Labour Supply and Incentives to work in Europe

Labour Supply and Incentives to Work in Europe

Labour Supply and Incentives to Work in Europe Edited by

Ramón Gómez-Salvador Principal Economist, Directorate General Economics, European Central Bank, Frankfurt am Main, Germany

Ana Lamo Senior Economist, Directorate General Research, European Central Bank, Frankfurt am Main, Germany

Barbara Petrongolo Lecturer, Department of Economics and Research Associate, Centre for Economic Performance, London School of Economics, UK

Melanie Ward Senior Economist, Directorate General Economics, European Central Bank, Frankfurt am Main, Germany

Etienne Wasmer Professor, Département des Sciences Economiques, Université du Québec à Montréal, Canada

Edward Elgar Cheltenham, UK • Northampton, MA, USA

© Ramón Gómez-Salvador, Ana Lamo, Barbara Petrongolo, Melanie Ward and Etienne Wasmer, 2005 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited Glensanda House Montpellier Parade Cheltenham Glos GL50 1UA UK Edward Elgar Publishing, Inc. 136 West Street Suite 202 Northampton Massachusetts 01060 USA

A catalogue record for this book is available from the British Library

ISBN 1 84542 129 9 Printed and bound in Great Britain by MPG Books Ltd, Bodmin, Cornwall

Contents ix xii

List of contributors Acknowledgements Introduction Ramón Gómez-Salvador, Ana Lamo, Barbara Petrongolo, Melanie Ward and Etienne Wasmer PART I 1

INCENTIVES TO WORK

A matching model of non-employment and wage pressure Andrew Brigden and Jonathan Thomas Discussion Javier Ortega

2

1

9 41

Tax effects on work activity, industry mix and shadow economy size: evidence from rich country comparisons Steven J. Davis and Magnus Henrekson Discussion Pietro Garibaldi

44 105

PART II FACTORS AFFECTING LABOUR FORCE PARTICIPATION 3

Mothers’ changing labour supply in Britain, the USA and Sweden Shirley Dex, Siv Gustafsson, Andrew Jenkins, Heather Joshi, Eiko Kenjoh and Mark R. Killingsworth Discussion Marc Gurgand

4

115

151

Women’s hours of market work in Germany: the role of parental leave Monika Merz

v

153

vi

Contents

Discussion Holger Bonin 5

6

The determinants of labour force participation in the European Union Véronique Genre, Ramón Gómez-Salvador and Ana Lamo

219

Hiring incentives and labour force participation in Italy Piero Cipollone, Corrado Di Maria and Anita Guelfi

222

Discussion Jan C. van Ours

268

NEW FORMS OF EMPLOYMENT RELATIONSHIPS

Recent developments in part-time work in EU countries: trends and policy Hielke Buddelmeyer, Gilles Mourre and Melanie Ward Discussion Rob W. Euwals

8

195

Discussion Barbara Petrongolo

PART III 7

191

Matching workers to jobs in the fast lane: the operation of fixed-term contracts José M. Varejão and Pedro Portugal Discussion Julián Messina

273 309

312 332

PART IV PANEL DISCUSSION – LABOUR SUPPLY IN AN ENLARGED EUROPE Labour supply in a new Europe Francesco Paolo Mongelli

339

Challenges for labour markets in the new Europe Juan J. Dolado

353

Causes and consequences of international labour mobility in an enlarged EU Herbert Brücker

361

Contents

Does EU enlargement imply a ‘race to the bottom’ in EU labour markets? Dennis J. Snower

vii

377

The effects of enlargement in imperfect labour markets Etienne Wasmer

379

Index

391

Contributors Holger Bonin, Senior Research Associate, Deputy Program Director Evaluation of Labor Market Programs, IZA, Bonn Andrew Brigden, Manager, Monetary Assessment and Strategy Division, Bank of England, London Herbert Brücker, Research Associate, German Institute for Economic Research (DIW Berlin), Department of International Economics; Visiting Professor, Aarhus Business School, Aarhus Hielke Buddelmeyer, Research Fellow, Melbourne Institute of Applied Economic and Social Research, University of Melbourne; Research Fellow, IZA, Bonn Piero Cipollone, Senior Economist, Research Department, Banca d’Italia, Rome Steven J. Davis, William H. Abbott Professor of International Business and Economics, University of Chicago, Graduate School of Business, Chicago; Research Associate, National Bureau of Economic Research, Cambridge, Massachusetts Shirley Dex, Professor of Longitudinal Social Research, Institute of Education, University of London Corrado Di Maria, PhD candidate, Tilburg University, Department of Economics, Tilburg Juan J. Dolado, Professor of Economics/Catedrático de Economía, Departamento de Economía, Universidad Carlos III de Madrid Rob W. Euwals, Research Associate, Centraal Planbureau (Netherlands Bureau for Economic Policy Analysis), Den Haag; Research Fellow, IZA, Bonn; Research Affiliate, Centre for Economic Policy Research, London Pietro Garibaldi, Professor of Economics, University of Turin; Head of Labour Studies, Fondazione Rodolfo Debenedetti, Milano; Consultant on Labour Issues, the Italian Treasury, Rome Véronique Genre, Economist, Directorate General Economics, European Central Bank, Frankfurt am Main ix

x

Contributors

Ramón Gómez-Salvador, Principal Economist, Directorate General Economics, European Central Bank, Frankfurt am Main Anita Guelfi, Labour Economist, Confindustria, Research Department, Rome Marc Gurgand, Paris-Jourdan Sciences Economiques (joint research unit CNRS-EHESS-ENPC-ENS); Research Fellow, Crest, Paris and Université Catholique de Louvain Siv Gustafsson, Professor of Economics, Comparative Population and Gender Economics, Universiteit van Amsterdam; Research Fellow, Labour, Region and Environment, Tinbergen Institute Magnus Henrekson, Jacob Wallenberg Professor of Economics, Department of Economics, Stockholm School of Economics Andrew Jenkins, Research Fellow, Centre for the Economics of Education, Institute of Education, University of London Heather Joshi, Professor of Economic Demography and Director, Centre for Longitudinal Studies, Bedford Group for Lifecourse and Statistical Studies, Institute of Education, Director Millenium Cohort Study Project, University of London Eiko Kenjoh, Research Fellow, Department of Economics, University of Amsterdam Mark R. Killingsworth, Professor of Economics, Department of Economics, Rutgers University, New Brunswick, New Jersey; Research Economist, National Bureau of Economic Research, Cambridge, Massachusetts Ana Lamo, Senior Economist, Directorate General Research, European Central Bank, Frankfurt am Main Monika Merz, Professor of Economics, University of Bonn; Research Fellow, IZA, Bonn Julián Messina, Economist, Directorate General Research, European Central Bank, Frankfurt am Main; Research Fellow, IZA, Bonn Francesco Paolo Mongelli, Adviser, Directorate General Economics, European Central Bank, Frankfurt am Main Gilles Mourre, Economist, Directorate General for Economic and Financial Affairs, European Commission, Brussels Javier Ortega, Associate Professor, Université de Toulouse (GREMAQ, IDEI); Research Affiliate, Centre for Economic Policy Research, London; Research Fellow, IZA, Bonn

Contributors

xi

Jan C. van Ours, Professor of Labor Economics, Department of Economics, Tilburg University; CentER, Tilburg; Research Fellow, Centre for Economic Policy Research, London; Research Fellow, IZA, Bonn Barbara Petrongolo, Lecturer in the Department of Economics and Research Associate in the Centre for Economic Performance at the London School of Economics; Research Affiliate, Centre for Economic Policy Research, London; Research Fellow, IZA, Bonn Pedro Portugal, Senior Researcher, Bank of Portugal; Full Professor, Faculdade de Economia da Universidade Nova de Lisboa Dennis J. Snower, President, Institute of World Economics, Kiel; Professor of Economics, Christian-Albrechts University, Kiel, Germany; Research Fellow, Centre for Economic Policy Research, London; Research Fellow, IZA, Bonn Jonathan Thomas, Manager, Monetary Assessment and Strategy Division, Bank of England, London José M. Varejão, Assistant Professor, Faculdade de Economia, Universidade do Porto and CETE Melanie Ward, Senior Economist, Directorate General Economics, European Central Bank, Frankfurt am Main; Research Fellow, IZA, Bonn; Research Affiliate, Centre for Economic Policy Research, London Etienne Wasmer, Canada Research Chair, Professor, Département des Sciences Economiques, Université du Québec à Montréal; Research Fellow, European Center for Advanced Research in Economics and Statistics, Université de Bruxelles; Research Affiliate, Centre for Economic Policy Research, London; Research Fellow, IZA, Bonn

Acknowledgements We would like to take this opportunity to thank the large number of people involved in the production of this collected volume. In addition to the authors and discussants, we wish to acknowledge the contributions of participants in the ECB/CEPR labour market workshop ‘What Explains the Patterns of Labour Supply in Europe?’, particularly ECB Executive Board Member Otmar Issing, for his introductory statement, and Gabriel Fagan, Francesco Mongelli and Philippe Moutot, for their support and valuable contributions to the workshop, including chairing a session. Thanks also go to the CEPR, to Per Anders Edin and Gilles Saint-Paul, and especially to the then labour economics programme director Juan Dolado. The ECB’s labour group provided enthusiastic support and input to the conference: Véronique Genre, Julián Messina, Ricardo Mestre, Julian Morgan, Gilles Mourre, Marcin Przybyla, Roberta Serafini and Jarkko Turunen. Invaluable assistance in the planning and organization of the workshop was provided by Birgitte Ilving and Janet Seabrook. Finally, and perhaps most importantly, we would like to thank our assistant editor, Annette Hochberger, whose hard work and attention to detail has made this volume possible.

xii

Introduction Ramón Gómez-Salvador, Ana Lamo, Barbara Petrongolo, Melanie Ward and Etienne Wasmer This volume consists of a series of papers and discussions presented at the workshop entitled ‘What Explains the Pattern of Labour Supply in Europe?’ held at the European Central Bank (ECB) in Frankfurt am Main, Germany, on 23 and 24 June 2003. This was the first labour market workshop jointly organized by the ECB and the Centre for Economic Policy Research (CEPR). The aim of the workshop was to analyse the developments of labour supply in Europe and to assess their link with economic policies and labour market institutions. Notably, differences in tax systems (labour income) and in social protection systems (non-labour income) are expected to strongly affect individuals’ decisions to supply hours in the labour market. In turn, labour supply developments have key implications for wages, prices, potential output and economic and social welfare in general. In the short run, the cyclical sensitivity of labour supply can affect labour market tightness and thereby influence the outlook for wages and prices. In the longer run, labour supply (together with other factors) is a key determinant of potential output. Policy decisions need to account for this interplay between institutions and market outcome. The study of labour supply patterns in Europe was broken down into three main themes within the workshop. The first covered incentives to work. The second considered several economic and institutional factors affecting labour force participation. The third addressed the role played by the emergence of new forms of employment relationships in labour market developments. These themes were mainly addressed at the general European level or within individual European countries, and in some cases by comparing the European labour market experience to that of other OECD countries. In view of the possible changes that the European labour market may face with the increase in the number of countries participating in the EU, a panel discussion on the topic ‘Labour supply in an enlarged Europe’ closed the workshop. 1

2

Labour supply and incentives to work in Europe

INCENTIVES TO WORK It is frequently argued that in many European countries, labour market institutions – such as state pension benefits, unemployment benefits and disability benefits – reduce the incentives to work and participate in the labour market. At the same time active labour market policies (ALMPs) – consisting of public training programmes, job search assistance, subsidies to employment and direct job creation and designed to bring the unemployed back into work – have been put in place in these countries. Such policies may offer only piecemeal solutions since they do not address the underlying structural problems determining incentives to work. Moreover, such policies and benefits need to be funded via taxes, which may also influence the incentives to participate. Issues surrounding incentives and disincentives to work and/or to participate in the labour market are at the forefront of the first two chapters in this volume. In Chapter 1 Andrew Brigden and Jonathan Thomas consider the role of inactive individuals in labour market outcomes in ‘A matching model of non-employment and wage pressure’. They develop a matching model with variable search intensity assuming two types of non-employed people: those with low levels of search effort and high values of leisure time and home production – defined as inactive – and those with higher search effort and a lower value of leisure time and home production – defined as unemployed. They calibrate the model for a range of different shocks, using the UK as a case study, and examine the behaviour of inactivity, unemployment and wages. Their results suggest an important role for unemployment benefits in the observed labour market outcomes. Chapter 2 comprises Steven J. Davis’s and Magnus Henrekson’s study ‘Tax effects on work activity, industry mix and shadow economy size: evidence from rich country comparisons’. This chapter investigates the long-run total response of working time and other economic activity indicators to differences in tax rates on labour income, payrolls and consumption. To guide their investigation, the authors develop a theory of time allocation and task assignment between market and non-market production sectors. The theory, which is confirmed by empirical evidence from 19 rich countries in the 1990s, implies that higher tax rates reduce working time in the market sector, increase the size of the shadow economy, shift the industry mix of market activity and alter the composition of labour demand so as to amplify negative effects on hours of work and employment, particularly for the less skilled. Specifically, a unit standard deviation tax rate change of 12.8 percentage points is found to be associated with 122 fewer hours of market work per adult per year, a 4.9 percentage point reduction in the employment–population ratio, an increase

Introduction

3

in the size of the shadow economy amounting to 3.8 per cent of GDP and a 10 to 30 per cent drop in employment shares in some sectors.

FACTORS AFFECTING LABOUR FORCE PARTICIPATION Four chapters in this volume seek to shed some light on the factors affecting labour force participation. Chapters 3 and 4 illustrate the significant changes observed in women’s participation, both in Europe and the USA, over the second half of the 1990s and relate these changes to cohort effects, personal characteristics and changes in the labour market institutional framework. Chapters 5 and 6 investigate how recent developments in labour market institutions may have affected participation trends for different demographic groups, using a macroeconomic and then a microeconomic perspective. In Chapter 3, Shirley Dex, Siv Gustafsson, Andrew Jenkins, Heather Joshi, Eiko Kenjoh and Mark R. Killingsworth investigate changes in the labour supply of mothers in Britain, the USA and Sweden during the latter part of the twentieth century. They detect sizeable changes in labour supply from one cohort to the next, particularly among women with less than higher education and among women with children. The authors’ purpose is to investigate whether these changes are explained by inter-cohort changes in measured characteristics, particularly education, or whether there are substantial cohort effects per se. For that purpose, they focus on two important margins of adjustment in female labour supply, that is, women’s time to first birth and time to entry (or re-entry) into the labour market after the first birth. The fact that women with higher educational attainment tend to postpone motherhood is the only consistent finding, while the relationship between the level of education and the probability of re-entry and cohort effects are less clear-cut. In Chapter 4, ‘Women’s hours of market work in Germany: the role of parental leave’, Monika Merz documents empirical facts on women’s involvement in the labour market in Germany, looking at both participation and hours worked, and the link between these empirical facts and some changes in the institutional framework. The evidence from the 1950s illustrates a trend increase in women’s participation, which differs depending on their marital status and age, and which has been accompanied by a notable decline in hours worked, especially for those with young children. It is concluded that the decline in married women’s weekly hours worked is to some extent related to institutional changes in the federal legislation governing parental leave.

4

Labour supply and incentives to work in Europe

Chapter 5, ‘The determinants of labour force participation in the European Union’, by Véronique Genre, Ramón Gómez-Salvador and Ana Lamo, illustrates recent trends in labour force participation in the EU and studies the role of economic factors and of several labour market institutions in shaping these trends during the past two decades. The chapter relates the participation rates of women and men of different age groups to economic and institutional factors using country-level panel data. The rich data set used by the authors contains sufficient time variation in measured labour market institutions, and allows them to identify the impact of institutions on participation after controlling for country fixed-effects. The main finding of the chapter is that the generosity of the unemployment benefit system, the strictness of employment protection, and union density tend to be associated with lower participation rates of groups with presumably lower labour market attachment such as women and older workers. Chapter 6, ‘Hiring incentives and labour force participation in Italy’, by Piero Cipollone, Corrado Di Maria and Anita Guelfi, investigates how the quality composition of available jobs has recently affected labour force participation in Italy. In particular, the authors focus on the likely impact of a labour market programme introduced in Italy in 2000, which offered a substantial tax credit to firms hiring workers under open-ended contracts (as opposed to fixed-term contracts). This reform is supposed to improve participation incentives by increasing the share of permanent jobs in total job opportunities. The authors test this idea on micro-data from the Italian Labour Force survey, using a difference in difference approach. This is possible thanks to the cross-sectional variation in the applicability of the tax credit, which was only available for hires of workers aged 25 or more. The authors’ estimates show that the introduction of the tax credit raised the participation of eligible groups by almost 2 percentage points above that of non-eligible groups.

NEW FORMS OF EMPLOYMENT RELATIONSHIPS Until the mid-1970s the basic legal instrument regulating employment relationships in Europe was the full-time employment contract of indefinite duration. Increased economic integration, intensified competitive pressures and rapid technological progress on the demand side, along with the expansion of higher education, increased mobility and the effect of an ageing society on the supply side, have catalysed the need for new forms of employment relationships. Against this background, policy changes in EU countries over the last two decades have aimed to promote new forms of employment relationships, such as part-time and temporary employment.

Introduction

5

Important issues surrounding these new forms of employment relationships are the topic of two chapters within this volume. Hielke Buddelmeyer, Gilles Mourre and Melanie Ward shed some light on the experiences of part-time work in Europe in Chapter 7, ‘Recent developments in part-time work in EU countries: trends and policy’. They find that institutions and policy developments in Europe contribute to shaping both the time and cross-country development of part-time rates and have facilitated the growth of part-time employment. The authors argue that this supports economic performance in Europe, helping to raise the employment rate. In addition, part-time employment contributes to the achievement of higher levels of productivity by allowing workers to balance their professional life and private life and allowing employers to adjust working hours to business requirements. Nevertheless, policies designed to promote part-time work could have perverse effects for long-term productivity if part-time workers suffer lower hourly remuneration, insufficient access to training, lack of career development, and so on, with the consequent distortions in human capital investment. The authors argue that to be fully effective, employment policy should support voluntary part-time work. In Chapter 8, ‘Matching workers to jobs in the fast lane: the operation of fixed-term contracts’, José M. Varejão and Pedro Portugal investigate the use of fixed-term contracts in Portugal. The Portuguese labour market is particularly interesting in this case due to the relative strictness of employment protection legislation on permanent positions and the recent increase in the use of fixed-term contracts, which currently account for nearly two-thirds of newly offered positions. Analysis of worker flows between labour market states reveals that temporary jobs are associated with a higher degree of job churning than permanent positions, but that a non-trivial share of all fixed-term contracts become permanent. Varejão and Portugal argue that there is significant evidence for the use of fixedterm contracts as screening devices by workers and firms. Interestingly however, for workers in more advanced stages of their careers, holding a fixed-term position following a job loss episode implies the loss of returns to experience, harming both wage and future employment prospects.

PANEL DISCUSSION – LABOUR SUPPLY IN AN ENLARGED EUROPE In the panel discussion devoted to ‘Labour supply in an enlarged Europe’, several, if not all, aspects of labour market analysis are addressed: employment, wages, human capital, migration, labour market institutions and incentives to undertake reforms.

6

Labour supply and incentives to work in Europe

Francesco Paolo Mongelli investigates the evolution of employment and wages in a survey of various works on the effects of the European Monetary Union on labour markets. The bottom line of his discussion is that, although it is too early to fully assess the impact of monetary unification on labour markets, we can be confident that the most pessimistic forecasts have not materialized. Indeed, one can see some evidence of convergence to the Lisbon target of a 70 per cent employment to population ratio, although progress has been slower than expected. Mild optimism is shared by Dennis J. Snower, who dismisses the view that enlargement to the East should necessarily involve a ‘race to the bottom’ in making labour markets more flexible. For this to hold, factors of production and goods must be very mobile across countries. Snower stresses that capital flows between the former European Union and Central and Eastern European countries have remained low until recently and that migration flows will also remain at low levels in the near future. He agrees with Herbert Brücker in this, who distinguishes between static and dynamic estimates of migration flows in order to take account of transitional periods imposed in most countries during the enlargement process. His conclusion is that the East–West migration potential is moderate, that is, around 1 per cent of the EU-15 population. Etienne Wasmer also stresses the low mobility potential of enlargement, due to ageing populations both in the East and in the West. Wasmer expresses doubts about the neglect of transition costs in the enlargement process. Notably, the existence of specific skills in traditional sectors and the absence of ambitious redistribution schemes of the magnitude of past enlargements (Spain, Portugal, Greece and Ireland), combined with large GDP per capita differentials between the East and the West, will make the transition costly. Finally, most contributors, and notably Juan J. Dolado and Francesco Paolo Mongelli, insist on the necessity of labour market reforms given the slow progress made with these to date. Dolado makes a distinction between the demand for social protection from private agents (firms and workers) and the supply of protection from institutions and governments, indicating a possible mismatch between the former and the latter.

PART I

Incentives to Work

1.

A matching model of non-employment and wage pressure1 Andrew Brigden and Jonathan Thomas2

INTRODUCTION The contrasting behaviour of unemployment across European countries over the last decade has attracted considerable comment and analysis. For example, while the UK, the Netherlands, Ireland, Denmark and Spain have experienced sharp falls in unemployment since the mid-1990s, the jobless rate has moved much more sluggishly in countries such as France and Italy, while actually increasing a little in Germany (Table 1.1). The explanations proffered for this divergence include changes in the pattern of wage bargaining, reforms of the benefit system, skill mismatch, labour taxes, active labour market policies and employment protection legislation (Nickell and van Ours, 2000; Pissarides, 2003). Less attention has been paid to the fact that inactivity has been much more stable over the period (Table 1.1). While the average 1993–2002 change in the unemployment rate across the thirteen countries shown in Table 1.1 stood at around
4.3 percentage points, the corresponding change in inactivity was
1.8 percentage points. One consequence of this relative stability of inactivity has been that the ratio of the inactivity rate to the unemployment rate has risen in each country over the period, except in Germany where it has been basically flat (Figure 1.1). What are the implications of these developments for monetary policy? Nickell (2001) focuses on the role of a relatively high inactivity rate on restraining the growth of potential output. However, it might also have implications for aggregate wage pressure. One possibility is that a relatively high inactivity rate has helped to moderate any extra wage pressure arising from the sharp decline in unemployment. On the other hand it is arguable that the inactive are so detached from the labour market that they have no moderating impact on wage bargaining. Unfortunately, standard labour market models cannot address such issues because they do not distinguish between the inactive and the 9

10

Table 1.1

Incentives to work

Unemployment and inactivity in Europe (%) Unemployment

Belgium Denmark Finland France Germany Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland UK

Inactivity

1993

2002

1993

2002

12.0 10.7 16.2 11.4 7.9 15.7 10.0 6.2 6.0 5.4 22.4 9.5 3.8 10.3

10.9* 4.7 9.0 8.6* 8.6 4.2 9.0 3.1 3.9 5.0 11.3 5.2 2.5* 5.5*

48.2 32.8 38.9 45.1 42.0 47.0 51.6 43.3 38.8 41.7 50.1 38.3 28.2 39.3

48.0* 34.7 38.4 43.9* 42.8 40.6 51.0 36.8 34.5 38.2 47.1 38.7 29.3* 39.3*

Notes: OECD data. The unemployment rate is defined as ILO unemployed/15 activity. The inactivity rate is defined as (15  population
15  activity)/(15  population). *1999 for Belgium, 2000 for the UK, and 2001 for France and Switzerland.

unemployed. To address this deficiency we develop a simple matching model of the labour market with two types of non-employed workers according to their levels of job search. Individuals with low levels of search effort (L-types), who can loosely be interpreted as inactive, adopt this strategy because they attach a higher value to home production or to leisure than their counterparts with high search effort (H-types), who can loosely be interpreted as the unemployed. In addition to lower search effort, the higher value that L-types place on home production or leisure means that they set higher reservation wages.3 The key feature of the model is that wage offers made to the unemployed and inactive are interdependent. This co-dependency flows from the fact that an employer can match with either type of job seeker. One consequence is that the impact of a given shock on wage pressure depends on the mix of each type in the non-employed population. For example, a reduction in the benefits available to L-types puts downward pressure on L-type wages and raises their search effort as might be expected. However, it also puts upward pressure on H-type wages. This is because firms are now more likely to match with a more expensive L-type, which increases the leverage of H-types. We illustrate the properties of the model via simulating the

A matching model of non-employment and wage pressure 16

11

Belgium Denmark Finland France Germany Ireland Italy

14 12 10 8 6 4 2 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 (a)

UK Sweden Spain Portugal Norway Netherlands

16 14 12 10 8 6 4 2 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 (b)

Figure 1.1

Ratio of the inactivity rate to unemployment rate

steady-state impacts of several exogenous shocks, including a permanent increase in the productivity of a job match and a reduction in hiring costs. These simulations demonstrate that the relationship between unemployment, inactivity, and wage pressure, which are all endogenous, depends critically on the source of the underlying exogenous shock. For example, unemployment and wage pressure are unrelated in the face of a productivity disturbance, while a cut in hiring costs leads to lower unemployment and higher wage pressure. We then use the model to examine the behaviour of inactivity, unemployment, and wage growth in the UK over 1994–2000. The UK provides a useful case study because it is often cited as an example of how the

12

Incentives to work

appropriate policy reforms can significantly reduce the sustainable level of unemployment.4 Specifically we attempt to identify the underlying shocks that can explain the observed declines in unemployment and inactivity over this period. We consider shocks to benefits, hiring costs, firing costs and the share of low search effort individuals in the working-age population. The most plausible impulses involve a rise in the fraction of low search effort individuals, and a reduction in benefits to H-type non-employed. A larger L-type share could reflect the rise in the proportion of students among the working-age population, while the reduction in benefits could reflect the stricter benefit regime since the mid-1990s, alongside active labour market policies to increase labour supply. Under these shocks the 1994–2000 movements of unemployment and inactivity would not be associated with a rise in wage pressure. This increase in the proportion of students appears to have been broadly shared by other European countries.5 However, there has been far more diversity in the extent to which the benefit regime has been reformed (de Koning et al., 2004). The layout of the chapter is as follows. The following section develops the matching model and derives the steady-state equilibrium conditions. We then calibrate the model with UK data and simulate a variety of shocks to illustrate its key properties. The next section explores the relationship between unemployment, inactivity, and wage pressure, and a final section concludes.

THE MODEL We start our description of the model by considering the job-matching process. In particular we shall use a matching function that relates the number of job matches formed to the search activity of unemployed and inactive workers. One might believe that inactive workers do not take part in the matching process. However, according to many individual-level surveys of labour market activity, such as the Eurostat Labour Force Survey, some positive fraction of those non-employed people classified as inactive at the beginning of a three-month period will find work before the end of the period. Some of these people will have been waiting for the results of job applications. But some of them will claim that at the beginning of the period they were not actively seeking work, while others will claim that they did not want to work at all.6 Why is it that people who are not looking for work, and even people who say they do not want to work, find work three months later? One perspective, which we call the ‘missing data’ view, is that inactive people do not enter work directly. All transitions that appear to be from I to E actually

A matching model of non-employment and wage pressure

13

involve a short period in U that we do not observe owing to the discrete nature of many labour market surveys. Consider an individual who is temporarily sick in month one, and hence classified as inactive. He or she then recovers in month two and starts searching again. In month four the individual finds a job. If, like the UK Labour Force Survey (LFS), the survey is run quarterly and this individual happens to be interviewed only in months one and four, he or she will appear to have entered employment directly from inactivity. In reality, this was not the case.7 An alternative possibility, which we call the ‘right place, right time’ view, is that occasionally non-employed people who are supplying little or no search effort will receive job offers simply by virtue of being in the right place at the right time. They might, for example, meet the proverbial ‘man in the pub’ who just happens to have a suitable vacancy.8 In this instance simply signalling that one is out of work can be enough to obtain a viable job offer. Although these two explanations for observed transitions from I to E are not mutually exclusive, they do have differing implications for modelling purposes. Under the missing data view, inactive people do not enter a standard continuous time-matching function.9 Non-employed individuals choose whether or not to participate in the labour market as a function of the net returns to participation. If the labour market is tight, and these returns are high, they are likely to participate. If the labour market is loose, and these returns are low, they are likely not to participate. In that sense, the stock of inactive people is a function of labour market tightness, rather than a determinant. In this example the stock of inactive people does not tell us anything about market tightness that we could not already have known from inspection of (say) the vacancy to unemployment ratio. A recent paper that implicitly adopts this approach is Garibaldi and Wasmer (2005). In their model, individuals are employed, unemployed and searching for work, or inactive and engaging in home production. The returns to home production vary across individuals and across time, and it is this that causes individuals to switch from one non-employment state to another and hence contributes to the economic cycle. This chapter leans towards the ‘right place, right time’ view of the observed transitions from I to E. We believe that certain categories of the inactive can enter employment directly, if the right opportunity comes their way, with little or no job search. These categories might include, among others, students who say they would like to work but are not searching, those individuals who are at home looking after their family, and the discouraged (those who say they are not searching because they believe no work is available). Our approach is based on a model outlined in Chapter 5 of Pissarides (2000). In his model the number of matches per period (m) is a function of

14

Incentives to work

(1) the product of the average search intensity of the unemployed and the number of unemployed people (su), and (2) the product of the average advertising effort per vacancy and the number of vacancies (av). The key extension we make is to substitute all non-employed people (n) for the unemployed (u). Search intensity is now averaged across all non-employed people. For simplicity, we drop the assumption that firms can vary advertising effort per vacancy. Hence, the matching function we use is of the form: m  m(sn, v)

(1.1)

We assume there are two types of individual: L-types, who comprise a fraction L of the population, and H-types, who comprise a fraction (1
L) of the population. The size of the total population is P. L-types are either employed or non-employed. While non-employed, they choose to supply a low level of search effort (sL). H-types are either employed or non-employed. While non-employed they choose to supply a high level of search effort (sH ). Both types of individual choose these search intensities optimally as a function of their characteristics. The economy proceeds in the following way. Each period, firms that want to recruit post vacancies. To do so, they must pay a fixed amount pc per period (p is the output of a filled job, so advertising costs are assumed to be proportional to the output of a filled job). The per-period probability that any given firm matches with a non-employed person, which we denote by qF, is equal to the number of matches divided by the number of vacancies. Hence: qF 

m(sn, v) v

(1.2)

Total search effort is a weighted sum of the search of high and low effort searchers, thus snsLnL sHnH, where nL and nH are the number of low and high effort searchers, respectively. While firms are posting vacancies, non-employed individuals are receiving state benefits. L-types receive bL and H-types receive bH. Differences in these benefits reflect the possibility that H-types are formally claiming unemployment-related benefits, for which L-types are ineligible. Non-workers also receive utility from not working that is diminishing in the amount of search effort supplied. This utility may arise through home production, through enjoyment of leisure time, or through some combination of the two. If they supply no search effort at all, non-employed L-types receive a baseline amount lL and nonemployed H-types receive a baseline amount lH. We assume that lL lH, and it is this that causes high effort searchers to search harder and thus enter employment more quickly than low effort searchers.10 So the flow utility to

A matching model of non-employment and wage pressure

15

non-employed L-types (zL) and H-types (zH) has two components: a fixed component (representing state benefits) and a variable component (representing the value of home production and/or leisure time). We write these flow utilities as: zL  bL  (sL, lL) zH  bH  (sH, lH)

(1.3) (1.4)

where the function (s, l) has the following properties: s(s, l)  0 ss(s, l)  0 l (s, l)  0 Utility from home production and/or leisure time is decreasing in the time spent searching, and increasingly so. Again following Chapter 5 of Pissarides (2000), we assume that the perperiod probability that any given non-employed person matches with a vacancy is proportional to the fraction of total search effort that he or she has supplied. Hence these probabilities, denoted by qL for an L-type and qH for an H-type, can be written as: sL m(sn, v) sn sH qH  m(sn, v) sn qL 

(1.5) (1.6)

When a non-employed person matches with a firm posting a vacancy, they will agree on a wage rate. We denote the wage for L-types by wL and the wage for H-types by wH. Once this wage has been agreed, firms must also pay a fixed hiring cost (hL for L-types and hH for H-types).11 This is analytically equivalent to a training cost. hL and hH are paid only once, in the first period of a new hire. In this respect they differ from advertising costs ( pc) which are paid each period that a vacancy is open. An interesting point to consider is whether a non-employed person and a firm that have just met can always agree on some division of output that is acceptable to both parties. In particular, will it always make sense for a firm to hire an L-type it has just met, given that in equilibrium L-types tend to have a higher wage than H-types (reflecting the increased value of their outside option)? We return to this issue later on. Finally we turn to filled jobs. Each period, a filled job produces a fixed amount p. This is shared between firm and worker according to the wage

16

Incentives to work

rates wL and wH. These wage rates are continuously renegotiated and will change in response to economic shocks. In addition, a job that has been filled faces an exogenous probability  of being destroyed each period.12 If the job is destroyed, the firm must pay a fixed firing cost13 ( f L for L-types, and f H for H-types).14 The employed person then becomes nonemployed and must start searching for work again. The firm can no longer produce and must decide whether to post another vacancy. We are now in a position to write down the seven value functions that together describe the present discounted value of expected future utility available to employed L-types, employed H-types, non-employed L-types, non-employed H-types, firms employing an L-type, firms employing an H-type, and firms currently posting a vacancy. These are written out in full below.15 rWL  wL  (NL
WL) rWH  wH  (NH
WH) L rN  bL  (sL, lL)  qL(WL
NL) rNH  bH  (sH, lH)  qH(WH
NH ) rJL  p
wL  (V
JL
f L) rJH  p
wH  (V
JH
f H ) rV 
pc  qF [ L(JL
V
hL)  (1
L)(JH
V
hH)]

(1.7) (1.8) (1.9) (1.10) (1.11) (1.12) (1.13)

Equation (1.7) says the rental value of a job to an L-type (WL is the capital value, and r the real rate of interest) is equal to his or her per-period wage, plus the capital loss that would occur if the job were destroyed, times the per-period probability that the job is destroyed. Equation (1.8) gives the rental value of a job to an H-type. Equation (1.9) says the rental value of non-employment to an L-type is equal to state benefits received, plus the value of home production and/or leisure time, plus the capital gain that would occur if he or she were to enter employment, times the probability that he or she does enter employment. Equation (1.10) gives the rental value of non-employment to an H-type. Equation (1.11) says the rental value to the firm of a job that is filled by an L-type is equal to per-period profits net of the wage ( p
wL), plus the capital loss that would occur if the job were destroyed, times the per-period probability that the job is destroyed. Equation (1.12) gives the rental value to the firm of a job that is filled by an H-type. Finally, equation (1.13) says the rental value to the firm of a vacancy is equal to the (negative of) the per-period cost of advertising a vacancy ( pc), plus the expected capital gain that would occur if the vacancy were filled, times the probability that the vacancy is filled. This capital gain is uncertain because the employer does not know whether he or she will meet an L-type or an H-type. The expected capital gain is the weighted

A matching model of non-employment and wage pressure

17

average of the asset value of a job filled by an L-type (net of V and hiring costs), and the asset value of a job filled by an H-type (net of V and hiring costs). The weights depend on L, which we use to denote the conditional probability that a firm meets with an L-type, given that it meets with someone. L is simply the fraction of all non-employed people who match in a given period that are L-types. Hence: L 

nLqL nLqL  nHqH

(1.14)

Using (1.5) and (1.6) in (1.14) we obtain: L 

sLnL sn

(1.15)

Our model has seven endogenous variables: two wage rates (wL and wH ), two optimally chosen search efforts (sL and sH ), two non-employment stocks (nL and nH ), and a stock of vacancies (v). To solve the model we need seven equilibrium conditions. The conditions we use, which are a natural extension of those in Pissarides (2000), are as follows. The two wage rates (wL and wH ) are determined by a Nash Bargaining process, such that a fraction of the total surplus generated from each match goes to the worker (whether L-type or H-type), and a fraction (1
) goes to the firm. The two search efforts (sL and sH ) are chosen to maximize the value of non-employment (NL for an L-type and NH for an H-type). Firms post vacancies until the asset value of a vacancy is zero. This is effectively a free entry condition. Finally, nL and nH evolve according to the processes governing matching and job destruction described above. The conditions are written out in full below: (WL
NL)  [(JL
V)  (WL
NL)] (WH
NH )  [(JH
V)  (WH
NH )]

(1.16) (1.17)

NLi 0 sLi

(1.18)

NH i 0 sH i

(1.19)

V0 nL  (LP
nL)
qLnL t

nH  [(1
L)P
nH]
qHnH t

(1.20) (1.21) (1.22)

18

Incentives to work

In the conditions for optimal search effort, (1.18) and (1.19), each individual is indexed by i. It is necessary to retain these subscripts until the calculus has been performed since each individual i must take the actions of others as given when choosing si. Since all L-types are identical to all other L-types, and all H-types are identical to all other H-types, we shall find that in equilibrium there are only two search efforts: sL and sH. Equation (1.21) says that the change in L-type non-employment is equal to the number of inflows (the constant job destruction rate times the number of L-types in employment) minus the number of outflows (the probability that an L-type matches with a vacancy times the number of L-types who are non-employed). There is an equivalent expression for the change in H-type non-employment (1.22). By setting (1.21) and (1.22) to zero we derive steady-state conditions for employment: LP   qL

(1.23)

(1
L)P   qH

(1.24)

nL  nH 

We can now use the equilibrium conditions (1.16) to (1.20) together with the value functions (1.7) to (1.13) to derive expressions for the remaining endogenous variables (wL, wH, sL, sH, v). These are reported below (the full workings are available upon request).



wL  (1
)[bL  (sL, lL)]  p
f L  L



pc  qF LhL
qF(1
L)






 

p
wH
f H
hH r

(1.25)

wH  (1
)[bH  (sH, lH)]  p
f H  H



pc  qF(1
L)hH
qF( L) s(sL, lL)  s(sH, lH) 








 

p
wL
f L
hL r

m(sn, v) wL
bL
(sL, lL) sn r    qL

 

m(sn, v) wH
bH
(sH, lH ) sn r    qH

(1.26)

(1.27)

(1.28)

A matching model of non-employment and wage pressure





pc 1  qF r

p
L[wL  f L  hL(r  )]
(1
L)[wH  f H  hH(r  )]



19

(1.29)

Equations (1.25) and (1.26) are the wage equations for L-types and H-types respectively. While they may look cumbersome, they do have intuitive appeal. Equation (1.25) says that an L-type’s wage is a weighted average of his or her reservation wage (the term in square brackets, pre-multiplied by 1
) and the potential gain from the match (the term in braces, premultiplied by ). The weights are (1
) and . If the worker has no bargaining power ( 0) he or she gets the reservation wage, which is equal to the sum of state benefits and the value of leisure time adjusted for search effort. If the firm has no bargaining power ( 1) the worker gets the entire potential gain from the match. The ‘potential gain from the match’ needs some elaboration. If the match goes ahead, an amount p will be produced, out of which an amount f L must be paid in firing costs with fixed probability  per period. But in addition to producing, the match saves on advertising costs. To see how large these costs might be, imagine the case where the worker refuses the match and the firm is forced to hire another L-type. The L-type who walks away is likely to have to wait 1/qL periods until he finds another firm. The firm he finds is likely to have spent, in total, an amount pc/( LqF ) hiring him. Hence, the expected cost of advertising for an L-type per period that the L-type would be non-employed is given by (pc/ LqF )/(1/qL). This simplifies to pc times v/nL, the ratio of vacancies to non-employed L-types, which we label L for convenience.16 Unfortunately this is not the end of the story. In addition to the pc advertising costs that must be paid each period that the firm is searching for another L-type, L-type hiring costs equal to hL are also paid with probability qF L. One further wrinkle, unique to our model, is that the firm is not forced to recruit another L-type. It may find an H-type instead. This occurs with probability qF(1
L) per period. The capitalized value of matching with an H-type is equal to (1/(r ))( p
wH
f H ) hH which must be subtracted from the other cost items. We can see from (1.25) and (1.26) that the two wage rates are interdependent. One interesting implication is that changes in state benefits available to L-types (the inactive) have implications for the wage rate open to H-types (the unemployed) and hence for the steady-state non-employment rate of H-types. We shall return to this issue below. The remaining equations are more straightforward. The optimal search equations (1.27) and (1.28) say that a non-employed worker will supply search effort until the marginal cost of doing so (in terms of leisure time forgone) just equals the expected gain from doing so (in terms of the probability adjusted mark-up of the wage over the reservation wage). To see this,

20

Incentives to work

note that the first term on the right-hand side is the probability that a given unit of search effort results in a match, while the second term is the capitalized value of the mark-up of the wage rate over the reservation wage. Equation (1.29) is the job creation condition. It says that firms will post vacancies until the expected total advertising cost is just equal to the present discounted value of expected future profits (this term reflects the fact that the firm does not know whether it will meet an L-type and pay wL, or an H-type and pay wH ).

MODEL CALIBRATION AND PROPERTIES We do not attempt to solve the model analytically. Instead we aim to find plausible parameter values that will allow us to obtain numerical solutions of the initial steady state and the new steady state following a given shock. We turn first to the calibration. For illustrative purposes we use UK data, mostly drawn from the UK Labour Force Survey. Similar data can be obtained for other countries. Calibration The first step is to specify appropriate functional forms, both for the matching function (1.1) and for the value of leisure function used in (1.2) and (1.3). In common with most authors, we specify a Cobb-Douglas matching function: m(sn, v)  (sn) v1


(1.30)

In a recent survey of the empirical literature, Petrongolo and Pissarides (2001) argue that Cobb-Douglas matching functions have tended to fit the data well, in most countries and in most studies. Recall from the discussion above that we want to find a value of leisure function such that the value of leisure is decreasing in the amount of search effort supplied, and increasingly so. One function that delivers this result is shown below: (s, l)  l(1
(s)2)

(1.31)

The parameter determines the sensitivity of the value of leisure time to changes in search effort. We set 1. In this case s can be viewed as the fraction of time spent searching. For s 0, the non-employed person enjoys his or her maximum value of leisure time l. For s1, the non-employed

21

A matching model of non-employment and wage pressure

person gets no utility from leisure time at all. By construction, the marginal disutility from extra search time is increasing in the amount of search being undertaken. That means that it would cost an H-type more to raise his or her search time by (say) 30 minutes than it would cost an L-type. This aspect of the cost of search function turns out to have important implications for model properties. Counting up across the seven equations of our model, and the two functions outlined above, we have a total of 18 exogenous parameters. These 18 parameters are listed in Table 1.2, together with the benchmark values we assign to them. There is a distinction between the way in which values were assigned to the first 11 parameters (shown in bold in the third column), and the way in which values were assigned to the last seven parameters (shown in italics in the third column). Parameter values shown in bold are either convenient normalizations or were assigned by direct observation. The estimate of training costs, for example, comes from Bentolila and Bertola (1990). But some parameter values, such as the maximum value of leisure time available to L-types (lL), or the fraction of L-types in the whole population (L), are much less tangible. These parameter values, Table 1.2

Values of the exogenous parameters in the benchmark case

Parameter

Description

P p



Population size Output of a filled job Parameter in value of leisure function Fraction of total surplus that goes to the worker Job destruction rate Training costs for L and H-types Firing costs for L and H-types Elasticity of job matches with respect to total search effort Real discount rate Parameter of the matching function State benefits paid to non-employed L-types State benefits paid to non-employed H-types Maximum value of leisure time/home production available to L-types Maximum value of leisure time/home production available to H-types Per-period cost of posting a vacancy Fraction of population that are L-types

 hL, hH f L, f H  r  bL bH lL lH pc L

Quarterly value 1.000 1.000 1.000 0.500 0.035 0.333 1.000 0.500 0.010 1.094 0.121 0.242 0.771 0.537 0.601 0.378

22

Incentives to work

shown in italics, were chosen so that a number of steady-state conditions hold in the benchmark case. We now deal with the selection of each parameter in turn. The first two parameter values listed in Table 1.2 (P1.000, and p1.000) are convenient normalizations. The selection of ( 1.000) has already been discussed. In common with Pissarides (1998) we set the worker’s share of the total surplus ( ) equal to 0.500. This is slightly lower than the value chosen by Millard and Mortensen (1997), who set 0.584 on the grounds that it yields a steady-state unemployment rate equal to the UK average over the period 1983–92. Our results are not that sensitive to small variations in . We set the quarterly rate of job destruction () equal to 0.035, on the grounds that this is the average transition rate from employment into non-employment across successive waves of the UK Labour Force Survey from 1993 to 1999.17 We take our estimates of training costs (hL and hH ) from US survey information reported in Hammermesh (1993). This suggests that average training costs were $2500 per worker in 1990. In the same year average US wages plus benefits were equal to $31 200. With parameters set to their benchmark values, the steady-state wage in our model is around 93 per cent of output. That suggests we set our training cost parameters equal to around one-third of quarterly output (that is, hL hH 0.333).18 We take estimates of firing costs ( f L, f H ) from a cross-country study by Bentolila and Bertola (1990). They suggest the following specification for expected firing costs (F ) F  N  (1
Pa)SP  Pa[(1
Pu)(SP  LC )  Pu(UP  LC )]

(1.32)

where N is pay for the notice period, Pa is the probability that the dismissal is appealed to the courts, Pu is the probability that the dismissal is ruled unfair by the courts, SP is the severance payment, LC represents the employer’s legal costs from going to court, and UP is the payment for unfair dismissal. They use the following parameter values for the UK: N  1.5 months’ pay, SP 1.5 months’ pay, Pa 0.0923, Pu 0.11, LC 2 months’ pay, and UP2 months’ pay. Working through the arithmetic, we get an expected figure equal to 3.2 months’ pay. We round down and set both f L and f H equal to output in one quarter. Consequently, f L f H 1.000. The parameter  captures the elasticity of job matches with respect to total search effort sn (see equation (1.30)). More conventional matching functions use the unemployment rate in place of sn. Empirical estimates of the elasticity of job matches with respect to the unemployment rate tend to be close to one-half (Petrongolo and Pissarides, 2001). We choose to

A matching model of non-employment and wage pressure

23

set 0.5 also.19 Following the literature (Millard and Mortensen, 1997; Neiss and Nelson, 2001) and empirical evidence, we set the real discount rate r to 1 per cent per quarter.20 We have directly assigned values to 11 of our 18 parameters. Benchmark values are chosen for the remaining seven parameters such that seven conditions hold in steady state. These conditions are as follows: nL  0.080 nH  0.064 sH  2.343 sL v  0.549 m(sn, v) bH  0.260 wH bL  0.5 * bH v c  0.333 m(sn, v)

(1.33) (1.34) (1.35) (1.36) (1.37) (1.38) (1.39)

Conditions (1.33) and (1.34) put restrictions on the fraction of the population that are non-employed L-types and H-types in steady state. Condition (1.35) puts a restriction on the probability that an H-type enters employment in a given period relative to the probability that an L-type enters employment in a given period.21 Restrictions (1.33) to (1.35) were all derived from Labour Force Survey (LFS) data. From the LFS it is possible to obtain a 26-group breakdown of the non-employed population. The unemployed can be categorized according to the current duration of their unemployment spell (less than six months and greater than six months), while the inactive can be categorized according to the stated reason for their inactivity (with up to 24 different reasons available). The categories are shown in Table 1.3 together with the average quarterly transition rate into employment and the average fraction of the working-age population that each category represented during the period 1993 to 1999 (Schweitzer, 2003). After ranking each of the 26 categories of the non-employed listed in Table 1.3 according to the rate at which they transition into employment, we define a cut-off point and label those above this point high effort searchers (H-types) and those below low effort searchers (L-types). We aim, so far as possible, to label as H-types all those who transition at a rate close to the rate of the short-term unemployed (34.1 per cent per quarter). Most of the remainder we label as L-types. There is one exception to this rule: we

24

Incentives to work

Table 1.3 Average transition rates and average stocks of 26 categories of non-employment (1993–99) Status

Average quarterly transition rate into employment (%)

Average stock (as percentage of working-age population)

Type

34.1 14.2

3.0 2.8

H H

Seeking work, but unavailable to start Student 27.5 Looking after family 16.4 Sick (temporary) 11.8 Sick (long-term) 3.9 Other reason 35.2 No reason 25.9

0.3 0.1 0.0 0.0 0.1 0.0

H L L – H H

Not seeking work, and would like to work Waiting on application 25.2 Student 16.4 Looking after family 5.6 Sick (temporary) 7.6 Sick (long-term) 1.3 Discouraged 4.3 Not started looking 16.4 Other reason 12.4 No reason 19.0

0.0 0.7 2.0 0.3 1.8 0.2 0.3 0.5 0.0

H L L L – L L L L

Not seeking work, and would not like to work Waiting on application 29.5 Student 13.4 Looking after family 3.5 Sick (temporary) 4.8 Sick (long-term) 0.7 Job not needed 3.4 Retired 1.9 Other reason 8.8 No reason 45.6

0.0 3.1 5.0 0.2 4.0 0.5 1.4 0.5 0.2

H L – L – – – L H

Unemployed 6 months  6 months

count the long-term unemployed (at least six months) as H-types despite the fact that a few L-types have higher transition rates. Finally, six categories marked with a dash in the ‘Type’ column are excluded from our model on the grounds that they have very low transition rates. These individuals are mainly either long-term sick or early retirees.

A matching model of non-employment and wage pressure

25

It is now reasonably straightforward to see how we obtained restrictions (1.33) to (1.35). Taken from Table 1.3, non-employed L-types represent on average 8 per cent of the working-age population.22 Non-employed H-types represent on average 6.4 per cent of the working-age population. Moreover, L-types transition at a weighted average rate of 10.8 per cent per quarter while H-types transition at a weighted average rate of 25.3 per cent per quarter. Taken together, this means we would like our model to generate steady-state non-employment rates of nL 0.080 and nH 0.064. We would also like H-types to enter employment 25.3/10.8 (2.343) times more frequently than L-types, which in turn means they must search with 2.343 times more effort than L-types. Restriction (1.36) says that in steady state the expected duration of a vacancy should be equal to 0.549 per quarter or a little over seven weeks. This is the average expected duration of UK Jobcentre vacancies over the period 1993 to 1999, where the expected duration is defined as the inverse of the probability that a vacancy is filled, that is, the inverse of the number of placings divided by the previous period stock of unfilled vacancies.23 Restriction (1.37) says that in steady state the replacement ratio (state benefits available to high effort searchers divided by the high effort searcher’s wage, conditional on receiving benefits) equals 0.26 as suggested by Millard and Mortensen (1997). We believe that state benefits available to low effort searchers should be less than those available to high effort searchers (that is, bH bL).24 However, we have little or no idea as to the relative magnitudes. For simplicity we arbitrarily set L-type benefits equal to one half of H-type benefits (see restriction (1.38)). Survey information reported in Hammermesh (1993) for the United States suggests that the total cost of advertising a vacancy is equal to around one month’s output. This restriction is imposed by (1.39). Table 1.4 shows the steady-state values of the endogenous variables in the model. Notice that wL wH in the steady state. This result, which may seem counter-intuitive, flows from the fact that L-types must be compensated for their higher value of home production/leisure while both types of worker are equally productive. One way of reversing this result would be to allow hL hH so that L-types are more expensive to hire.25 This might reflect the possibility that L-types require more training to perform a given task because they are less attached to the labour market. The wage of both L and H-types is above 90 per cent of output (recall that p1). That is considerably higher than empirical estimates for the UK labour share of around 70 per cent. This difference largely reflects the fact that capital is not present in this model. The only cost firms face is the cost of posting vacancies. They do not have to pay interest and depreciation on capital.26 Consequently it is more sensible to think of p as output net of capital costs.

26

Incentives to work

Table 1.4 Steady-state values of the endogenous variables in the benchmark case Variable

Description

wL wH sL sH v NL NH

Wage rate of L-types Wage rate of H-types Search effort of L-types Search effort of H-types Vacancy rate Non-employment rate of L-types Non-employment rate of H-types

Quarterly value 0.944 0.930 0.199 0.466 0.017 0.080 0.064

The steady-state vacancy rate (v0.017) is approximately equal to the number of vacancies per head of working-age population over the period.27 sH/sL, nH and nL are at their steady-state values by construction. Model Properties To illustrate the properties of the model we examine the steady-state responses of the seven endogenous variables in the model (v, sL, sH, nL, nH, wL, wH) to three permanent exogenous shocks. We consider (1) a permanent 1 per cent increase in worker productivity ( p), (2) a 50 per cent cut in benefits to L-type non-employed (bL), and (3) a 50 per cent cut in the hiring costs of both L and H-types (hL and hH ). Impact of a productivity rise Our model would be productivity neutral were it not for the fact that a number of important (opportunity) cost parameters are not indexed to productivity. As it is, when productivity rises, employing people becomes more attractive since (1) wages are in part held down by unchanged state benefits, (2) hiring costs are lower compared to the output of a filled job, and (3) firing costs are lower compared to the output of a filled job. Taken together, this encourages firms to post more vacancies in steady state (Figure 1.2a). The increase in vacancies induces greater search effort (when vacancies rise, the marginal return to an extra unit of search is higher). The search effort of L-types rises by more than the search effort of H-types because, given the nature of the cost of search function, it is cheaper for L-types to increase their search effort (Figure 1.2b). Because sL rises by more than sH, nL falls more quickly than nH (Figure 1.2c). The final panel (Figure 1.2d), which plots the two wage rates for each type (wL and wH ) and the aggregate wage rate (w), is a little harder to understand.

27

A matching model of non-employment and wage pressure 6 12

v

5

sl

% change

4 3 2

6 sh

4 2

0

0

(b) Search effort

(a) Vacancies

Percentage point change

8

1

0

1.25


0.2

1

wh
0.4

nh


0.6
0.8

wl

w

0.75 0.5 0.25

nl 0


1

(c) Non-employment Figure 1.2

% change

% change

10

(d) Wages

Responses to 1 per cent productivity rise

First, note that both wage rates rise by an amount close to the change in productivity (1 per cent). That is because p is a major determinant of the surplus from the match that is split in the same ratio as before (50:50). Second, note that wH rises by more than wL. With reference to the wage equations (1.25) and (1.26) this is at first glance surprising. The reservation wage of H-types declines by proportionately more than the reservation wage of L-types (because the value of their home time falls by proportionately more). Moreover, L rises by proportionately more than H (because nH falls by proportionately more than nL). Both of those effects put proportionately more upward pressure on wL than on wH. But these effects are dominated by the increase in L, which measures the probability that a firm matches with an L-type given that it matches with someone. Essentially, the probability that the firm matches with an L-type rises by more than the probability that the firm matches with an H-type because the L-types increase their search effort by more than the H-types. This

28

Incentives to work

improves the bargaining power of H-type individuals because a higher surplus is generated from a match between H-types and firms. Thus high effort searchers can get a share of this surplus and both parties will still be better off.28 Impact of a cut in benefits for low effort searchers Figure 1.3 shows the impact of a 50 per cent cut in the benefits paid to L-types (bL). Such a cut may embody the recent policies of restricting the availability of student grants. The cut immediately increases the search effort of L-types, which encourages firms to create more jobs. The extra vacancies induce extra search effort by H-types although their proportional response is much lower since their benefits are unchanged. The extra search of both groups helps to reduce their non-employment rates, but the proportional fall in nL dominates. Aggregate wages decline as might be expected. But it is interesting to note that the decline in wL is partly offset 16

80

v

14 60 % change

% change

12 10 8 6

sl

40 20

4 2

sh 0

0

(a) Vacancies

(b) Search effort 0.5 nh


1
2
3

nl


4

wh
0.5
1

wl


1.5

(c) Non-employment Figure 1.3

w

0 % change

Percentage point change

0

(d) Wages

Responses to 50% fall in low effort benefits

29

A matching model of non-employment and wage pressure

by a rise in wH. As before, the increase in wH reflects the increased likelihood that the firm will run into a more costly L-type. Impact of a hiring subsidy Finally we consider the impact of a hiring subsidy to employers. This is modelled as a 50 per cent cut in both hL and hH.29 Figure 1.4 shows that vacancies and search effort rise. sL rises by proportionately more than sH, which leads to a proportionately larger decline in nL compared to nH. It is important to note that these falls in non-employment hinge critically upon our assumption of exogenous job destruction so that a hiring subsidy increases job creation but has no impact upon job destruction. Mortensen and Pissarides (1999) develop a matching model with endogenous job destruction where hiring subsidies can increase unemployment by encouraging firms to cut existing jobs sooner in order to receive the subsidy for creating new jobs. This rise in the incidence of unemployment dominates the 6 12

v

5

sl

4 % change

% change

10 3 2

6

2

0

0

(b) Search effort

0

1.25 wh


0.2 nh


0.6 nl


1

wl

0.75 0.5 0.25 0

(c) Non-employment Figure 1.4

% change

1


0.4


0.8

sh

4

1

(a) Vacancies

percentage point change

8

(d) Wages

Responses to 50% fall in hiring costs for non-employed

w

30

Incentives to work

decline in the duration of average spells. In any case, the reservation wages of both groups decline, which puts downward pressure on wL, wH, and w. But this is more than offset by the rise in the vacancy/non-employment ratios ( L, H ) so that all wages rise. Once again wH increases by proportionately more than wL because L increases. All three exogenous shocks (productivity rise, L-type benefit cut and a hiring subsidy) lead to a larger percentage point decline in nL compared to nH. This suggests that such shocks are unlikely to account for the observed movements in inactivity and unemployment. The next section investigates these issues in more depth.

NON-EMPLOYMENT AND WAGE PRESSURE In this section we use our model to investigate the relationship between (changes in) unemployment, inactivity and wage pressure. Particular attention is paid to the recent UK experience of a falling unemployment rate coupled with a relatively constant inactivity rate, which is broadly typical of the European experience. Figure 1.5 plots a long-run unemployment rate series and a long-run inactivity rate series alongside nH (the H-type non-employment rate) and nL (the L-type non-employment rate). The denominator in each case is the working-age population and all series are derived from the LFS. As explained above the categorization of individuals into H and L-types is based on a ranking of the observed transition rates of 26 different categories of non-employment (Table 1.3). Essentially H-types are the unemployed plus one or two others such as inactives who are waiting for the outcome of a job application. L-types are a subset of the inactive that excludes those with very low transition rates (principally the long-term sick). While nH and nL differ from actual unemployment and inactivity, each constructed series has a similar profile to its ‘real world’ counterpart. In what follows, the terms nH and the unemployment rate, and nL and the inactivity rate, are used interchangeably. This framework highlights the well-known fact that there is no reducedform relationship linking either the unemployment rate or the inactivity rate to wage pressure (by which we mean the productivity-adjusted real wage or w/p).30 This is because the ‘trade-off ’ between the unemployment rate and/or the inactivity rate and wage pressure depends on the source of the shock that has caused the unemployment rate and/or the inactivity rate to move. We can illustrate this point with a simple diagram. Figure 1.6 plots a sequence of the long-run unemployment rate/wage pressure outcomes following three different types of shock.31 They are not

31

10 9 8 7 6 5 4 3 2 1 0 1979

30

U

nL 25

I

nH

20 15

%

%

A matching model of non-employment and wage pressure

10 5 0 1984

1989

1994

1999

Note: Inactivity rate is on the right-hand scale. All other rates are on the left-hand scale.

Figure 1.5

Unemployment, inactivity, nH and nL

Phillips curves in the usual sense of the word since they are drawn in real space. The top line represents the sequence of long-run unemployment/ wage pressure outcomes as the hiring cost is reduced (by means of a hiring subsidy) first by 5 per cent, then by 10 per cent and so on down to zero. The middle line represents the sequence of long-run unemployment rate/wage pressure outcomes as productivity rises first by 0.25 per cent, then by 0.50 per cent and so on up to 5.00 per cent. Finally, the bottom line represents the sequence of long-run unemployment rate/wage pressure outcomes as state benefits to the inactive are reduced first by 2.5 per cent, then by 5.0 per cent and so on up to 50.0 per cent.32 Figure 1.6 indicates that we cannot look at the recent movements in the actual unemployment and inactivity rates apparent in Figure 1.5 and draw reliable inferences on ensuing changes in wage pressure. A more reliable approach, and one that we follow here, is to concentrate instead on finding some combination of plausible shocks that could account for the 3.1 percentage point reduction in nH and the 0.7 percentage point reduction in nL that took place during the period 1994 to 2000. Of course it is (nearly) always possible to generate a 3.1 percentage point reduction in nH and a 0.7 percentage point reduction in nL by shocking just two parameters in the model.33 Moreover, once the two parameters that are to be shocked have been chosen, then the shocks to those parameters are uniquely determined. That is to say, if we hypothesized that the 3.1 percentage point reduction in nH and the 0.7 percentage point reduction in nL were explained solely by

32

Incentives to work

1 hL/H shock

Cumulative change in w/p (%)

0.8 0.6 0.4 0.2

p shock

0 3

4

5


0.2
0.4

6

nH (%) 7

bL shock


0.6 Figure 1.6

Unemployment/wage pressure loci

variations in state benefits (that is, solely by variations in bH and bL), then we could solve for that unique change in bL and that unique change in bH that would deliver these reductions in nH and nL.34 With 18 exogenous parameters there are up to 153 different shock pairings that could have caused the observed movements in nH and nL.35 In order to narrow the search somewhat, we focus on just seven of the 18 (L, bL, bH, f L, f H, hL and hH ). Our justification for taking this approach is simply that, with the exception of L (which measures the fraction of L-types in the population), these are the main policy parameters in the model. Moreover, to simplify things further, we assume that any change in hiring or firing costs affects H and L-types equally. That means we are left with just ten possible shock pairings. These shock pairings, together with the size of each shock necessary to cause the observed reductions in nH and nL, are presented in Table 1.5. Table 1.5 should be read as follows. If one takes the view that the falls in nH and nL observed during the period 1994–2000 were due entirely to changes in state benefits then, looking at the fifth row, unemployment benefits would need almost to have halved while inactive benefits would need to have risen by more than 10 per cent. Reading across to the final two columns we see that, if these shocks had occurred, vacancies should have risen by a quarter while the labour share should have fallen slightly. The model would not solve for the final shock pairing. Intuitively, if we

33

A matching model of non-employment and wage pressure

Table 1.5 Shocks that could have generated the falls in n H and nL observed in the UK over 1994–2000 Shock pairing


L, bH L, bL L, f L/H L, hL/H bH, bL bH, f L/H bH, hL/H bL, f L/H bL, hL/H f L/H, hL/H

Adjustment necessary to generate observed falls in nH and nL

X

X


L ↑ 9.4%, bH ↓ 40.9% L ↑ 60.6%, bL ↓ 62.0% L ↑ 40.6%, f L/H ↓ 124.7% L ↑ 29.4%, hL/H ↓ 83.6% bH ↓ 47.5%, bL ↑ 10.7% bH ↓ 52.9%, f L/H ↑ 38.0% bH ↓ 77.3%, hL/H ↓ 100.0%, bL ↑ 105.8%, f L/H ↓ 327.0% bL ↑ 28.9%, hL/H ↓ 128.8% N/A

Implications for v(a) ↑

25.0% 18.8% ↑ 18.8% ↑ 43.4% ↑ 25.0% ↑ 25.0% ↑ 12.5% ↑ 25.0% ↑ 68.75% N/A ↑

w/p

↓

0.3% 0.2% ↑ 4.5% ↑ 0.8% ↓ 1.4% ↓ 2.8% ↓ 2.5% ↑ 10.9% ↑ 0.5% N/A ↓

Note: (a) Over the period 1994–2000, the stock of vacancies at job centres more than doubled, while the labour share rose by around 3%.

impose the restrictions f H f L and hH hL, then there is no combination of (common) shocks to H-type and L-type hiring and firing costs that would deliver such a markedly different response from H-type and L-type non-employment. We can rule out some of the shock pairings in Table 1.5 on the grounds that the magnitude of the implied shocks is implausible. For example, if one takes the view that the falls in nH and nL were due to a combination of a change in L and a change in f L/H, or that they were due to a change in bL and a change in f L/H, then firing costs would have to fall so much that they turned negative. In other words, the government would be granting a firing subsidy. These pairings are marked with an ‘X’ in the second column. The most intuitively appealing pairing involves a negative shock to bH and a positive shock to L.36 This pairing, which appears in bold in Table 1.5, also leads to a rise in vacancies and an easing of wage pressure. Although the real value of unemployment benefits has not changed much since the mid-1990s (Nickell and van Ours, 2000), there have been steps to tighten eligibility which help to reduce the value of being unemployed relative to working. Elements of this increased strictness include the introduction of the Jobseeker’s Allowance (JSA) in 1996, which replaced unemployment benefit and income support for the unemployed. JSA tightened eligibility partly by requiring recipients to sign a ‘jobseeker’s agreement’ detailing the steps that the claimant will take in order to find work. This agreement is

34

Incentives to work

assessed fortnightly. If the claimant cannot prove that all reasonable steps have been taken to satisfy the agreement, or refuses to accept a reasonable offer of work, then JSA payments can be terminated. The introduction of various welfare-to-work schemes since the late 1990s, such as the New Deal for Young People (NDYP) in 1998, has also made it much harder for the unemployed to receive benefits indefinitely. For example, under the NDYP, 18–24 year olds who have been continuously claiming unemployment benefit for six months are required to enter a Gateway programme for up to four months during which they are offered intensive job finding assistance.37 Those who have not found work within this period are then offered options including subsidized employment and full-time education/training. However they cannot return to receiving unemployment benefits. We can expect the NDYP to put downward pressure on real wages by reducing the incidence of long-term unemployment and encouraging job creation. Similar schemes aimed at other target groups, including lone parents, were also introduced in the late 1990s but were initially voluntary. There are also grounds for believing that L, the proportion of the working-age population who are L-types, has risen. L-types are principally individuals who place a relatively high value on leisure/home production. One factor likely to influence L is the number of full-time students of working age, which has risen by around 490 000 since 1994 from 7 per cent of the working-age population to 8 per cent. According to the model, a rise in the student share could be expected to moderate the upward pressure on search effort arising from a decline in bH. However, any moderating effect may have been partly offset by the decreasing availability of student grants over the period that may have simultaneously encouraged students to look harder for work, especially part-time jobs.38 According to the Labour Force Survey the share of part-time employees who were still in education rose from 10 per cent in 1984 to 13 per cent in 2000. The preferred shocks that generate a 3.1 percentage point reduction in the unemployment rate, coupled with a 0.7 percentage point reduction in the inactivity rate, imply a small decrease in wage pressure. But the actual labour share rose (by around 3 per cent) during the period under consideration. One way to address this might be to consider the possibility that more than two types of shock occurred. For example it seems plausible that recent government incentives have lowered hiring costs. Other things equal, our model predicts that this would put upward pressure on the labour share. As a sensitivity check on the findings, we conducted an alternative set of simulations where the quarterly firing costs ( f L, f H ) were reduced from one unit of output to half of one unit. Consequently we set f L f H 0.5. One reason for this reduction is that the parameters used to generate our benchmark values of firing costs in Table 1.2 are based on data from the 1970s

A matching model of non-employment and wage pressure

35

and 1980s.39 But the unionized share of the workforce, which is likely to be positively related to the probability that a dismissal is appealed to the courts, has fallen over the 1990s.40 In any case, the finding that shocks to L and bH provide the most plausible explanations for the falls in unemployment and inactivity over 1994–2000 continues to hold. However, the movements in both parameters are now more muted. For example, bH is now required to fall by 28.1 per cent and L to rise by 5.3 per cent (the shifts in Table 1.5 are
40.9 per cent and 9.4 per cent respectively). One possible explanation for these lower responses is that lower firing costs mean a higher equilibrium rate of job creation so that benefits and the L-type share do not have to change as much to achieve a given change in unemployment and inactivity. We also assumed that L-type non-employed receive no state benefits (bL 0). This immediately rules out an investigation into the effects of changing benefits to non-employed L and H-types. However, shocks to L and bH continue to provide the most plausible explanations for the falls in unemployment and inactivity over 1994–2000. In this case L rises by 1.3 per cent and bH declines by 40.1 per cent.41

CONCLUSIONS Since the mid-1990s the proportion of unemployed people among the population in most European countries has fallen, while the proportion of inactive people has remained relatively constant. This poses some important questions for policy-makers. Does the reduction in the pool of unemployed people mean that the labour market has necessarily tightened, putting upward pressure on wages as many models, which effectively ignore the inactive, would suggest? Or will any upward pressure on wages be moderated by the stock of inactive people? In an attempt to shed some light on these issues we have built and calibrated a theoretical model of the labour market that includes a role for the inactive. Specifically we have extended the model of variable search intensity described in Chapter 5 of Pissarides (2000) to include two types of non-employed individual: H-types and L-types. H-types (who represent the unemployed) and L-types (who represent the inactive) differ only in the value that they attach to leisure time/home production. The H-types have a lower value of leisure time/home production than the L-types, which means that they have a lower reservation wage, choose to search harder for work and therefore enter employment more readily. A key feature of our model is that the wage rates available to each type are interdependent. Among other things that means that if L-types are

36

Incentives to work

encouraged to search harder by a reduction in state benefits, such as student grants, then that will have implications for the wage rate open to H-types, and hence for the steady-state non-employment rate of H-types. One important result is that, in our model, it is impossible to write down a single reduced-form relationship linking either the unemployment rate or the inactivity rate (or some combination of the two) to wage pressure. That is because the relationship depends on the source of the shock that has caused either the unemployment rate or the inactivity rate to move. Consequently it is not possible to look at a plot of unemployment and inactivity and then draw unambiguous conclusions about the implications for wage pressure. We then conducted the following exercise. First we selected a subset of the 18 parameters in our model that, by and large, could have been affected by changes in government policy. Second we took all possible pairings of this subset of the parameters and calculated the size of the shock to each parameter in each pair that would have been necessary to generate the movements in the UK unemployment rate and the inactivity rate observed since the mid-1990s. Some of the shock pairings were then ruled out on grounds of plausibility. For example, if one hypothesized that the only two shocks were to inactive benefits and firing costs, then the government would need to be offering a substantial firing subsidy in order to generate the observed reductions in unemployment and inactivity. The most intuitively appealing pairing involved a 40 per cent reduction in unemployment benefits, coupled with a 10 per cent increase in the fraction of L-types in the population. The decline in unemployed benefits could reflect the introduction of the Jobseeker’s Allowance in 1996 and the New Deal for Young People in 1998. The rise in the share of those who place a relatively high value on leisure time/home production could reflect the increase of almost 500 000 in the number of full-time students over the period. To the extent that the rise in the inactivity rate/unemployment rate ratio in other European countries reflects similar shocks, then our analysis suggests that the implications for underlying wage pressure are relatively benign.

NOTES 1.

We are grateful to Ben Martin who supplied us with his routine nlsolve that can be used to solve systems of non-linear equations in Matlab. This chapter has benefited from discussions with Charlie Bean, Ian Bond, Mike Joyce, Jens Larsen, Steve Millard, Steve Nickell, Chris Pissarides and Mark Schweitzer. Thanks to Rebecca Hall for excellent research assistance. The views expressed are those of the authors and do not necessarily reflect the views of the Bank of England or the Monetary Policy Committee.

A matching model of non-employment and wage pressure 2. 3.

4. 5.

6. 7. 8. 9. 10. 11.

12.

13. 14.

15. 16.

17.

37

[email protected]; [email protected], Bank of England, Threadneedle Street, London, EC2R 8AH, UK. This suggests that the inactive (L-types) have lower transition rates into employment. But this mapping may not be exact because some categories of self-reported inactive workers have higher flow rates into work than the self-reported unemployed (Schweitzer, 2003). It is possible for the inactive to search less than the unemployed yet have higher transition rates because they have higher job offer probabilities. Our empirical work uses differences in realized transition rates to identify high and low effort searchers. See de Koning et al. (2004). The relative stability of UK inactivity over the recent upswing has also attracted some comment (Gregg and Wadsworth, 1999; Nickell, 2001; Brittan, 2001). Data from Eurostat suggest that the share of the working-age population who are in tertiary level education has increased steadily from 4.1 per cent in 1992 to 5.1 per cent in 2002. On this basis, the rate increased by 2.4 percentage points to 5.3 per cent in the UK, by 0.2 percentage points to 5.2 per cent in France, by 1.6 percentage points to 6.6 per cent in Spain, by 0.7 percentage points to 4.7 per cent in Italy, by 1.4 percentage points to 5.4 per cent in Denmark and by 0.1 percentage points to 3.8 per cent in Germany. A detailed analysis of the transition rates into employment of many different categories of the non-employed is contained in Schweitzer (2003). Under ILO definitions it is also possible to search for and find a job before one becomes available for work. This might be particularly relevant for students moving from college into employment. We thank Stephen Nickell for giving us this idea. Chris Pissarides has pointed out that the inactive may play a role in a discrete time framework where both the inactive and unemployed have the same intensity conditional upon searching. However, the unemployed are more likely to be searching in any given period. The assumption that lL  lH may reflect a higher taste for non-market activities among L-types. It is assumed that the firm can always identify L and H-types from the method by which both parties meet, and possibly their labour market histories. For example, H-types use formal job search channels and tend to have shorter spells of non-employment. Consequently both types can be paid different wages for doing the same job, and have different training costs. This assumption of a fixed and exogenous rate of job destruction means that shifts in hiring and firing costs will only affect non-employment through their impact on job creation. With endogenous job destruction, such movements will affect both job creation and job destruction (Mortensen and Pissarides, 1999; Pissarides, 2000). This means that our findings concerning the impacts of shifts in hiring and firing costs may not generalize to the case where job destruction is endogenous. In principle, firing costs can differ between L-types and H-types for several reasons, including differences in their pay rates during any period of notice. It is assumed that new hires are governed by the same wage structure as those workers already employed by the firm. In particular hiring and firing costs do not form part of the negotiations when the worker and firm first meet about the possibility of forming a match. All the value functions describe the steady state since it is assumed that the various asset values are constant over time. A numerical example might help. Imagine that I threaten to resign from my company. If I do this, I know that it will take me just two weeks to find another job. I also know that my company will have to advertise my job at a cost of £50 per week for four weeks before finding a replacement. That means each week that I am in employment, rather than nonemployed, I save society a total of 4*£50/2  £100 per week. This £100 surplus is then shared out according to my bargaining strength, , and added to my weekly wage. It is arguable that this is a lower bound on the true job destruction rate given the nature of the LFS which will not capture many transitions between quarterly surveys.

38 18.

19.

20. 21. 22. 23.

24. 25. 26. 27. 28. 29.

30. 31. 32. 33. 34.

35. 36.

Incentives to work This is derived as [2500/(31200/4)]*(100/93). It might be thought that training costs for L-types might be a little higher than training costs for H-types since L-types tend to have longer non-employment durations. This is something that could be subsequently investigated, though we do not believe it would have any material impact on the properties of the model. The partial effect of an increase in n is to lower search intensity (with more nonemployed people the returns to search are reduced: see (1.27) and (1.28)). That might suggest that we shade  up above 0.5. But, in general equilibrium the impact on s of a change in n will depend on what has caused n to change in the first place. Hence, we see no reason to move away from 0.5. The ten-year spot real interest rate derived from UK government index-linked gilts has averaged 3.5 per cent since they were first introduced. This implies a real rate of 0.88 per cent per quarter. From equations (1.3) and (1.4) it is clear that the relative probabilities of entering employment simplifies to the relative search effort supplied. Of course this 8 per cent figure is much lower than the actual inactivity. It has been suggested that only around one-third of all vacancies in the economy are advertised at Jobcentres. By calibrating our model in this way, we are implicitly assuming that non-Jobcentre vacancies are filled at broadly similar rates to Jobcentre vacancies. Particularly since we have excluded the long-term sick, who may be on more generous benefits, from our definition of L-types. It is also possible to reverse this result if we assume that L-types search less because they have a lower expected return to job search than H-types. Pissarides (2000) discusses extending the basic matching model to allow for capital. Assuming that the total number of vacancies is three times those listed with Jobcentres. Essentially H-types can credibly say, ‘Well, I think you ought to pay me a little more, because if you don’t give me the job, you’re likely to run in to an L-type next time, and they’re more expensive anyway.’ Such subsidies form part of many New Deal programmes that are currently available to the non-employed. For example, up to 20 per cent of non-Gateway participants on the New Deal for Young People have taken jobs with a private sector employer for six months where the wage was subsidized by up to £60 per week (see www.dfes.gov.uk/ statistics/DB/SFR/s 0277/index.html). Defined in this way, wage pressure is something distinct from labour market tightness. We believe the latter to be explicitly a disequilibrium phenomenon: see Brigden and Thomas (2003). More precisely, we plot the new steady-state H-type non-employment rate against the percentage difference between the new steady-state and the old steady-state aggregate productivity-adjusted wage rates. One interpretation of Figure 1.6 is that demand shocks lead to a negative relationship between wage pressure and unemployment while supply shocks lead to a positive relationship. Strictly, it is not always possible because some parameters may be constrained (for example, the two search effort parameters are bounded by 0 and 1). An alternative procedure might be to calibrate the model to match the 1994 nonemployment rates of both groups and the extent to which changes in all the parameters can account for movements in nH and nL over 1994–2000. This alternative is ruled out by the fact that we do not have good measures of how several of the parameters have actually evolved over the period. Calculated as 18!/(16!2!) 153. On the face of it, the pair of shocks involving a decline in bH and a rise in bL also seems plausible. However the real level of benefits available to inactive bL is unlikely to have risen since 1994. Apart from the changes to student maintenance, the disability benefit regime was sharply tightened in 1995.

A matching model of non-employment and wage pressure 37.

38. 39. 40. 41.

39

Blundell et al. (2001) find that the New Deal for Young People has raised the unemployment-to-employment transition rates for males by around 20 per cent. There is no significant effect for females who comprise around a quarter of the programme participants. Riley and Young (2001) provide evidence that the NDYP has reduced wage pressure. For example, since 1998 new entrants into full-time education have generally been expected to contribute around £1000 towards tuition costs. Between 1991 and 1998 the average student grant fell from around £1500 to £1200 per year. See Bentolila and Bertola (1990). This share fell from 36 per cent in 1992 to 30 per cent in 1998 (Wadhwani, 2000). In an earlier draft of the chapter we carried out these simulations in a simpler model without hiring and firing costs. This rules out an analysis of the 7 of the 10 shock pairings shown in Table 1.5 that assess the impacts of changes in hiring and/or firing costs. This framework produced similar results for the shock pairings (L), (bH) and (L), (bL) to those in the first two rows of Table 1.5. The model would not solve for the (bH ), (bL) pairing.

REFERENCES Bentolila, S. and G. Bertola (1990), ‘Firing costs and labour demand in Europe: how bad is eurosclerosis?’, Review of Economic Studies, 57, 381–402. Blundell, S., M. Dias, C. Meghir and J. Van Reenen (2001), ‘Evaluating the Employment Impact of a Mandatory Job Search Assistance Program’, Institute for Fiscal Studies Working Paper No. 01/20. Brigden, A. and J. Thomas (2003), ‘What Does Economic Theory Tell Us about Labour Market Tightness?’, Bank of England Working Paper No. 185. Brittan, S. (2001), ‘The rise of the inactive man’, Financial Times, 21 June, p. 23. de Koning, J., R. Layard, S. Nickell and N. Westergård-Nielsen (2004), ‘Policies for Full Employment’, Department for Work and Pensions, http://cep.lse.ac.uk/ full_employment/layard_report.pdf. Garibaldi, P. and E. Wasmer (2005), ‘Equilibrium search unemployment, endogenous participation and labor market flows’, Journal of the European Economic Association, 3 (4). Gregg, P. and J. Wadsworth (1999), ‘Economic inactivity’, in P. Gregg and J. Wadsworth (eds), The State of Working Britain, Manchester, UK: Manchester University Press, pp. 47–57. Hammermesh, D. (1993), Labour Demand, Princeton, NJ: Princeton University Press. Millard, S.P. and D.T. Mortensen (1997), ‘The unemployment and welfare effects of labour market policy’, in D.J. Snower and G. de la Dehesa (eds), Unemployment Policy: Government Options for the Labour Market, Cambridge, UK: Cambridge University Press, pp. 554–72. Mortensen, D.T. and C.A. Pissarides (1999), ‘New developments in models of search in the labour market’, in O.C. Ashenfelter and D. Card (eds), Handbook of Labor Economics, Vol. 3B, Amsterdam: North Holland, pp. 2567–627. Neiss, K. and E. Nelson (2001), ‘The Real Interest Rate Gap as an Inflation Indicator’, Bank of England Working Paper No. 130. Nickell, S. (2001), ‘Has UK labour market performance changed?’, speech given to the Society of Business Economists, 16 May, London. Nickell, S. and J. van Ours (2000), ‘The Netherlands and the United Kingdom: a European employment miracle?’, Economic Policy, 30, 135–75.

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Petrongolo, B. and C.A. Pissarides (2001), ‘Looking into the black box: a survey of the matching function’, Journal of Economic Literature, 38, 390–431. Pissarides, C.A. (1998), ‘The impact of employment tax cuts on unemployment and wages: the role of unemployment benefits and tax structure’, European Economic Review, 42, 155–83. Pissarides, C.A. (2000), Equilibrium Unemployment Theory, second edition, Cambridge, MA, USA: MIT Press. Pissarides, C.A. (2003), ‘Unemployment in Britain: a European success story’, mimeo, London School of Economics. Riley, R. and G. Young (2001), ‘The Macroeconomic Impact of the New Deal for Young People’, National Institute of Economic and Social Research Discussion Paper No. 184. Schweitzer, M. (2003), ‘Ready, Willing, and Able? Measuring Labour Availability in the UK’, Bank of England Working Paper No. 186. Wadhwani, S.B. (2000), ‘British unemployment and monetary policy’, Bank of England Quarterly Bulletin, 40, 88–102.

A matching model of non-employment and wage pressure

41

DISCUSSION Javier Ortega This chapter has two main empirical motivations. First, it aims at modelling the transition to employment of individuals who declare themselves not to be searching for a job but who find one. This is particularly the case for students and individuals looking after their family. This observed transition has two possible interpretations. One interpretation is to consider that the individual goes from inactivity to unemployment and then to employment but that we cannot observe all these transitions since the survey questions are not asked ‘frequently enough’. Alternatively, the individual may have received a spontaneous offer (the ‘man in the pub’ hypothesis). The second empirical motivation of the chapter is to explain the evolution of the UK labour market between 1994 and 2000, which simultaneously experienced (i) a decline of the unemployment rate, (ii) a moderate reduction of the inactivity rate, and (iii) no change in wages (a low ‘wage pressure’). More precisely, the question addressed by the authors is that of determining which plausible shock(s) can explain this evolution. For that purpose, Andrew Brigden and Jonathan Thomas set up a matching model with search intensity à la Pissarides (2000). The economy is composed of two types of individuals. The first group is that of the ‘high effort searchers’, whose intrinsic characteristic is to have a low valuation of non-market activities. As a result, these individuals are ‘unemployed’ whenever they are non-employed. The second group is that of the ‘low effort searchers’, who are characterized by a high valuation of non-market activities. As a result, these individuals are ‘inactive’ whenever they are non-employed. Concerning the matching process, the authors assume on the one hand that there is a unique matching function for both types of non-employed, that is, that the channels of search for a job for both groups of individuals do not differ. Additionally, firms perfectly observe the candidate type (high effort searcher or low effort searcher) when meeting, and they can pay different wages to different types of agents. The combination of these two assumptions implies that the value of a filled vacancy is a weighted average of the value of employing each type of worker and that any change affecting any type modifies the wages of both types. I found this chapter very interesting and also very well structured. In addition, it is a very good example of integration of theoretical and empirical analysis. Its main contribution to the literature is to provide a new way of modelling inactivity which enables better exploitation of the data, at least in some dimension.

42

Incentives to work

I According to the individual’s intrinsic characteristic

E

U

Figure 1D.1

Brigden–Thomas’s labour market flows

The most general model for analysing labour market flows would be one that considers three states – inactivity (I), unemployment (U) and employment (E) – and which allows for transitions in both directions between these three states. However, such a model is in general not very tractable. An alternative model frequently used in the literature is one in which the direct transitions between inactivity and employment are not considered. The drawback of this second model is that it cannot represent the direct transitions from inactivity to employment observed in the data. The modelling choice made by Brigden and Thomas is a different one and can be represented in Figure 1D.1. The advantage of this specification with respect to the previous one comes from the fact that direct transitions between inactivity and employment are considered. The drawback is that the observed transitions between inactivity and unemployment cannot be exploited here. Then, the fundamental empirical question for the choice of a model is the quantitative relevance of the transitions between inactivity and employment as compared to the transitions between inactivity and unemployment. Concerning the matching structure, I am not convinced that the hypothesis of a unique matching function is the one that best suits the model. If the matching function is assumed to be unique, this means that the channels of search are the same for both types of individuals. If this hypothesis proved not to be realistic (for example, if the ‘man in the pub’ was much more important for the inactive than for the unemployed), an alternative would be to specify two matching functions. In that case, I expect the links between the market situation of ‘low effort searchers’ and the wages of ‘high effort searchers’ (and vice-versa) to be relatively weak.

A matching model of non-employment and wage pressure

43

Finally, and related to my preceding comment, it is important to underline that the classification of non-employed as ‘high effort searchers’ or ‘low effort searchers’ is done according to their observed probabilities of transition to employment. This is coherent with the modelling (since a unique matching function is assumed) but corresponds to only one of the possible interpretations of the data. Indeed, it is based on the implicit assumption that labour demand factors are not relevant (or are not the most relevant element) for explaining differences in transition rates among individuals. In other words, the differences in transition rates are assumed to depend on the different intrinsic characteristics of the agents regarding their valuation of non-market activities, that is, a supply-side element.

2.

Tax effects on work activity, industry mix and shadow economy size: evidence from rich country comparisons1 Steven J. Davis and Magnus Henrekson

INTRODUCTION Taxes on labour income and consumption expenditures encourage households to substitute away from the legal market sector in favour of untaxed activities – leisure, household production and the shadow economy. We investigate these substitution responses by relating measures of employment, market work hours, shadow economy size and the industry mix of market production activity to tax rate differences among rich countries. Our objective is to assess the long-run total response of these outcomes to persistent differences in tax rates on labour income, payrolls and consumption – collectively, personal taxes. By ‘total response’ we mean the direct effects that work through labour supply and demand plus indirect effects that involve government spending responses to available tax revenues. As in Brennan and Buchanan (1980), Krusell et al. (1996), Persson and Tabellini (2002) and Becker and Mulligan (2003) we recognize that taxing capacity affects government expenditures. In turn, many expenditure programmes affect labour supply incentives. Leading examples include government programmes for unemployment and disability insurance. Our sample of rich countries offers a modest number of data points. Despite this limitation, the broad-brush comparisons that we undertake are useful for several reasons. First, a focus on national outcomes provides information about the combined effect of taxes working through labour supply and labour demand channels. In this regard we stress that tax effects on hours worked and other outcomes cannot be inferred from labour supply elasticities alone. Our theory of task assignment implies that personal taxes have disproportionately large effects on the demand for less skilled workers. By most accounts, labour supply is also more elastic for less skilled workers. So, as personal taxes twist labour demand away 44

Tax effects on work activity, industry mix and shadow economy size

45

from less skilled workers, their negative effects on work hours and employment are amplified. Second, countries with high tax rates on labour and consumption have relatively generous tax-funded programmes for social security, disability insurance, sick leave assistance, unemployment insurance and general assistance. The benefit sides of these programmes alter labour supply incentives in ways that discourage market work activity, increase employment in the underground economy and alter the industry mix of market production activity. Insofar as government spending on these programmes responds to the availability of tax revenues, the full response to differences in taxing capacity includes the indirect effects that work through the expenditure side of government behaviour. Conceivably the indirect expenditure effects on the outcome variables under study are larger than the direct effects of taxes.2 Third, there are large, highly persistent differences between countries in tax rates on labour and consumption and in the scale of tax-funded social insurance programmes. This variation partly compensates for a modest number of data points. Moreover, labour market responses to persistent tax rate changes are probably bigger over the longer term, as imperfectly mobile factors of production migrate between sectors and activities in the wake of tax changes, and as slow-working welfare state dynamics come into play. The persistent character of national differences in personal tax rates makes them well suited for assessing long-run effects. These remarks suggest that national comparisons help to inform our thinking about the effects of taxes and taxing capacity. The evidence provides useful inputs for assessing the performance of economic theory and the success or failure of public policy – tax policy in particular. In this regard Prescott (2002, 2003) argues that French welfare would rise by 19 per cent in consumption-equivalent terms if France lowered its labour and consumption taxes to US levels. He bases this assessment on the cross-country empirical relationship between taxes, factor inputs and output per workingage person, as interpreted through the lens of a standard one-sector growth model. If Prescott’s assessment is correct, France and many other nations bear enormous costs for high tax rates on labour and consumption.3 In terms of Prescott’s framework and analysis, our study is useful for two reasons. First, the view that France’s relatively high tax rates cause its relatively low output per working-age person suggests other hypotheses that we address. Evidence on these hypotheses serves to support, qualify or undermine Prescott’s conclusion. Second, more detailed evidence about whether and how personal taxes affect work time and productive activity provides inputs for improved model-building and more refined policy analysis. Before proceeding, it will be useful to spell out some conventions regarding terminology. For our purposes ‘market production’ refers to output

46

Incentives to work

produced and incomes generated in legal markets, which are declared to the government and captured in the National Income and Product Accounts. The ‘shadow’ or ‘underground’ economy refers to the output and incomes generated in markets which are not declared to the government, particularly the taxing authorities. ‘Household production’refers to output produced for own consumption, as distinct from output produced and sold in formal or informal markets. ‘Leisure’refers to the time devoted to rest and intrinsically enjoyable activities that are otherwise non-productive. In line with this terminology, we think of household time as allocated between market production, underground production, household production and leisure. The chapter proceeds as follows. The following section provides background on the household and underground production sectors and additional motivation for our focus on long-run effects. The next section sketches a theory of task assignment and time allocation between market and non-market production sectors. The theory identifies characteristics of production technologies and factor inputs that lead to high or low tax responsiveness. Since these characteristics differ markedly across industries, the theory yields testable implications for the cross-country relationship between personal taxes and industry shares of employment and value added. The third section below describes conditions whereby OLS regressions yield consistent estimates for the total response to tax rate differences among countries. The fourth section describes the data in our sample of rich countries, and is followed by a section reporting evidence on the cross-country relationship of personal tax rates to the outcome measures. The next section reviews other evidence that speaks to the longrun total response to personal taxes, and a concluding section follows.

HOUSEHOLD PRODUCTION, UNDERGROUND ACTIVITY AND WELFARE STATE DYNAMICS Taxes on labour and consumption lead to tax avoidance and tax evasion on several margins. The tax-induced substitution of household production and leisure for market goods and services are legal forms of tax avoidance. The tax-induced substitution of underground work activity for employment in the legal market sector and the consumption of goods and services produced in the underground economy to escape taxation are illegal forms of tax evasion. The size of the household and underground sectors suggests the potential for a significant tax-induced diversion of productive activity away from the legal market sector. Eisner (1988, Table S.4) reports several estimates for the value of labour services supplied to the household production sector in the United States,

Tax effects on work activity, industry mix and shadow economy size

47

ranging from 24 to 48 per cent of official GNP. Juster and Stafford (1991) report that time devoted to household production by a typical US married couple is about three-quarters as much as hours worked for paid compensation. Greenwood et al. (1995) cite this evidence as motivation for business cycle models with home production in their review of work on the topic. As their survey attests, macroeconomics has increasingly recognized the significance of the home production sector. Nevertheless, few analyses treat both home production and taxation.4 Much economic analysis of taxation also neglects the underground sector. However, available evidence indicates that the shadow economy is sizeable, even in developed economies, and that taxes are a major stimulant to underground activity. In their survey of research on the shadow economy Schneider and Enste (2000, Table 7) report that the value of shadow economy output in the mid-1990s amounted to 16 per cent of official GDP in the average OECD country, ranging from about 7 per cent in Austria and Switzerland to 22 per cent or more in Belgium, Greece, Italy, Portugal and Spain.5 According to Giles and Tedds (2002, p. 66), ‘there is a consensus that in almost every country that has been studied the underground economy has been growing relative to GDP or GNP over the past two or three decades’. The importance of taxation is well established in previous research on the determinants of shadow economy size. In the words of Giles and Tedds (2002, p. 7), ‘Perhaps the single most commonly cited “driving force” of the underground economy is the actual, or perceived, tax burden.’ Likewise, Schneider and Enste (2000, p. 82) write that ‘In almost all studies, one of the most important causes of the increase of the shadow economy is the rise of the social security and tax burden.’ These research summaries strongly suggest that the impact of taxes on underground economic activity is an important part of the overall response to personal tax rate differences between countries.6 The foregoing remarks highlight the potential importance of tax-induced substitution away from production in the legal market sector to household and underground production activity. A distinct body of research on welfare state dynamics highlights the potential for such tax-induced substitution responses to cumulate over time, leading to much bigger tax effects in the long run than the short run. Lindbeck (1995) discusses several reasons for delayed private responses to the economic disincentives created by high tax rates and generous social insurance programmes. He argues that habits, attitudes and social norms restrain the influence of economic incentives on behaviour, and that these restraining influences can erode over time as a consequence of high tax rates and generous welfare state benefits. In this vein, Lindbeck et al. (1999) model the interplay between individual incentives and a social norm favouring

48

Incentives to work

work over welfare. The intensity of the norm, as felt by the individual, diminishes with the population share of welfare recipients. This interaction gives rise to the possibility of multiple equilibria and extended dynamics. Purely economic mechanisms can have similar effects. For example, Ljungqvist and Sargent (1995, section 4.3) model the effects of a breakdown in the monitoring process that deters abuse of the unemployment insurance system. In their analysis, an exogenous increase in unemployment leads to less effective monitoring of benefit claimants, which in turn allows for greater abuse. A sufficiently bad unemployment shock overwhelms the monitoring process and leads to a permanently higher rate of unemployment. Much other research on European unemployment stresses the potential for long and complex dynamic responses to shocks or to changes in unemployment insurance and other labour market institutions. Prominent examples include Ljungqvist and Sargent (1998) and Blanchard (2000). In the empirical work below we examine data on tax measures and outcome variables as of the middle 1990s. Broadly speaking, pronounced cross-country differences in tax burdens and social safety nets had been inplace for two decades or more by the mid-1990s – presumably long enough for any slow-working effects of taxation to have emerged.7 Moreover, the countries in our sample were hit by large negative shocks in the 1970s and early 1980s of the sort that could be expected to expose any latent instability or negative feedback loops that amplify long-run effects. In this respect a focus on outcomes in the mid-1990s is well suited to our main objective.

THEORY AND EMPIRICAL IMPLICATIONS How Personal Taxes Affect Time Allocation and the Choice of Production Sector Consider a household that chooses between market and non-market solutions for accomplishing a certain task such as painting its home exterior. Under the market solution, the household hires a professional and the transaction is subject to various taxes. Under the non-market solution, the household applies its own time to accomplish the task and avoids taxation. A third alternative is to hire someone under the table, thereby evading some or all taxes without incurring the time cost of a do-it-yourself approach. The analysis below focuses on the first two options, but a similar analysis could be applied to any choice between taxed and untaxed (or less taxed) alternatives. How do personal taxes affect the choice between market and household modes of production? To address this question, assume initially that labour

49

Tax effects on work activity, industry mix and shadow economy size

is the only input used to perform the task. For convenience, refer to the household making the choice of production mode as the ‘buyer’. Assume that the good or service in question is produced and consumed in a given quantity, and define the following notation: CH do-it-yourself cost in household production CM cost of buying the service in the market from a professional supplier WB buyer’s pre-tax wage per unit of time WP pre-tax wage of the professional supplier HB time required to accomplish the task by the buyer HP time required to accomplish the task by the professional tmarginal tax rate on the buyer’s labour income, including his or her mandatory contributions to social insurance funds s payroll tax rate levied on employers (that is, the buyer) m valued added tax (VAT) rate or sales tax rate. Note that (HB/HP ) measures the professional’s relative productivity at the task in question. When HB HP, the professional is more productive, and we may say that the professional has an absolute advantage in the activity. This case is likely to prevail in most circumstances but the buyer could enjoy an absolute advantage in certain cases. For example, the buyer might be highly able in many tasks, not just in her market speciality.8 The cost of production in the do-it-yourself case equals forgone aftertax wages: CH WB(1
t)HB.

(2.1)

That is, the time cost of self-supply amounts to WB(1
t)HB in forgone expenditures on other consumption goods. The cost of buying the service in a competitive market is CM WP(1s)(1m)HP.

(2.2)

It follows immediately that the buyer prefers the market solution when

CH  CM ⇔

WB HB (1  s)(1  m)  . WH HP 1
t

(2.3)

Equation (2.3) says that the market solution dominates when the professional’s comparative advantage – his relative productivity times the buyer’s relative wage – exceeds the tax factor [(1s)(1m)]/(1
t). For any

50

Incentives to work

given tax structure the comparative advantage ratio (WB/WP )(HB/HP ) determines task assignment and time allocation. Taxes alter private choices regarding time allocation and task assignment by changing the threshold comparative advantage ratio at which the market solution dominates. In the absence of taxes the privately optimal choice assigns the task to the person with comparative advantage. To see this point, observe that the right side of (2.3) equals one when s mt0. Observe also that the notax task assignment minimizes the opportunity value of the scarce time resources used up in accomplishing the task. In this sense privately optimal task assignments are socially efficient in the absence of taxes. In contrast, by raising the minimum comparative advantage required of the professional for the market solution to obtain, personal taxes drive a wedge between privately and socially optimal task assignments. Too few tasks are carried out in the market sector because of taxes and too little time is spent working in the market. Conversely too many tasks are carried out in the household (or underground) sector, and too much time is spent working outside the legal market sector. As taxes rise, marginal producers in the market sector are displaced by less efficient producers in the household sector, which raises the average cost of overall production while lowering average production costs in the market sector.9 Davis and Henrekson (2002) derive a version of (2.3) as a property of competitive equilibrium in a model with a continuum of consumption goods and households that allocate time between market production, household production and leisure. In their model, the market sector combines capital and multiple labour inputs to produce goods according to production technologies that exhibit constant returns to scale and smooth substitution among inputs. Households differ with respect to market wages, efficiency of home production technologies, and preferences over consumption goods. Thus, although we derived (2.3) in a very simple setting, it holds more generally. The key requirement underlying a condition like (2.3) is that the household be on the margin between working for paid compensation in the market and spending time at household production in some activity. Almost every working household is likely to satisfy this condition, especially in the long run when the household can exercise choice over market work hours. Choice of Production Sector with Capital Inputs Capital inputs lead to a more complex decision rule for the choice of production sector. When the professional enjoys an absolute productivity advantage at the task in question, capital requirements discourage a do-ityourself approach. Capital idleness in the home sector also discourages

Tax effects on work activity, industry mix and shadow economy size

51

self-supply. Taxes can raise or lower the relative capital costs of a do-ityourself approach depending on whether capital services are produced in the home or market sector and on the specifics of the tax system. Some additional notation will be helpful in developing these points: K units of capital applied during production PK price per unit of capital rreal interest rate geometric depreciation rate on capital Ta term that summarizes the effect of business-level taxes on capital income. Suppose first that capital services are supplied by the market sector regardless of who performs the task as, for example, when the household rents equipment but supplies its own labour services. Assuming that marketsupplied capital services are subject to the value added tax rate m, the cost expressions for the home and market options become10 CH WB(1
t)HB (r)(1
t)KPKH B

(2.4)

CM W P(1s)(1m)HP (r)KPKT(1m)H P.

(2.5)

and

The buyer now prefers the market solution when





W BH B (r  )KP K (1  m)T (1s)(1m) B
HP )  .  (H W PH P HPW P 1
t 1
t ˇ

(2.6)

The second term on the left side of (2.6) is positive so long as HB HP, that is, when the professional has an absolute advantage. The expression inside square brackets is the ratio of pre-tax capital costs to pre-tax labour costs in the market production mode. Clearly, greater capital intensity pushes the buyer toward the market solution when the professional has an absolute advantage. For given capital intensity and tax parameters, the impact of capital costs on the choice of production mode intensifies with the professional’s absolute advantage. When the buyer and professional are equally productive in the task, the capital cost effect on choice of production mode vanishes and the decision rule reduces to (2.3). The professional’s absolute advantage favours the market solution because a do-it-yourself approach engages capital inputs for a longer time

52

Incentives to work

spell, raising effective capital costs in household production relative to market production. This logic clearly extends beyond capital inputs. In particular whenever the buyer’s absolute disadvantage means that household production ties up cooperating factors for a longer time spell, the buyer is pushed toward the market solution. The cooperating factors of production could be capital inputs, but they could also be other workers required to accomplish the task. For this reason the allocation of time to household production is relatively unattractive for team production activities that require simultaneous application of multiple labour inputs. According to the decision rule (2.6), the relative cost of capital services in the home production mode rises with the tax expression (1m)T/(1
t), provided that the professional has an absolute advantage. For example, an increase in the value added tax rate raises the cost of capital services required to accomplish the task at home relative to the cost of capital services under the market solution. Equation (2.6) implies that this effect can be large enough to reverse the net effect of the value added tax on the choice between home and market production modes. Thus, in contrast to the case of labour-only production activities, the value added tax can push capital-intensive production activities toward the market solution. The decision rule (2.6) also implies that an increase in the effective business-level tax rate on capital income discourages a do-it-yourself approach to capital-intensive activities when the professional has an absolute advantage. However, this effect of business-level taxes on capital income rests on the assumption that capital services are produced in the market sector regardless of who supplies the labour services. If instead capital services are produced in the home sector under a do-it-yourself approach, then the buyer prefers the market solution provided that





(1s)(1m) WBHB (1m K )H B
T(1m)H P (r)KP K   (2.6) P P P P W H 1
t H W 1
t ˇ

where m K is the tax rate on capital purchases for the home sector. As reflected in (2.6), capital services produced in the home sector escape business-level taxes on capital income. As a result, an increase in the effective tax rate T now discourages market production in favour of home production. Equation (2.6) also reflects an implicit assumption that the idle time of capital goods is the same whether deployed in the household or market production sectors. In fact, the idle time of capital goods is often much greater in the household sector. In terms of the home painting example a professional might make use of a spray painter on a weekly basis whereas

Tax effects on work activity, industry mix and shadow economy size

53

the same piece of equipment might sit idle nearly all year round when acquired for household production. As a polar alternative, consider the situation with no rental or resale market for the capital input in question. Suppose that the professional supplier fully utilizes capital inputs in the market sector, and let  be a parameter that reflects the time interval between uses of the capital input in the household production sector. For example, if the time unit is one week, and a do-it-yourself handyman in the household sector makes use of a spray painter once every two years, then 104. In this case the decision rule governing choice of production mode has the same form as before, but now  replaces HB in the second term on the left side of (2.6). The strength of the capital cost effect now depends on the idle time of the capital input rather than the buyer’s absolute disadvantage. This idleness effect can be quite powerful for capital-intensive tasks. Returning to the example of the spray painter, suppose that H P equals one week, H B equals two weeks, and the household wants its home exterior painted once every two years. Then the capital cost component is twice as large in household production as in market production for the case of a frictionless resale market for capital goods, but it is 104 times as large for the case of no capital resale market. In practice, idleness will be low for frequently used capital inputs such as cooking equipment and for equipment with well-established rental markets such as light trucks for transporting household goods. In contrast, the prospect of high idleness and the absence of rental markets for, say, specialized wood-cutting equipment discourages the assignment of certain carpentry tasks to the household sector, even when the professional does not have a large comparative or absolute advantage. The basic character of the decision rules (2.3), (2.6) and (2.6) will be familiar to readers who are versed in the literature on assignment models; see Sattinger (1993) for an excellent synthesis of work in this area and Davis (1997) for a simple model of assignment based on absolute advantage in a setting with team production. The central ideas in the assignment literature appear to have little explicit application to questions about the effects of taxation although the concept of comparative advantage is widely appreciated. Empirical Implications The theory has interesting implications for which productive activities are most responsive to personal tax rates, that is, most easily shifted from market to household or underground production modes. In this regard the theory says that greater comparative and absolute advantage on the part of

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Incentives to work

professional suppliers, greater capital intensity in production and a higher degree of capital idleness in the household sector act as deterrents to substitution away from market production modes. A greater efficiency advantage for team production also discourages substitution away from the market sector. The comparative and absolute advantage of professionals is greater when the market production mode relies intensively on highly skilled and highly specialized labour inputs. Hence, the theory predicts that employment and value added in skill-intensive industries are relatively insensitive to personal tax rates, because few households are close to the margin of substitution between production modes. If we interpret firm and establishment size as proxies for the importance of team production methods, then the theory also predicts that employment and value added are relatively insensitive to personal tax rates in industries where large firms and establishments predominate. Based on these theoretical considerations, personal services, domestic household services, cleaning and laundry services, and eating and drinking establishments closely fit the profile of tax-responsive industries. Unfortunately the measurement and classification of these production activities is well harmonized across countries only for eating and drinking. In light of this fact, the empirical investigation below considers employment and value added shares in eating and drinking establishments but not in personal services, domestic household services or cleaning and laundry services. The empirical work also considers value added and employment shares in lodging and retail trade. Lodging is capital intensive, but three aspects of its production technology point to easy substitution away from the legal market sector.11 First, the production of lodging services relies intensively on less skilled labour, so that comparative and absolute advantages do not strongly deter non-market production modes. Second, scale economies and team production methods are of modest importance, as evidenced by the many small establishments that provide lodging. Third, many households have extra living space, so that lodging services supplied outside the formal market sector do not involve large capital rental costs. Thus, despite the capital-intensive nature of lodging, neither absolute advantage nor idleness strongly deters tax-induced substitution out of the legal market sector. Retail trade also exhibits some characteristics that, according to the theory, facilitate tax-induced substitution away from market production. As in lodging, the retail sector relies heavily on less skilled labour and small establishments are commonplace. These attributes lead to high tax responsiveness. Working in the other direction, the retail sector is capital intensive,

Tax effects on work activity, industry mix and shadow economy size

55

principally in the form of structures and inventories. On balance then, the theoretical presumption for high tax sensitivity in the retail sector is weaker than for the other sectors mentioned above. Another factor might play an important role in the tax responsiveness of the retail sector. Measured production in retail trade bundles the outputs of production processes that involve very different factor intensities. The inventory services produced by the sector are highly capital intensive, whereas the customer services are intensive in less skilled labour. Hence, even though the overall output bundle produced by retail trade is fairly capital intensive, the scope for tax-induced responses in the customer service component of retail output is probably large. If this is the case, the tax-responsiveness of employment and value added shares in the retail sector will be high, despite relatively high capital costs in the sector. Admittedly, a similar point could be made about other sectors, so our decision to single out retail trade in this respect involves some judgement. Our empirical investigation omits child care and elderly care from the analysis, even though these activities exhibit the characteristics identified by the theory as conducive to high tax sensitivity. Perhaps partly for this reason, rich countries with high tax rates tend to provide large direct or indirect subsidies for market (or state) provision of child and elderly care services. Rosen (1997) provides a detailed and provocative analysis of US and Swedish differences in this regard, and Rogerson (2003) argues that this observation helps to explain high Scandinavian employment rates in the face of generally high tax rates. We do not seek to identify tax effects on choice of production mode for these activities because we lack suitable and internationally comparable data on the effective tax rates applied to them, as well as on market-based employment and value added in these activities. Our last point about the theory pertains to the impact of personal taxes on relative labour demand and the interaction with heterogeneity in labour supply elasticity. In particular, the theory implies that personal taxes alter the composition of labour demand in ways that amplify negative effects on hours worked and employment. To see this point, recall that greater skill intensity in production implies less scope for tax-induced substitution away from the market sector. Team production technologies work in the same direction, and it is well established that skill intensity rises with employer size.12 These theoretical effects mean that personal taxes reduce the relative demand for less skilled labour. By and large, empirical studies find that labour supply is more elastic for less skilled workers. In short, personal taxes reduce the relative demand for less skilled workers, and market work activity by less skilled workers is more responsive to labour demand shifts. Consequently the tax-induced shift in the

56

Incentives to work

composition of labour demand magnifies the negative effects on employment and hours worked.

IDENTIFICATION The empirical investigation considers regression equations of the form HC  abTC C

(2.7)

where C indexes countries, T is a monotonic function of the tax factor and H is the average number of hours worked per adult or other outcome variable. Recall that our objective is to estimate the total response to tax rate differences among countries, inclusive of follow-on responses that involve government spending behaviour. Our approach to identification relies on the assumption that personal tax rates differ between countries for reasons that are exogenous to the outcome variables. Given this assumption, there remain at least two important issues of identification. First, the total response to personal tax rate differences between countries can depend on the reason for the tax rate differences. Second, personal tax rates are measured with error. We concentrate here on the first issue. With respect to the reasons for cross-country variation in personal tax rates, it is helpful to distinguish three categories: (i) Taxing capacity: exogenous differences in taxing capacity and the efficiency of tax collection, which can arise from constitutional provisions that affect taxing power, the degree of competition between autonomous tax authorities within the country, accidents of history when there is inertia in the political process that determines tax rates, and other causes. (ii) Welfare state preferences: exogenous differences in the desire or political support for social welfare programmes that distort labour supply decisions and perhaps alter the structure of labour demand. These differences can arise from constitutional provisions that affect the political feasibility of redistributive tax and transfer programmes, the degree of ethnic, linguistic and racial fragmentation of the population, accidents of history, and other causes.13 (iii) Revenue requirements: exogenous differences in net government revenues from non-distortionary (or less distortionary) sources. For example, at any given level of welfare state spending, higher revenue from petroleum export taxes or lower spending on national defence means less need to rely on distorting forms of taxation.

Tax effects on work activity, industry mix and shadow economy size

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In line with this three-way categorization, consider a simple structural model for the outcome variable H, the tax variable, welfare state spending and net revenue requirements: HC  H HTC  HWC uH C W W W WC   TC  GC uW C TC  TTWC  TGC uTC

(2.8) (2.9) (2.10)

where W is the welfare spending variable, G is an exogenous determinant of the government’s net revenue requirements, and u H, uW and uT are random disturbances that are uncorrelated with each other and with T, W and G. Equation (2.8) describes the structural dependence of the outcome variable H on the tax and distortionary spending variables. Equations (2.9) and (2.10) describe the joint determination of taxation and distortionary spending. The total response of hours worked to an exogenous tax rate difference is given by dH dT

|

T

 HH

W H H W  

T

(2.11)

where the T notation signifies that the variation originates with an exogenous difference in taxes. According to (2.11), the hours worked response to an exogenous tax difference is the sum of a direct effect and an indirect effect that works through government expenditures. The magnitude of the indirect effect rises with the impact of welfare spending on hours worked (H ) and the sensitivity of welfare spending to personal tax rates ( W ). To obtain the total response of hours worked to tax rate differences that originate with exogenous variation in welfare spending, compute the total derivative of (2.8) with respect to W and rescale to obtain a unit change in T: dH dT

|

W

 HH

1 . T

(2.12)

The total response of hours worked to tax rate differences that originate with exogenous differences in net revenue requirements is given by dH dT

|

 HH

G

W . T

(2.13)

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Incentives to work

Comparing (2.11), (2.12) and (2.13), we see that the total response to tax rate differences among countries is the same, irrespective of the reasons for the differences, when W 1/T. This condition says that welfare spending varies with personal tax rates in the same manner regardless of the source of tax rate variation across countries. We refer to this condition as the equal spending-response condition. We are now in a position to clarify the interpretation of regressions (2.7) estimated on cross-country data. Suppose that the data are measured without error and that the structure (2.8)–(2.10) describes the datagenerating process. If the equal spending-response condition holds, then an OLS regression on (2.7) provides a consistent estimate of the total response to tax rate differences across countries. (See Appendix B for a formal proof.) In this context ‘welfare state spending’ means any aspect of government behaviour that varies systematically with personal tax rates and that has a direct effect on the outcome variable. The equal spending-response condition strikes us as a reasonable basis for interpreting OLS regressions on (2.7), but it is hardly an unassailable identifying assumption. When the equal spending-response condition fails, then OLS on (2.7) yields a weighted average of the total response expressions in (2.11), (2.12) and (2.13). The precise weights depend on the relative importance of the underlying sources of tax rate variation. OLS still yields a consistent estimate of the average total response to personal tax rates and, for this reason, still provides useful information about long-run tax effects on market work activity, shadow economy size and the industry mix of market activity. Recent research on the constitutional and political determinants of government spending suggests why the equal spending-response condition might fail. Several models of political equilibrium imply that proportional elections (large voting districts) lead to more government spending and higher taxes (Persson and Tabellini, 2002). The model of Persson et al. (2000) implies that parliamentary regimes also lead to more spending and higher taxes than presidential regimes. Persson and Tabellini (2002) find empirical support for both propositions, but they also find weaker evidence that these two dimensions of constitutional design differ in their implications for the share of government spending devoted to welfare state programmes. Taken at face value, this empirical evidence means that the equal spending-response condition fails in a sample of countries that differ with respect to both electoral rules and the choice between parliamentary and presidential regimes. As this discussion also indicates, the identification issue is not resolved simply by finding an instrument for exogenous variation in tax rates across countries. Instruments that isolate different exogenous sources of tax rate variation can yield different total response estimates.

Tax effects on work activity, industry mix and shadow economy size

59

THE COUNTRY-LEVEL DATA Our empirical investigation considers data for 19 countries on several outcome variables: the ratio of employment to population of working age (15–64 years), annual hours worked per employed person, annual hours worked per adult of working age, size of the shadow economy relative to measured GDP, and value added and employment shares for selected industry groups. Except for shadow economy size, our outcome measures are drawn mainly from OECD sources. In turn, the OECD data derive from national sources that are not fully harmonized in the measurement of employment, hours worked and value added. Internationally comparable data on employment and value added shares in the industries that we identified above are not available for many countries. For this reason, our industry share comparisons involve smaller samples. By and large, more aggregated industry categories allow for larger samples and greater consistency among countries in the classification of production activities. We found reasonably consistent data for nine countries in ‘Retail Trade and Repair Services’, 14 countries in ‘Eating, Drinking and Lodging’, and 14 countries in a broader category that encompasses ‘Trade, Repair Services, Eating, Drinking and Lodging’. Wholesale trade activities plus vehicle trade and repair services are included in the broader category but excluded from Retail Trade and Repair Services. We were unable to construct usable samples for personal services, domestic household services or cleaning and laundry services.14 There are many methods for estimating the size of the shadow economy, as discussed at length in Schneider and Enste (2000) and Giles and Tedds (2002). We use data based on two quite different methods – the Currency Demand Method and the Electricity Method. To the best of our knowledge, these are the only methods that have been widely applied in a consistent manner to the countries in our samples. The Currency Demand Method has a long history that dates to Cagan (1958), but recent implementations follow Tanzi (1980). Under this method, the researcher specifies a time-series regression model for the ratio of currency to bank deposits or overall money holdings. The regression model relates the currency demand ratio to interest rates, per capita income, tax rate measures and other variables. The difference between the predicted currency value at actual tax rates and the predicted value at zero tax rates (or at tax rates in a base year with, by assumption, no shadow economy) yields an estimate for the currency demand arising from tax evasion in the underground sector. Given an assumption about income velocity in the underground sector, typically that it equals income velocity in the legal market sector, one obtains an estimate of shadow economy size

60

Incentives to work

by multiplying the underground currency demand by the underground income velocity. Under the Electricity Method, the ratio of electricity usage to GDP in a base period is used to estimate shadow economy size in other periods. In practice, this method typically relies on two assumptions: unit elasticity of total output (measured plus unmeasured) with respect to electricity usage, and total output equal to measured output in the base period. The gap between the total output implied by the posited relationship to electricity usage and official GDP then provides an estimate of shadow economy size. Obviously this method rests heavily on the posited relationship between total output and electricity usage. This relationship can be disturbed by changes in output composition, the relative price of electricity and the technological requirements for electricity usage. For our purposes the Electricity Method also suffers from a conceptual problem in that it fails to distinguish between household production and other production activity that takes place outside the legal market sector. For example, if personal taxes shift the preparation of meals from restaurants to home cooking, they also shift electricity usage from the market to household production sectors. This substitution response shows up as a larger shadow economy under the Electricity Method, but it is more appropriately characterized as a shift in favour of production for own use and away from markets altogether. For country-level data on average personal tax rates we rely on Nickell and Nunziata (2001) and Schneider (2002). Roughly speaking, the data from Schneider measure average tax rates paid by the average worker. But major components of personal taxes for a typical worker, such as payroll taxes, are proportional to earnings. And most consumption taxes are proportional to expenditures. Hence, we think that Schneider’s data capture much of the cross-country variation in marginal tax rates for the average worker. Schneider’s tax data are also better suited for our purposes in other respects, because they provide enough detail to construct the tax factor in (2.3) and (2.6), and because they do not mix taxes on capital income with taxes on labour income. Schneider’s tax data lack a panel dimension and are available for few countries. Hence, we also consider data from Nickell and Nunziata, who measure the sum of average tax rates on payrolls, consumption expenditures and household income using data from national accounts. Their data run from 1960 to 1995 (with some missing observations), which enables us to characterize broad trends in the evolution of country-level personal tax rates. Appendix A provides a fuller description of the tax measures and other variables in our study.

Tax effects on work activity, industry mix and shadow economy size

61

Table 2.1 reports descriptive statistics by variable and sample. (See Table A.2 in Appendix A for the composition of each sample.) There is much variation in both the outcome variables and the tax variables. Focusing on sample D, the standard deviation across countries is 162 hours per adult for annual work time, 9.8 percentage points for the employment–population ratio and 5.1 percentage points for the shadow economy relative to GDP. In sample E our broadest industry group accounts for 19.8 per cent of employment and 14.0 per cent of GDP, on average, with standard deviations of 3.1 and 3.6 percentage points, respectively. The standard deviation of the average personal tax rate is 11.3 percentage points in the Nickell–Nunziata data and 12.8 points in the Schneider data. As we proceed to the empirical relationship between tax rates and the outcome variables, it should be kept in mind that the data undoubtedly contain considerable noise. At a minimum, national differences in the measurement of the outcome variables lead to spurious variation in the Table 2.1

Descriptive statistics for country-level data

Variable

Sample A

Annual work hours per adult Annual hours per employed person 100 employment– population ratio Shadow economy, % GDP, Currency Demand Method Shadow economy, % GDP, Electricity Method, 1990 Value added share: Trade, Repairs, Eating, Drinking, Lodging Value added share: Eating, Drinking, Lodging Value added share: Retail Trade and Repairs

1140 (48) 1768 (43) 64.7 (2.6)

B

C

D 1067 (162)

1703 (166) 65.4 (2.4)

1124 (176) 1641 (142) 65.7 (8.7) 15.9 (4.9)

E

F

64.8 (9.8) 16.2 (5.1) 14.7 (4.4) 14.0 (3.6)

2.4 (1.1) 5.2 (1.6)

62

Table 2.1

Incentives to work

(continued)

Variable

Sample A

Employment share: Trade, Repairs, Eating, Drinking, Lodging Employment share: Eating, Drinking, Lodging Employment share: Retail Trade and Repairs Employment share: all manufacturing industries Sum of Tax Rates from Nickell and Nunziata (2001) Sum of Tax Rates from Schneider (2002) Tax Factor, based on data from Schneider (2002) Number of countries Years Number of observations

B

C

D

E

F

19.8 (3.1)

4.4 (1.7) 8.2 (1.1) 19.6 (2.6) 50.8 (3.2)

13 1977, 83 90, 95 52

50.1 (3.2)

49.6 (14.0)

53.7 (11.3)

54.7 (11.2)

54.2 (11.4)

62.0 (12.8)

62.3 (12.9)

61.3 (11.4)

1.91 (.24)

1.92 (.24)

1.92 (.26)

17 1977–94

19 1995

14 1995

14 1995

9 1995

306

19

14

14

9

Notes: Aside from the bottom three rows, the table entries report means (standard deviations) for the indicated variables and samples. For samples A and B, the value in parentheses reports the standard deviation after sweeping out year and country fixed effects. (See Table A.2 in Appendix A for sample compositions.) Annual work hours per adult equal the ratio of employment to working-age population (15 to 64 years old) times the average annual hours of market work by employed persons. Sum of Tax Rates is the sum of average tax rates on income, payrolls, and consumption expenditures. The Tax Factor equals the product of (1 payroll tax rate) and (1  consumption tax rate) divided by (1
income tax rate). See the text for an explanation and derivation of this tax variable. Income equals labour income in the data from Schneider and household income in the data from Nickell and Nunziata. The tax rate data from Schneider are for 1996. See Appendix A for additional information about the data and sources. Source: Authors’ calculations.

Tax effects on work activity, industry mix and shadow economy size

63

data. However, there is no apparent reason why this source of measurement error in the outcome variables is correlated with the explanatory tax variables. Measurement error in the tax variables is a more serious concern, and it may well lead us to understate the impact of personal taxes on the outcome variables.

CROSS-COUNTRY EVIDENCE ON THE EFFECTS OF PERSONAL TAX RATES Personal Tax Rates and their Evolution in Recent Decades Table 2.2 reports average personal tax rates (the sum of t, s and m) by country and decade. For each year we measure the average personal tax rate as the sum of tax rates on payrolls, consumption expenditures and household incomes, as computed by Nickell and Nunziata. We then average over years within the decade to obtain the reported values. Table 2.2 documents three key points. First, average personal tax rates vary greatly across countries, ranging from 31 per cent in Japan to 77 per cent in Sweden in the 1990s. Second, there has been a broad and pronounced upward drift in personal tax rates during recent decades, but the pace of drift slowed greatly or halted after 1985. The simple average of national tax rates rose by only 1.4 percentage points from 1985 to 1990 and then fell slightly from 1990 to 1995. Third, the structure of relative tax rates has been fairly stable since the 1970s, as seen by comparing the last two columns in the table. The main outliers are Italy, Portugal and Spain, which experienced relative tax increases of 10 percentage points or more between the 1970s and 1990s, and the Netherlands, which experienced a relative tax decrease of 17 percentage points over the same time interval. No other country underwent a relative tax change of more than 6 percentage points between the 1970s and 1990s. In short, Table 2.2 establishes that average personal tax rates differ greatly among the countries under study and that these pronounced differences were largely intact for more than a decade prior to 1995. Moreover, the overall level of personal tax rates changed little after 1985. Taken together, these observations imply that our data from the mid-1990s are reasonably well suited for an investigation into the long-run effects of personal tax rates. Employment and Hours Response to Personal Tax Rates Empirical studies on the relationship between aggregate outcomes and personal tax rates typically use the sum of t, s and m, or something similar, as

64

Table 2.2

Incentives to work

Average personal tax rates by country and decade

Country

Australia (AU) Austria (AT) Belgium (BE) Canada (CA) Denmark (DE) Finland (FI) France (FR) Germany (GE) Ireland (IR) Italy (IT) Japan (JA) Netherlands (NE) New Zealand (NZ) Norway (NO) Portugal (PO) Spain (SP) Sweden (SW) Switzerland (SZ) United Kingdom (UK) United States (US) Simple mean

1960s

1970s

1980s 1990–95

29 49 41 34 37 41 56 43 26 57 24 50 – 49 – 22 47 30 38

35 55 44 42 53 54 59 47 32 55 26 56 29 60 26 28 65 324 45

39 59 46 43 60 60 65 50 38 57 34 55 31 65 35 41 78 36 51

35 38

42 44.2

44 49.3

Deviation from mean in 1970s

Deviation from mean in 1990s

– 59 50 51 59 64 67 53 40 68 31 45 – 60 40 47 77 35 47


10 11
1
3 9 9 15 3
13 10
18 12
15 16
19
16 21
10 1


11 9
1 0 9 13 17 2
10 18
19
5
19 10
10
4 26
15
4

45 50.3


3


5

Notes: The 5-year changes in the simple mean of the average personal tax rates for the countries listed in the table are as follows: Interval Change

1960–65 3.0

1965–70 5.3

1970–75 1.4

1975–80 3.2

1980–85 3.0

1985–90 1.4

1990–95
0.4

Table entries report the sum of average tax rates on labour income, payrolls and consumption expenditures using data from Nickell and Nunziata (2001). Before computing the average value over countries in each decade and the country-specific deviations, we fill in missing values using the nearest available observation for the same country. Source: Authors’ calculations based on data from Nickell and Nunziata (2001).

the explanatory tax variable. This sum equals the natural log of the tax factor up to a first-order approximation. We experimented with both the sum of tax rates and the tax factor as explanatory variables. The regression fit is typically as good or better for the sum of rates when the dependent variable is

Tax effects on work activity, industry mix and shadow economy size

65

a measure of work hours, the employment rate or shadow economy size. In contrast, the tax factor usually yields a better fit when the dependent variable is an employment or value added share. Hence, we focus on the tax factor variable for the share regressions and the sum of rates otherwise. Table 2.3 reports cross-country regressions of the employment rate and hours worked measures. Figure 2.1 displays the regression line and corresponding scatter plot of annual work hours per adult against the tax variable. As seen in the table, the measure of the tax variable based on Schneider’s data yields better regression fits and larger tax effects. Partly for this reason, and partly because it is closer to the theoretical tax measure, our discussion in the text focuses on results for the Schneider-based measure when both tax sources are available. According to the sample D results, a unit standard deviation tax difference of 12.8 percentage points lowers annual work time in the market Table 2.3 Cross-country regressions of work hours and employment rates on tax rates (1995 data) Independent variable: sum of tax rates on income, payrolls and consumption Dependent variable

Sample

Source for tax rates

Annual work hours per adult

A C D D

N&N N&N N&N S

Annual hours per employed adult

A C D D

N&N N&N N&N S

100  employment– population ratio

A C D D

N&N N&N N&N S

Slope coefficient

Standard error

P-value


6.7
7.4
4.1
9.5

3.3 2.6 4.0 2.4

.07 .01 .32 .00

.21 .27 .01 .52


6.5
6.5
3.9
4.9

2.1 2.6 3.4 2.9

.01 .02 .28 .11

.42 .23 .02 .13

.54 .29 .77 .07


.05 .01
.08 .19


.12
.17
.07
.38

.19 .15 .25 .19

Adj. R2

Notes: All regressions are by ordinary least squares. The P-value reports the marginal significance level in a test of the null hypothesis that the coefficient on the tax variable equals zero. N & NNickell and Nunziata (2001); S  Schneider (2002). Source: Authors’ calculations.

66

35

40

sz

us

ca

45

50

ir no ge

sp

ne

fr

de

55 60 65 70 Labourpayroll consumption tax rate

uk

Hours1655
9.5*(sum of tax rates) (2.4)

Tax rates and annual work hours per adult (Sample D: 14 countries in 1995)

Tax data from Schneider (2002).

800

900

1000

1100

1200

1300

1400

Figure 2.1

Source:

Hours

it 75

be

80

sw

85

67

Tax effects on work activity, industry mix and shadow economy size

sector by (12.8  9.5 ) 122 hours per adult. This large effect amounts to three weeks of full-time work per adult per year. The effect of taxes operates on the intensive hours margin and the extensive employment margin. In particular, the estimates imply that a unit standard deviation tax difference reduces the employment–population ratio by 4.9 percentage points and work time per employed person by 63 hours per year. For reasons explained above, we think the cross-sectional regressions in Table 2.3 provide a useful basis for inference about the long-run effects of personal taxes and a better basis than panel regressions that exploit highfrequency time variation within countries.15 Nevertheless, some readers may want to consider panel regressions of the outcome variables on average personal tax rates. Table 2.4 reports these panel regressions for available data and Figure 2.2 displays one of the corresponding scatter plots. Since Table 2.4 Panel regressions of work hours and employment rates on tax rates Independent variable: sum of tax rates on income, payrolls and consumption Dependent variable

Sample

Years

Annual work hours per adult

A

77, 83 90, 95 77, 83 90, 95

Annual hours per employed adult

A

100  employment– population ratio

A

77, 83 90, 95 77, 83 90, 95

A A A B B

77, 83 90, 95 77, 83 90, 95 1977–94 1977–94

Year Country Slope St. PAdj. effects effects coeff. error value R2 No

No


3.1

1.7

.07

.04

Yes

Yes


4.3

3.0

.16

.83 (.03)

No

No


9.1

1.0

.00

.61

Yes

Yes


2.2

1.9

.25

.89 (.01)

No

No

.17

.08

.05

.05

Yes

Yes


.21

.13

.09

No Yes

No Yes

.15
.22

.04 .04

.00 .00

.88 (.05) .05 .94 (.08)

Notes: All regressions are by ordinary least squares, with and without fixed effects, as indicated. The tax rate variable is from Nickell and Nunziata (2001). The right-hand column reports in parentheses the adjusted R2 value after sweeping out the fixed effects. Source: Authors’ calculations.

68

20

ja

ja

30

ir

spja

be

uk causus us sp ussp ir ca ca ca uk uk it uk bebe be fi it no

no no

fi fr

no

fr

fi fi fr it fr it

sw sw

80

sw

Hours 2230
9.1*(sum of tax rates) (1.0)

40 50 60 70 Labourpayroll consumption tax rate

ir sp ir at at atat

ja

Tax data from Nickell and Nunziata (2001).

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200

Figure 2.2 Tax rates and annual hours per employed person (Sample A: 13 countries with data for 1977, 1983, 1990 and 1995)

Source:

Hours

sw

90

69

Tax effects on work activity, industry mix and shadow economy size

the Schneider data pertain only to the mid-1990s, all of the panel regressions make use of the Nickell–Nunziata tax data. Standard errors are large in the panel specifications that isolate withincountry time variation. This is unsurprising in the light of the stable relative tax structure documented in Table 2.2. The only panel regressions with country and year-fixed effects that yield statistically significant coefficients on the tax variable are for the employment–population ratio. In these regressions, the estimated tax effects are about half as large as the ones in Table 2.3, using the Schneider data, but larger than the ones using the Nickell–Nunziata data. The sign of the estimated tax effect on the employment–population ratio reverses when the panel specification omits fixed effects. Coupled with the results in Table 2.3, this reversal implies a positive cross-sectional relationship between personal tax rates and the employment–population ratio in the earlier years of the sample period. Note that the pattern of results differs for the measures of hours worked. In fact, the negative relationship between tax rates and hours worked per employed person is much stronger when the regression specification omits fixed effects. Tax Effects on Industry-level Employment and Value Added Shares Table 2.5 reports cross-country regressions of the industry-level employment and value added shares on the tax variables, and Figures 2.3a–c and 2.4a–c Table 2.5

Cross-country regressions of industry shares on tax measures

Dependent variable: percentage of employment or value added in the industry group A

Employment shares regressed on tax measures

Industry group Trade, Repairs, Eating, Drinking, Lodging Trade, Repairs, Eating, Drinking, Lodging Eating, Drinking and Lodging Eating, Drinking and Lodging Retail Trade and Repairs Retail Trade and Repairs

Sample E E E E F F

Tax measure

Slope Standard P-value Adj. coeff. error R2

Sum of
.198 rates Tax factor
9.6

.038

.00

.67

2.5

.00

.52

Sum of
.095 rates Tax factor
5.4

.026

.00

.49

1.3

.00

.55

Sum of
.055 rates Tax factor
2.8

.025

.07

.32

1.2

.06

.34

70

Incentives to work

Table 2.5 B

(continued)

Value added shares regressed on tax measures

Industry group Trade, Repairs, Eating, Drinking, Lodging Trade, Repairs, Eating, Drinking, Lodging Eating, Drinking and Lodging Eating, Drinking and Lodging Retail Trade and Repairs Retail Trade and Repairs

Sample

Tax measure

Slope coeff.

E

Sum of rates Tax factor


.142

.069

.06

.20


7.4

3.8

.07

.18

Sum of rates Tax factor


.044

.021

.06

.20


2.7

1.1

.03

.29

Sum of rates Tax factor


.100

.029

.01

.57


5.3

1.3

.00

.67

E E E F F

Standard P-value Adj. error R2

C Value added share regressions, excluding Canada and the USA in Sample E Industry group Trade, Repairs, Eating, Drinking, Lodging

Tax measure

Slope coeff.

Standard error

P-value

Adj. R2

Sum of rates Tax factor


.103
4.8

.066 3.5

.15 .20

.12 .08

D Manufacturing employment shares regressed on tax measures in Sample E Industry group All manufacturing industries

Tax measure

Slope coeff.

Standard error

P-value

Adj. R2

Sum of rates Tax factor

.027 1.7

.058 3.2

.66 .59


.07
.06

Note: Tax rate measures calculated from data in Schneider (2002). See also notes to Table 2.3. Sources:

Authors’ calculations and statutory tax rates.

71

1.5

sz

us

1.6

ca

1.7

1.8

uk

no

ir

fr

ge

itau

sw

be

2.2

ne

% of employment38.3
9.6*(tax factor) (2.5)

1.9 2 2.1 Tax factor a) Trade, Repairs, Eating, Drinking and Lodging for 14 countries

Tax data from Schneider (2002).

13

15

17

19

21

23

25

27

Figure 2.3 Personal taxes and employment shares in 1995

Source:

%

2.3

de

72

sz

us

1.6

(continued)

Tax data from Schneider (2002).

6 1.5

7

8

9

10

Figure 2.3

Source:

%

1.7

fr

ge

au

it

2.1

2.2

ne

2.3

de

% of employment  13.6
2.8*(tax factor) (1.2)

1.9 2 Tax factor b) Retail Trade and Repairs for 9 countries

1.8

uk

73

2 1.5

3

4

5

6

7

8

sz

us

1.6

ca

(continued)

Tax data from Schneider (2002).

Figure 2.3

Source:

%

1.7

no

ir

fr

it

ge

1.9 2 2.1 Tax factor c) Eating, Drinking and Lodging for 14 countries

1.8

uk

au

be sw

2.2

ne

% of employment  14.8
5.4*(tax factor) (1.3)

2.3

de

74

sz

us

1.6

ca

1.7

1.8

uk no ir

fr ge

it au

sw

be

% of value added  28.3
7.4*(tax factor) (3.8)

1.9 2 2.1 Tax factor a) Trade, Repairs, Eating, Drinking and Lodging for 14 countries

Tax data from Schneider (2002).

10 1.5

12

14

16

18

20

22

24

Figure 2.4 Personal taxes and value added shares in 1995

Source:

%

2.2

ne

2.3

de

75

3 1.5

4

5

1.6

1.7

uk

(continued)

ge

au

1.9 2 2.1 Tax factor b) Retail Trade and Repairs for 9 countries

1.8

fr

2.2

ne

it

6

us % of value added  15.4
5.3*(tax factor) (1.3)

sz

7

8

Tax data from Schneider (2002).

Figure 2.4

Source:

%

2.3

de

76

1 1.5

2

3

4

5

6

sz

us

1.6

ca

(continued)

Tax data from Schneider (2002).

Figure 2.4

Source:

%

1.8

no

ir

fr

ge

1.9 2 2.1 Tax factor c) Eating, Drinking and Lodging for 14 countries

1.7

uk

it

au

be sw 2.2

ne

% of value added  7.6
2.7*(tax factor) (1.1)

2.3

de

Tax effects on work activity, industry mix and shadow economy size

77

display several of the scatter plots. The results in panels A and B of Table 2.5 show a uniformly negative relationship between personal tax rates and the industry shares, as predicted by the theory above. Every regression shows a statistically significant effect at the 10 per cent level, despite small sample sizes. The point estimates imply sizeable tax effects on the industry mix of market activity. Consider an increase in the tax factor of 25 basis points, about one standard deviation. According to Table 2.5 (and using Table 2.1), this increase lowers the employment share in the broadest industry group by 2.4 percentage points, or 12 per cent of industry employment evaluated at the mean. A 25 basis point rise in the tax factor lowers the employment share by 1.4 points (31 per cent) in Eating, Drinking and Lodging and by 0.7 points (9 per cent) in Retail Trade and Repairs. Similarly, a 25 basis point rise lowers the value added share by an estimated 1.9 points (13 per cent) in the broad industry group, by 0.7 points (28 per cent) in Eating, Drinking and Lodging and by 1.3 points (25 per cent) in Retail Trade. Specifications that are linear in the tax rates imply similar quantitative responses in the industry shares. As suggested by Figures 2.3 and 2.4, the industry share regressions are more fragile for value added than for employment. Figure 2.4a for example reveals that Canada and the United States are large outliers in the value added regression for the broad industry group. Panel C of Table 2.5 shows that the tax effect on the value added share for the broad industry group is smaller and statistically insignificant when we delete Canada and the United States from the sample. In contrast, the corresponding employment share regression is not sensitive to the exclusion of data for Canada and the United States. These results support the view that labour and consumption taxes twist the mix of market employment and production away from activities that are, according to the theory above, relatively easy to carry out in the home (or underground) sector. It is possible, however, that higher tax rates lead to lower employment and value added shares in all industries that are not carried out or are heavily subsidized by the public sector. It would remain useful to quantify the impact of tax rates on the mix of market production activity in this case, but the evidence would not then favour a theory that emphasizes differences among activities in the ease of substitution between market and home production. To investigate this issue, we now consider the relationship between tax rates and the share of total employment in manufacturing industries. According to the theory above, the manufacturing sector is relatively insensitive to personal tax rates because manufacturing production is highly capital intensive, larger firms and establishments predominate and

78

Incentives to work

the workforce is highly specialized. Two other considerations motivate our choice of the manufacturing sector for this purpose. First, manufacturing accounts for a sizeable fraction of total employment, essentially the same as Trade, Repairs, Eating, Drinking and Lodging combined in the average country (see Table 2.2). Second, the classification of manufacturing activities is well harmonized across countries, so that classification inconsistencies are unlikely to distort the results. Panel D in Table 2.5 reports the results of regressing manufacturing’s share of total employment on the tax rate measures. In sharp contrast to the results for the tax-responsive service industries, the cross-country data show a positive, statistically insignificant effect of labour and consumption tax rates on manufacturing’s share of total employment. This evidence reinforces the view that personal taxes alter the industry mix of market activity by inducing a substitution toward home or underground production modes in activities characterized by low capital intensity, relatively unskilled and unspecialized labour inputs and less benefit from large-scale production teams. That is, taxes on consumption and labour have a much more powerful depressive effect on market employment and production in certain industries and activities rather than a uniform effect across industries and activities. Let us now return to the question of whether higher tax rates on consumption and labour income reduce the relative demand for less skilled workers. Table 2.6 reports industry-level statistics on hourly wages and years of schooling for selected industries based on US data. The table shows that labour inputs in Retail Trade, Repair Services, and Eating, Drinking and Lodging establishments are much less skilled than labour inputs in the average industry, whether skill is measured by hourly wages or years of schooling. Taken together, Tables 2.5 and 2.6 imply that personal taxes shift the industry mix of market activity away from sectors that intensively use less skilled workers. This evidence is consistent with the view that personal taxes twist the structure of labour demand in a way that concentrates negative effects on less skilled workers. Tax Effects on Shadow Economy Size Table 2.7 reports cross-country regressions of shadow economy measures on the sum of personal tax rates, and Figure 2.5 displays one of the corresponding scatter plots. Using the Currency Demand Method and Schneider’s tax data, a unit standard deviation increase in the average personal tax rate raises shadow economy size by (12.80.30 ) 3.8 per cent of measured GDP, which corresponds to a 24 per cent increase in the size of the shadow economy evaluated at the mean. This is a large effect and

79

Tax effects on work activity, industry mix and shadow economy size

Table 2.6

Labour skill measures for selected industries, US data

Industry

Mean hourly wage, log deviation from average ( 100) (1)

Hourly wage rank

Years of schooling rank

(2)

Mean years of schooling, deviation from average (3)

4
28
19

28 51 49

.03
.38
1.15

28 38 52


36

53


.94

47


51

60


1.16

54

108 log points 25 log points

1–61

4.9 years 1.2 years

1–61

Wholesale Trade Retail Trade Automotive and Miscellaneous Repair Services Hotels and Lodging Establishments Eating and Drinking Establishments Range Between-Industry Standard Deviation

(4)

Notes: We calculated the industry-level wage and schooling statistics from individual-level data in the 1985–87 March files of the US Current Population Survey; see Davis and Henrekson (2003) for details. Column (1) reports the difference between the hoursweighted mean of the log hourly wage in the indicated industry and the average over industries of the log hourly wage. There are 61 industries that cover the entire economy. Column (3) reports the difference between the hours-weighted mean years of schooling in the indicated industry and the average over industries of mean schooling years. Years of schooling is based on the highest grade completed. Source: Authors’ calculations.

it implies that differences in the level of personal tax rates are a major determinant of differences in the extent of shadow economy activity between rich, industrialized countries. Recall that the shadow economy estimates based on the Currency Demand Method derive from fitted, country-specific time-series models of the form

ˆ

(Currency–Deposits Ratio)ct (Tax Rates)ct c (Other Variables)ct

ˆ

c

(2.14)

where c indexes countries, t indexes time and a hat denotes an estimated

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Incentives to work

Table 2.7 Cross-country regressions of shadow economy size on tax rates Dependent variable: shadow economy as a percentage of official GDP; independent variable: sum of tax rates on labour income, payrolls and consumption Source for tax rates

Sample Shadow economy Slope Standard P-value measure coefficient error

Nickell and Nunziata Nickell and Nunziata Schneider

C

D

Schneider

D

Nickell and Nunziata Schneider

D

D

D

Currency Demand Currency Demand Currency Demand Currency Demand (1996) Electricity Method (1990) Electricity Method (1990)

Adj. R2

.20

.08

.02

.25

.25

.11

.04

.26

.30

.08

.00

.53

.31

.08

.00

.53


.04

.11

.75


.07

.16

.09

.09

.15

Notes: Data on the size of the shadow economy are averages of 1994 and 1995 values unless otherwise indicated. For the shadow economy size measure based on the Electricity Method the results are nearly identical whether we use the Nickell–Nunziata tax measure for 1990 or 1995. As explained in the text, the regressions that rely on the Currency Demand Method have a different interpretation from the ones that rely on the Electricity Method; see also notes to Table 2.3. Source: Authors’ calculations.

parameter value. Given (2.14) the estimated size of the shadow economy is

ˆ

(Shadow)ct Vct [(Actual
Base Tax Rates)ct c]

(2.15)

where V is the income velocity in the underground sector, and the base tax rates are the values associated with no shadow economy. In practice, model specification, variable measurement, base tax rates, income velocity and sample period differ between countries. It is instructive, however, to consider the case with the same tax rate measures and the same values for base tax rates, velocity and the estimated tax-response parameter . In this case, cross-country regressions of the type reported in Table 2.7 and Figure 2.5 using the Currency Demand Method yield a perfect fit. More generally, the fit of such a regression informs us about the homogeneity in the values of V,  and the base

ˆ

ˆ

81

35

40

sz

us

45

ca ne

fr

de

55 60 65 70 Labourpayroll consumption tax rate

ge

sp

it

75

be

Tax rates and shadow economy as percentage of GDP (Sample D: 14 countries in 1995)

50

uk

ir

no

100*(shadow/GDP)
2.4.3*(sum of tax rates) (.08)

Tax data from Schneider (2002).

6

10

14

18

22

26

30

Figure 2.5

Source:

% of GDP

80

sw

85

82

Incentives to work

tax rates that underlie the shadow economy estimates in (2.15). Such cross-country regressions do not provide any additional statistical evidence about the relationship between tax rates and shadow economy size – all of the evidence about tax effects on shadow economy size is contained in the underlying country-specific time-series regressions. Instead, the cross-country regression provides a convenient way to summarize the size (and similarity) of the estimated tax effects in the underlying time-series models. In contrast, the cross-country regressions based on the Electricity Method provide additional statistical evidence – over and above the evidence in the country-specific studies – about the impact of taxes on shadow economy size. Moreover, the evidence in Table 2.7 for the Electricity Method reflects cross-country variation rather than within-country timeseries variation. That said, the cross-country evidence in Table 2.7 based on the Electricity Method is not particularly powerful. When using the Nickell–Nunziata tax measure, the estimated tax effect on shadow economy size is small and statistically insignificant. When using the Schneider tax measure, the estimated tax effect is positive and statistically significant at the 10 per cent level, but it is only half as large as the results for the Currency Demand Method. As remarked above, many previous studies find evidence that personal taxes boost the size of the shadow economy. In this regard, our main contribution is to clarify the interpretation of cross-country regressions of shadow economy size on taxes and to place the empirical relationship between taxes and shadow economy size into a larger context. In particular, our study indicates that the tax-induced stimulus to the shadow economy is part of a broader response pattern that includes important effects on market work hours, market employment and a systematic shift in the industry mix of market activity. Controls for other Policies and Institutions that Affect Work Activity The regressions in tables 2.3–5 and 2.7 do not control for minimum-wage laws, job-security provisions or other policies and institutions that can discourage work activity in the legal market sector. If these omitted factors have important effects on the outcome variables, and if they are coincidentally correlated with the tax measures, then the previous regression results yield biased estimates of the total tax response. Of course, if higher personal taxes are causally related to the adoption of more or less burdensome policies and institutions, then the effects of these omitted factors can be viewed as part of the total response to taxes. In this case, the identification issues raised by omitted policies and institutions are

Tax effects on work activity, industry mix and shadow economy size

83

analogous to the ones discussed above for distortionary government expenditures. We now consider controls for four types of policies and institutions that have attracted much attention in previous research. (i) Minimum wage laws can lower work activity in the legal market sector by raising labour costs for less skilled workers. Because legal wage minimums have greater bite in sectors that rely more heavily on less skilled workers, they can also alter the industry mix of market activity. To capture these potential effects we use the ratio of a country’s legal wage minimum to its average wage. (ii) Collective bargaining institutions compress wage differentials (see Blau and Kahn, 1999), which can lower work activity in the legal market sector by pricing certain workers out of the market. Davis and Henrekson (2005), among others, find that institutionally induced wage compression alters the industry mix of employment. To control for these effects we use the percentage of a country’s workers who are covered by collective bargaining agreements. (iii) Job security provisions can reduce work activity in the legal market sector by impeding the efficient allocation of labour, raising labour costs and discouraging new hires. Because job creation and destruction intensity is greater in lower wage industries,16 uniformly applied job security provisions are likely to alter the industry distribution of employment. To control for these potential effects we use an OECD index of the overall strictness of a country’s employment protection legislation. (iv) Product market regulations that impede entry and hamper competition in output markets can also lower employment and alter the industry mix of market activity. To control for these effects we use an index of competition-retarding product market regulations reported in Nicoletti and Pryor (2001). The index reflects a detailed codification of central government regulations in OECD countries. It is intended to capture state ownership and control of productive enterprises, barriers to entrepreneurship, ownership restrictions and barriers to trade and investment. Product market regulations can affect employment outcomes through several channels. Consider first our simple model of time allocation and task assignment. If barriers to competition result in a price-cost markup percentage , then the tax factor on the right side of equations (2.3) and (2.6) is scaled up by (1 ). That is, weaker competition in product markets displaces production activity from the legal market sector in the same way

84

Incentives to work

as higher personal taxes. Blanchard and Giavazzi (2003) and Gersbach and Schniewind (2001) show how barriers to product market competition lead to higher markups and lower employment in general equilibrium settings. In an interesting study of the French retail sector, Bertrand and Kramarz (2002) find that local entry barriers increase seller concentration, raise consumer prices, and lower employment. Their evidence suggests that entry barriers lower employment by raising markups and by curtailing market provision of labour-intensive customer services. In addition to these direct effects, the rents created by product market regulations help sustain political support for job-security provisions and stiffen resistance to labour market reforms, as stressed by Blanchard and Giavazzi (2003). In considering controls for other policies and institutions, our main goal is to investigate whether and how the controls affect the estimated tax effects in the cross-country regressions. To that end, Table 2.8 reports regressions of annual hours per adult, shadow economy size and the industry-level shares on tax variables and controls. Each panel considers a particular control variable. Table 2.8 conveys two main messages. First, with the partial exception of panel B, the magnitude of the estimated tax effects is quite similar to our Table 2.8 Cross-country regressions with controls for other policies and institutions A

Control variable: ratio of minimum to average wages in 1991–94

Dependent variable Work hours per adult Shadow economy size Broad employment share Broad VA share Employment share in Eating, Drinking, Lodging VA share in Eating, Drinking, Lodging Employment share in Retail VA share in Retail

Tax variable

Ratio of F-test minimum to P-value average wages

Adj. R2

Sample D D E

Coeff.
9.5 .19
8.2

S.E. 2.9 .10 3.0

Coeff. .08
.08
.06

S.E. 3.40 .12 .07

.01 .21 .01

.48 .11 .51

E E


9.0
5.3

4.8 1.7

.07
.00

.11 .04

.19 .01

.13 .51

E


3.0

1.4

.01

.03

.09

.23

F


2.9

1.6

.00

.04

.19

.23

F


6.6

1.3

.06

.03

.01

.75

Tax effects on work activity, industry mix and shadow economy size

85

Table 2.8 (continued) B Control variable: percentage of workers covered by collective bargaining agreements in 1994 (earlier for some countries) Tax variable

Dependent variable Work hours per adult Shadow economy size Broad employment share Broad VA share Employment share in Eating, Drinking, Lodging VA share in Eating, Drinking, Lodging Employment share in Retail VA share in Retail

Collective bargaining coverage

F-test P-value

Adj. R2

Sample D D E

Coeff.
9.3 .17
2.8

S.E. 5.1 .18 3.9

Coeff.
.10
.00
.08

S.E. 2.60 .10 .04

.01 .26 .00

.48 .07 .63

E E


0.2
4.4

6.6 2.5


.08
.01

.06 .02

.09 .01

.24 .52

E


1.8

2.0


.01

.02

.09

.24

F


0.7

2.1


.02

.02

.11

.37

F


4.4

2.4


.01

.02

.02

.63

C Control variable: OECD index of employment protection legislation in the late 1990s Tax variable

Dependent variable Work hours per adult Shadow economy size Broad employment share Broad VA share Employment share in Eating, Drinking, Lodging VA share in Eating, Drinking, Lodging Employment share in Retail VA share in Retail

Index of employment protection

F-test P-value

Adj. R2

S.E. 44.4 1.8 0.9

.00 .24 .01

.60 .09 .53

Sample D D E

Coeff.
5.8 .13
7.7

S.E. Coeff. 3.0
80.8 .12 0.7 3.0
0.9

E E


7.8
4.9

4.9 1.7

0.2
0.3

1.4 0.5

.22 .01

.10 .52

E


2.5

1.4


0.1

0.4

.10

.23

F


3.3

1.5

0.3

0.5

.15

.29

F


6.2

1.5

0.5

0.4

.01

.68

86

Table 2.8

Incentives to work

(continued)

D Control variable: index of competition-retarding product market regulations, 1997–98 Tax variable

Index of F-test Adj. product market P-value R2 regulations

Dependent variable Sample Coeff. S.E. Coeff. S.E. Work hours per adult D
8.4 2.5
140.0 102.0 Shadow economy size D .18 .10
2.2 4.3 Broad employment share E
8.8 2.2
3.8 1.8 Broad VA share E
7.5 4.0 0.4 3.3 Employment share E
5.0 1.2
1.8 1.0 in Eating, Drinking, Lodging VA share in Eating, E
2.6 1.2
0.5 0.9 Drinking, Lodging Employment share F
2.6 1.2
1.1 1.1 in Retail VA share in Retail F
5.6 1.2 1.6 1.0

.01 .23 .00 .22 .00

.55 .09 .63 .10 .61

.09

.24

.12

.35

.01

.73

Notes: All regressions by OLS. For regressions with annual work hours per adult or shadow economy size as the dependent variable, the tax variable is the sum of tax rates on payrolls, labour incomes and consumption expenditures. The share regressions use the tax factor. Both tax variables are based on data from Schneider (2002). The measure of shadow economy size is based on the Electricity Method. Means (standard deviations) for the control variables in Sample D: ratio of minimum to average wages, 51 (11); percentage of workers covered by collective bargaining agreements, 72 (25); index of employment protection, 1.95 (.87); index of product market regulations, .58 (.29). See Table 2.1 for descriptive statistics of all other variables. Sources: Authors’ calculations. Minimum wage variable: Nickell and Layard (1999, Table 9) and Dolado et al. (1996, Table 1). Bargaining coverage: Blau and Kahn (1999, Table 2); value for Ireland set to the mean in the top third of the coverage values in Blau and Kahn based on description in Glyn (2002, p. 6). Index of employment protection legislation: summary indicator of Overall EPL Strictness, Version 2, as reported in OECD (1999, Table 2.5). Index of product market regulations: Total Product Market, O-NSB Scores, as reported in Table 1 of Nicoletti and Pryor (2001).

earlier results. Large standard errors prevent sharp inferences in some cases, especially with respect to shadow economy size, but the basic pattern of results survives the inclusion of the controls. Second, none of the control variables show a pattern of statistically significant tax effects on the

Tax effects on work activity, industry mix and shadow economy size

87

outcome variables. This does not mean that the policies and institutions that motivate the control variables are unimportant; indeed, we are persuaded by previous research that these factors have powerful effects in some instances. Rather, Table 2.8 suggests that these other factors have less powerful, less pervasive effects than tax differences, or that the control variables suffer from more serious problems of measurement error. Several additional remarks in connection with Table 2.8 are in order. First, while few of the slope estimates in panel B are individually significant, the F-tests typically reject the hypothesis that taxes and collective bargaining are jointly insignificant. As in the other panels, the tax variable and the control jointly account for a large fraction of the outcome differences among countries. Second, unreported results for hours per worker and the employment–population ratio are more sensitive to the inclusion of controls than the results for hours per adult. Third, the Table 2.8 regressions make use of tax rate data from Schneider (2002). When we substitute the Nickell–Nunziata tax variable, the results show almost no evidence of tax effects; the tax variable is statistically significant at the 10 per cent level in only one of 32 regressions. The measurement of tax variables appears to be a key issue for research into the role of taxes as a driving force behind national differences in work activity. We also examined regressions that include all four controls. None of the variables are individually significant in the annual hours regression that includes all of the controls, nor are the controls jointly significant. However, the controls and tax variable are jointly significant at the 5 per cent level. None of the variables are individually or jointly significant in the shadow economy regression. In contrast, the industry regressions show negative effects (10 per cent significance level) of bargaining coverage on employment and value added shares in the broad industry group, of product market regulations on employment shares in the broad industry group and in Eating, Drinking and Lodging, and of the tax factor on the employment share in Eating, Drinking and Lodging. To sum up, the tax and control variables are jointly significant in all of the regressions except those for the shadow economy measure. Taken together, the tax and control variables account for much of the cross-country variation in the outcome measures. The size and statistical significance of the estimated tax effects remain largely intact when we add a single control to the basic regression but not when we simultaneously include all four controls. On balance, the regression evidence supports the view that long-run tax effects on work activity are large, and the view that tax rate differences between rich countries explain much of the international variation in work activity outcomes.

88

Incentives to work

OTHER EVIDENCE This section briefly describes other evidence about two issues: how personal taxes affect the amount of time devoted to household production activity, and the effects of personal taxes on the mix of market production activity. Piggott and Whalley (2001) analyse Canada’s 1990 switch from a 13.25 per cent sales tax on manufactured goods, which offers little scope for production outside the legal market sector, to a broad-based consumption tax at a 7 per cent rate. They report that the percentage of food dollars spent on restaurant meals fell from 42 per cent prior to the Canadian tax reform to 35 per cent afterwards. This change in the composition of consumption expenditures indicates that the Canadian VAT induced a large substitution away from the legal market provision of food preparation and dining services. Karoleff et al. (1994) and Spiro (1993) report a sizeable increase in underground activity following Canada’s 1990 switch from a tax on manufactured goods to a broad-based consumption tax. Freeman and Schettkat (2005) investigate the large gap in female employment rates between high-tax Germany and the low-tax United States. They find that German women actually work as many hours as US women after accounting for time devoted to household work. Housework activities like cleaning and cooking account for a major part of the extra time worked at home by German women. Freeman and Schettkat also find that expenditure shares on restaurant meals and personal services are much lower for German than for US households. In a follow-up study Schettkat (2003) finds that the entire gap in market work time between US and German women can be explained by wage dispersion and tax wedges. Neither the level of real income nor culture (that is, countryspecific effects) turns out to have significant explanatory power. These US–German differences in household work activity and the composition of consumption expenditures are consistent with our cross-country evidence and they fit the implications of the theory above. In a similar vein Olovsson (2004) claims that total work time, inclusive of home production activity, is only 1 per cent lower in Sweden than in the United States, even though Swedes work 10 per cent fewer hours in the market sector. Olovsson’s claim reiterates Juster and Stafford’s (1991, p. 498) conclusion that ‘Swedish men, compared to US men, have less market work time, more home production time, and less leisure time.’ While more research is needed to settle the issue, the widely held view that Europeans enjoy more leisure than Americans may be a myth founded on an overly narrow conception of work activity and a lack of comprehensive, internationally comparable time-use data.

Tax effects on work activity, industry mix and shadow economy size

89

Aguiar and Hurst (2004) provide evidence from a different direction on the scope for substitution between market provision and home production. As in other research on life-cycle consumption behaviour, they find that food expenditures fall by an average of 17 per cent at retirement, an observation that is often interpreted as a departure from consumption smoothing over time. But Aguiar and Hurst also show that food consumption, as measured by the nutritional content of food intakes and by the income elasticity of different foods and dining experiences, does not drop at retirement. They also find that the decline in food expenditures ‘is accompanied by a 53 per cent increase in time spent in home production (shopping for and preparing food) by individuals during retirement’. In other words, the drop in the opportunity cost of time at retirement precipitates a large shift from market provision to home production of food services. In another line of research, empirical studies find that tax evasion is relatively prevalent in retail trade, restaurants and hotels (and, to a lesser extent, in taxi services and professional consulting): see Skolka (1985) and Giles (2000). This evidence is usually interpreted to mean that tax evasion is relatively easy in these sectors. Our theoretical analysis points to another interpretation – namely, that the costs of underground production activity in terms of forgone comparative and absolute advantages and capital idleness are relatively low in these sectors. Of course these two explanations are not exclusive and it seems likely that both the relative ease of tax evasion and the relatively modest efficiency consequences of underground activity play a role in explaining the high incidence of tax evasion in these sectors.

CONCLUDING REMARKS Our study relies on the large size and persistent character of tax rate differences between rich countries to draw inferences about long-run tax effects. In summary terms, the data tell this story: higher tax rates on labour income and consumption expenditures lead to less work time in the market sector, more work time in the household sector, a bigger underground economy and smaller value added and employment shares in industries that rely heavily on low wage, low skill labour inputs. The estimated tax effects are large for our preferred tax measures. In cross-country regressions for the mid-1990s, a unit standard deviation tax hike of 12.8 percentage points leads to 122 fewer hours of market work per adult per year, a 4.9 percentage point drop in the employment– population ratio, an increase in shadow economy size amounting to

90

Incentives to work

3.8 per cent of measured GDP, and 10–30 per cent smaller value added and employment shares in Retail Trade and Repairs, in Eating, Drinking and Lodging, and in a broader category that includes Wholesale Trade and Motor Trade and Repair. This estimated tax-response pattern is broadly consistent with the simple theory of task assignment and time allocation sketched above. The industry mix evidence fits the theoretical implication that personal taxes twist the demand for labour away from less skilled workers. All of the variables in this study are susceptible to considerable measurement error. The outcome variables are not measured in exactly the same way in each country and the classification of service sector activities is not fully harmonized. The shadow economy variable is especially fraught with measurement problems, which may explain why the estimated tax effect on shadow economy size is often statistically insignificant. The Nickell–Nunziata tax measure captures average tax rates, as calculated from national income accounts, while the Schneider data are probably closer to the marginal and average tax rates for a typical worker. None of our tax measures captures the heterogeneity of tax rates within countries. In view of the measurement problems we are struck by the consistent pattern and large size of the estimated tax effects when using the Schneider data to measure the tax variables. Controls for minimum wages, collective bargaining coverage, stringency of job security provisions and the extent of competition-retarding product market regulations do not greatly alter the size or pattern of the estimated tax effects. In contrast, the estimated tax effects based on the Nickell–Nunziata data are smaller, the regression fits are poorer and the results are more sensitive to the inclusion of controls. In fact, once we include controls for other policies and institutions, the Nickell–Nunziata data yield no statistical evidence of a separate tax effect on the outcome variables. This sensitivity to the measurement of the tax variable merits greater attention in future research.17 We emphasize that our estimated tax effects are not pure labour supply responses. Theory and evidence suggest that personal taxes operate partly by twisting labour demand away from workers with relatively elastic labour supply. It follows that the aggregate labour supply response to a uniform tax hike is bigger than suggested by the simple cross-sectional mean elasticity of labour supply. As a separate point, countries with higher tax rates also have bigger expenditures on government programmes that are likely to discourage labour supply. We do not isolate expenditure-side effects on work activity, but they are probably large. In their recent review, Krueger and Meyer (2002, pp. 2384–5) conclude that labour supply elasticities with respect to benefits for unemployment insurance, worker compensation and disability insurance ‘are substantially higher than the labor supply

Tax effects on work activity, industry mix and shadow economy size

91

elasticities typically found for men in studies of the effects of wages or taxes on hours of work . . . [and] are also larger than the consensus range of estimates of the labor supply elasticity for women’. Lastly, let us return to the recent studies by Prescott (2002, 2003) which consider the output, employment and welfare consequences of personal taxes in an equilibrium model with one production sector and a simple labour/leisure choice for the representative household. Our evidence supports the view that tax rate differences among rich countries are a major reason for large international differences in market work time. In this respect our results are very much in line with Prescott’s analysis. At the same time however, our evidence strongly suggests that labour and consumption taxes operate with powerful effect on several margins: substitution between legal and underground activity, substitution between home and market production, the mix of market production activity and the composition of market expenditures. Prescott’s model collapses these response margins to a single choice between work and leisure. It remains to be seen whether a model that accommodates several active response margins will sustain Prescott’s conclusion that European-level tax rates on labour income and consumption cause giant welfare losses. Two features of Prescott’s model contribute to big tax effects on market work activity. First, he adopts logarithmic preferences over consumption and leisure, which implies high labour supply elasticity. Second, a large portion of government revenues are redistributed to households via lumpsum transfers, which partly negates the income effect of taxes on labour supply. Interpreted literally, the high labour supply elasticity is difficult to square with micro evidence, but one might interpret the preference specification as an approximate reduced form in which the high labour supply elasticity stands in for substitution possibilities between market and home (or underground) production sectors. The lump-sum redistribution of tax revenues helps to capture the negative effects of certain government expenditures on market work activity. It would be useful to explicitly integrate non-market production and expenditure-side inventive effects on labour supply into macroeconomic models of taxes and work activity. Olovsson (2004) takes a step in that direction with respect to non-market production activity. He considers an equilibrium model with two consumption goods and two production sectors. One good is produced only in the market sector and the second good is produced in both market and household sectors. There is imperfect substitution in consumption between the home-produced and marketproduced variants of the second good. In this setup Olovsson shows that US–European tax rate differences can lead to large differences in market work time (and little difference in total work time) with a small Frisch

92

Incentives to work

elasticity of labour supply. He also shows that the market output losses generated by observed tax differences are large.

APPENDIX A: THE DATA Tax Measures We obtain data on tax rates from two sources that differ in coverage, sample period and method of calculation. Table 10 in Schneider (2002) reports (a) the value added tax rate (average sales tax rate in the USA), (b) the direct tax rate on labour income, (c) the social security contribution rate of employees and (d) the social security contribution rate of employers. The notes to his table define (b) as the sum of all income taxes paid on wages and salaries (including income of the self-employed), divided by ‘gross labour costs of an average income earner in the country’. The notes also state that (c) and (d) are ‘calculated on the basis of the annual gross earnings of an average income earner’. The sources for the data, covering 16 countries in 1996, are Schneider’s ‘own calculations and OECD working paper 176’. In calculating the tax factor on the right side of equations (3) and (6) in the main text, we use (a) to measure m, the sum of (b) and (c) to measure t, and (d) to measure s. There are at least three issues with respect to Schneider’s tax data. First, the meaning of ‘gross labour costs’ and ‘annual gross earnings’ is unclear. Our tax factor calculations interpret both terms as synonymous with workers’ pre-tax earnings. Second, the US figure of 13.8 per cent for the employer social security contribution rate in column (5) of Schneider’s Table 10 appears to be too high. Mandatory employer contributions in the USA consist mainly of payments for the federal Old-Age, Survivors and Disability Insurance (OASDI) and Medicare Hospital Insurance (HI) programmes, the federal and state unemployment insurance system, and state worker compensation programmes. As of 1996, the OASDI rate was 6.2 per cent on the first $62 700 in earnings and the HI rate was 1.45 per cent on all earnings. The federal unemployment insurance tax rate was 0.8 per cent on the first $7000 of a worker’s annual earnings, zero at the margin for most workers, and less than 0.3 per cent of covered wages (US House of Representatives, 2000, pp. 305–6). The average state unemployment insurance tax rate in 1995 was 2.2 per cent of taxable wages and 0.8 per cent of total wages (US House of Representatives, 1996, Table 5.10). Employer costs for worker compensation programmes averaged 1.66 per cent of covered wages in 1996 (Williams et al., 2003, Figure 1). Using the 0.8 per cent and 2.2 per cent figures for unemployment insurance taxes,

Tax effects on work activity, industry mix and shadow economy size

93

US payroll tax rates sum to 12.3 per cent, which is 1.6 percentage points lower than Schneider’s figure. Our discussion also implies that average marginal payroll tax rates in the USA are much less than 12.3 per cent. Third, the notes to Schneider’s Table 10 indicate that payroll and manpower taxes are counted as part of direct taxes on labour income. From this description it sounds as if certain payroll taxes are counted in both columns (3) and (5) of his Table 10 but this interpretation appears incorrect in light of the fact that Schneider includes columns (3) and (5) in calculating the total tax and social security burden. On this basis we treat his columns (3) and (5) as measuring different aspects of the tax system. Nickell and Nunziata (2001) report the average tax rate on payrolls, household income and consumption expenditures. They measure tax rates using the London School of Economics CEP-OECD database, which draws on the OECD National Accounts and other sources. Their ‘tax wedge’ measure is the sum of three components: an ‘employment tax rate’, employer contributions to social security, welfare plans and private pensions , total employee compensation, in cash or in kind, less the numerator a ‘direct tax rate’ on household income, employee contributions to social security  household income taxes , current receipts of households and an ‘indirect tax rate’ on household consumption, indirect taxes less subsidies . private final consumption expenditures The employment tax rate corresponds to the variable s in the tax factor formula, except that it includes employer contributions to private pensions as part of the tax on payrolls. The direct tax rate corresponds imperfectly to the variable t in the tax factor formula in that it includes taxes on all household income. Likewise, the base in this tax rate measure includes all household income.18 The indirect tax rate corresponds to the variable s in the tax factor formula. Nickell and Nunziata report the sum of these three tax rates as the ‘tax wedge’, which we use as a measure of the average personal tax rate. Nickell and Nunziata report annual data on 20 OECD countries for varying sample periods over the time period from 1960 to 1995. For

94

Incentives to work

Australia we impute the 1985 value to 1990 and 1995 (samples A and C). For New Zealand we impute the 1986 value to 1995 (sample C). National Employment, Population and Work Hours Our data on the employment–population ratio and hours worked in the market sector are drawn or derived from the following OECD sources: ● ● ●

OECD (1998, 1999, 2002), Employment Outlook, Statistical Annex, and online source at: www1.oecd.org/scripts/cde/viewbase.asp? DBNAME  lfs_data.

Sample A contains data on annual hours per employed person, annual work hours per adult and the ratio of employment to working-age population (15–64 years old) for 13 countries in 1977, 1983, 1990 and 1995. We impute hours worked for Belgium and Ireland in 1977 based on their 1983 values. Sample B contains data on the employment–population ratio from 1977 to 1994. The remaining samples contain data for 1995. Size of Shadow Economy Unless noted otherwise, our data on shadow economy size as a percentage of GDP are from the column headed ‘Average 1994/95’ in Table 3 of Schneider (2002). These estimates of shadow economy size are based on the Currency Demand Method, except for New Zealand which uses an average of values based on the Currency Demand and MIMIC Methods. Our data on shadow economy size for 1996 are drawn from Schneider’s Table 10. Our data on shadow economy size in 1990 based on the Electricity Method are drawn from Table 6 in Schneider and Enste (2000). We use observations on shadow economy size in Great Britain for the UK. Industry Value Added and Employment Shares For data on value added and employment by industry group, we rely mainly on the 2000 and 2001 editions of the OECD report Services: Statistics on Value Added and Employment. The coverage of these data includes ‘nonmarket services’ produced by the government and non-profit institutions and provided free of charge or at a fee well below production costs. We use data on value added at current prices and data on total employment which includes ‘working proprietors and unpaid family workers of unincorporated

Tax effects on work activity, industry mix and shadow economy size

95

units as well as employees’. There are differences between countries in the methods used to measure value added and employment. Many countries do not report service sector data that are sufficiently disaggregated for our purposes. In addition, even for the broad industry groups we consider, there are inconsistencies among reporting countries in the classification of certain service sector activities such as trade in motor vehicles, repair services, and eating and drinking establishments. Classification inconsistencies led us to suppress the Canadian data for Retail Trade and Repairs. In addition we suppressed the Spanish data because they are highly implausible. For example, the reported Spanish figures imply that Restaurants and Hotels is two-thirds larger than Retail Trade and Repairs based on value added data, in sharp contrast to other countries, but one-third smaller based on employment data. For the UK we used data on number of employees because industry-level employment data are not reported. For the USA classification inconsistencies led us to construct our own figures for value added and employment in Retail Trade and Repairs and in Eating, Drinking and Lodging. Details are available from the authors upon request. For data on the share of employment accounted for by manufacturing industries, we rely on the 2003 edition of OECD Labour Force Statistics, 1982–2002. For France we used data on the number of manufacturing employees because French manufacturing employment is not reported. This leads to a slight understatement of the French manufacturing share. Table 2A.1 reports our country-level data on value added and employment shares. Table 2A.2 shows the composition of each sample used in our study.

96

20.44 16.50 23.88 18.55 16.13 19.13 20.65 20.33 19.38 16.87 15.35 22.19 22.27 25.94

Trade, Repairs, Eating, Drinking, Lodging (1) 6.00 2.83 6.26 2.84 3.08 3.77 5.44 5.07 2.97 2.54 2.61 5.68 5.48 7.72

Eating, Drinking, Lodging (2)

8.96 9.87 9.11

8.84 7.56

7.02 6.66 8.72

20.83 18.69 13.92 20.32 17.95 24.12 18.82 21.79 19.13 17.02 18.61 22.99 21.91 17.99

(4)

(3) 7.30

All Manufacturing

Retail Trade and Repairs

Employment shares

15.84 12.81 11.77 13.28 12.17 11.24 10.94 16.16 14.01 11.36 10.83 18.82 12.97 23.59

Trade, Repairs, Eating, Drinking, Lodging (1) 3.69 1.51 2.29 1.57 2.40 1.22 2.35 3.14 1.74 1.22 1.22 3.00 2.58 5.14

Eating, Drinking, Lodging (2)

Value added shares

7.85 4.50 7.76

6.02 3.89

4.79 3.85 4.27 3.92

4.48

(3)

Retail Trade and Repairs

Sources:

OECD (2000, 2001, 2003) and authors’ calculations, as described in Appendix A.

Notes: Column (1) covers everything in columns (2) and (3) plus wholesale and commission trade, trade in motor vehicles, and motor vehicle repair services. Column (3) excludes motor vehicle repair services but includes other consumer repair services. Column (3) does not include eating and drinking establishments.

Austria Belgium Canada Denmark France Germany Ireland Italy Netherlands Norway Sweden Switzerland UK USA

Country

Table 2A.1 Data on industry-level employment and value added shares, 1995

97

Tax effects on work activity, industry mix and shadow economy size

Table 2A.2

Sample compositions for country-level data

Country

Sample A

Australia Austria Belgium Canada Denmark Finland France Germany Ireland Italy Japan Netherlands New Zealand Norway Portugal Spain Sweden Switzerland UK USA No. of countries Years Observations

B

D

E

F

X X X

X X X X

X

X X

X

X X X X X X

X X X X X X X

X X X X X X X X X X X X X X X X X X

X

X

X X X X X

X X X X

X X X

17 1977–94

19 1995

14 1995

14 1995

9 1995

306

19

14

14

9

X

C X

X X

X X X X X X X X X X

X X X X X

X X X X X 13 1977, 83 90, 95 52

X X X X

X

X X

APPENDIX B: PROPOSITION ON IDENTIFICATION ‘Tax effects on work activity’ estimates OLS regressions of the form HC abTC C

(2B.1)

where C indexes countries, T is a monotonic function of the tax factor and H is an outcome variable of interest. This appendix proves, under conditions spelled out below, that b is a consistent estimator for the total response of H to an exogenous variation in T, inclusive of indirect effects that work through government spending responses to tax revenues.

98

Incentives to work

Proposition Assume that the data generating process is given by the following structural model for the outcome variable of interest H, the tax variable T, distortionary government spending W and net revenue requirements G: HC  H  HTC  HWC  uH C W W W W WC   TC GCuC TC  TTWC TGCuTC

(2B.2) (2B.3) (2B.4)

where uH, uW, uT and G are exogenous, mutually uncorrelated random disturbances with finite variances. If 2 0 (no exogenous variation in net revenue requirements a) G2 0 and W or distortionary spending)

or b) W  1T (equal spending-response condition), then plim(b) H  H W. That is, the OLS slope parameter in (2B.1) provides a consistent estimator for the total response of H to an exogenous variation in T. Proof Solve for the reduced form of the subsystem (2B.3) and (2B.4) to obtain

W W T( W T W )GC WuTCuW C 1
T W T

TT W(T W T )GCTuW C uC TC  1
T W

WC 

(i) (ii)

Next, substitute from (i) and (ii) into the right side of (2B.2) to obtain an expression for H in terms of parameters and exogenous variables:

H T HC H { TT W(T W T )GCTuW C uC} 1
T W H H { W W T( W T W )GC WuTCuW  C }uC 1
T W

(iii)

Tax effects on work activity, industry mix and shadow economy size

99

Now use (ii) and (iii) to calculate the probability limit of the OLS slope estimator in (2B.1): plim(b)  HH

(T W T )( W T W )2GT2W W2T (T W T )2 2G(T )2 2W2T

(iv)

It is immediate from (iv) that condition (a) yields the result. Alternatively, impose the equal spending-response condition (b) in (iv) and simplify to obtain (( W W ) T )( W T W )2G(1 W )2W W2T (( W W ) T )2 2G(1 W )2 2W2T ( W W T )( W T W )2G2W( W )2 2T  HH W ( W W T )2 2G2W( W )2 2T H H W  

(v)

plim(b)  HH

NOTES 1. We thank Pietro Garibaldi, Alberto Alesina, Alan Auerbach, Roland Benabou, Robert Hall, Narayana Kocherlakota, Erik Hurst, Jonathan Guryan, Lars Ljungqvist, Casey Mulligan, Kevin Murphy, Christopher Sims, Peter Spiro, Mark Watson and seminar participants at the European Central Bank, Federal Reserve Bank of Minneapolis, Princeton University, NBER Labor Studies program meeting, University of Chicago, University of Missouri, and University of Wisconsin for many helpful comments on earlier drafts. We also thank Conny Olovsson for helpful remarks on the relationship between Olovsson (2004) and Prescott (2003) and Jonathan Gruber for helpful pointers to US data sources. Bin Chen, Arvid Malm, Mickael Salabasis and Shelly Yang provided able research assistance. We gratefully acknowledge financial support from ‘Handelns Utvecklingsråd’ (HUR). Needless to say, we are responsible for errors and shortcomings. 2. We make no effort to summarize the vast body of research on the labour supply incentives associated with social insurance programmes, but studies of the Swedish case by Aronson and Walker (1997) and Henrekson and Persson (2004) highlight many of the issues. Krueger and Meyer (2002) review much of the relevant literature. 3. Prescott’s assessment is by no means universally shared among professional economists. Lindert (2002), for one, advances a much more favourable assessment of economic policy and performance in developed economies with high taxes and social spending. Blanchard (2004) offers a more mixed assessment that acknowledges some negative effects of high tax rates in Europe but places greater emphasis on regulations in product, labour and financial markets. 4. Boskin (1975) provides an early analysis of tax incidence and efficiency in a two-sector general equilibrium model with market and home production. Sandmo (1990), Piggott and Whalley (2001) and Kleven et al. (2000) analyse optimal taxation in models with home production. McGrattan et al. (1997) estimate an equilibrium business cycle model with home production and use it to evaluate the effects of distortionary taxation. Tax effects on the choice between market and household production activity play a central role in Rosen’s (1997) assessment of the Swedish welfare state and in Sørensen’s (1997) analysis of European unemployment. Olovsson (2004) considers the impact of personal taxes on market work activity in a dynamic equilibrium model with household production.

100 5.

6.

7. 8.

9.

10.

11. 12. 13.

14.

15.

Incentives to work Schneider and Enste (2000) report estimates of shadow economy size based on several different methods and types of data. Two methods that have been applied to many countries – the Physical Input (Electricity) Method and the Currency Demand Method – yield similar values for the average size of the shadow economy in the OECD and a similar pattern across countries. See their Tables 6 and 7. Johnson et al. (1998) argue that the administrative burden of taxation and the scope for corruption and abuse by the tax authorities, as distinct from tax rates, are key determinants of shadow economy size. We do not dispute this assessment for the sample of countries considered by Johnson et al., but the problems that they emphasize are much less important and probably much less variable among the countries in our sample. However, there is a broad upward drift in personal tax rates during the decades that precede our sample period, as we show below in the section on cross-country evidence. In addition, many households have strong preferences for the self-supplied version in activities such as meal preparation and child care. And household production can yield utility directly, as in gardening for enjoyment. It would not be hard to incorporate these considerations into the analysis. Palda (1998) shows that taxes also lower productive efficiency and raise average costs when firms (or alternative market technologies) differ in the ability to evade taxes. In Palda’s model, unlike the model sketched here, taxes can raise average production costs in the legal market sector. This formulation is consistent with standard user cost treatments of capital income taxation. For example, in the case of a self-financed business we have T  (1
k
Z)/ (1
) where  is the tax rate levied on business income net of depreciation costs, k is the rate of tax credit on new capital expenditures, and Z is the present value of depreciation allowances per dollar of capital expenditures. When k Z  1, T is an increasing function of the tax rate . Under full expensing of capital goods (k 0, Z  1), T drops out of equations (2.4)–(2.6). See Auerbach (1983) for a fuller discussion. Also, note that depreciation costs that are proportional to production do not influence the choice of production sector. The same point applies to market-supplied intermediate inputs that are used up in production. As a practical matter, the data on Lodging are aggregated with eating and drinking establishments for many of the countries in our sample. See for example Troske (1999) and the discussion on pp. 33–6 in Brown et al. (1990). Alesina et al. (2001) argue that greater ethnic, linguistic and racial fragmentation leads to less political support for social insurance and redistribution. The model of Persson et al. (2000) implies that a presidential-congressional regime entails greater separation of powers than a parliamentary regime and, as a result, leads to smaller government and less redistribution in political equilibrium. Persson and Tabellini (2002) discuss theory and evidence related to the impact of political regimes and electoral rules on the size and composition of government spending. The best we could do from OECD sources, by combining all three types of activities, results in a sample of only seven countries with data on our preferred tax measure. Regressions for this sample show a negative relationship between taxes and the employment and value added shares, as predicted by the theory, but the results are not statistically significant. In a previous draft, we reported a statistically significant effect of personal taxes on the shares for this industry group. However, upon further review of the data, we deleted two countries from our original sample because of incompatible classifications and we corrected the US data. We have milder concerns about the consistency of the classification and measurement of these activities in the remaining countries. For these reasons, we concluded that a sample for this industry group does not provide a sound basis for inference. In principle, panel methods and country-level case studies that investigate longer-term responses to persistent tax rate changes are potentially quite useful as a basis for inference about long-run tax effects. However, as Table 2.3 shows, there is not much low-frequency country-specific variation in personal tax rates in our sample.

Tax effects on work activity, industry mix and shadow economy size 16. 17.

18.

101

See for example Section 3.3 in Davis et al. (1996). We recently became aware of a paper by Nickell (2003) which concludes that a 10 percentage point rise in personal tax rates lowers the employment–population ratio by about 2 percentage points. Nickell’s estimate is half as large as our preferred estimate based on the Schneider tax data, but it is somewhat larger than our estimates in Table 2.3 based on the Nickell–Nunziata data. This comparison reinforces the conclusion that the measurement of tax variables is a key issue for future empirical work on how taxes affect work activity. We are uncertain about the precise meaning of ‘current receipts of households’ and ‘household income taxes’, the terms that Nickell and Nunziata use in describing the construction of their ‘direct tax rate’ measure.

REFERENCES Aguiar, M. and E. Hurst (2004), ‘Consumption vs. expenditure’, University of Chicago Graduate School of Business, unpublished paper, June. Alesina, A., E. Glaeser and B. Sacerdote (2001), ‘Why doesn’t the US have a European style welfare state?’, Brookings Papers on Economic Activity, Fall. Aronson, T. and J.R. Walker (1997), ‘The effects of Sweden’s welfare state on labor supply incentives’, in R.B. Freeman, R. Topel and B. Swedenborg (eds), The Welfare State in Transition: Reforming the Swedish Model, Chicago: The University of Chicago Press. Auerbach, A. (1983), ‘Taxation, corporate financial policy and the cost of capital’, Journal of Economic Literature, 21 (3), 905–40. Becker, G.S. and C.B. Mulligan (2003), ‘Deadweight costs and the size of government’, Journal of Law and Economics, 46 (2), 293–340. Bertrand, M. and F. Kramarz (2002), ‘Does entry regulation hinder job creation? Evidence from the French retail industry’, Quarterly Journal of Economics, 117 (4), 1369–413. Blanchard, O. (2000), ‘The Economics of Unemployment, Shocks, Institutions and Interactions’, Lionel Robbins Lectures, October. Blanchard, O. (2004), ‘The Economic Future of Europe’, NBER Working Paper No. 10 310 (March). Blanchard, O. and F. Giavazzi (2003), ‘Macroeconomic effects of regulation and deregulation in goods and labor markets’, Quarterly Journal of Economics, 118 (3), August, 879–909. Blau, F.D. and L.M. Kahn (1999), ‘Institutions and laws in the labor market’, in O.C. Ashenfelter and D. Card (eds), Handbook of Labor Economics, Volume 3A, Amsterdam: Elsevier. Boskin, M.J. (1975), ‘Efficiency aspects of the differential tax treatment of market and household economic activity’, Journal of Public Economics, 4 (1), 1–25. Brennan, H.G. and J.M. Buchanan (1980), The Power to Tax: Analytical Foundations of a Fiscal Constitution, Cambridge: Cambridge University Press. Brown, C., J. Hamilton and J. Medoff (1990), Employers Large and Small, Cambridge, MA: Harvard University Press. Cagan, P. (1958), ‘The demand for currency relative to the total money supply’, Journal of Political Economy, 66 (3), 302–28. Davis, S.J. (1997), ‘Sorting, learning and mobility when jobs have scarcity value’, Carnegie-Rochester Conference Series on Public Policy, 46, 327–37.

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Davis, S.J. and M. Henrekson (2002), ‘Personal taxes and the scope for market activity’, incomplete working paper. Davis, S.J. and M. Henrekson (2005), ‘Wage-setting institutions as industrial policy’, Labour Economics, forthcoming. Davis, S.J., J.C. Haltiwanger and S. Schuh (1996), Job Creation and Destruction, Cambridge, MA: The MIT Press. Dolado, J., F. Kramarz, S. Machin, A. Manning, D. Margolis and C. Teulings (1996), ‘Minimum wages: the European experience’, Economic Policy, 23 (October), 319–72. Eisner, R. (1988), ‘Extended accounts for national income and product’, Journal of Economic Literature, 26 (4), 1611–84. Freeman, R.B. and R. Schettkat (2005), ‘Marketization of production and the US–Europe employment gap’, Economic Policy, 20 (41), 6–52. Gersbach, H. and A. Schniewind (2001), ‘Product Market Reforms and Unemployment in Europe’, IZA Discussion Paper No. 255 (February). Giles, D.E.A. (2000), ‘Modeling the tax compliance profiles of New Zealand firms: evidence from audit records’, in G.W. Scully and P.J. Caragata (eds), Taxation and the Limits of Government, Boston: Kluwer Academic Publishers. Giles, D.E.A. and L.M. Tedds (2002), Taxes and the Canadian Underground Economy, Toronto: Canadian Tax Foundation. Glyn, A. (2002), ‘Labour Market Success and Labour Market Reform: Lessons from Ireland and New Zealand’, CEPA Working Paper No. 2002–03. Greenwood, J., R. Rogerson and R. Wright (1995), ‘Household production in real business cycle theory’, in T.F. Cooley (ed.), Frontiers of Business Cycle Research, Princeton, NJ: Princeton University Press. Henrekson, M. and M. Persson (2004), ‘The effects of sick leave changes in the sickness insurance system’, Journal of Labor Economics, 22 (1), 87–113. Johnson, S., D. Kaufmann and P. Zoido-Lobaton (1998), ‘Regulatory discretion and the unofficial economy’, American Economic Review, 88 (2), 387–92. Juster, F.T. and Stafford, F.P. (1991), ‘The allocation of time: empirical findings, behavioral models, and problems of measurement’, Journal of Economic Literature, 29 (2), 471–522. Karoleff, V., A. Snow and G.A. Trandel (1994), ‘Tax evasion and the size of the underground economy’, Canadian Public Policy, 20 (3), 235–52. Kleven, H.J., W.F. Richter and P. Birch Sørensen (2000), ‘Optimal taxation with household production’, Oxford Economic Papers, 52 (3), 584–94. Krueger, A.B. and B.D. Meyer (2002), ‘Labor supply effects of social insurance’, in A.J. Auerbach and M. Feldstein (eds), Handbook of Public Economics, Volume 4, Amsterdam: North-Holland. Krusell, P., V. Quadrini and J.-V. Rios-Rull (1996), ‘Are consumption taxes really better than income taxes?’, Journal of Monetary Economics, 37 (3), 475–503. Lindbeck, A. (1995), ‘Hazardous welfare-state dynamics’, American Economic Review, 85 (2), 9–15. Lindbeck, A., S. Nyberg and J.W. Weibull (1999), ‘Social norms and economic incentives in the welfare state’, Quarterly Journal of Economics, 114 (1), 1–35. Lindert, P. (2002), ‘Why the Welfare State Looks Like a Free Lunch’, UC-Davis Department of Economics Working Paper No. 02-7. Ljungqvist, L. and T.J. Sargent (1995), ‘The Swedish unemployment experience’, European Economic Review, 39 (5), 1043–70.

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Ljungqvist, L. and T.J. Sargent (1998), ‘The European unemployment dilemma’, Journal of Political Economy, 106 (3), 514–50. McGrattan, E.R., R. Rogerson and R. Wright (1997), ‘An equilibrium model of the business cycle with household production and fiscal policy’, International Economic Review, 38 (2), 267–90. Nicoletti, G. and F.L. Pryor (2001), ‘Subjective and objective measures of the extent of governmental regulations’, at www.aei-brookings.org/admin/authorpdfs/ page.php?id424. Nickell, S.J. (2003), ‘Employment and Taxes’, CESifo Working Paper No. 1109, December. Nickell, S. and R. Layard (1999), ‘Labor market institutions and economic performance’, in O.C. Ashenfelter and D. Card (eds), Handbook of Labor Economics, Volume 3C, Amsterdam: Elsevier. Nickell, S.J. and L. Nunziata (2001), ‘Labour Market Institutions Database’, Centre for Economic Performance, September 21. OECD (1999), Employment Outlook, Paris: OECD Publications Service. OECD (2000, 2001), Services: Statistics on Valued Added and Employment, Paris: OECD Publications Service. OECD (2003), Labour Force Statistics, 1982–2002, Paris: OECD Publications Service. Olovsson, C. (2004), ‘Why Do Europeans Work so Little?’, Institute for International Economic Studies Working Paper, Stockholm University. Palda, F. (1998), ‘Evasive ability and the efficiency cost of the underground economy’, Canadian Journal of Economics, 31 (5), 1118–38. Persson, T. and G. Tabellini (2002), ‘Political institutions and policy outcomes: what are the stylized facts?’, mimeo, Stockholm University. Persson, T., G. Roland and G. Tabellini (2000), ‘Comparative politics and public finance’, Journal of Political Economy, 108 (6), 1121–61. Piggott, J. and J. Whalley (2001), ‘VAT base broadening, self supply, and the informal sector’, American Economic Review, 91 (4), 1084–94. Prescott, E. (2002), ‘Prosperity and depression’, American Economic Review, 92 (2), 1–15. Prescott, E. (2003), ‘Why Do Americans Work So Much More than Europeans?’, Federal Reserve Bank of Minneapolis Staff Report 321, November. Rogerson, R. (2003), ‘Taxation and market work: is Scandinavia an outlier?’, work in progress, Arizona State University. Rosen, S. (1997), ‘Public employment, taxes, and the welfare state in Sweden’, in R.B. Freeman, R. Topel and B. Swedenborg (eds), The Welfare State in Transition: Reforming the Swedish Model, Chicago: University of Chicago Press. Sandmo, A. (1990), ‘Tax distortions and household production’, Oxford Economic Papers, 42 (1), 78–90. Sattinger, M. (1993), ‘Assignment models of the distribution of earnings’, Journal of Economic Literature, 31 (4), 831–80. Schettkat, R. (2003), ‘Differences in US–German Time-Allocation: Why Do Americans Work Longer Hours Than Germans?’, IZA Discussion Paper No. 697, January. Schneider, F. (2002), ‘The Size and Development of the Shadow Economies of 22 Transition and 21 OECD Countries’, IZA Discussion Paper 514, June. Schneider, F. and D.H. Enste (2000), ‘Shadow economies: size, causes, and consequences’, Journal of Economic Literature, 38 (1), 77–114.

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Skolka, J. (1985), ‘The parallel economy in Austria’, in W. Gaertner and A. Wenig (eds), The Economics of the Shadow Economy: Proceedings of the International Conference on the Economics of the Shadow Economy, Berlin: Springer-Verlag. Sørensen, P.B. (1997), ‘Public finance solutions to the European unemployment problem?’, Economic Policy, 25, 223–64. Spiro, P. (1993), ‘Evidence of a post-GST increase in the underground economy’, Canadian Tax Journal, 41 (2), 247–58. Tanzi, V. (1980), ‘The underground economy in the United States: estimates and implications’, Banca Nazionale del Lavoro Quarterly Review, 135 (4), 427–53. Troske, K. (1999), ‘Evidence on the employer size-wage premium from workerestablishment matched data’, Review of Economics and Statistics, 81 (1), 15–26. US House of Representatives (1996, 2000), The Green Book: Background Materials and Data on Programs within the Jurisdiction of the Committee on Ways and Means, Washington, DC: US Government Printing Office. Williams, C., V. Thompson, P. Reno and J.F. Burton, Jr (2003), WorkersCompensation: Benefits, Coverage, and Costs, 2001, Washington, DC: National Academy of Social Insurance.

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DISCUSSION Pietro Garibaldi The chapter is a nice contribution to the growing literature on the adverse macroeconomic effects of total taxation (Prescott, 2003; Nickell, 2003). It sets forth a very simple theory of task allocation and shows that market taxation takes away activity from the market towards home production.1 There are strong empirical predictions on the adverse effects of taxation on the aggregate employment rate, as well as on the employment rate in specific sectors of the economy. Using simple bi-variate relationships between employment rates and total taxation, the chapter finds empirical support for the adverse effects of taxation. I organize my comments in three parts. First, I present a graphical illustration of the theory of task allocation presented here by Davis and Henrekson. Second, I discuss the importance of a critical maintained assumption of the authors – namely the elasticity of labour supply – and show how the basic results would change with a more empirical plausible assumption. Third, I offer a further robustness check on the empirical results of the chapter by considering the role of start-up costs, a product market institution that, from a theoretical standpoint, has similar empirical predictions on the labour market. A Theory of Task Allocation Let us assume that there exists a continuum of tasks that can be carried out in two different ways. In other words, we can imagine that each particular task (task B, for example) can be accomplished with two solutions: the market solution, which involves a cost CM(B) and the non-market solution, which requires a cost equal to Ch(B). Clearly, the market solution will be preferred as long as CM(B)Ch(B). The choice between the two solutions is easily depicted in Figure 2D.1. The horizontal axis reports the cost of the market solution, while the vertical axis reports the cost of the non-market solution. Each task can then be described by a point in the space (CM, CH ). If there is no taxation, the choice between the two technologies is simply described by the position of each activity relative to the dotted line: all activities that lie above the 45 degree line will be performed in the market. As a result, the activity labelled B will be done in the market while the activity labelled C will be performed at home. In reality, many activities are not easily performed with both solutions, and their position is very close to one of the axes. For example, international telecommunications is an activity that is very expensive to be done

106

Incentives to work

CH

Market activities

D

s 0, m 0, t  0 s m t  0

B A Do-it-yourself activities

C

F CM

Figure 2D.1 Task allocation between household production and the market by oneself at home, and thus its position can be described by a point such as D. At the other extreme, one can easily imagine that there are many activities that can only reasonably be performed at home. Dressing oneself in the morning (or brushing one’s teeth) is an activity that is far too expensive to be performed in the market, and it is thus easily described by a point such as F in the figure. This suggests that such activities have a natural allocation, independently of market taxation. Let us now consider what happens when taxation is introduced. As Davis and Henrekson show, the decision between the market and the non-market solutions is distorted by the tax factor, which the authors indicate with (1s)(1 m)/(1
t) where s is the payroll tax levied on employers, m is the value added tax rate (or sales tax) and t is the marginal income tax rate. In terms of Figure 2D.1, the effect of taxation is akin to an anticlockwise shift of the 45 degree line, as clearly described in the figure. The presence of taxation distorts the natural choice between the two solutions. All activities that lie below the continuous line (the one with positive tax factor) in Figure 2D.1 will be done in the market while activities that are below the line will be done at home. This is a visual description of the fact that taxation shifts away from the market a well-defined set of activities. In terms of Figure 2D.1, an activity such as A will be performed at home when taxation is present, while it will be performed in the market

Tax effects on work activity, industry mix and shadow economy size

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without taxation. The key empirical implication of this theory is thus very simple: taxation moves away from the market solution all those activities that involve similar costs at home and in the market. The larger is taxation, the larger is the share of activities that are performed at home. Clearly, the activities that can potentially be done at home as well as in the market are those activities that belong to specific low-skill labourintensive service sectors, such as retail trade, lodging, eating and drinking, and so on. Such a prediction is the key empirical prediction presented in the chapter. Before turning to the discussion of the empirical implications, let me turn to the second issue. Wage Elasticity of Labour Supply In the authors’ theory, Wb represents the buyer’s pre-tax wage per unit of time. While the theory presented is obviously very simple, some of the maintained assumptions are probably too strong. Indeed, the authors’ analysis assumes that the pre-tax wage is independent of taxation in general, and of the size of the payroll tax in particular. This is a very strong assumption. Most textbooks of labour economics clearly show that an increase in payroll taxation is likely to be passed on to firms’ labour costs, unless labour supply is perfectly inelastic. In other words, the authors are assuming that labour costs are independent of payroll taxation, or that labour supply is perfectly rigid. This assumption is in general not true, and it is not likely to hold for the specific type of labour supplied in the sectors where the possible substitution of market activity with home activity is high. Let me explain this further. The activities that are close substitutes between the market and the home sectors are those activities that are undertaken by people with relatively low formal education who are also more likely to be female workers. Such workers’ characteristics are associated with a relatively high value of the elasticity of labour supply, as the survey by Blundell and MaCurdy (1999) makes clear. To take this effect into account, the formal specification of the cost at home of a general activity j should be written as Ch( j)Wb(t)(1
t)Hb, with Wb /t 0.

(1)

Taking this effect into consideration, the impact of taxation on the cost Ch( j) would be Ch/t(1
t)Hb Wb /t
tHb,

(2)

108

Incentives to work

an expression whose overall sign is ambiguous (Hb is simply the amount of time required to perform the activity at home). Clearly, if one assumes that Wb /t0, as the authors seem to do in their chapter, then the total effect of taxation on the cost of producing at-home activity j is certainly negative. Despite this shortcut, I do believe that the authors’ results makes sense, even with endogenous labour supply, but it seems that the total effect is likely to be more complicated than what derives from the simple theory. The Role of Start-up Costs Let me now turn to the third issue. As the authors recognize, there are additional institutional differences across countries that are consistent with the empirical results found in the chapter. Indeed, the authors do recognize the existence of such additional institutions, but they argue that their empirical relevance on the labour market is weaker. Specifically, after performing a simple comparison of the effects on the employment rate and on hours worked of taxation and of various other institutions (employment protection, collective bargaining, overall product market regulation, minimum wage) the authors conclude that the adverse effect of taxation is quantitatively more important. In these last few lines I address an extra robustness check. The specific empirical implications of the authors are very similar to the implications of the literature that argues that start-up costs and administrative burdens on firm creation are key variables for understanding the bad employment peformance of many European countries in the service sector. Pissarides (2002) and Lopez Garcia et al. (2001) present theory and evidence that is fairly comparable to the one presented in this chapter. The logic of the effect of start-up costs is simple: by making firm creation in specific service sectors very costly, start-up costs increase the incentives to carry out those services inside the household (eating and so on). The index on product market regulation used by Davis and Henrekson is a more general index of the role of the state in the produce market, while the theory I am referring to is specifically concerned with start-up costs. In what follows I use the index on administrative burden on start-up costs compiled by the OECD (1998). The index records the number of procedures and the number of days necessary to set up a firm in different countries. In Figure 2D.2, I plot the cross-country correlation between start-up cost and the employment rate in 1995, where the latter figure is taken from Nickell and Nunziata (2001). The negative correlation is impressive. A simple linear regression with a constant features a negative and highly significant coefficient (Table 2D.1). In Figure 2D.3, the relationship between start-up costs and total hours features a similar negative

Tax effects on work activity, industry mix and shadow economy size 80

Employment rate

75

Fitted values

sz de

109

no

us sw

70

ca

uk

ne

65

ge

60

fr be

ir

55

it 50 sp

45 .5

Figure 2D.2

1

1.5

2 2.5 Start-up costs

3

3.5

4

4.5

Start-up costs and employment rate in 1995 (14 countries)

Table 2D.1 Cross-country regressions with start-up costs and taxation in 1995 Employment rate a

Hours workedb

Eating c

Start-up costs P-value Observations R2


4.23 0.02 20.00 0.22


97.50 0.01 14.00 0.48


0.17 0.63 14.00 0.02

Start-up costs P-value Total tax rate P-value Observations R2


4.02 0.06
0.24 0.20 14.00 0.45


72.35 0.01
6.22 0.02 14.00 0.69

0.20 0.48
0.10 0.01 13.00 0.68

Independent variables

Note: All regressions include a constant. Sources: Start-up costs are from OECD (1998). Tax rates are from Schneider (2002). a. Employment rates are from Nickell and Nunziata (2001). b. Hours worked per person are those used in Davis and Henrekson (this chapter). c. Employment share in Eating and Drinking are from Davis and Henrekson (this chapter).

110

Incentives to work Fitted values

1400 us sz

1300

Hours

1200 de

uk sw ca

1100 ir

no

ge

1000

fr

ne

be

900 it

sp 800 .5

1

1.5

2

2.5 3 Start-up costs

3.5

4

4.5

Figure 2D.3 Start-up costs and annual work hours per adult in 1995 (14 countries) correlation, with a highly significant coefficient. Perhaps surprisingly, the correlation between start-up costs and the employment share in specific retail trade sectors is not significant. Finally, I performance a race test between the start-up cost index and the total tax rate used by Davis and Henrekson in this chapter (Table 2D.1). Note that in the simple regressions the tax rate used is the tax rate from Schneider (2002) since Davis and Henrekson argue that is the best fitting variable for capturing the adverse effect of taxation on total employment. The results show that it is very difficult to judge which of the two effects is quantitatively more important. In the regressions on the employment rate only the start-up cost index is significant, while in the regression on the employment share in eating and drinking the only significant variable is the tax rate. Finally, in the regression on the hours worked, both variables are highly significant. These results suggests that further work is needed to discriminate between the two variables, which both appear to be very important for understanding the cross-country variation in the employment rates. Note 1. Recent papers that have addressed the impact of taxation on home production include Freeman and Schettkat (2002) and Garibaldi and Wasmer (2004).

Tax effects on work activity, industry mix and shadow economy size

111

References Blundell, R. and T. MaCurdy (1999), ‘Labor supply: a review of alternative approaches’, in O.C. Ashenfelter and D. Card (eds), Handbook of Labor Economics, Vol. 3A, pp. 1559–695. Freeman, R.B. and R. Schettkat (2002), ‘Marketization of Production and the US–Europe Employment Gap’, NBER Working Paper No. 8797. Garibaldi, P. and E. Wasmer (2004), ‘Raising female employment: reflections and policy tools’, Journal of the European Economic Association, Vol. II, 320–30, April. Lopez Garcia, P., C. Pissarides and R. Fonseca (2001), ‘Entrepreneurship, start-up costs and employment’, European Economic Review, 45 (May), 692–705. Nickell, S. (2003), ‘Employment and Taxes’, CESifo Working Paper Series No. 1109, December. Nickell, S. and L. Nunziata (2001), ‘Labour Market Institutions Database’, Centre for Economic Performance, September 21. OECD (1998), Fostering Entrepreneurship, Paris: OECD Publications Service. Pissarides C.A. (2002), ‘Company start-up costs and employment’, in P. Aghion, R. Frydman, J. Stiglitz and M. Woodford (eds), Knowledge, Information and Expectations in Modern Macroeconomics: In Honor of Edmund S. Phelps, Princeton: Princeton University Press. Prescott, E. (2003), ‘Why Do Americans Work So Much More than Europeans?’, Federal Reserve Bank of Minneapolis Staff Report No. 321, November. Schneider, F. (2002), ‘The Size and Development of the Shadow Economies of 22 Transition and 21 OECD Countries’, IZA Discussion Paper No. 514, June.

PART II

Factors Affecting Labour Force Participation

3.

Mothers’ changing labour supply in Britain, the USA and Sweden Shirley Dex, Siv Gustafsson, Andrew Jenkins, Heather Joshi, Eiko Kenjoh and Mark R. Killingsworth*

Changes in female labour supply were one of the most dramatic features of the labour market in Europe and the USA during the twentieth century, and are likely to be so during the twenty-first century as well. Labour supply among men changed relatively little during the last third of the twentieth century. In contrast, labour supply among successive cohorts of women grew more or less continuously, and by substantial amounts. How has this increase in female labour supply come about? What are some of the factors underlying this change? Has this change proceeded more or less equally across the board, or has it occurred at different rates in different countries and for different groups of women (for example, for mothers, the less-educated and so on)? To address these questions, we consider the experience of Britain, Sweden and the USA. Although they have different social systems, in some important respects female labour supply in these three countries is relatively similar (see Table 3.1, which shows male and female labour force participation rates). We believe that a careful look at changes in female labour supply in these countries is useful for several reasons: it will increase our understanding of how female labour supply has been changing in these countries; it may help us determine the future course of female labour supply in these countries; and it may also provide a forecast of the likely course of labour supply in other countries in which female labour supply is currently lower.

FEMALE LABOUR SUPPLY: A LOOK AT COHORT CHANGES By most measures – labour force participation, weeks worked per year, hours worked per week – female labour supply in most countries increased 115

116

Source:

Note:

46.1 59.6

US Bureau of Labor Statistics.

All series are adjusted to follow US concepts.

38.1 59.8

31.0 59.5

81.8 72.6 n/a 56.4

n/a 73.1 52.4 48.5

84.3 75.5 n/a 49.2

n/a 64.2

France

41.0 48.8

82.7 66.3

Germany

40.1 55.2

87.1 68.1

Japan

Women 1961 2001

82.9 74.4

Australia

86.6 70.8

Canada

Men 1961 2001

Sweden

Britain

Year

USA

Civilian labour force participation rates (%) by country and sex, 1961 and 2001

Table 3.1

32.5 36.3

81.8 61.4

Italy

n/a 55.7

n/a 75.4

Netherlands

Mothers’ changing labour supply

117

substantially during the last third of the twentieth century, both in absolute terms and relative to male labour supply. How much of this can be traced to changes across (rather than within) cohorts? The data for Sweden – which are somewhat limited in several respects – suggest that the labour force participation rates of successive birth cohorts of both men and women have changed very little relative to those of persons born in the first several decades of the twentieth century. These data come from the Statistikdatabasen of Statistics Sweden, which provides information on labour force participation at different ages. By combining these data for different years and ages (for example, persons born in 1956–65 at ages 16–24, 25–34, 35–44 and so on), one can construct agelabour force participation profiles for seven synthetic birth cohorts, starting with persons born in 1916–25 and ending with persons born in 1976–85. Figures 3.1, for men, and 3.2, for women, show very little change in labour force participation from one birth cohort to the next. In contrast, data for the USA (which are somewhat more detailed than the Swedish data) tell a very different story. These data come from the Annual Demographic Files of the US Current Population Survey (CPS) for 1968–2002. The CPS is a large national sample survey that provides, inter alia, data on sex, age, and hours and weeks worked per year. By combining the CPS data for different years and ages, one can construct data for ten synthetic birth cohorts, starting with persons born in 1925–29 and ending with persons born in 1970–74. Graphs of annual hours worked for successive cohorts show very clearly which aspects of labour supply have – and have not – changed appreciably in the last 35 years. First consider Figure 3.3, which graphs annual hours of work1 for each synthetic birth cohort of US men from 1925–29 to 1970–74. Each cohort’s labour supply2 follows the familiar inverted-U-shaped pattern. Note that each cohort’s behaviour is (at least in this visual presentation) virtually indistinguishable from any other’s. Now consider Figure 3.4, for US women, which makes it very clear that labour supply for more recent female birth cohorts is substantially greater than that of earlier cohorts. Measured at age 45, for example, the labour supply of the most recent female cohorts is about 600 hours of work per year greater than that of the oldest cohort. Note also that, for the most recent birth cohorts, the oft-mentioned M-shaped dip in female hours of work during the prime child-bearing and child-rearing years (ages 25–35) is virtually nonexistent. Table 3.2 summarizes Figures 3.3–3.4 by means of OLS regressions, in which the dependent variable – annual hours worked – is regressed on a cubic in age and dummies for birth cohort. For men, the difference between the oldest cohort (born 1920–24) and more recent cohorts (as measured by the coefficients for the latter) is generally small (less than 100 annual hours);

118 25–34

35–44 Age

Labour force participation rates by age and birth cohort, Swedish men

40 16–24

50

60

70

80

90

100

Figure 3.1

Percentage in the labour force

1966–75

1956–65

45–54

55–64

1946–55

1936–45

1976–85

1926–35

1916–25

119 25–34

35–44 Age

Labour force participation rates by age and birth cohort, Swedish women

40 16–24

50

60

70

80

90

100

Figure 3.2

Percentage in the labour force

1946–55 1966–75

1936–45 1956–65

45–54

1976–85

1926–35

1916–25

55–64

120

Figure 3.3

Hours of work per year

25

27

29

31

33

35

37

39

41

43

Annual hours of work, by birth cohort, US men

600

900

1200

1500

1800

2100

2400

45 47 Age

49

51

57

59

61

1970–74

1965–69 55

1960–64

1955–59

53

1950–54

1940–44

1935–39 1945–49

1930–34

1925–29

63

65

121

300

600

900

1200

1500

1800

25

Figure 3.4

Hours of work per year

29

31

33

35

1930–34 1955–59

37

39

41

1935–39 1960–64

43

45 Age

47

1940–44 1965–69

Annual hours of work, by birth cohort, US women

27

1925–29 1950–54

49

51

1945–49 1970–74

53

55

57

59

61

63

65

122

Factors affecting labour force participation

Table 3.2 OLS regression analyses of annual hours worked for synthetic birth cohorts, by sex (USA) Variable

Men

Women

Parameter estimate

Standard error

t

Parameter estimate

Standard error

t


158.07 5.32
0.05

17.74 0.40 0.00


8.91 13.04
17.67


285.49 8.02
0.06

12.61 0.29 0.00


22.63 27.63
31.82

Cohort dummies (reference: born 1920–24) 1925–29
76.32 20.32
3.76 1930–34
94.31 19.64
4.80 1935–39
82.31 19.34
4.25 1940–44
61.38 19.75
3.11 1945–49
77.47 20.58
3.76 1950–54
91.22 21.53
4.24 1955–59
76.49 22.69
3.37 1960–64
34.53 24.11
1.43 1965–69
3.86 26.03
0.15 1970–74 8.91 29.50 0.30

20.97 93.94 209.38 319.18 443.21 548.50 617.43 690.49 745.16 809.27

14.44 13.96 13.75 14.04 14.63 15.30 16.13 17.14 18.51 20.97

1.45 6.73 15.22 22.73 30.28 35.83 38.26 40.28 40.25 38.59

3767.72

175.67

21.45

age age2 age3

Intercept R2,

3382.81

247.10

number of observations

13.69

0.9641, 237

0.9767, 237

indeed, the difference between the oldest cohort and the three youngest cohorts (born 1960–64 or later) is statistically indistinguishable from zero. In contrast, for women, the upward climb in cohorts’ annual hours of work is dramatic; the difference in annual hours between the youngest and oldest cohorts (those born 1970–74 vs. those born 1925–29) is more than 800 (t 38.6). In sum, as Table 3.2 reminds us, men certainly constitute a greater fraction of total labour supply than do women: the labour market as a whole is still predominantly male. However, as Figure 3.4 shows very clearly, women constitute a greater fraction of the change in total labour supply during the last 40 years; and it is for this reason that we think that female labour supply is particularly important. Education and motherhood are almost always considered to be important determinants of labour supply, particularly of women. The synthetic cohort data from the CPS highlight some very interesting possibilities about these two factors (although they also require some careful caveats). First consider Figures 3.5 and 3.6. Figure 3.5 shows annual hours worked, by birth cohort, for synthetic cohorts of US women with less than

123

300

600

900

1200

1500

1800

Figure 3.5

Hours of work per year

27

29

31

33

35

37

1930–34 1955–59

39

41

43

1935–39 1960–64

45 Age

47

49

1940–44 1965–69

51

53

55

1945–49 1970–74

57

Annual hours of work, by birth cohort, US women with less than 16 years of education

25

1925–29 1950–54

59

61

63

65

124

Figure 3.6

Hours of work per year

25

27

29

31

1925–29 1950–54

33

35

37

1930–34 1955–59

39

41

43

1935–39 1960–64

45 47 Age

49

1940–44 1965–69

51

53

55

1945–49 1970–74

57

59

Annual hours of work, by birth cohort, US women with 16 years of education or more

300

600

900

1200

1500

1800

2100

61

63

65

125

Mothers’ changing labour supply

Table 3.3 OLS regression analyses of annual hours worked for synthetic birth cohorts, by education (US women) Variable

16 years of education or more

Less than 16 years of education

Parameter estimate

Standard error

t

Parameter estimate

Standard error

t


543.69 13.75
0.11

25.90 0.59 0.00


20.99 23.06
24.88


214.64 6.44
0.05

11.67 0.26 0.00


18.38 23.96
28.68

Cohort dummies (reference: born 1920–24) 1925–29
44.18 29.66
1.49 1930–34 47.88 28.68 1.67 1935–39 124.58 28.24 4.41 1940–44 192.97 28.83 6.69 1945–49 299.68 30.05 9.97 1950–54 395.01 31.43 12.57 1955–59 444.19 33.13 13.40 1960–64 468.50 35.20 13.31 1965–69 525.94 38.01 13.84 1970–74 608.98 43.06 14.14

18.39 75.69 183.60 284.28 393.00 488.32 566.28 641.21 679.90 732.91

13.37 12.92 12.73 12.99 13.54 14.17 14.93 15.86 17.13 19.41

1.38 5.85 14.42 21.87 29.01 34.46 37.91 40.41 39.68 37.75

2732.92

162.62

16.81

age age2 age3

Intercept R2,

7917.40

360.74

number of observations

21.95

0.9056, 237

0.9773, 237

a university education (that is, less than 16 years of schooling); Figure 3.6 shows annual hours for synthetic cohorts of US women with at least a university education. Figures 3.5 and 3.6, like Figure 3.4, show steady increases in annual hours of work for successive female cohorts. Comparison of Figures 3.5 and 3.6 suggests that hours of work may have increased somewhat more among women with less than 16 years of schooling (Figure 3.5) than among those with 16 or more years (Figure 3.6). Table 3.3 uses OLS regression to summarize Figures 3.5 and 3.6; note that – as measured by their differences relative to the reference birth cohort (those born in 1920–24) – growth in labour supply for recent cohorts is indeed greater among women with less than 16 years of education than among women with 16 or more years of education. (For example, for women born in 1970–74, the difference in annual hours relative to those born in 1920–24 is 609 (t 14.14) among women with 16 or more years of education, while it is 733 (t 19.41) among women with less than 16 years of education.) Figures 3.7 and 3.8 summarize annual hours of work for synthetic cohorts of US women, first with (Figure 3.7) and then without (Figure 3.8)

126

600

800

1000

1200

1400

1600

Figure 3.7

Annual hours worked

27

29

31

1935–39 1960–64

33

1940–44 1965–69

35 Age

37

1945–49 1970–74

Annual hours of work, by birth cohort, US women with children

25

1930–34 1955–59

39

1950–54

41

43

45

127

1000

1200

1400

1600

1800

2000

Figure 3.8

Hours of work per year

27

29

31

1935–39 1960–64

33

1940–44 1965–69

35 Age

37

1945–49 1970–74

Annual hours of work, by birth cohort, US women without children

25

1930–34 1955–59

39

1950–54

41

43

45

128

Factors affecting labour force participation

children under 18 in the home. It should be noted that, to an even greater degree than is the case with the other synthetic cohort profiles shown in this chapter, the profiles in Figures 3.7 and 3.8 should be interpreted with caution, because the absence or presence of children can itself vary with age. This means that the synthetic cohort profiles in Figures 3.7 and 3.8 do not necessarily give one a good idea of the age profile of any actual cohort.3 With this important caveat in mind, it is nevertheless interesting to consider Figures 3.7 and 3.8. They suggest – subject to the qualifications just noted – that labour supply of successive cohorts of US women with children, although still relatively low, has increased considerably more than labour supply of successive cohorts of childless US women. The OLS regressions in Table 3.4 make the point numerically: as measured by their cohort dummy-variable coefficients, the difference in annual hours relative to those born in 1920–24 is 800 (t 37.78) for women born in 1970–74 with children, vs. 497 (t20.75) for women born in 1970–74 without children. In sum, this relatively impressionistic and informal review of the data suggests that, in the USA, cohorts with low levels of labour supply (women, Table 3.4 OLS regression analyses of annual hours worked for synthetic birth cohorts, by presence of children (US women) Variable

No children under 18 Parameter estimate

With children under 18

Standard error

t

Parameter estimate

Standard error

t

72.61 2.13 0.02

5.22
4.93 4.61


169.77 6.21
0.06

64.21 1.88 0.01


2.64 3.30
3.41

Cohort dummies (reference: born 1930–34) 1935–39 168.51 22.47 7.50 1940–44 264.91 21.71 12.20 1945–49 386.43 21.33 18.11 1950–54 452.35 21.27 21.26 1955–59 486.25 21.27 22.85 1960–64 512.53 21.74 23.57 1965–69 497.97 22.65 21.98 1970–74 496.92 23.94 20.75

149.74 257.76 393.42 498.82 562.03 634.13 705.81 800.03

19.87 19.20 18.86 18.81 18.81 19.23 20.03 21.17

7.53 13.42 20.85 26.51 29.87 32.97 35.23 37.78

1590.55

721.00

2.21

age age2 age3

Intercept R2,

378.90
10.51 0.09


3230.38

815.27

number of observations


3.96

0.9501,120

0.9793,120

129

Mothers’ changing labour supply

women with less than a university education, women with children) have nevertheless increased their labour supply to a greater degree than have cohorts with higher levels of labour supply (men, women with a university education or more, women with no children). What is much less clear is whether these phenomena are purely cohort trends or whether other factors are at least partly responsible. Education receives at least some attention in virtually every discussion of changes and trends in female labour supply. On a naive view, education is an important component of one’s market wage, so, by raising the wage, greater levels of education would be expected to attract women into market work, inducing them to postpone motherhood and return to work quickly after childbirth (particularly when it is necessary to purchase child care at market prices). However, education may also raise the home wage (the ‘opportunity cost of time’), thereby pulling women back into the home. As in conventional labour supply models, the net effect of education on the hazard of motherhood and of labour market re-entry will therefore depend on its relative impact on home and market wages. (Note also that wives’ education may be associated with both their own and their husbands’ lifetime earning power, generating income effects.) To see some of these issues in the starkest possible terms, consider the following very simple model. There are three periods; a woman can have at most one child, born in either period 1 or 2 (but not period 3). Utility in each period U(N, C) depends on the number of children N ( 0 or 1) and on consumption C. Income is Y for a childless woman; during period t ( 1 or 2), childbirth entails lost income K (for consumption, forgone earnings and so on); during any period s after childbirth, earnings are reduced by an amount D due to skill depreciation caused by prior absence from the workforce for child-rearing. There is no discounting, saving or borrowing. There are, then, three choices (and, thus, three possible equilibria), with implications for utility in each of the three periods (Table 3.5). Table 3.5 Utility of alternative choices in the simple model of work and fertility Choice

Have a child in period 1 Have a child in period 2 Do not have a child

Utility in Period 1

Period 2

Period 3

U(1, Y
K ) U(0, Y ) U(0, Y )

U(1, Y
D) U(1, Y
K ) U(0, Y )

U(1, Y
D) U(1, Y
D) U(0, Y )

130

Factors affecting labour force participation

The decision to have a child in period 1 vs. period 2 will depend on the relative magnitudes of U(1,Y
D) and U(0,Y ); clearly, women with substantial earnings losses arising from childbearing (large D) are more likely to postpone, whereas women with high marginal utility for children (large U/N) are more likely to have their child in period 1. How might education E affect the decision to have a child, or to postpone childbearing? It seems natural to assume that earnings Y are increasing in E; following the discussion above, it also seems natural to assume that education also increases the marginal utility of children U/N. The first effect will encourage postponement of childbearing; the second will encourage early childbearing. The net effect of education on childbearing is therefore indeterminate a priori. In the next sections, we use formal statistical tools to examine the empirical relation between motherhood, education, and female labour supply more closely. We focus on several key questions: ceteris paribus, have more recent cohorts of women increased their labour supply relative to past cohorts by either (a) postponing the start of motherhood, or (b) working (or returning to work) for pay sooner after entering into motherhood? Are these effects greater or smaller at higher levels of education? To what extent are differences in childbearing and labour supply the result of factors other than cohort effects per se, such as educational attainment?

THE HAZARD OF MOTHERHOOD In this section, we consider factors related to, and changes in, entry into motherhood. Are higher levels of education associated with postponement of entry into motherhood? Have successive cohorts of women tended increasingly to postpone entry into motherhood? We investigate these questions using data for Sweden, Britain and the USA. We focus on time to birth of one’s first child, that is, the hazard of (first) motherhood.4 The detail in the data gives us the opportunity to examine this using several different models. Models 1A and 1B focus on birth cohort as an independent variable, estimated either without (1A) or with (1B) the female unemployment rate. Models 2A and 2B add ‘completed’ education (usually measured as of the last survey wave available to us). In Models 3A and 3B, education is instead measured as a time-varying covariate.5 Treating education as a time-varying covariate of course entails measuring the actual level of education (for persons still in the risk set) at each date. Since education (in either the ‘completed’ or the time-varying sense) and fertility may be interrelated, this raises complicated questions about causality that argue for considerable caution in interpreting the results.

131

Mothers’ changing labour supply

Sweden Table 3.6 presents results for Cox proportional hazard models of first birth – equivalently, the ‘hazard of motherhood’ – for Swedish women born in 1950–75.6 Each model controls for mother’s educational level (classified as ‘medium’ or ‘high’, relative to the reference category ‘low’). Note that, as indicated in Table 3.6, educational level in these analyses is measured as Table 3.6

Cox proportional hazard models of time to first birth, Sweden Hazard ratio

Standard error

z

Pr z

0.720

0.054


4.41

0.000

Model 1B: cohort and lagged female unemployment rate Born 1963–75 0.786 0.062 Unemployment rate 0.928 0.023


3.06
3.07

0.002 0.002

Model 2A: cohort and education at survey date Born 1963–75 0.771 0.058 Education: medium 0.597 0.050 high 0.488 0.040


3.46
6.16
8.83

0.001 0.000 0.000

Model 2B: cohort, education, and lagged female unemployment rate Born 1963–75 0.831 0.066
2.34 Education: medium 0.607 0.051
5.97 high 0.490 0.040
8.78 Unemployment rate 0.935 0.023
2.73

0.019 0.000 0.000 0.006

Variable Model 1A: cohort Born 1963–75

Notes: Born 1963–75dummy for birth cohort (reference: born 1950–62) Education: medium dummy for ‘medium’ education high dummy for ‘high’ education (reference  ‘low’ education) Unemployment rate female unemployment rate, lagged one year Data refer to women born during 1950–75 (n  1539). Education is measured as of the survey date, when fertility history was also collected (mostly at entry into the panel). As shown below, this varies from 1984 to 1998. See Appendix A for further details. Data on education and fertility are derived from the following surveys: Born 1950–62

Born 1963–75

All

1984 1986 1993 1996 1998

259 102 283 12 205

38 56 347 50 187

297 158 630 62 392

Total

861

678

1539

132

Factors affecting labour force participation

of the date of the survey in which respondents were interviewed about their fertility history (generally, this was at entry into the panel). In all of the models considered here, the more recent (1963–75) cohort of women has a significantly lower hazard of first birth than does the older (1950–62) cohort. Likewise, in all of these models, both higher unemployment and higher education are also statistically significantly associated with a lower hazard. Thus, these results are entirely consistent with the notion that the hazard of motherhood is significantly lower both for the more highly educated and for more recent birth cohorts. In contrast, Heckman et al. (1985) found a higher hazard of first birth associated with a university education in data for the 1941–45 birth cohort in Sweden; however, controlling for individual heterogeneity rendered this effect statistically insignificant, perhaps reflecting the impact of assortative mating. Heckman and Walker (1990, p. 1412; 1991, p. 5), who consider birth cohorts from 1936 to 1960, report evidence of ‘pronatal cohort drift in the parameters of fitted models that is consistent with the introduction of pronatal Swedish policies’. Our own results suggest, however, that Sweden’s unexpected and unprecedented sharp rise in unemployment during the 1990s may have discouraged childbirth during more recent times. Britain Table 3.7 presents our results for Cox proportional hazard models of the hazard of motherhood for two cohorts of women in Britain. We find that in Britain, to an even greater extent than in Sweden, higher levels of education are strongly associated with a lower hazard of motherhood regardless of which model, and which set of other covariates, we consider. We also find, again as in Sweden, that in all of our models the more recent British cohort has a significantly lower hazard of motherhood.7 In sharp contrast with Sweden, however, the results for Britain indicate that higher unemployment is associated with a significantly higher hazard of motherhood: in other words, in Britain poor labour market prospects appear to attract young women towards earlier, rather than delayed, motherhood. This contrast with Sweden could arise because in Britain employment and childbearing have been seen as substitutes rather than as complements, as in Sweden, with its range of policies – from parental leave to subsidized childcare – to promote the combination of parenthood and work. The USA Table 3.8 presents results for Cox analyses of the hazard of motherhood for cohorts of women in the USA. In all models, the evidence concerning

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Mothers’ changing labour supply

Table 3.7

Cox proportional hazard models of time to first birth, Britain

Variable Model 1A: cohort Born 1970

Hazard ratio

Standard error

z

Pr z

0.788

0.019


9.71

0.000


8.14 6.60

0.000 0.000

Model 1B: cohort and lagged female unemployment rate Born 1970 0.816 0.020 Unemployment rate 1.051 0.008 Model 2A: cohort and education at age 30 Born 1970 0.885 Education: medium 0.670 high 0.396

0.022 0.019 0.013


4.94
13.89
27.40

0.000 0.000 0.000

Model 2B: cohort, education at age 30 and lagged female unemployment rate Born 1970 0.917 Education: medium 0.668 high 0.396 Unemployment rate 1.052

0.023 0.019 0.013 0.008


3.43
13.98
27.42 6.82

0.000 0.000 0.000 0.000

Model 3A: cohort and time-varying education Born 1970 0.877 Education: medium 0.633 high 0.420

0.022 0.018 0.015


5.29
15.78
25.03

0.000 0.000 0.000

Model 3B: cohort, time-varying education and lagged female unemployment rate Born 1970 0.907 Education: medium 0.631 high 0.422 Unemployment rate 1.049

0.023 0.183 0.015 0.008


3.87
15.89
24.91 6.38

0.000 0.000 0.000 0.000

Notes: Born 1970dummy variable for birth cohort (reference: born 1958) Education: medium dummy variable for ‘medium’ educational attainment highdummy variable for ‘high’ educational attainment (reference ‘low’ educational attainment) Unemployment rate female unemployment rate, lagged 12 months Data refer to women born in 1958 and 1970 in the combined NCDS (1958) and BCS (1970) surveys (n11 390 in total). See Appendix A for further details.

education is clear-cut: regardless of how education is measured (either as of 2000, or as a time-varying covariate), women with relatively high levels of education (as of the date measured) have a lower hazard of motherhood.8 In contrast, the results concerning cohort differences are by no means uniform, for they are sensitive to which other variables are included in the

134

Table 3.8

Factors affecting labour force participation

Cox proportional hazard models of time to first birth, USA

Variable Model 1A: cohort Born 1961–64

Hazard ratio

Standard error

z

Pr z

0.971

0.041


0.73

0.466


1.04
1.72

0.301 0.085

Model 1B: cohort and lagged female unemployment rate Born 1961–64 0.958 0.041 Unemployment rate 0.964 0.021 Model 2A: cohort and education at 2000 Born 1961–64 0.980 Education: medium 0.568 high 0.303

0.041 0.072 0.081


0.50
7.87
14.67

0.616 0.000 0.000

Model 2B: cohort, education at 2000 and lagged female unemployment rate Born 1961–64 0.967 Education: medium 0.568 high 0.303 Unemployment rate 0.965

0.041 0.072 0.081 0.021


0.81
7.87
14.67
1.70

0.419 0.000 0.000 0.089

Model 3A: cohort and time-varying education Born 1961–64 0.852 Education: medium 0.559 high 0.630

0.043 0.060 0.081


3.75
9.74
5.71

0.000 0.000 0.000

Model 3B: cohort, time-varying education and lagged female unemployment rate Born 1961–64 0.837 Education: medium 0.555 high 0.626 Unemployment rate 0.955

0.044 0.060 0.081 0.021


4.10
9.84
5.79
2.18

0.000 0.000 0.000 0.030

Notes: Born 1961–64 dummy for birth cohort (reference  born 1957–60) Education: medium dummy for ‘medium’ education high dummy for ‘high’ education (reference ‘low’ education) Unemployment rate  female unemployment rate, lagged 12 months Data refer to women born during 1957–64 in the NLSY79 survey (n  3108). All analyses include dummies for black race and Hispanic ethnicity (reference category is white non-Hispanics). See Appendix A for further details.

analysis. In particular, in Models 1A and 1B (in which the only other covariates are demographic variables and the unemployment rate), the cohort difference is not statistically significant at conventional test levels. Likewise, in Models 2A and 2B, which include education measured as of 2000, the cohort effect is again not statistically significant. In contrast, in Models 3A

Mothers’ changing labour supply

135

and 3B, which include variables for educational attainment as a time-varying covariate, the cohort effect is not only statistically significant, but also much larger in magnitude than in the other models. The results concerning unemployment are somewhat more uniform: in all models, unemployment is associated with a lower hazard of motherhood, although in some instances (Models 1B and 2B) the effect is somewhat imprecisely estimated. The procyclical tendency of fertility in the USA cannot be attributed to any public policy supporting employed mothers, though the greater use of market childcare by mothers of very young children may be relevant here. General Discussion Are the results for our three countries consistent with, or at odds with, each other? In a nutshell, the results for both Sweden and Britain provide strong support for the notion that the hazard of motherhood is lower both (a) for more recent cohorts and (b) for women with more education. On the other hand, higher unemployment is associated with a significantly lower hazard of motherhood in Sweden, but a significantly higher hazard in Britain. In contrast, the evidence in our US analyses is somewhat puzzling. On the one hand, no matter how education is measured – as a time-varying covariate, or as of 2000 – the results indicate that greater educational attainment is associated with a significant reduction in the hazard of motherhood, as is also the case for both Sweden and Britain. On the other hand, the results concerning cohort effects are quite sensitive to how education is measured: if education is measured as of 2000, there is no indication of a significant cohort effect for the USA; but if education is measured as a time-varying covariate, the results imply that women in the more recent cohort have a significantly lower hazard of motherhood than do women in the earlier cohort with the same level of education. Finally, like those for Sweden – but unlike those for Britain – the results for the USA suggest that higher unemployment is associated with a slower entry into motherhood, although this effect is not always precisely estimated.

THE HAZARD OF WORKING FOR PAY AFTER FIRST CHILDBIRTH In this section, we consider another margin on which female labour supply may have adjusted over time, and which may have contributed to the increase in female labour supply: changes in working (or returning to work) for pay after childbirth. Is education associated with working sooner after

136

Factors affecting labour force participation

childbirth? Relative to previous cohorts, have more recent cohorts entered (or returned to) paid work sooner after childbirth? Ceteris paribus, do women who postpone childbearing until later ages tend, after giving birth, to re-enter the labour market sooner or later than other women? We estimate three variants of each of three different models. Model 1 is the basic model, with a variable for birth cohort; Model 2 includes variables for completed education; Model 3 treats education as a time-varying covariate.9 Within each model, variant A includes only basic variables; variant B adds a variable for the female unemployment rate; and variant C includes variables for both the female unemployment rate and the age of the mother as of the birth of her first child (for short, ‘time to first birth’). Sweden Table 3.9 presents Cox proportional hazard models for market work after the first childbirth for Sweden. The sample (167 women) is rather small – work history data in this phase are available only for women who had their first child after entering the study – which may help explain why most of the effects are quite imprecisely estimated. For what it is worth, the coefficient for birth cohort implies a negative-signed effect that is almost (but not quite) significant at conventional test levels until one adds the unemployment variable – which, inter alia, registers the adverse labour market conditions in Sweden during the 1990s. This reduces cohort differences still further in terms of both magnitude and statistical significance. There is a tendency, sometimes significant, for the hazard of market work to decline (at a decreasing rate) as time to first birth increases, whether or not education is also held constant (see Models 1C and 2C). In general, differences by educational attainment in the hazard of work after the first birth are not statistically meaningful (see Models 2A–2C in Table 3.9).10 Britain Next consider Table 3.10, which presents our results for proportional hazard models of work after the first birth in Britain. Our data pool event histories from two cohorts born 12 years apart. In our basic analysis (Model 1A), the later cohort has a greater chance of entering employment, though not quite significantly so. This cohort effect disappears in Model 1B, which adds unemployment (which itself has the expected negative effect on working). Model 1C adds time to (that is, mother’s age at) first birth, for which it is especially important to control in these data. (The earlier cohort is observed up to age 42, whereas the later cohort is observed only up to age 30.) Once this is taken into account, the younger (1970) cohort appears to

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Mothers’ changing labour supply

Table 3.9 Cox proportional hazard models of work after first birth, Sweden Hazard ratio

Standard error

z

Pr z

0.717

0.129


1.86

0.063

Model 1B: cohort and female unemployment rate Born 1963–75 0.818 0.166 Unemployment rate 0.959 0.029


0.99
1.37

0.321 0.170

Model 1C: cohort, female unemployment rate, and childbirth variables Born 1963–75 0.843 Unemployment rate 0.955 Time to first birth 0.696 Time to first birth, squared 1.006

0.238 0.032 0.148 0.004


0.61
1.37
1.70 1.76

0.544 0.170 0.089 0.078

Model 2A: cohort and education at first birth Born 1963–75 0.716 Education: medium 0.796 high 1.054

0.129 0.176 0.218


1.86
1.03 0.25

0.063 0.303 0.801

Model 2B: cohort, education at first birth, and female unemployment rate Born 1963–75 0.848 Education: medium 0.747 high 1.087 Unemployment rate 0.947

0.173 0.168 0.226 0.030


0.81
1.30 0.40
1.73

0.420 0.193 0.690 0.084


0.59
1.33 0.81
1.70
2.06 2.12

0.552 0.183 0.416 0.088 0.039 0.034

Variable Model 1A: cohort Born 1963–75

Model 2C: cohort, education at first birth, female unemployment rate, and childbirth variables Born 1963–75 0.843 0.242 Education: medium 0.740 0.167 high 1.197 0.265 Unemployment rate 0.943 0.032 Time to first birth 0.632 0.141 Time to first birth, squared 1.008 0.004

Notes: Born 1963–75 dummy for birth cohort (reference: born 1950–62) Education: medium dummy for ‘medium’ education high dummy for ‘high’ education (reference ‘low’ education) Unemployment rate female unemployment rate Time to first birth mother’s age at birth of first child Data refer to women born in 1950–75 who gave birth to their first child during 1984–97 (n  167). Uses monthly information on labour force status. See Appendix A for further details.

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Factors affecting labour force participation

Table 3.10 Cox proportional hazard models of work after first birth, Britain z

Pr z

0.028

1.84

0.065

Model 1B: cohort and female unemployment rate Born 1970 1.008 0.029 Unemployment rate 0.974 0.008

0.29
3.36

0.780 0.000

Model 1C: cohort, female unemployment rate, and childbirth variables Born 1970 1.197 Unemployment rate 0.992 Time to first birth 1.171 Time to first birth, squared 0.998

0.041 0.009 0.027 0.000

5.31
0.88 6.97
5.13

0.000 0.380 0.000 0.000

Model 2A: cohort and education at age 30 Born 1970 1.025 Education: medium 1.204 high 1.330

0.027 0.038 0.050

0.91 5.80 7.68

0.360 0.000 0.000

Model 2B: cohort, education at age 30 and female unemployment rate Born 1970 0.990 Education: medium 1.202 high 1.320 Unemployment rate 0.978

0.029 0.038 0.049 0.008


0.36 5.76 7.45
2.85

0.720 0.000 0.000 0.000

Model 2C: cohort, education at age 30, female unemployment rate and childbirth variables Born 1970 1.169 Education: medium 1.104 high 1.138 Unemployment rate 0.992 Time to first birth 1.164 Time to first birth, squared 0.998

0.040 0.036 0.044 0.009 0.027 0.000

4.52 3.06 3.34
0.88 6.69
5.02

0.000 0.000 0.000 0.380 0.000 0.000

Model 3A: cohort and time-varying education Born 1970 1.029 Education: medium 1.205 high 1.380

0.027 0.039 0.051

1.08 5.83 8.63

0.280 0.000 0.000

Variable Model 1A: cohort Born 1970

Hazard ratio

Standard error

1.050

139

Mothers’ changing labour supply

Table 3.10 Variable

(continued) Hazard ratio

Standard error

z

Pr z

Model 3B: cohort, time-varying education and female unemployment rate Born 1970 0.996 Education: medium 1.203 high 1.368 Unemployment rate 0.979

0.029 0.038 0.051 0.008


0.15 5.79 8.36
2.69

0.879 0.000 0.000 0.007

Model 3C: cohort, time-varying education, female unemployment rate, and childbirth variables Born 1970 1.169 Education: medium 1.106 high 1.163 Unemployment rate 0.993 Time to first birth 1.162 Time to first birth, squared 0.998

0.040 0.036 0.046 0.009 0.026 0.000

4.52 3.10 3.82
0.85 6.60
4.97

0.000 0.002 0.000 0.395 0.000 0.000

Notes: Born 1970 dummy variable for birth cohort (reference: born 1958) Education: medium dummy variable for ‘medium’ educational attainment highdummy variable for ‘high’ educational attainment (reference ‘low’ educational attainment) Unemployment rate  female unemployment rate Time to first birth  mother’s age at birth of first child Data refer to women born in 1958 and 1970 in the combined NCDS (1958) and BCS (1970) surveys who gave birth to at least one child (n  7846 in total). See Appendix A for further details.

have a significantly greater propensity to work after the first birth. Greater time to first birth is also, at least initially, significantly associated with working sooner after the first birth. This result remains after educational attainment is added (Model 2C), whereas greater education is also unambiguously and significantly related to a greater hazard of working after the first birth. In the final model (Model 3C), unemployment becomes insignificant although all of the other covariates – cohort, education, and time to first birth – are quite precisely determined. The results for educational attainment and time to first birth are remarkably robust, and are essentially the same regardless of whether they are considered separately or together. These results also do not depend on whether education is measured as of age 30 (Model 2) or as a time-varying covariate (Model 3).

140

Factors affecting labour force participation

The USA Our results for Cox proportional hazard models for the USA of work after the first birth appear in Table 3.11. In addition to the other variables considered in variant C of our models (that is, time to first birth and its square), we add an indicator for whether the birth occurred during or after 1993 (when Federal legislation promoting unpaid family leave after childbirth took effect). In many cases, the different models produce quite similar results. In each model, the more recent cohort (born in 1961–64) has a significantly higher hazard of work after the first birth. Similarly, in all cases, a higher female unemployment rate significantly reduces the hazard of work after childbirth. Likewise, in all three models, the coefficient for the ‘time to first birth’ variable is positive but not statistically significant (see the results for Models 1C, 2C and 3C).11 The coefficient for births after 1992 is always both negative and significant; taken at face value, this is consistent with the hypothesis that Federal legislation providing for limited unpaid family leave tended to reduce the hazard of (returning to) work after the first Table 3.11

Cox proportional hazard models of work after first birth, USA

Variable

z

Pr z

0.046

9.33

0.000

Model 1B: cohort, and female unemployment rate Born 1961–64 1.129 0.047 Unemployment rate 0.162 0.042

2.58
43.26

0.010 0.000

Model 1C: cohort, female unemployment rate, and childbirth variables Born 1961–64 1.191 Unemployment rate 0.158 Time after 1992 0.672 Time to first birth 1.001

0.064 0.067 0.114 0.001

2.74
27.70
3.49 1.27

0.006 0.000 0.001 0.204

Model 2A: cohort, and education at 2000 Born 1961–64 1.650 Education: medium 1.288 high 2.607

0.047 0.089 0.098

10.71 2.97 9.77

0.000 0.005 0.000

Model 2B: cohort, education at 2000, and female unemployment rate Born 1961–64 1.121 Education: medium 0.891

0.047 0.090

2.42
1.29

0.015 0.197

Model 1A: cohort Born 1961–64

Hazard ratio

Standard error

1.541

141

Mothers’ changing labour supply

Table 3.11

(continued) Hazard ratio

Standard error

z

Pr z

0.874 0.160

0.102 0.043


1.32
42.47

0.187 0.000

Model 2C: cohort, education at 2000, female unemployment rate, and childbirth variables Born 1961–64 1.194 Education: medium 0.875 high 0.859 Unemployment rate 0.158 Time after 1992 0.666 Time to first birth 1.002

0.064 0.090 0.103 0.067 0.114 0.001

2.77
2.20
1.48
27.65
3.56 1.47

0.006 0.138 0.140 0.000 0.000 0.142

Model 3A: cohort, and time-varying education Born 1961–64 1.668 Education: medium 1.453 high 3.154

0.047 0.081 0.091

10.95 4.63 12.66

0.000 0.000 0.000

Model 3B: cohort, time-varying education, and female unemployment rate Born 1961–64 1.115 Education: medium 0.833 high 0.814 Unemployment rate 0.159

0.047 0.082 0.096 0.043

2.30
2.24
2.15
42.37

0.021 0.025 0.031 0.000

Model 3C: cohort, time-varying education, female unemployment rate and childbirth variables Born 1961–64 1.198 Education: medium 0.812 high 0.793 Unemployment rate 0.159 Time after 1992 0.660 Time to first birth 1.002

0.064 0.083 0.098 0.067 0.114 0.001

2.83
2.52
2.37
27.63
3.64 1.68

0.005 0.011 0.018 0.000 0.000 0.094

Variable high Unemployment rate

Notes: Born 1961–64dummy for birth cohort (reference  born 1957–60) Education: medium dummy for ‘medium’ education high dummy for ‘high’ education (reference ‘low’ education) Unemploymentfemale unemployment rate Time after 1992dummy for first birth occurred in 1993 or later Time to first birth mother’s age at birth of first child Data refer to women born during 1957–64 in the NLSY79 survey who gave birth to at least one child (n2191). All analyses include dummies for black race and Hispanic ethnicity (reference category is white non-Hispanics). See Appendix A for further details.

142

Factors affecting labour force participation

birth – although it should be noted that this may be confounded with other changes that occurred after 1992. The results concerning education are sensitive to which other variables are included in the analysis. In Models 2A and 3A, which control only for cohort and education (as of 2000 or as a time-varying covariate, respectively), education is positively and significantly associated with a greater hazard of work after the first birth. In contrast, in Models 2B and 3B, which control for cohort and unemployment as well as education, education is negatively associated with a greater hazard (albeit significantly so only in Model 3B). Finally, in Models 2C and 3C, which control for cohort, unemployment and time to first birth, the coefficients on the education variables are both negative and statistically significant. General Discussion Our results concerning work after the first birth vary considerably from one country to another, and even from one model to another for the same country. Perhaps the most uniform finding is that higher female unemployment tends to discourage women from working after the first birth – although this effect is statistically significant in all models only for the USA. In most other respects, the results are mixed. For example, the results for Sweden imply that, ceteris paribus, more recent birth cohorts do not significantly accelerate or postpone work after the first childbirth, relative to earlier cohorts. In contrast, the US results imply that the more recent cohort tends to work (or resume work) after the first childbirth significantly sooner than the earlier cohorts, other things being equal. The same appears to be true in Britain, although there the cohort difference is statistically significant only in variant C. Likewise, in Sweden and the USA, the relation between time to first birth and the hazard of (resuming) work is often not significant; whereas in Britain, the hazard of work after motherhood significantly increases (at a diminishing rate) with the mother’s age at first birth. Finally, the relation between education and the hazard of work after first birth is never significant in Sweden; is always positive and significant in Britain; and in the USA is positive and significant in some models, negative but not significant in some models, and both negative and significant in still other models.

OVERALL SUMMARY: IN LIEU OF A CONCLUSION Table 3.12 summarizes our overall results. The only truly uniform finding is that, in all three countries, education is significantly associated with a

143

Mothers’ changing labour supply

Table 3.12

Summary of results in Tables 3.6–3.11

Outcome, effect

Sweden

Britain

USA

Hazard of first birth More recent cohort


and SS all models


and SS all models


and SS all models
and SS all models


and SS all models  and SS all models


all models (NS except when education is TVC)
and SS all models
all models (sometimes NS)

More education Higher unemployment

Hazard of work after first birth More recent
but NS cohort all models More education NS all models

Higher unemployment


but NS all models

Older at birth of first child


then  (often NS)

 but SS only in Models C  and SS all models


and SS in Model B
and NS in Model C  then
SS in all models

 and SS all models  and SS in Models 2A, 3A
but NS in Model 2B
and SS in Models 2A, 3B
C
and SS all models  but NS all models

Notes: SSstatistically significant at 0.05 level NS not statistically significant at 0.05 level TVC time-varying covariate Models C include variables for unemployment and for time to first birth and its square. Models B include a variable for unemployment but not time to first birth. Models A do not include variables for either unemployment or time to first birth.

lower hazard of first motherhood (or equivalently, with a longer time to first birth) for persons in the same birth cohort. There is some evidence (some of it statistically significant) that suggests that, ceteris paribus, the hazard of first motherhood is lower for more recent cohorts. However, whereas the ceteris paribus hazard of work after the first birth is significantly higher for more recent cohorts in the USA, in Sweden there are no significant cohort differences in this hazard. The evidence on the ceteris paribus relation between education and the hazard of market work is likewise not clear: in Britain it is always positive and significant; in

144

Factors affecting labour force participation

Sweden it is never significant; in the USA it is positive and significant in some models, but negative and significant in others. Almost 20 years ago, Heckman et al. (1985, p. 179) commented that ‘unlike many other areas of knowledge in economics and social science, there are few widely accepted or carefully confirmed “stylized facts” in fertility dynamics to guide economic model builders’. The situation does not seem to have changed much since then. What might account for these apparent differences in behaviour among the three countries we have analysed, and for the seeming absence of stylized facts? One obvious answer is that social and labour market institutions in these three countries are very different. Of course, it is one thing to speculate that institutional differences might help explain cross-country behavioural differences, and quite another to identify particular institutional differences that do in fact explain such behavioural differences. In this connection, it should be noted that economic theory provides surprisingly little guidance as to the likely direction of the relation between a factor such as education and the hazard of either motherhood or work after childbirth. First consider the relation between education and motherhood in a purely static setting. On a naive view, education is an important component of one’s market wage, and so – by raising the wage – greater levels of education would be expected to pull women towards market work, inducing them to postpone motherhood and (return to) work quickly after childbirth. A million wage regressions support this view; but a moment’s thought suggests that education may also raise the home wage (home productivity, or the ‘opportunity cost of time’), thereby pulling women into the home. As in conventional labour supply models, the net effect of education on the hazard of motherhood and on the hazard of market work after motherhood will now depend on its relative impact on the home and market wages. However, this is only the beginning of the story, for education also affects the availability of substitutes in child care and the elasticity of substitution between inputs. For example, as Leibowitz (1974) has noted, more-educated women may feel that there are fewer good substitutes for their own time, leading them to spend almost as much of their own time in child care as do women with less education. In a dynamic framework, matters become still more complicated. For example, in a life cycle perspective, higher education may entail a lifetime wealth effect, both in its own right and via assortive mating, that will enable women to spend less time in the labour market over their lifetimes and thereby tending to offset the substitution effect of education, operating through higher wage rates. Note also that questions about timing are likely to be just as relevant to education as they are to motherhood: just as women may postpone childbirth, they may also postpone (or accelerate) education.

Mothers’ changing labour supply

145

Hence, completed education will not necessarily be related to childbirth or to the return to work after childbirth in the same way as education measured at other points in the life cycle (for example, treated as a time-varying covariate, as in some of our analyses). Our results (for example, for the USA in Table 3.11) show that this is indeed the case. In sum, simple theories about how education will affect the hazard of motherhood or of work after childbirth are almost certain to be oversimplified; and it is likely to be very difficult to tease unambiguous predictions about such effects from any comprehensive model of the role of education in motherhood and work decisions. One final point concerns the relation between these theoretical notions and the econometric techniques used in the analyses described here. Particularly in a life cycle perspective, work, education and motherhood are all choices; yet the analyses presented in this chapter follow much prior work in taking education as given in ‘explaining’ motherhood, and in taking both education and motherhood as given in ‘explaining’ work choices. (For example, many of the analyses in this chapter control for education in analysing the hazard of motherhood, and control for both education and mother’s age at first birth in analysing the decision to work after childbirth.) From an empirical point of view, the fundamental problem with this approach is that it ignores unobservables. For example, women who desire to have children may plan on making only small investments in education (at least, if education raises home productivity less than market productivity) and on working relatively little. This will generate a strong negative correlation between education and children, suggesting that the former ‘causes’ the latter, even though – at least in this story – both education and children are both determined by unobservable preferences. Likewise, our results for Britain show that, ceteris paribus, women who have their first child relatively late will return to the work force statistically significantly sooner than women who have their first child relatively early; but this negative relationship is not necessarily ‘structural’ or ‘causal’. Rather, it may be ‘spurious’, arising simply from the neglect of unobservables (above-average tastes for work, above-average market productivity and so on) that affect both the hazard of motherhood and the hazard of work after first birth (Heckman and Walker, 1990, 1991). Evidence from other analyses of fertility suggests that ‘controlling for unobservables in a robust nonparametric fashion vitally affects the sign and statistical significance of the estimated effect of early life cycle events on subsequent fertility outcomes’ (Heckman et al., 1985, p. 180). Developing a life cycle econometric model that controls for unobservables and treats work, education and motherhood as interrelated choices will be difficult; but then again, pretending that each is exogenous to the others is difficult too.

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Factors affecting labour force participation

APPENDIX A: DATASETS USED IN THE ANALYSES Data for Britain The data for our analyses of Great Britain come from two major cohort datasets. The British Cohort Study (BCS) provides data on persons who were all born in the same week in April 1970. They were interviewed most recently in 1999/2000. This sweep contains detailed information on birthdates of any children, and on work histories from age 16 to age 30. The National Child Development Study (NCDS) provides data on persons who were all born in a single week in March 1958. They have been interviewed at various points in their lives, with the three most recent sweeps occurring in 1981, at 23; 1991, when cohort members were 33; and in 1999/2000, when cohort members were 42. Monthly work histories were compiled at each of these sweeps and information on dates of any pregnancies was collected in 1991 and 2000. We exclude from our sample NCDS cohort members who were present at the 2000 sweep but not at the 1991 sweep, since these persons were asked only about births since age 33 (rather than about all births). Our total sample consists of 11 390 women, with 5802 from NCDS and 5588 from BCS. Variables Education The work history data for each cohort was used to measure the number of months spent in full-time education from the September after the 16th birthday up to age 30. The minimum school-leaving age in Britain of 16 implies a minimum of 11 years in full-time education. Categorical variables were constructed for years of education up to the age of 30. ‘Low’ education is defined as no more than 11 years of full-time education (that is, no time spent in full-time education beyond age 16); ‘medium’ education is defined as 12 to 13 years of education (that is, up to two years beyond the minimum); and ‘high’ education is defined as 14 or more years of full-time education. Our time-varying education variable is measured by counting months of full-time education. The minimum is again assumed to be 11 years of fulltime education at age 16; the low, medium and high educational attainment categories are defined in the same way as for the fixed education covariate, but for NCDS cohort members interviewed in 2000 all months in full-time education up to age 42 are counted. Labour force participation after childbirth The work history data was used to find out whether the woman returned to work at some point from three

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months after childbirth onwards. Employed status could be either full-time or part-time. Those not in employment are categorized as either ‘not employed’ or ‘in full-time education’. Time series unemployment data Our unemployment variable is the monthly UK female claimant count unemployment rate, obtained from the Office for National Statistics (ONS). Data for the USA The data for our analyses of the USA come from the National Longitudinal Study of Youth 1979 (NLSY79). This followed persons born during 1956–64 from 1978 to the present (the last available observations currently available refer to 2000). NLSY79 provides the month and year of birth of all persons (both adults and their offspring); we adopt the convention that all births occur in the middle of the month. Variables Education NLSY measures education as years of schooling completed as of May of each year (education as of birth of first child refers to education in May of the year of the first child’s birth). We use three education categories: ‘Low’ (under 12 years, that is, less than high school); ‘Medium’ (12–15 years, that is, high school graduate, possibly with some higher education, but no undergraduate degree); and ‘High’ (16 years or more, that is, an undergraduate degree, possibly with some post-graduate education). Work experience NLSY categorizes each individual as working or not working (a category that can include leave of absence from paid work as well as not in the labour force, unemployed and so on) for each week during 1978–2000. We defined the date of first work (or returning to work) after childbirth as the first week after childbirth on which the individual is observed to be working. Unemployment rates The monthly unemployment rate data for females age 16 and over are not seasonally adjusted, and are derived from the US Bureau of Labor Statistics (www.bls.gov). Data for Sweden Our data for Sweden are derived from the HUS (HUShållens ekonomiska levnadsförhållanden), a longitudinal survey that covers the years 1984–1998 at biennial or triennial intervals.

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Variables Age of mother at birth of the first child The age of the mother at birth of the first child is calculated from the difference between the birth year of the children and that of the mother. We select only biological children of women for the analyses. The information on biological children is collected at entry to the survey and for individual women it can be any of the surveys HUS 1984, 1986, 1993, 1996 and 1998. Education We created variables for three levels of education: ‘Low’ (11 years of schooling or less, that is, less than a three-year gymnasium course), ‘Medium’ (12–14 years, that is, three-year gymnasium or more, but less than college or university training), and ‘High’ (college or university training of two years or more). Labour force participation after childbirth To measure labour force participation, we used a spell file on employment. The first month of women being at work (‘gainfully employed’ in the HUS) since month 4 is regarded as the month that she started working. Employment status in the HUS is categorized as ‘gainfully employed’, ‘vacation, work leave, sick leave, etc.’, ‘unemployed’, and ‘not gainfully employed incl. schooling, house-keeping, retired’. Unemployment rate Data on female unemployment rates are derived from OECD, Labour Force Statistics 1966–1986 (pp. 392–3) and Labour Force Statistics 1979–1999 (pp. 252–3).

NOTES *

1.

2.

We thank, without implicating, Marc Gurgand and Monika Merz for helpful comments and suggestions on an earlier version of this chapter; Fernando Galindo-Rueda for supplying cleaned work history data from the National Child Development Survey (NCDS); and Fiona Steele, who provided us with linked birth history data from the NCDS sweeps 5 and 6. Killingsworth gratefully acknowledges the Economic and Social Research Council’s award of a Visiting Fellowship in support of his work on this chapter. We follow the usual practice of deriving annual hours of work by multiplying weeks worked during the year and average hours of work during the week. Note that this does not make any adjustment for paid vacations (which are not measured by the CPS), and thus measures hours of paid work rather than hours actually spent at work. For both men and women, results for labour force participation rates are similar to the results for hours of work which we discuss in the text. Both here and in the analyses discussed below, we define ‘labour supply’ as time actually worked; we do not analyse labour force participation per se.

Mothers’ changing labour supply 3.

4. 5. 6. 7.

8.

9. 10. 11.

149

For example, a woman who has no children at age 25, but then gives birth to a child at age 26, will move from Figure 3.8 to Figure 3.7, only to move back to Figure 3.8 later on as her child grows up and leaves her household. For many purposes, the ideal ‘cohort’ data on women with children would refer to women who will actually have (or expect to have) exactly n children (n 0, 1, 2, . . .) during their lifetimes. However, we know of no simple way to trace out cohort profiles of this kind using conventional survey data such as the CPS. It is certainly feasible to use our data to consider time to second or third childbirth and so on, but this poses some rather complicated analytical issues that do not arise in analyses of time to first birth (Heckman and Walker, 1990, 1991; Heckman et al., 1985). The Swedish data do not permit us to measure education as a time-varying covariate, so we do not estimate a ‘Model 3’ for Sweden. See Appendix A for discussion of the data used in our analyses of Sweden, Britain and the USA. In their analyses of data from the British Household Panel Survey for 1991–98, Aassve et al. (2003) find that women from more recent birth cohorts or with higher levels of education have a significantly lower hazard of births. (Their equation for the hazard of a birth distinguishes between first, second, and so on, births using dummy variables for parity, and is part of a system of equations that considers union formation and employment transitions as well as births per se.) Caucutt et al. (2002, p. 828) argue that ‘women’s wages play a key role in both the quantity and timing of children, and the time trend for recent birth cohorts has been toward giving birth at later ages. These phenomena cannot be explained by women’s education decisions’. However, in their regression analyses for the proportion of children born after age 27 that control for wages and total number of children, a variable for years of education is positive (implying that women with more education tend to have more children after age 27) and significant for all three birth cohorts considered. (Caucutt et al. do not test for cohort differences.) The Swedish data do not permit us to measure education as a time-varying covariate, so we do not estimate a ‘Model 3’ for Sweden. In a probit analysis by Gutiérrez-Domènech (2003, p. 19, Table 11) of the probability of employment for Swedish women within eight years after the first birth, neither education effects nor cohort effects turn out to be statistically significant. We found no evidence of nonlinearity in the hazard of work with respect to time to first birth: the coefficient for the square of ‘time to first birth’ was never significant at reasonable test levels in any of the models considered here.

REFERENCES Aassve, Arnstein, Simon Burgess, Matt Dickson and Carol Propper (2003), ‘Employment, Family Union and Childbearing Decisions in Great Britain’, CEPR Discussion Paper 4011. Caucutt, Elizabeth M., Nezih Guner and John Knowles (2002), ‘Why Do Women Wait? Matching, Wage Inequality, and the Incentives for Fertility Delay’, Review of Economic Dynamics, 5, 815–55. Guttiérrez-Domènech, Maria (2003), ‘Employment after Motherhood: A European Comparison’, CEPR Discussion Paper 567. Heckman, James J. and James R. Walker (1990), ‘The Relationship between Wages and Income and the Timing and Spacing of Births: Evidence from Swedish Longitudinal Data’, Econometrica, 58 (6), 1411–41.

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Heckman, James J. and James R. Walker (1991), ‘Economic Models of Fertility Dynamics: A Study of Swedish Fertility’, Research in Population Economics, 7, 3–91. Heckman, James J., V. Joseph Hotz and James R. Walker (1985), ‘New Evidence on the Timing and Spacing of Births’, American Economic Review, 74 (2), 243–50. Leibowitz, Arleen (1974), ‘Education and Home Production’, American Economic Review, 64 (2), 243–50.

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DISCUSSION Marc Gurgand Changes in women’s labour supply is not only one of the most dramatic features of recent decades: it is also one of the hardest to document in an analytic perspective. In the context of an ageing labour force, however, women’s participation is rightly considered a major issue and research that can clarify the underlying mechanisms is warranted. Still, this chapter illustrates that even to come up with stylized facts about three intertwined phenomena – participation, fertility, education – is a hard task. Although the authors provide gross evidence about raising participation, they do not document trends in the two other dimensions of interest. These are well known: in 30 years, the synthetic fertility index (average number of children per woman) has decreased from 2.48 to 2.13 in the USA, from 2.43 to 1.65 in the UK and from 1.92 to 1.54 in Sweden. In the USA, 42 per cent of females 25 to 34 years old have attained tertiary education in 2001, 29 per cent in the UK and 39 per cent in Sweden; only ten years back, these ratios were 31 per cent, 18 per cent and 28 per cent respectively. The individual behaviour that underlies these trends is extremely complex because each of the three dimensions considered here is dynamic in nature and involves potentially substantial individual heterogeneity. The simple model presented in the chapter illustrates the analytical difficulties implicit in this topic and the theoretical indeterminacy of the size and direction of many effects. The purpose of the chapter, however, is not to identify causality, nor to provide structural relationships, but to document the simultaneity of the phenomena. Although obvious at an aggregate level, the simultaneity is hard to interpret, in particular because of composition effects. The cohort analysis neutralizes the age structure, but micro data is indeed preferable to go further along this path. Unfortunately, the first part of the microeconometric analysis is limited to the fertility–education relationship, participation being no longer in the picture. As a result, it is hard to know if the occurrence of motherhood is dependent on education because childbirth is simply delayed to allow for studying or because those graduating then delay childbirth to take a step into the labour market. This might be one of the reasons why the US result is sensitive to whether education is included as a time-varying variable or not. Generally, the effect of a time-varying covariate in duration analysis is identified only if survival does not influence the path of the covariate process, which is unlikely in the present case. In view of the strong aggregate stylized facts it comes as a surprise that the results are so little robust, especially over the three countries, and this is

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a most interesting feature of the chapter. One reason is probably methodological, because there is substantial variation as to the time in the life cycle at which the education level is evaluated, and this can be related to the preceding remark. But it also points to the fact that the economic and institutional contexts matter a lot, which is of major importance for public policy. Among the topics of importance, I would emphasize the following: ●

●

●

●

●

Returns to education Not only are returns to education different between countries, but so is the wage gap. Flat private returns to higher education reported by the OECD for females are in the order of 10 per cent in Sweden and of 15 per cent in the USA and UK. Also, the gender wage gap is much higher in the latter two countries than in Sweden. Child care policies Public spending on child care is a subsidy to female participation that differs widely. Maternity leave legislation is also certainly of importance. Fiscal incentive Most fiscal systems generate high marginal implicit tax rates on the second earner. In Sweden, this source of disincentive was removed during the 1970s and this must be important for comparisons with other countries. Part-time work The flexibility of working time is potentially important for the joint labour supply–fertility decision. There is substantial variation between the countries considered here: according to the OECD, the incidence of part-time working among participating women is 19 per cent in the USA, 40 per cent in the UK and 20 per cent in Sweden in 2003. Unemployment Actually, the chapter tends to identify hours worked with labour supply. At least since the 1970s, unemployment should enter the picture.

Overall, the chapter points to the usefulness of a systematic analysis of basic facts, but also to the challenge and potential of international comparison on this topic. In spite of the data limitations they face, the authors take steps along the right path: micro-data analysis together with a careful account of varying contexts.

4.

Women’s hours of market work in Germany: the role of parental leave Monika Merz1

INTRODUCTION The literature on female labour supply often departs from the presumption that in most industrialized countries women’s market hours worked have steadily risen since the early 1970s. A commonly quoted example is the experience of married women in the USA. Their average hours worked have risen by over 60 per cent during the last three decades (McGrattan and Rogerson, 1998, 2004). For married women with children this rise has been even more extreme. The secular increase in women’s hours worked has become the subject of a growing body of literature which uses dynamic economic models in an effort to explain the driving forces underlying this trend (see for example Olivetti, 2001, or Jones et al., 2003). Often quoted exceptions to the above-mentioned empirical observations are Germany, Italy and Spain where women’s total market hours worked have remained constant at best. Given that fertility in these countries has declined over the past thirty years and reached the bottom of the distribution in Europe, the observations on Germany, Italy and Spain are considered a puzzle, because at first glance it is hard to perceive why women who have few children do not participate more actively in the labour market. At second glance, there are likely candidates that may help explain the observed differences, such as child care availability, the income tax system, parental leave policies, the legal length of the work week, or the availability of parttime jobs. In this chapter we explore the development of women’s involvement in the German labour market since the late 1950s. We pursue two goals. Firstly we document the trends of market hours worked of different groups of women. Contrary to most of the existing literature, we not only study women’s labour force participation, but also their weekly hours worked in the market. We provide compelling evidence that exclusively focusing on female labour force participation in an effort to understand women’s labour market involvement can be misleading. Towards this end, we decompose 153

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women’s market hours worked into hours worked per female employee – the so-called intensive margin – and the product of the employment rate and the labour force participation – the so-called extensive margin. Secondly we investigate the link between the observed decline in women’s weekly hours and three major changes in the federal regulation governing parental leave which have taken place since the late 1970s. To do so, we illustrate the changes in the actual weekly hours of married females with children below the age of ten – the group for whom average weekly hours have declined the most since the mid-1970s – and contrast them with the respective changes in the control group, that is, married females without young children. We use micro census data from the German Federal Statistical Office (Statistisches Bundesamt). At a highly aggregated level, these data are available from 1957 to 2001 for the former West Germany. They allow us to study long-term trends of the variables of interest, and also structural changes. Our findings suggest that the near constancy in aggregate series of hours worked per working age woman that has been observed since the late 1950s hides a vast set of different trends in the intensive and extensive margins of women’s involvement in the German labour market. Labour force participation has steadily risen among female employees regardless of marital status and of consecutive cohorts. The rise in women’s participation has been the strongest for married and widowed or divorced women. The observed rise in female participation has been accompanied by a notable decline in women’s weekly hours worked. We identify married women with young children as the group of female employees for whom this decline has been the most severe. Unlike in many other industrialized countries – notably the USA, where married women with very young children have drastically increased their weekly hours worked since World War II – this group of female employees in Germany has steadily and significantly reduced its weekly hours’ involvement since 1975. We attribute much of this decline to institutional changes in the federal legislation governing parental leave which occurred in 1986, 1992 and 2001. The chapter proceeds as follows. First we present the data underlying the study. The next section sets the scene for taking a closer look at women’s hours worked in the market by studying long-term trends of the aggregate series that are decomposed by gender, age and marital status. We then take a closer look at changes in the federal legislation governing parental leave and study the impact of these changes on married women’s labour market behaviour. The final section concludes.

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THE DATA All data underlying our study originate from the micro census of the German Federal Statistical Office. The micro census is the official statistics of the population and the labour force in Germany; it has been available since 1957 for former West Germany and since 1991 for former East Germany.2 The most recent results currently available are for 2001. In terms of data collection method applied, it is comparable to the Current Population Survey of the USA. Data are collected by annual interviews of 1 per cent of all households that are randomly drawn. In every year, about 370 000 households with 820 000 individuals participate in the interviews. Out of these, about 70 000 households with 160 000 individuals primarily live in former East Germany. Interviews take place between April and July. They are typically staged with the head of a chosen household who is asked to respond to questions relating to the various household members. Hence, the data are gathered through a household’s self-reporting. The response rate regularly reaches at least 97 per cent. Responses to questions relating to labour market activities relate to an ex ante chosen week of the year, the so-called reference week. That particular week has varied over time – it is currently the last week of April in which there are no public holidays. A quarter of all participating households are rotated each year, so that a chosen household participates in the sample on average for four years.3 Questions relating to the number of weekly hours worked when employed cover actual as well as normal hours worked during the reference week. In case these two figures deviate from each other, the interviewees give reasons for the deviation such as overtime, vacation, illness, flexible work hours or other reasons, including parental leave. The micro census results that have been available since 1957 are based on individual, regional or time aggregation. Individual data are aggregated based on characteristics such as gender, age, marital status or sector of employment. The time-series have the advantage of consistently reporting the variables of interest over 45 years for the former West Germany. They obviously suffer from the fact that they are not available at a degree of disaggregation that currently is of interest to many researchers. This deficiency has led the German Federal Statistical Office to make excerpts of the original micro-level data – so-called scientific use files – available to academic institutions. A scientific use file contains a randomly drawn 70 per cent of those households that participate in the micro census of the respective year. In order to render compatibility with the micro census data, the relevant entries in the sub-sample are multiplied by the factor 10/7. Scientific use files are available for 1989, 1991, 1993, 1995, 1996, 1997 and 2000. The micro-level data provide detailed information on an individual’s

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gender, age, marital status, educational attainment, whether or not children in various age categories are present, and also on an individual’s labour market status. If applicable, the data also capture the economic situation of a person’s partner. For those individuals who are employed, the data contain information on the actual number of weekly hours worked, and whether their job is full-time or part-time. Our empirical work is based on the aggregate time-series of the micro census covering the period from 1957 to 2001. We use the aggregate timeseries in order to illustrate trends in the evolution of gender-specific labour force participation rates, or weekly hours worked per employee. Most importantly we can divide market hours worked for each of these groupspecific characteristics into labour force participation (extensive margin) and hours worked per employee (intensive margin), thereby searching for empirical regularities. We restrict our analysis to individuals and households who reside in the former West Germany, primarily because our analysis requires consistent data over a rather long time period. Naturally such data are more readily available for former West Germany than for the former East. A comment on the informational content of the aggregate time-series underlying this chapter is appropriate here. The composition of the female population in former West Germany has undergone major changes since the official micro census was started in 1957. Those changes are mostly due to three main waves of immigration. Starting in the 1970s so-called guest workers from southern European countries and Turkey came to Germany to work. Many of those guest workers and their families have settled permanently. Since the mid-1980s many German descendents from Russia have immigrated into Germany. Lastly, German unification has led to much migration from former East Germany into the Western states. Since data on West Germany cover all those who reside in the former West, they include migrants and immigrants.

TRENDS IN HOURS OF MARKET WORK Macroeconomists commonly use the total number of hours worked in the market as a measure of labour input in the production of goods and services. This measure is typically reported in relation to the working age population. In Germany, where retirement at the age of 65 is mandatory in the public sector and common practice in all other sectors, the working age population covers all individuals who are between 15 and 64 years old. Figure 4.1 depicts the ratio of total weekly hours worked to persons of working age together with the pendant for men and women. All three series

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Male Female Sum

Figure 4.1

Weekly market hours worked per person (age 15–64)

0 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 Year

5

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declined markedly between 1957 and the mid-1970s. Thereafter, the measure for men continued to decline – albeit at a reduced pace – whereas the measure for women remained constant. Although an important measure of the economy’s total labour input, the aggregate series on hours worked hides much of the dynamics associated with their underlying components. To be specific, total hours worked per person (H/Pop) can be decomposed into total hours worked per employee (H/E) (the intensive margin), employees as a fraction of the total labour force, the so-called employment ratio (E/L), and labour force participation (L/Pop) (the extensive margin):





 H H E L    Pop i E i L i Pop

i

i {w, m, s}

(4.1)

where the index i represents women w, men m, or the sum of the two s. We argue that, in the case of Germany, studying hours worked per employee as a measure for the intensive margin of adjustment is much more informative and appropriate than simply distinguishing between part-time and fulltime work. That is so because the exact classification of a particular job depends on the normal hours worked, and normal hours vary by sector. For example, a person working 35 hours a week is classified as a part-time worker in a sector where the normal work week consists of 38 hours. However, that same person would be classified as full-time worker if she worked in a sector where the normal work week consisted of 35 hours. To avoid this ambiguity, we simply look at actual weekly hours worked. While the employment ratio has remained roughly constant during the period of observation, the remaining two components have undergone remarkable changes.4 As illustrated in Figure 4.2, women’s labour force participation has increased, while that of men has steadily declined. Weekly hours worked per employee have declined for both men and women. As Figure 4.3 shows, this decline has been much more drastic for women than for men. Between the late 1950s and the mid-1960s – a time that has become known as post-World War II ‘Wirtschaftswunder’ in Germany – male and female employees each worked well over 45 hours per week. During the following four decades, women on average reduced their weekly hours worked to less than 30, while men reduced theirs effectively to 40. The reduction in weekly hours worked was a continuous process which was accelerated in the 1980s. Two factors mainly contributed to this development. Firstly, starting in the mid-1980s, the length of the work week was gradually reduced across most sectors. In 1985 for example, the metal industry negotiated an agreement to switch from 40 to 38.5 hours per week. In 1993 the number of weekly hours worked was

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Labour force participation (age 15–64)

20 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 Year

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Figure 4.2

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Figure 4.3

Weekly market hours worked per employee (age 15–64)

20 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 Year

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Women’s hours of market work in Germany

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further reduced to 36. Similar agreements were adopted in other sectors. The reduction in the work week came along with the opportunity for firms to negotiate longer or shorter hours per week with a small fraction of their work force. Secondly, in 1979 the country’s first law on parental leave became effective, enabling employed women who had given birth to take leave from their job following maternity leave. This leave policy was extended and improved from the employees’ perspective through various amendments which became effective in 1986, 1992 and 2001. The launch of the new policy was accompanied by a steady increase in the number of part-time jobs that have been occupied by women much more than by men. Taken together, these changes accelerated the decline in weekly hours worked per employee. Theoretically at least, the decomposition of total hours worked per person as summarized in equation (4.1) can be arbitrarily refined not just according to gender, but according to any other individual characteristic. In practice, the degree of refinement is limited by data availability. The aggregate series from the micro census allow us to decompose total weekly hours worked by gender and marital status, but not any further. At this level of disaggregation, the data have been available since 1975 for women and since 1990 for men. Decomposing the aggregate hours series by gender and marital status helps to shed light on key differences in the labour market behaviour of women and men. Data availability lets us consider an individual’s marital status to be one of the following three states: single, married, widowed or divorced. A distinction between widowed or divorced is not possible at the aggregate data level. Women’s Hours of Market Work Figure 4.4 suggests that the constancy in all women’s weekly hours worked per person is related to the fact that this measure has remained constant for married women, and that the decline in the measure for single women has been offset by the increase in the measure for widowed or divorced women.5 As far as the intensive margin of adjustment is concerned, single women have always worked more hours per week than widowed or divorced women whose weekly hours, in turn, have typically exceeded those of married women. In spite of these differences in levels, all three groups of female employees have reduced their number of hours worked per week to a comparable extent. These trends are summarized in Figure 4.5. Married female employees with children below the age of ten have undergone the most severe reduction in weekly hours worked. As depicted by the solid line in Figure 4.5, this group of employees cut its weekly hours by close to 45 per cent between 1975 and 2000. The observed reduction gained

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Figure 4.4

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Figure 4.5

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Single Married no children 10 Married with children  10 Widowed/divorced

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momentum in the late 1970s and again around 1986 and 1992 – three points in time which coincide with major modifications in the federal legislation governing parental leave. We will focus on this group in particular when trying to assess the quantitative implications that changes in the parental leave policy have had on women’s labour market involvement. It should be emphasized that, even though the various groups of female employees have undergone different degrees of adjustment in their weekly hours worked during the time period considered, their respective fractions of total female employment remained remarkably constant. Single females constitute 30 per cent of total female employment. Widowed or divorced females make up 11 per cent and married females without children below the age of ten years make up 42 per cent. The remaining 18 per cent are attributable to married females with young children. The picture for changes in labour force participation differs significantly across the three female groups considered. Single women’s participation in the labour force hardly changed between 1975 and 2000; it has remained constant at about 65 per cent. The participation rate of married and widowed or divorced women, on the other hand, has undergone a significant increase. For married women, this rate rose from 44 to 60 per cent. The increase for widowed or divorced women was even more distinct: their participation rate rose by over 20 percentage points from 45 to 66 per cent. As a result, a woman’s marital status nowadays is less decisive for her participation in the labour force than for the number of hours she works per week. These trends are summarized in Figure 4.6. Figure 4.7 illustrates the life-cycle profile of women’s labour force participation for particular cohorts in different years when women of all marital states are grouped together.6 This figure nicely illustrates how labour force participation has changed from older to younger cohorts of women. Older women cohorts have tended to participate less in the labour market at every stage in their life, and they also have tended to withdraw more frequently around the age when they had children. On the other hand, younger cohorts participate less at a younger and an older age, indicating an increased level of education as well as a reduced retirement age. The prima facie evidence suggests that younger cohorts’ labour force participation much more closely resembles that of men than did the participation behaviour of older cohorts. However, this latter statement is subject to a major caveat. Since 1992, employed women who have had a child can take parental leave up to three years after the child’s birth (see below for details). This generous parental leave policy has become very popular among younger female employees, leading to a steadily growing fraction of them who actually take such a leave when they have a child. The official statistic treats women on parental leave as employed, even though most of them

165

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Figure 4.6

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166

0

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Figure 4.7

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2535

3545 4555 Age intervals

5565

Life-cycle profiles of women’s labour force participation for different birth cohorts

15 25

65

1955–65

1945–55

1925–35

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Women’s hours of market work in Germany

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do not work in the market at all during that time. One can argue that employees should be treated as out of the work force while being on parental leave. That of course would imply that the life-cycle pattern of different women’s cohorts effectively has not changed much over time. The extent of the change in women’s labour market involvement that has occurred since the late 1950s becomes all the more evident if it is contrasted with that of men. Men’s labour market involvement has remained remarkably constant over the past 25 years. Their labour force participation has remained constant over different birth cohorts; younger cohorts have slightly reduced their participation, but their overall involvement remains high. This observation is mirrored in the development of men’s labour force participation, which has remained largely unchanged since 1975. Single men have increased their participation while married men have decreased theirs, leaving the average participation rate for men unaffected. A similar picture emerges when looking at weekly hours worked. This measure has only slightly decreased, from 42 hours to about 40 hours per week since 1975, and the decrease has been borne proportionately by men of different marital status.

THE ROLE OF PARENTAL LEAVE There are several indications in the data that the introduction of parental leave has contributed to the observed decline in the number of weekly hours worked by married women with young children. We want to assess the impact that this institutional change has had on married women’s engagement in the labour market. Towards this end, we first provide a brief overview of the timing and the extent of the institutional changes that occurred and then carefully study the data. Some Institutional Details Parental leave is closely tied to maternity leave. In former West Germany, female employees who expect a child have been entitled to a total of 14 weeks of maternity leave, six weeks of which have to be taken prior to delivery. While on maternity leave, women receive their full salary and are protected against dismissal. Starting in January 1979, female employees on maternity leave could opt to take an additional four months of leave immediately following their maternity leave. While on leave, they received a monthly income of DM 750 from the government and could not be dismissed. They also had the right to return to their employer, albeit not to

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their previous job. Although this leave policy can be viewed as an early form of parental leave, effectively it was nothing but an extension of maternity leave. Starting in January 1986, the first federal law on governmental transfer payments to new parents became effective (Bundeserziehungsgeldgesetz BerzGG). According to this law, new parents were entitled to receive a transfer payment (Erziehungsgeld) from the federal government, regardless of their labour market status. The transfer equalled DM 600 for the first six months and was means tested thereafter; the transfer was paid for at most one year; it has explicitly been aimed at providing a financial incentive for new parents to raise their children themselves at home. The same law also regulated parental leave for the first time. Married mothers and fathers and unmarried mothers who were employed and who had an infant were entitled to a total of two years of parental leave following the mother’s maternity leave. Until the end of 1991, only those parents could opt to take the leave who were entitled to receive the government’s parental subsidy. Starting in 1992, those subsidies were extended to a maximum of 24 months, and eligible parents could take parental leave up to three years after the birth of their child. The opportunity to take parental leave was disentangled from the eligibility to receive transfer payments for raising the child. If married, a child’s parents could decide on how to split up the parental leave time between them. Parental leave has been compatible with part-time work. Between 1986 and the end of 1988 a mother or a father on parental leave could work up to 15 hours per week. Between 1989 and late 2000 they could work up to 19 hours per week. Another modification of the BerzGG became effective in January 2001. Employed mothers and their male partners – regardless of whether or not they are married – are entitled to take parental leave up to three years after the birth of their child. In fact, up to a third of this leave can be taken until the child turns eight years old, if the employer agrees. Part-time work of up to 30 hours per week is compatible with being on parental leave. Married parents with an annual income of at least €51 130 (DM 100 000) and all other parents with an annual income of at least €38 350 (DM 75 000) are excluded from receiving a parental subsidy; all eligible parents can choose between receiving a monthly transfer of €307 (DM 600) for at most two years and €460 (DM 900) for at most one year. It is important to note that, following ILO convention, individuals on parental leave count as employed in the official statistics for Germany. In what follows, we document the impact that these legal changes have had on the extent to which employed females take parental leave, and hence on their market hours worked.

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Empirical Evidence Assessing the quantitative importance of parental leave in former West Germany is difficult, because direct evidence from aggregate time-series data is available for the period from 1987 to 1991 only. During that time, receiving the parental subsidy was a prerequisite for being eligible to take parental leave. The German Federal Statistical Office reports in its statistical yearbook (Statistisches Jahrbuch) the socio-economic profile of those who received the subsidy and those who subsequently took the leave. With the legal changes becoming effective in 1992, the opportunity for employed new parents to take parental leave was disentangled from their entitlement to receive the parental subsidy. Therefore, the federal statistics on parental subsidies (Bundesstatistik Erziehungsgeld) which are maintained by the Federal Ministry of Family Affairs no longer convey reliable information on the extent of parental leave. We therefore supplement the direct evidence by indirect evidence from the official employment statistics in order to gain insight into the quantitative importance of parental leave among female employees, and how it has evolved over time. Such evidence is available because, when reporting their actual weekly market hours worked, employees also report whether or not their actual hours deviate from their normal hours. If they deviate, employees indicate the main reason for that deviation such as illness, vacation, flexible work hours, overtime or parental leave. We further use this detailed information in order to disentangle the general rise in part-time work of women which started in the 1980s, and business cycle movements in hours worked, from parental leave. We centre our attention on married women when studying the quantitative implications of changes in parental leave policies on women’s labour market involvement. That is because married women are the only group for which the official statistics provide separate information on the labour market behaviour of those who have children below the age of ten years and those who don’t. Married employees without young children can be viewed as the control group for married employees with small children who may be exposed to the treatment ‘receiving a parental subsidy’ or ‘taking parental leave’. Moreover, it is also well known that between 1987 and 1991 over 98 per cent of all recipients of a parental subsidy were women. Out of these women, about 90 per cent were married and lived together with their spouse. Figure 4.8 draws a clear picture of how the real monthly parental subsidy per recipient – female and male – has developed since this transfer payment was introduced in 1986. The average subsidy drastically increased between 1987 and 1991 and dropped significantly when the legal changes became

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Figure 4.8

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effective in 1992. In fact, there are clear signs of a structural break occuring in the early 1990s. Since then, the average subsidy received has remained rather stable. Because the nominal monthly payment has not been adjusted since 1986, these figures suggest that, during the first five years after the first law on parental subsidies became effective, eligible individuals adjusted their behaviour such as to receive an increasing monthly payment. They could do so, for example, by reducing their market hours worked, and thereby their income, prior to becoming a new parent. The observed decline in the average monthly payment which occurred in the early 1990s is due to the fact that, since 1992, the parental subsidy can be received for a maximum of two years, and that any payment after six months is means tested. Furthermore, entitled individuals have been able to ex ante choose between receiving a maximum monthly amount of DM 900 (€460) for at most one year, and a maximum of DM 600 (€307) per month for at most two years. Taken together, these factors have driven down the average monthly payment per recipient. The fraction of new mothers who receive a parental subsidy equals the number of new female recipients of a parental subsidy in a given year relative to the total number of births in that year. A careful look at this fraction reveals the following two phenomena. Firstly and not surprisingly, the fraction is significantly higher for all new mothers than for those mothers who were employed at the time of delivery. For example, in 1987 the ratio equalled 96 per cent for the first group and 82 per cent for the latter group. Secondly, this ratio has declined over time; it reached 85 per cent in 2002 for all mothers and 80 per cent for employed mothers in 1991. (No separate evidence on this latter group is available beyond 1991.) This drop is most likely due to the recent legal changes which allow mothers on parental leave to work up to 30 hours per week. Finally, when restricting our attention to married female employees, between 1987 and 1991, close to 7 per cent of them were on parental leave and worked zero hours.7 These figures are the clearest direct evidence on the quantitative importance of parental leave among married female employees which we report in this chapter. They are consistent with the figures which we infer from indirect evidence (see Table 4.1). We provide indirect evidence on the quantitative importance of parental leave among married women which we derive from the German Federal Statistical Office’s employment statistics. We look at the fraction of employed married females working at most 20 hours per week and compare the actual hours with the normal hours worked. We focus on female employees working at most 20 hours per week, because women on parental leave could work up to 19 hours (15 hours) per week until the end of 2000 (1988).

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Table 4.1 Incidence of parental leave without part-time work (upper bounds) (%)

1986 1991 1992 2000 2001

Married women with children 10

Married women without children10

4.4 10.1 12.5 16.1 14.7

0.67 0.69 0.92 0.96 0.95

Note: The entries denote the fraction of the respective group of married female employees working zero hours during the survey week for ‘other reasons, including parental leave’. Source: German Federal Statistical Office, Bevölkerung und Erwerbstätigkeit, Fachserie 1, Reihe 4.1.1, various issues.

Next, we select the fraction of married female employees for whom the actual hours worked are less than their normal hours and indicate the reason for this deviation. We provide this evidence for the three years in which major changes in the law on parental support (Bundeserziehungsgeldgesetz) became effective, that is, for 1986, 1992 and 2001, and also for the years preceding those legal changes. This enables us to assess the impact that the various legal changes had on the labour market behaviour of married women with and without children below the age of ten. Table 4.1 presents upper bounds on the incidence of parental leave without part-time work among married women with young children, illustrating how this incidence was linked to changes in the federal law regulating parental leave. All figures are based on the German Federal Statistical Office’s employment statistics. They illustrate that, following the introduction of parental leave in 1986, married women only gradually adjusted their labour market behaviour to this new opportunity of taking time off from their job while raising their children at home. Between 1986 and 1991 the fraction of married women who went on leave rose from 4.4 per cent to over 10 per cent. With the new opportunity to take parental leave without being eligible for parental subsidies, that fraction jumped by 2.5 percentage points in 1992 and continued to rise until the turn of the century. When new parents were allowed to combine parental leave with part-time work of up to 30 hours per week in 2001, the fraction of married women with young children who took leave without part-time work started to decline. Figures 4.9–4.11 illustrate the distribution of married female employees’ weekly hours worked for the three points in time when changes in the law

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on parental support became effective; each figure shows the distribution for married women with children below the age of ten and for married women without those children. Figures 4.12–4.15 indicate the extent to which the actually observed weekly hours coincided with the normal hours worked. They also indicate the reason for deviations between actual and normal weekly hours. Figure 4.9 suggests that the legal changes which became effective in 1986 left the hours distribution for the two groups of married female employees almost unaffected. Prior to 1986, married women with young children were more likely to work less than 20 hours per week and also less likely to work around 40 hours per week than women from the control group. In 1986 both groups’ hours distribution changed in a similar fashion: weight shifted away from the tails towards the centre of the respective distribution. While for married women with young children the fraction of weekly hours in the category ‘between ten and 20’ increased by more than the fraction in the category ‘between 30 and 40’, the opposite held true for the control group. Figures 4.12 and 4.13 shed some light on the motives underlying the observed changes. On the one hand, the fraction of married employees working at most 20 hours per week, for whom the actual hours are less than their normal hours, declined between 1985 and 1986 to less than 20 per cent, indicating that an increasing fraction of these women reduced their regular weekly hours. On the other hand, among the married women with young children for whom the observed hours were less than their normal hours, the fraction who attributed this discrepancy to parental leave significantly increased from 60 per cent in 1985 to 72 per cent in 1986. This observation clearly shows that many eligible married women took advantage of parental leave when it was first introduced. Figures 4.10, 4.14 and 4.15 convey the respective information for 1992. By 1991 well over 50 per cent of married female employees with small children worked at most 20 hours a week as opposed to only 30 per cent of the women in the control group. Also, over 10 per cent of married women with young children worked zero hours, as opposed to only 3 per cent of married women without young children. Figure 4.14 suggests that much of this observed difference in the hours distribution of the two groups was due to parental leave policy. For over 25 per cent of married women with young children working at most 20 hours per week these were less than their normal hours, and 80 per cent of them attributed this discrepancy to ‘other reasons’, including parental leave. According to Figure 4.15, with the legal changes becoming effective in 1992, the fraction rose to over 30 per cent. In sum, there is compelling evidence that the legal separation between parental leave and parental subsidy together with the increase of the maximum leave period from two to three years led to an overall rise in parental leave among

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Figure 4.11a Distribution of weekly hours worked by married women with children 10 yrs, across hours intervals, 2000 vs. 2001

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Figure 4.13b Reasons for deviations from normal hours of married women working at most 20 hours per week, as %, 1986

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Figure 4.15a Actual hours vs. normal hours worked by married women working at most 20 hours per week, as %, 1992

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married women, thereby contributing to the decline in the weekly hours worked by that group. By the year 2000, the fraction of married women with young children who worked less than 20 hours per week had risen to almost 70 per cent; 20 per cent worked zero hours, and over 35 per cent worked betweeen ten and 20 hours per week. Figure 4.11 supports the evidence stated in Table 4.1 that the legal changes which be0came effective in 2001 successfully induced some married women with young children who took parental leave to start working part-time. Remarkably, even though individuals on parental leave were entitled to work up to 30 hours per week, the reform of 2001 reduced the fraction of married employees working between 20 and 30 hours per week and instead increased the fraction working between one and ten, or between ten and 20 hours per week.

CONCLUSIONS This chapter provides strong evidence that drawing a reliable picture of women’s labour market involvement in Germany requires one to look not only at their labour force participation, but also at their hours worked if employed. Using aggregate time-series data from the German micro census covering the period from 1957 to 2001, the chapter illustrates that labour force participation has steadily risen among female employees regardless of marital status and of consecutive cohorts. The rise in women’s participation has been the strongest for married and widowed or divorced women. Average female labour force participation in Germany has moved towards that of men, and has almost reached that of women in other industrialized countries. The evidence further suggests that the observed rise in female participation has been accompanied by a notable decline in women’s weekly hours worked. The chapter points to married women with young children as a significant group of female employees for whom this decline has been the most severe. Unlike in many other industrialized countries – notably the USA, where married women with very young children have drastically increased their weekly hours worked since World War II, thereby contributing to an overall rise in aggregate hours worked – this group of female employees in Germany has steadily reduced its weekly hours’ involvement by over 40 per cent since 1975. In this chapter we argue that institutional changes in the federal legislation governing parental leave contributed to the observed decline in married women’s weekly hours worked. We provide ample evidence to support this argument. The strong increase in monthly real payments of parental subsidies to new parents which took place between 1986 and 1991

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coincided with a big drop in married women’s weekly hours worked and with a doubling of the fraction of married females taking parental leave without part-time work. When in 1992 the maximal length of parental leave was extended from two to three years and the opportunity of taking this leave was disentangled from the eligibility for a governmental transfer payment, this fraction jumped significantly. Similarly, the legal changes which enable new parents on parental leave to work a considerable number of weekly hours while on leave have caused fewer married females to take such a leave and not to work at all. In sum, even though labour force participation has steadily risen across the different groups of female employees, there is strong evidence that married females have reacted with their intensive labour market involvement to changes in incentives to work. These reactions have considerably affected the development of average hours worked by female employees in Germany. The results presented leave several important questions unaddressed. For example, it is to be expected that changes in the parental leave policy not only affected the labour market behaviour of married women, but also of single and widowed or divorced women. Have their reactions been similiar to those illustrated for married women? Furthermore, the extent to which women react to parental leave policies most likely is linked to their educational level, or to the labour market status of their spouse or partner. All of these issues require further exploration in an effort to complete the picture of the impact that parental leave has had on women’s total labour market involvement in Germany. However, such an exploration requires analysing individual data from the micro census or from the German SocioEconomic Panel. We leave it to future research.

NOTES 1. Correspondence: [email protected]. I thank Chinhui Juhn, Guy Laroque and seminar participants at Arizona State University and IGIER at Università Bocconi for useful comments. Financial support from the German Science Foundation (Deutsche Forschungsgemeinschaft) is gratefully acknowledged. This project was started while I visited RUESG at the University of Helsinki. I thank Erkki Koskela and Seppo Honkapohja for their generous hospitality. Markus Demary provided able research assistance. All errors and omissions are my own. 2. Former West Germany comprises the states Bremen, Hamburg, Schleswig-Holstein, Northrhine-Westfalia, Hessia, Lower Saxony, Rhineland-Palatine, Baden-Wuerttemberg, Bavaria, the Saarland and West Berlin. 3. There may be a source of systematic misreporting, simply because the time of the interview often does not coincide with the point in time for which the interviewee provides the information. However, whether or not the data actually suffer from such a mistake has not yet been analysed. 4. The time variation of employment rates is driven more by business cycles than by changes in long-term trends. We therefore abstract from employment rates in our effort to link

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long-term developments in total weekly hours worked to the extensive and intensive margins of adjustment. 5. Between 1975 and 2000 the share of single women in the female population declined from 36 to 34 per cent, while the share of widowed or divorced women rose from 16 to 18 per cent. Nothing is known about the composition of the latter group and how it may have changed over time. But it seems sound to assume that the share of divorced women in this group has risen, given that the divorce rate in the population has drastically increased. 6. The data for 1950 originate from the census which was conducted on 13 September 1950. They do not cover the Saarland and West Berlin. 7. This figure is computed as follows: 8.7 per cent of all married female employees had a child in 1987; 82 per cent of them received a parental subsidy; and 96 per cent of those women worked zero hours.

REFERENCES German Federal Statistical Office (1953), ‘The population of the Federal Republic of Germany according to the census of September 13, 1950’, Official Statistics of the Federal Republic of Germany Series, 35 (1), Stuttgart: Kohlhammer. German Federal Statistical Office (1953), ‘The occupational and social composition of the population of the Federal Republic of Germany according to the census of September 13, 1950’, Official Statistics of the Federal Republic of Germany Series, 36 I (2), Stuttgart: Kohlhammer. Jones, L., E. McGrattan and R. Manuelli (2003), ‘Why Are Married Women Working so Much?’, Federal Reserve Bank of Minneapolis, Research Department Staff Report No. 317. McGrattan, E. and R. Rogerson (1998), ‘Changes in hours worked since 1950’, Quarterly Review of the Federal Reserve Bank of Minneapolis, Winter, 2–19. McGrattan, E. and R. Rogerson (2004), ‘Changes in hours worked, 1950–2000’, Quarterly Review of the Federal Reserve Bank of Minneapolis, Summer, 14–33. Olivetti, C. (2001), ‘Changes in Women’s Hours of Market Work: The Effect of the Return to Experience’, Boston University Working Paper, December.

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DISCUSSION Holger Bonin In international comparison, Germany has one of the most generous parental leave systems (Waldfogel, 2001). Since employed mothers became eligible for protected leave beyond the traditional eight weeks after delivery in 1979, a series of reforms sequentially has extended the period of protected leave from six months to three years. Protected leave implies that the employer–employee relationship is on hold and the employee cannot make any claims for wage payments, but she has the option to return to the job held before pregnancy until the protected leave expires. At the same time, parents are eligible for child-rearing benefits. While initially only women on maternity leave were entitled to up to six months of payments, the current scheme is more generous: transfers can be paid irrespective of employment status and for up to two years, though benefits are means tested from the sixth month onwards. For comparison, mothers in the United States need to rely on short-term disability benefits during maternity leave. These usually run out after six weeks. Only since the introduction of the Family and Medical Leave Act in 1993, certain workers have been eligible for unpaid family leave that expires 12 weeks after birth. Very few states, like California, offer paid family leave of up to six weeks. In this institutional context it is not surprising that new mothers tend to return to work quickly (Berger and Waldfogel, 2004). That parental leave and benefit policies are so much more generous in Germany is probably the result of a combination of preferences, social norms and opportunities. Different fertility patterns may be especially important. Whereas birth rates in the United States are close to replacement, fertility levels in Germany have been among the lowest in the world since the fertility decline of the late 1960s. The significant expansion of support for parents after childbirth over the years has been justified as a means to promote fertility. In this regard, parental leave acts as a substitute for day care facilities, which are in shorter supply than in other nations. Several economic studies have analysed the effects of parental leave policies not only on fertility behaviour but also on women’s labour supply. The chapter by Monika Merz adds to this literature by decomposing labour supply decisions into adjustments at the extensive margin (participation) and at the intensive margin (hours worked by participants). Using aggregate results taken from the German micro census, she convincingly demonstrates that this distinction is indeed crucial to draw an accurate picture of women’s position in the German labour market. While women’s participation rates have steadily increased since the 1950s, hours worked by

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participants have fallen such that hours worked per capita have remained basically constant. The chapter draws attention to the fact that much of this decline in working hours since 1975 comes from working mothers with young children. It is tempting to conclude that this trend is a response to the expansion of parental leave policies. However, the observed combination of rising participation and falling hours of working mothers with young children would be consistent with a composition effect which perhaps already worked when there was only basic maternity leave but weekly hours per worker were in strong decline (see Figure 4.1). Because of the advances in women’s educational attainment, employment has become more valuable over the generations. As a result, at the extensive margin women with ever-higher costs of working have been entering the labour market. Among these are the mothers with young children who simply could not supply full-time hours due to lack of childcare facilities. Moreover, the secondearner model in Germany gets strong financial support by the provisions for income taxation of married couples. In order to make a case for a causal relationship between parental leave policies and labour supply at the intensive margin, the chapter basically uses a difference-in-differences strategy. This means that it exploits amendments of parental leave legislation to identify labour supply effects by comparing the behaviour of the treatment group (women with young children) relative to that of a control group (women without children) before and after the policy change. Though the changes in behaviour before and after the reforms of 1986, 1992 and 2001 generally point in the expected direction that parental leave and support policies create disincentives to work at the intensive margin, in my view the empirical results are not thoroughly convincing for a number of reasons. First of all, the macro perspective does not yield any proper test of statistical significance. As a substitute it may help to study changes for the treatment and control groups between non-reform years, to check how much year-to-year variation is normally in the data. Furthermore, the notorious critique to any difference-in-differences study applies. For identification of policy effects, it has to be assumed that there are no simultaneous unobserved factors affecting the treatment and the control group differently. Yet such factors may well exist, considering that the treatment group most probably differs not only in children from the control group, but also in relevant characteristics like age, cohort and education that cannot be controlled for. Finally, the official statistics used do not exactly cover either the treatment group or the treatment. Though the policy changes only apply to new mothers, the study takes all mothers with children younger than ten as the

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treatment group due to data limitations. Given that the non-treated in this group should not respond to the policy, a back-of-the-envelope calculation of the treatment effect on the treated would be to inflate the observed changes by a factor equal to the inverse population share of the treated. Doing so, several of the year-to-year changes studied in the chapter appear to be too large to be reasonably attributed only to institutional change. Regarding the treatment, evidence on parental leave is by and large only indirectly inferred from women counted as part-time employed in the micro census but working less than normal hours in the survey week for a bunch of reasons that includes parental leave. Of course, the revealed incidence of parental leave would be biased if mothers with young children resorted more frequently to absence from work for ‘other reasons’ without actually being on parental leave than the control group. In principle, the validity of the identifying assumption could be verified, since parental leave is recorded explicitly in the most recent waves of the micro census. Overall, the analysis could substantially benefit from an exploration of the ample micro data on parental leave available for Germany. Apart from the micro census at the individual level and the German Socio-Economic Panel, a useful, less standard source to identify employment relationships on hold due to maternity leave is the IAB Employment Sample, which has the advantage of providing a large number of observations and long employment histories, and the disadvantage of not recording exact working hours. Even if one accepts the empirical evidence presented in the chapter, one may ask, on a more conceptual level, is it relevant? It appears that a very large share of women on parental leave do not work at all rather than work part time, not the least in order to remain entitled to the means-tested parental leave benefits. One may argue that these women are de facto nonparticipants having made their decision at the external margin. If they nevertheless count as employed in the official records, any extension of parental leave generates a spurious increase in participation rates. As a corollary, the measured decline in weekly working hours per employee (Figure 4.3) turns out too large. From this perspective, in order to arrive at an accurate picture of the dynamics of women’s total labour supply, the essential task would be to adjust the underlying components for the plain classification effects associated with maternity leave. From an economic policy perspective, the fact that more generous parental leave impacts negatively on current hours worked does not seem to be the main issue of concern. After all, most decision-makers would claim that giving women the chance to temporarily exit from the labour market to raise their offspring is exactly the purpose of the policy. The key question to answer then is how the interruption of working careers through

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parental leave impacts on the development of employment status and hours worked over the life cycle. In this regard, the finding of Kunze and Ejrnaes (2004) that women returning to work after exiting for an extended maternal leave period in Germany face a wage drop of approximately 10 to 20 per cent is not comforting. An empirical analysis focusing on the extensive and intensive margins of the labour supply decision at expiry of parental leave, respectively parental leave benefits, therefore, seems a worthwhile undertaking. The various institutional changes of the past will ease identification of any labour supply effects. In particular, the amendment of 2001 may allow studying whether encouragement of part-time work, while being on protected leave, is a fruitful policy to reduce the potentially large long-term income penalty associated with the very generous parental leave system in Germany. References Berger, L.M. and J. Waldfogel (2004), ‘Maternity leave and the employment of new mothers in the United States’, Journal of Population Economics, 17 (2), 331–49. Kunze, A. and M. Ejrnaes (2004), ‘Wage Dips and Drops around First Birth’, IZA Discussion Paper No. 1011. Waldfogel, J. (2001), ‘What other nations do: international policies toward parental leave and child care’, The Future of Children, 11 (1), 99–111.

5.

The determinants of labour force participation in the European Union1 Véronique Genre, Ramón Gómez-Salvador and Ana Lamo

INTRODUCTION In 2000 and 2001 the Lisbon and Stockholm Councils set three ambitious targets for employment policy in Europe over the following ten years. By 2010, employment rates in the European Union should be raised to an average 70 per cent for the working age population as a whole, to 60 per cent for women and 50 per cent for older workers. According to Eurostat (2001) estimates, these targets imply an increase in employment of about 20 million people, drawing not only from those who are currently unemployed, but also from the 76 million people aged between 15 and 64 who are currently not actively participating in the labour market, the so-called ‘inactives’.2 To achieve the Lisbon and the Stockholm targets, the EU is facing the major challenge of attracting these inactives into the labour force. The evolution of the labour force is determined by demographic developments and participation behaviour.3 Demographic developments could influence the labour force directly by driving the growth of working age population, and indirectly by affecting the age composition of the working age population and contributing to lowering or increasing the relative share of specific age groups with a certain participation behaviour. A recent study by Genre and Gómez-Salvador (2002) however shows that demographic developments have not played a major role in labour force developments over the last two decades in the euro area. Rather, participation behaviour, which depends on economic and institutional factors, is driving the evolution of the euro area labour force. The achievement of the EU targets will require important developments in policy instruments and the reform of legal and institutional frameworks to encourage participation. Clearly, identifying the key economic and 195

196

Factors affecting labour force participation

institutional factors influencing labour force participation appears a prerequisite to the successful design of efficient policy measures which can help attract potential labour supply into the labour force. This chapter provides an empirical study of the impact of economic and institutional factors determining participation decisions in the EU. The analysis is performed by estimating participation equations for five different demographic groups – namely, young (15–24 years old), female and male prime-aged (25–54) and female and male older age groups (55–64) – using annual data for a panel of 12 EU countries over the period 1980–2000.4 Unlike in most other studies we use macro panel data. Our focus is not to model the participation decisions of particular individuals, which would be the case with micro panel data, but rather to study the evolution of participation for different demographic groups across a set of European countries. Our approach allows us to account for unobserved country-specific effects as well as a great number of observed country-specific effects. We provide a comprehensive study for all the relevant demographic groups. The breakdown of population by demographic groups is essential for understanding the pattern of participation rates. Many reasons could justify this breakdown: the participation of young workers is much more variable than the participation of prime-age males, the availability of parttime jobs and fertility decisions are key for prime-age females, while other factors such as retirement programmes are important determinants for older workers. The existing literature on labour force participation has been aware of the need for this breakdown and it is not uncommon to find studies for a particular group. We include institutions as explanatory factors of participation rates. The analysis of the effects of institutions on unemployment and employment has largely developed in recent years since the datasets compiled by Blanchard and Wolfers (2000) and Nickell and Nunziata (2001) have become available. Nickell (1997), Elmeskov et al. (1998), Belot and van Ours (2000, 2001) and Bertola et al. (2002) are some examples of studies that focus on the direct effect of institutions, while Blanchard and Wolfers (2000), Jimeno and Rodriguez-Palenzuela (2002) and Nunziata (2002) follow an approach based on the interactions between institutions and shocks. To our knowledge our paper is the first to look at the impact that institutions could have on participation decisions (with the exception of Blöndal and Scarpetta (1999) who analyse retirement decisions). We exclusively focus on the direct impact of institutions. According to our findings, rigid labour market institutions do not seem to encourage participation for all groups considered. Indeed, labour market institutions in recent years have undergone changes in rules or in features that increased their overall flexibility (for example, declining union density,

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decreasing employment protection, tightening of eligibility criteria for unemployment benefits). These changes have generally supported participation. Moreover, other dominant forces determining the evolution of participation rates during the sample period were the ratio of part-time employment for prime-age females and cohort effects for oldest females. The chapter is organized as follows. We first review the stylized facts regarding participation developments over the last two decades across our country sample and for various age and gender groups. The next section discusses the determinants likely to influence labour market participation. Following that we describe the empirical strategy and present our results. The last section summarizes and gives some initial conclusions.

LABOUR MARKET PARTICIPATION: STYLIZED FACTS Since the early 1980s, labour market participation in the European Union has increased from below 67 per cent to nearly 70 per cent in 2001 (see Figure 5.1a). This increase has been led by the positive contribution of female participation, which grew by around 12 percentage points to reach 60.7 per cent in 2001, while male participation rates fell by around 4 percentage points to 77.8 per cent (see Figure 5.1b). Apart from these trend movements, the effects of the cycle are also evident from the figures. In fact, the aggregate participation rate exhibits some decrease in periods of slower economic growth (that is, first half of the 1980s and of the 1990s). This decrease, in relative terms, seems more marked for male than for female participation. Figure 5.2 displays descriptive graphs of average participation rates for the 1990s, plotted against the average rates for the 1980s for all EU countries in our sample. Averaging the two decades is an attempt to control for the cyclical movements in participation.5 The participation rate is shown for three specific age groups – the young (15–24 years old), prime-age (25–54 years old) and older age groups (55–64) – and for both genders. A 45-degree line is added to all figures in order to facilitate the comparison of the developments. Points above the line imply an increase in participation between the two decades, while points below the line indicate a decline. Figures 5.2a and b display information on youth. Between the 1980s and the 1990s, participation rates of young people fell in virtually all countries, with the remarkable exception of the Netherlands. There seems to be little difference in developments and levels across genders, although the decline in young male participation is on average somewhat larger than that of females. Looking at national participation levels, Nordic countries and the UK in particular report participation rates significantly higher than

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Factors affecting labour force participation

71 70 69 68 67 66 65 64 63 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 Sources: Eurostat, OECD and ECB calculations.

Figure 5.1a Labour force participation in the EU (as percentage of working age population)

83

Males (left-hand scale) Females (right-hand scale)

65

81

60

79

55

77

50

45 75 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 Sources: Eurostat, OECD and ECB calculations.

Figure 5.1b Labour force participation in the EU by gender (as percentage of working age population)

199

The determinants of labour force participation in the EU (a) Men, 15–24 years old

(b) Women, 15–24 years old 80

75 70 65 60 55 50 45 40 35 30

75

DK NL

70

UK DE SE PT IE FI

ES

DK NL

65 1990s

1990s

80

60

UK

55

GE

50 IE

45

IT

SP

40

FR

35

BE

BE

30 30 35 40 45 50 55 60 65 70 75 80 1980s

IT

FR

1980s

(c) Men, 25–54 years old

(d) Women, 25–54 years old 90

96

80

SE DEPT

FR 70

94 93

NL

92

BE

PT

1990s

1990s

PT

30 35 40 45 50 55 60 65 70 75 80

97

95 DE ES

DK

UK

93

BE

ES IE IT

40 SE

90 92

94

95

30 96

30

97

40

50

60

70

80

90

1980s

1980s

(e) Men, 55–64 years old

(f) Women, 55–64 years old

90

90

80

80

SE

70

70

DK PTUK IE ES FI DE NL IT FR BE

50 40 30

50 40 30

20

20

10

10 10

20

30

40

50 60 1980s

70

80

SE

60 1990s

60 1990s

FR UK

NL

50

IT FI 91

60

FIDK

IE

91 90

SW

FI

90

DK PTUK FI DE NL IE FR BE 10

ES IT 20

30

40

50

60

70

80

1980s

Sources: Eurostat, OECD and ECB calculations.

Figure 5.2 Labour force participation by gender and age groups, 1980s versus 1990s (as a percentage of working age population)

90

200

Factors affecting labour force participation

those of Belgium, Spain, France and Italy, in both the 1980s and the 1990s. Danish participation rates for young men, for example, reached 74.1 per cent on average in the 1990s while Belgian ones stood no higher than 36.5 per cent. Generally countries with high youth participation rates saw less of a change in participation between the 1980s and the 1990s, if any. For instance youth participation rates in Denmark and the UK barely moved over the two decades. Figures 5.2c and d report developments in participation rate for primeage groups. Unlike for the youth, there is a clear difference in participation developments across genders. Participation rates of prime-aged men have generally fallen from the 1980s to the 1990s, while those of women have generally increased. Also, movements recorded by male participation are substantially smaller than those of females and over the last two decades prime-age male participation remained largely concentrated above 90 per cent. The largest declines in male participation were recorded in some Nordic countries (Finland, Sweden), Italy and the UK, although they account for at most 3–4 percentage points. There was less of a decline in male participation in Portugal and Germany, while it remained stable in the Netherlands. Conversely, the increase in prime-age female participation contrasts markedly with other age groups. It has been substantial from one decade to the next, with variations of average participation rates close to 20 percentage points, for example in Spain and the Netherlands. This significant increase in female participation is common to all countries except Sweden, Finland and Denmark. However, these three Nordic countries already had a very high prime-age participation rate in the 1980s, leaving little room for a further rise. Considering the broadly-based movement of increase in females’ participation, the national ranking of participation rate levels has hardly changed from one decade to the next. However, the countries with lowest participation rates have also witnessed the highest increases between the 1980s and the 1990s, indicating some degree of catch-up and convergence. This is specific to the prime-age female age group, as none of the other groups considered give any sign of particular convergence or divergence. Finally, prime-age female participation rates are substantially less concentrated than those of males, ranging from below 60 per cent in Spain, Ireland and Italy to more than 80 per cent in Finland, Sweden and Denmark. Finally, Figures 5.2e and f report changes in participation rates for the older age group. Older male participation declined in all countries, while older female participation generally increased, with the exception of France and Finland. However, the variation from one decade to the next remained smaller than that of males, where decreases in participation rates of around 10 percentage points or more are common (Italy, France, Spain).

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Once again participation rate developments did not affect the overall national ranking in terms of level. Sweden, Denmark, Ireland and the UK remained countries with a high average participation rate, while Belgium, France and the Netherlands did not catch up. Moreover, older women’s participation rates are systematically lower than those of older men.

PARTICIPATION DETERMINANTS: ECONOMIC AND INSTITUTIONAL FACTORS This section briefly discusses the relevance and developments of some economic and institutional factors that can determine changes in participation rate developments as well as differences in levels across EU countries.6 From a demand-side viewpoint, business cycle fluctuations are expected to affect participation decisions via the discouragement effect, that is, positive economic developments are associated with a higher probability of finding a job and therefore the participation rate rises, while negative economic developments have the opposite effect. In addition, the increase in the relative importance of service activities has fostered certain types of occupations and work arrangements (for example, part-time jobs) that are expected to have a different impact on labour demand between genders, and in particular to favour female participation. From the supply side, economic theory gives an important role to potential earnings. The level of education could be a good proxy of potential earnings, but unfortunately there is a lack of available data to measure trends in education since the 1980s. Alternatively, we focus on real wages, in spite of the inaccuracy of this to measure potential earnings.7 Moreover, family compositional changes – including trends in marriage, family size and fertility rates – could influence participation decisions, especially the participation of women. Finally, gender discrimination, which has been referred to as an impediment for women in finding a job, has been taken into account, using as a proxy the percentage of parliamentary seats occupied by women. In addition to economic factors, institutional factors have proved to be very relevant not only for employment and unemployment as a whole, but also in terms of the relative effects on age and gender groups. We expect the institutional framework also to play a role in the decision to participate in the labour market, via incentives to participate. The existing literature summarizes this institutional framework by referring mainly to union density, labour taxes, employment protection, unemployment benefit systems and retirement systems.8 The expected effects of institutions on participation can be discussed on the basis of an insider–outsider model, such as for example, the one in

202

Factors affecting labour force participation

Bertola et al. (2002). According to this model, the combination of optimizing behaviour by unions and differences in group-specific labour market attachment leads to a situation with higher wage compression and larger employment differences between unionized and non-unionized workers. One could think of prime-age males as the insiders, and more attached to the labour market, while the youngest, prime-age females and oldest workers tend to be less attached, either because they show more frequent transitions from activity to inactivity (youngest and prime-age females) or because they face incentives to leave the labour market before the retirement age (oldest). The model implies that the presence of unions will compress wages by age and gender groups, resulting in relatively large employment losses among the young, older and female groups. In sum, labour market institutions are then expected to have an impact on the bargaining power of insiders and therefore on the expectations of outsiders as regards their opportunity of getting a job. The extent and character of union density is expected to be very important.9 Union involvement in relative wage-setting should concentrate employment losses on ‘outsider’ workers, while union power may affect employment and participation developments of demographic groups by influencing which of them bear the burden of layoffs. In terms of participation, we would expect that more unionized economies would experience lower participation rates among the less protected workers, that is, the youngest and females. Labour taxes drive a wedge between gross (paid by the employer) and net (received by employees) wages. The impact of taxes on net wages and, therefore, on labour force participation, depends on institutional factors, such as the wage bargaining process, wage distribution and tax and benefit schemes. If taxes result in net wage changes, participation decisions will be influenced by an income and a substitution effect. According to the income effect, lower wages will increase participation in order to keep the income level constant. However, the substitution effect will induce a lower participation, as lower wages make leisure relatively cheaper in terms of labour. We would expect the effect of taxes to be negative but small; lower for men than women. Employment protection and unemployment benefits10 are also expected to have an effect on participation patterns, although the net effect of these institutions seems less clear. On the one hand, more stringent employment protection reduces employers’ willingness to hire and fire workers, which means that employment and participation rates of young labour market entrants and women with discontinuous participation should be negatively affected. In the same way, to the extent that a more generous unemployment benefit (UB) coverage strengthens union bargaining strategies, it

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203

would reduce the employment rates of the most vulnerable groups and, indirectly, their participation rates. On the other hand, however, more employment protection and the existence of more generous unemployment benefit systems could represent a positive incentive to participate in the labour market from the point of view of workers. Finally, retirement systems are expected to have an impact on the participation rates of older workers. The greater the coverage, the lower the participation rates. Retirement systems can be measured from different perspectives. Available information allows for measuring replacement rates, the official retirement age, and financial costs of early retirement programmes. As available replacement rate indicators do not have a time dimension for our period of study, we substitute these indicators by the expenditure on social protection for old-aged people (as a percentage of GDP). This variable is expected to capture country differences in social protection, which can affect incentives to work, while the official retirement age captures differences in legal systems. Early retirement programmes, measured as a percentage of GDP, could also partly explain differences in participation between countries.

EMPIRICAL EVIDENCE In this section we estimate participation equations for five different demographic groups using time-series of annual macro data for a panel of EU countries.11 The basic equation follows the general form: log(PRit /1
PRit ) Xit bt ci it. The participation rate is a variable bounded between zero and one (see Bover and Arellano, 1995). To account for this we define our dependent variable as the logit transformation of participation rates of each group, for country i and period t.12 bt and ci are period and country effects respectively. The basic explanatory variables (Xit) include economic factors and institutional variables that are plausible determinants of each group’s participation rate. Common to all the groups are the following variables: a measure of the business cycle, wages, unemployment benefit replacement rates, unemployment benefit duration index, union density, an index of employment protection and a measure of labour taxes.13 Data on unemployment benefit replacement rates, unemployment benefit duration index, union density and the index of employment protection are only available until 1998; to enlarge the series we have maintained constant their values in 1999 and 2000.14 To control for business cycle effects we include overall

204

Factors affecting labour force participation

unemployment rates. This is expected to capture cyclical labour market conditions better than alternative measures such as the output gap. The potential problem of endogeneity of this variable is diminished by the fact that our dependent variable is defined for a specific population group and consequently its potential feedback to aggregate unemployment is small. Nevertheless, we use unemployment lags as instrumental variables. The error term in our equations would contain slow moving cohort effects that result in autocorrelation; the population of our demographic groups moves (in and out of the group) slowly over time. We do not include lags of the dependent variable among the explanatory variables because they are likely to capture these slow moving components of the error term and bias the estimates. Estimation is performed using feasible GLS allowing for autoregressive, AR(1), and heteroskedastic residuals. We implemented an unit root and cointegration test for our panel to ensure that our results are not based on spurious regressions. However, it should be noted that because of the short time-series dimension of our panel the outcome of the test must be interpreted with caution. The rest of this section shows the results for all demographic groups and discusses specific relevant variables in each case. Table 5.1 presents basic results for each demographic group; alternative equation specifications were also estimated and some of them are commented on in the text.

Table 5.1

Panel data estimates of participation rates a,b Prime-age Females (1)


0.013 (
3.41) Real wage 0.056 (0.62) Fertility rate (Nordic0.764 the Netherlands) (4.78) Fertility rate
0.254 (
3.21) Female part-time ratio 0.031 (9.64) Parliamentary % 0.003 (1.39) Long-term unemployment Unemployment rate

Aged 55–64

Aged 15–24

Males (2)

Males (3)

Females (4)

Both genders (5)

0.005 (0.926) 0.377 (0.39)

0.003 (0.84) 0.120 (1.55)

0.001 (0.16) 0.114 (1.32)


0.030 (
5.34) 0.036 (0.40)


0.004 (
3.16)

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The determinants of labour force participation in the EU

Table 5.1

(continued) Prime-age Females (1)

Males (2)

Part. rate 25–54 lagged 10 years Old-age social protection (% GDP) Retirement age

Aged 55–64 Males (3)

Females (4)


0.001 (
0.08) 0.198 (7.96)

0.490 (3.35)
0.025 (
1.81) 0.135 (7.44)


0.280 (
2.71)
0.401 (
3.74)
0.008 (
3.50)


0.684 (
4.33)
0.053 (
0.35)
0.339 (
1.00) 0.054 (0.38)
0.293 (
0.92)

0.085 (0.65)
0.112 (
1.00)
0.586 (
2.61)
0.216 (
2.01)
0.558 (
2.01)


0.196 (
1.52)
0.258 (
2.31)
1.186 (
5.29)
0.505 (
4.54)
0.896 (
3.27)

196

191

203

203

172

Part-time ratio 15–24

UB duration Union density Empl. protection Labour taxes Obs. No.

Both genders (5)


0.031 (
4.23) 0.013 (5.21) 0.235 (1.75)
0.213 (
1.88) 0.572 (1.75)
0.012 (
0.08)
0.066 (
0.18)

Young in education

UB replc. ratio

Aged 15–24

Notes: a. Except when indicated estimations are performed by FGLS including time and country dummies. Countries include Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Portugal, Spain, Sweden and the UK. For variable definitions see the Appendix. b. Stages GLS. Fertility and unemployment rates instrumented. t-statistics in parentheses.

Prime-age Females This group comprises females aged between 25 and 54. Participation rates for this group have experienced an important increase in all the countries of our sample for the period 1980–2000, which is expected to be related to the general increase in labour demand and the relative importance of service activities, as well as to the increase in more flexible job arrangements. Apart from the common set of explanatory variables mentioned above, some specific variables have been considered for this group,

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Factors affecting labour force participation

including fertility rates, part-time employment rates, ratio of employment in services and the percentage of parliamentary seats occupied by women. Other relevant variables would have been measures of child care availability and maternal/parental leave. Child care and maternal/parental leave combined with part-time jobs could enable women to reconcile motherhood with professional life, and therefore have a positive impact on female participation. Unfortunately lack of data prevented us from testing this hypothesis.15 Estimation results are presented in column 1 of Table 5.1. It can be seen that female participation is found to be sensitive to cyclical conditions. The estimated coefficients on unemployment rates are negative and statistically significant, reflecting the discouragement effects described in the previous section. The magnitude of the coefficient is rather low. This is reasonable for this particular group, for which participation presents an upward trend during the whole period of analysis, suggesting that the dominant determinants of the participation development for this group are not cyclical but rather medium to long term. The vacancy rate was also considered in order to capture the effects of job availability. Its coefficient was positive and significant and the coefficients on the remaining variables were not substantially affected. Nevertheless, the inclusion of this variable forces us to drop two countries, Italy and Ireland, from the sample, due to data unavailability and we therefore decided to leave it out of our estimates. The coefficient on overall real wages turns out to be positive as expected, but quite unstable and insignificant. Fertility decisions are essential for this group’s participation. In fact, having children and/or participating could be joint decisions. We include a fertility variable (fertility rates) among the explanatory variables and try to alleviate endogeneity biases by instrumenting fertility using lags. When looking at the relationship between fertility and participation at the individual country level for our data set we found that countries with higher female participation also have higher fertility rates, while for the rest of the countries the relationship is negative. Consequently, we allow for an asymmetric effect of fertility in our equation specifications by interacting fertility rates with a dummy for Sweden, Denmark and the Netherlands. Fertility rates turn out to be significant and positive for this group of countries, with a net coefficient of around 0.5, and negative and also statistically significant for the rest of the countries. Moreover, we find a very significant and positive effect of the variable measuring the percentage of females in part-time jobs. The ratio of employment in services over total employment, when significant, lowers the coefficient of the part-time variable. This effect is to be expected because part-time jobs are concentrated in the services sector.16 The percentage of

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207

parliamentary seats occupied by women is introduced as a proxy for gender discrimination and is found to be positive but not statistically significant. Turning to labour market institutions, rigidity appears to have negative effects on the participation of prime-age females. Both unemployment benefit variables are quite significant, although the coefficient on unemployment benefit duration shows more variability across specifications. Union density has a significant but very low impact on participation, while the effect of employment protection and labour taxes is not significant. The latter could be due to measurement issues; since our tax variable measures country average tax revenue including income and consumption taxes, we drop it from the regressions for this group. Finally, a variable that measures the child benefit in each country as a percentage of GDP was also tried in several variants of this specification and found to be not statistically significant. We checked integration and cointegration properties in our panel using the method suggested by Maddala and Wu (1999). This method combines the results of any kind of individual countries’ unit root test using a Fisher test: twice the sum of the log of p-values from the individual tests is distributed chi-squared with degrees of freedom twice the number of cross-sections (12 countries). Combining p-values (MacKinnon’s approximations) from the Phillips Perron test we accepted the null of a unit root for core age female participation. We implemented the same exercise for the residuals of the regression and rejected the null hypothesis of no cointegration. Therefore we reject the hypothesis of spurious regression results.17 Prime-age Males By contrast with women, men aged 25 to 54 have generally witnessed a slight decline in participation rates since the early 1980s. This decline has been moderate and has not offset the fact that European prime-age males are strongly participating in the labour force. Our specification for this group included real wages, the unemployment rate and the set of labour market institutions. In addition, we tested a number of other variables likely to have some influence on male participation. In particular, we tested female part-time ratio and female labour force participation without much success, as none of these variables appeared to influence overall male labour force participation. It would seem sensible that these variables influence behaviour at the individual level, but their effect is unlikely to be captured in a macro analysis. A feature of prime-aged male participation is its absence of sensitivity to cyclical variations in job opportunities. In our estimation (column 2, Table 5.1) the unemployment rate coefficient is not significant. Other measures of the cycle or of variations in

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Factors affecting labour force participation

job availability, such as the output gap or the vacancy rate, did not perform any better. However, long-term unemployment enters the specification significantly, even though the corresponding coefficient is rather small. It appears that male participation tends to respond negatively to a rise in longterm unemployment, suggesting that overall variations in the economic cycle barely influence prime-aged males’ participation, but the most structural component of unemployment has some negative effect on participation. In addition, the coefficient on real wages together with a number of variables that could be plausible factors influencing primed-aged male participation turned out to be non-significant. This is the case with disability benefits, as entering a sickness and a disability benefit plan might be seen as a way to withdraw from the labour market. Interestingly however, this variable seems to be relevant for only one country, the Netherlands.18 Moving to labour market institutions, they also seem to have little effect on prime-age male participation. Indeed, with the exception of the unemployment benefit replacement ratio, none of them significantly enters our specifications. Against the background of the discussion above, this may not be wholly surprising as we expect prime-age males to be ‘insiders’ in the labour market. Labour market institutions are thus less likely to influence them than other ‘outsider’ groups. The negative response of male labour force participation to the unemployment benefit replacement ratio is, however, significant, indicating that the lower the unemployment benefit replacement ratio, the higher the incentive to participate. Males and Females Aged 55–64 Apart from the common set of variables, two additional variables have been considered as relevant determinants of participation decisions in the oldest group. These are the official retirement age and the expenditure on old age programmes as a percentage of GDP, which are expected to have a positive and negative relationship with participation rates, respectively. Moreover, an additional variable has been used specifically in the females’ equation, namely the participation of prime-aged females lagged ten years.19 This variable is expected to capture the change in females’ participation across cohorts. In other words, the fact that the participation behaviour of females aged 55–64 today is linked with the way these females were behaving when they were 25–54 years old. Figure 5.3 illustrates the cohort effect in females for the euro area as a whole. The horizontal movement along each line shows how participation evolves with age, showing a sizeable decline for the oldest groups. The vertical distance between lines reflects the cohort effect, which is non-negligible. For instance, for those aged 45–49, we find that the cohort

209

The determinants of labour force participation in the EU 80

2000

1995 Participation rate

70 60

2000

2000

1990 1985

2000 1985

1985

50

2000 1985 1985 2000

40 30

2000

20 10 25–29

Figure 5.3

30–34

35–39

40–44 45–49 Age groups

50–54

55–59

60–64

The cohort effect on females’ participation

effect on participation has been above 15 percentage points in the past 15 years. Starting from the top line, the chart reports the participation rate for those aged 45–49 in 2000 (69 per cent), next the participation rate for the same age group in 1995 (63 per cent) and, then, successively in 1990 (57 per cent) and 1985 (51 per cent). Columns 3 and 4 in Table 5.1 show the estimation results both for males and females. The retirement age, as expected, has a positive effect on the participation of both gender groups, and appears to be the most significant variable. The expenditure on old-age social protection programmes only appears to be significant for females. Blöndal and Scarpetta (1999) also find that this variable does not influence participation decisions for older males. This can possibly be explained by a lower degree of attachment of older females to the labour market, and therefore old-age social protection could have a greater effect on this group. The cohort effect also appears to be significant and to have a positive influence on the participation rate of oldest females. By contrast, cyclical effects, captured by the unemployment rate, are found to be non-significant for both males and females. This suggests that participation of the oldest groups is driven by factors of a more structural nature. As regards labour market institutions, they have the expected negative impact on participation for both genders. Union density, employment protection and labour taxes are the most significant for males, while for females, on top of these, unemployment benefit duration and, to a lesser extent, unemployment benefit replacement ratio are also significant.

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Males and Females Aged 15–24 Finally, for the youngest group, the specific factors that have been considered as influencing participation decisions include education enrolment, flexible types of work and minimum wages. The idea is that enrolment in education can be seen as an alternative to participation in the labour market, that is, young people may decide either to continue studying in order to reach higher skills that will allow them to get a job more easily in the future, or as an alternative to being unemployed. Education therefore presents endogeneity problems. In an attempt to control for this problem we used lags of this variable as instruments. In addition, the introduction of flexible types of contracts (that is, temporary and part-time jobs) is expected to be an incentive both for firms, as the associated labour costs are lower, and for workers, as a temporary attachment or a ‘stepping stone’ to get a better job. Minimum wages, finally, have been referred to in the literature as being a barrier to hiring young people, who generally have lower skills and productivity. Participation behaviour of the youngest does not differ substantially by gender (as reported in the descriptive part) and consequently we estimate both genders together. As shown in column 5 of Table 5.1, cyclical conditions have a significant impact in the participation rate. The lower the unemployment rate, the higher participation. As expected, deciding to be in education negatively affects the participation rate. Therefore, the sustained rise in the proportion of young people in education observed in our sample has reduced the participation rate for this group. The results also show that part-time contracts have a positive influence on participation. In an alternative estimation we also added the temporary employment ratio but its effect was not significant. As regards labour market institutions, they do not appear to have a significant influence on the participation rate of youths. When a variable measuring minimum wages is included, as a labour market institution that can mainly affect the young, it turned out to be statistically nonsignificant. However, its inclusion reduces considerably the number of observations, as this information is only available for a limited number of countries. Discussion of the Results The empirical results in this section allow us to examine the net contribution that the development of different factors considered in the regressions makes to the total change in labour force participation for each demographic group in the last two decades. Figure 5.4 compares

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changes in participation predicted by our models with those obtained had specific explanatory variables remained constant since the beginning of the 1980s. The accumulated contribution of the cycle to changes in participation over the last two decades is basically zero, as expected. Other variables, such as institutions, have more long-lasting effects. For instance, if institutions had remained at the 1980s level the participation of older females would have increased by less than 3 percentage points, rather than the 4.5 percentage points suggested by the model. In other words, the average increase in older female participation rates would have been 35 per cent less, had the institutional framework not changed. Similarly, average participation of older males would have decreased by around 1.4 percentage point (or 16 per cent) more. On the other hand, the net effect of institutions in the accumulated change of participation is very low for prime-age males and females and for the young. Looking at more specific influences, had the share of part-time jobs stayed unchanged since 1980, the increase in prime-age female participation would have been around 25 per cent less and the decline in youth participation would have been almost twice as large. Finally, keeping cohort effects and education attachment at early 1980s levels would have maintained the participation rate of old females almost constant at the level of the 1980s and increased the participation of the youngest.

CONCLUSIONS Using annual data for a panel of 12 EU countries over the period 1980–2000, this chapter has looked at the role that both economic and institutional factors have played in explaining participation rates and their development in the last two decades in the EU. The analysis was performed by estimating participation equations for five different demographic groups – namely, young (15–24 years old), female and male prime-aged (25–54) and female and male older age groups (55–64). The influence of general economic variables on participation is mixed. The unemployment rate, which is meant to capture mainly cyclical developments, appears to have some relevance in explaining participation, albeit only for the young and prime-age females. This seems however reasonable as the cycle is likely to influence more those who are less attached to the labour market. The estimated impact of real wages on the other hand is generally found to be not significantly different from zero. This is likely to be due to a mismeasurement of aggregate wages that does not capture ageand gender-specific potential earnings.

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8.0 (e) young Note: This figure compares predicted changes in participation with those obtained if specific explanatory variables are maintained at the levels of 1980 or at minimum levels, in order to show the relevance of the developments in each selected variable to explain changes in participation.

Figure 5.4 Contribution of different factors to participation developments (in average percentage points) Participation actually seems to be more influenced by variables specific to different age groups. In particular, labour demand developments, measured by the ratio of part-time employment, appear to have substantially favoured the participation of the youngest and prime-age females. For these two groups, alternative decisions to participation, such as education

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for the youngest or fertility for prime-age women, have also been found significant. Interestingly, however, fertility does not appear to be an impediment to participation in Nordic countries. The inclusion of a proxy to capture cohort effects is crucial to explain oldest females’ participation. These findings strengthen the idea that participation incentive policies need to be carefully designed according to targeted groups. Finally, the influence of labour market institutions on participation must be stressed. It is shown that the rigidity of labour market institutions negatively affects the participation rate for most of the groups studied; an exception being, as expected, prime-age males, in line with their assumed role of ‘insiders’ in the design of these institutions. Thus, as the labour market institutional framework has reduced its tightness in the last two decades (for example, declining union density, decreasing employment protection, tightening of eligibility criteria for unemployment benefits), it seems to have had a positive impact on participation, in particular for the oldest groups.

APPENDIX: DATA SOURCES AND DEFINITIONS Participation rate Defined as the percentage of people in the labour force in the total working age population. The total labour force (or currently active population) comprises all persons aged 15 to 64 who are either employed or actively looking for a job. The working age population comprises all people aged between 15 and 64 years old. The data on participation rates and unemployment rates are derived from the annual European Community Labour Force Survey, which has been conducted every year in the spring since 1983.20 OECD and ILO data have been used to extend participation rates back to 1980. Unemployment rate Defined as the percentage of people actively looking for a job in the labour force. The data on unemployment rates are derived from the annual European Community Labour Force Survey, which has been conducted every year in the spring since 1983. OECD data have been used to prolong the unemployment rate back to 1980. Real wage Defined as nominal wages deflated by the GDP deflator, obtained from the European Commission AMECO database. Output gap Defined as deviations from trend growth, obtained from the European Commission AMECO database. Vacancy rate Defined as the number of vacant jobs as a percentage of the labour force. Vacancy data are obtained from the OECD.

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Fertility rate Defined as the average number of children that would be born alive to a woman during her lifetime if she were to pass her childbearing years conforming to the fertility rates by age of a given year. It is obtained from the NewCronos database, Eurostat. Part-time ratio Defined as the fraction of part-time workers in total employment, obtained from the annual European Community Labour Force Survey. Self-employment ratio Defined as the fraction of self-employed in total employment, obtained from the annual European Community Labour Force Survey. Long-term unemployment ratio Defined as the fraction of unemployed more than a year in total unemployment, obtained from the annual European Community Labour Force Survey. Services ratio Defined as the percentage of employment in the services sector, obtained from the European Commission AMECO database. Old-age social protection Includes old age pensions and the provision of goods and services (other than medical care) to the elderly, as a percentage of GDP. These data are drawn from European Social Statistics, Eurostat. Children benefit Includes support (other than medical care) in connection with pregnancy, childbirth and the care of children and other dependent family members, as a percentage of GDP. These data are drawn from European Social Statistics, Eurostat. Women parliamentary seats Defined as the percentage of seats in the parliament occupied by women. United Nations Statistics and ECB calculations. Unemployment benefit replacement ratio Refers to first year of unemployment benefits, averaged over family types of recipients, as a percentage of average earnings before tax. Data are taken from Labour Market Statistics Database, Nickell and Nunziata (2001). Unemployment benefit duration Obtained as a weighted average of the replacement rate received the second and third years of unemployment and in the fourth and fifth years, relative in both cases to the replacement rate in the first year. Data are taken from Labour Market Statistics Database, Nickell and Nunziata (2001).

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Union density Defined as the ratio of union members to total employees. Data are taken from Labour Market Statistics Database, Nickell and Nunziata (2001). Employment protection Measures in a range between 0 and 2 the strictness of employment protection. Data are taken from Labour Market Statistics Database, Nickell and Nunziata (2001). Labour taxes Defined as the sum of the employment tax rate, the direct tax rate and the indirect tax rate. Data are taken from Labour Market Statistics Database, Nickell and Nunziata (2001). Retirement age Defined as the official retirement age. Data are taken from Blöndal and Scarpetta (1999). Minimum wage Defined as the ratio of the minimum wage to the median wage, OECD.

NOTES 1.

2.

3. 4. 5. 6. 7. 8. 9.

10.

The views expressed in this chapter are those of the authors and do not necessarily reflect those of the European Central Bank (ECB). We would like to thank Neale Kennedy, Barbara Petrongolo, Jarkko Turunen and the participants of the 2003 CEPR/ECB Labour Market Workshop for useful comments. Any errors are of course the sole responsibility of the authors (at European Central Bank). The inactives make up a rather heterogeneous population. About one-sixth are willing to work but unable to find a job and the rest have withdrawn from the labour force for a wide variety of reasons: personal or family responsibility (20 per cent), education or training (27 per cent), own illness or disability (9 per cent) and retirement (16 per cent). The labour force, which comprises the employed and unemployed, can be thought of as potential participants (working age population) times the probability of participating (participation rates). Countries include Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden and the UK. This is for illustrative purposes. All the regression analysis is performed using annual data. See the Appendix for a more detailed description of the data sources and definitions. Average wages for a specific group could reflect skill composition and self-selection rather than participation behaviour. Data on union density, labour taxes, employment protection and unemployment benefits are taken from Nickell and Nunziata (2001) and variables on retirement systems are from Blöndal and Scarpetta (1999) (see Appendix). We considered, at an early stage, the degree of wage bargaining coordination, but it was found to be insignificant in the estimations and in general highly correlated with other institutions. It should be noted that union density is a quite imperfect measure of unionization, as there are countries with low affiliation rates like Spain or France where union agreements apply at a national level to all workers. The unemployment benefits are measured using two variables, that is, the replacement rate and the duration of the benefits.

The determinants of labour force participation in the EU 11. 12. 13. 14. 15. 16. 17. 18.

19. 20.

217

The panel is unbalanced for some of the explanatory variables. The estimations in this section were also performed using the participation rate as a dependent variable; the results did not show any relevant changes with respect to the ones presented in the chapter. For a general description of the variables and expected effect on participation rates see the previous section, ‘Participation determinants’. In order to check whether this was distorting the results, all the estimations in this section were also performed for a shorter sample ending in 1998. We didn’t find any relevant changes in the results. Recent studies using micro data have found evidence in support of this: see for example Pissarides et al. (2005) and Del Boca et al. (2005). Results including the ratio of employment in services are not shown, but are available from the authors. Similar tests were performed for each demographic group and in every case we rejected the hypothesis of spurious regressions. The number of disability benefits recipients in the Netherlands rose by more than 30 per cent between 1980 and 1990, as both employees and employers had an interest in using this insurance to avoid redundancies. In the early 1990s, measures were taken to redefine and tighten the eligibility criteria of the disability insurance scheme, leading to a major drop in the number of recipients (Barrell and Genre, 1999). This variable has been constructed using ten-year data published by the International Labour Office (ILO), which have been interpolated to obtain annual data by dividing the change homogeneously through time. Eurostat compiles these data and a detailed description of the sampling methods and adjustment procedures can be found in the Labour Force Survey – Methods and Definitions (1998). There is no information available for countries before they joined the European Union (that is, for Spain and Portugal prior to 1986, for Austria and Finland prior to 1995). The series were extended to 1980, whenever possible, using OECD Labour Force Statistics or, directly, relevant national sources. German data prior to 1991 only refer to West Germany.

REFERENCES Barrell, R. and V. Genre (1999), ‘Employment strategies for Europe: lessons from Denmark and the Netherlands’, National Institute Economic Review, 168, April, 82–98. Belot, M. and J.C. van Ours (2000), ‘Does the Recent Success of some OECD Countries in Lowering their Unemployment Rate Lie in the Clever Design of their Labour Market Reforms?’, IZA Discussion Paper No. 147. Belot, M. and J.C. van Ours (2001), ‘Unemployment and labor market institutions: an empirical analysis’, Journal of the Japanese and International Economies, 15 (4), 403–18(16). Bertola, G., F.D. Blau and L.M. Kahn (2002), ‘Labour Market Institutions and Demographic Employment Patterns’, CEPR Working Paper No. 3448. Blanchard, O. and J. Wolfers (2000), ‘The role of shocks and institutions in the rise of European unemployment: the aggregate evidence’, Economic Journal, 110, C1–33. Blöndal, S. and S. Scarpetta (1999), ‘The Retirement Decision in OECD Countries’, OECD Economics Department Working Papers No. 202. Bover, O. and M. Arellano (1995), ‘Female labour force participation in the 1980s: the case of Spain’, Investigaciones Económicas, 19, 171–94.

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Del Boca, D., R. Aarberge, U. Colombino, J. Ermisch, M. Francesconi, S. Pascua and S. Strøm (2005), ‘Labour market participation of women and fertility’, in European Women at Work, Part B, Oxford: Oxford University Press and Milan: Fondazione Rodolfo Debenedetti, forthcoming. Elmeskov, J., J.P. Martin and S. Scarpetta (1998), ‘Key lessons for labour market reforms: evidence from OECD countries’ experience’, Swedish Economic Policy Review, 5 (2), 205–52. Genre, V. and R. Gómez-Salvador (2002), ‘Labour Force Developments in the Euro Area since the 1980s’, ECB Occasional Paper No. 4. Jimeno, J.F. and D. Rodriguez-Palenzuela (2002), ‘Youth Unemployment in the OECD: Demographic Shifts, Labour Market Institutions and Macroeconomic Shocks’, ECB Working Paper Series No. 155. Maddala, G.S. and S. Wu (1999), ‘A comparative study of unit root test with panel data and a new simple test’, Oxford Bulletin of Economics and Statistics, Special Issue, 631–59. Nickell, S. (1997), ‘Unemployment and labor market rigidities: Europe versus North America’, Journal of Economic Perspectives, 3, 55–73. Nickell, S. and L. Nunziata (2001), ‘Labour Market Institutions Database’, Centre for Economic Performance, LSE, September. Nunziata, L. (2002), ‘Unemployment, labour market institutions and shocks’, mimeo. Pissarides, C., P. Garibaldi, C. Olivetti, B. Petrongolo and E. Wasmer (2005), ‘Women in the labour force: how well is Europe doing?’, in European Women at Work, Part A, Oxford: Oxford University Press and Milan: Fondazione Rodolfo Debenedetti, forthcoming.

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DISCUSSION Barbara Petrongolo This chapter empirically studies recent trends in labour force participation in the European Union. There have been several important developments in labour force participation in Europe over the last three decades. Among these, the most remarkable is the rising participation of women, from a 46 per cent EU average in 1976 to 62 per cent in 2002, followed by falling participation of both men (from 85 to 80 per cent) and youths (from 54 to 47 per cent). This chapter looks at the role of economic indicators and several labour market institutions in shaping these trends during the 1980s and 1990s. In particular, the authors estimate the determinants of participation for five demographic groups, namely youths, prime-age men and women, and older men and women. They find that the generosity of the unemployment benefit system, the strictness of employment protection, and union density tend to be associated with lower participation rates of groups with presumably lower labour market attachment such as women and older workers. Although establishing causality is a delicate matter, this chapter provides an interesting description of recent participation trends in the EU. The chapter fits in the recent macroeconomic literature on ‘employment and institutions’, which regresses aggregate labour market indicators on a number of economic and institutional factors using country-level panel data. As there is typically insufficient within-country variation in the labour market institutions usually considered, this literature aims at estimating the impact of institutions on, say, unemployment or participation, by exploiting their cross-country variation. But given that both labour market outcomes and institutional variables are potentially correlated with country-specific, unobserved factors, one should ideally control for country-specific unobserved heterogeneity, which is only feasible when there is enough time variation in the institutional indicators used. The authors of this chapter are using a fairly rich data set, with sufficient time variation in measured labour market institutions, which allows them to identify the impact of institutions on participation after controlling for country fixed-effects. Although the chapter does not propose a unified theoretical framework for understanding the impact of institutions on participation, it provides well-focused discussions on how we should expect a number of factors to affect the labour demand and labour supply. Among these factors, some may affect labour supply directly, such as unemployment insurance, retirement schemes or the availability of parttime jobs. Others primarily affect labour demand, such as employment

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protection or union density, and through labour demand and equilibrium wages they affect the incentives for participation. And finally other factors directly affect both labour demand and labour supply (such as average wages and the tax wedge). Moreover, there are variables that are clearly endogenous with respect to participation, such as wages, fertility rates, part-time employment rates, the proportion of youths in education and so on. With these considerations in mind, it is not straightforward to identify a causal link between right-hand side variables and participation. For example, average real wages do not seem to have a significant impact on participation in any of the demographic groups considered. In principle, real wages stimulate labour supply and decrease labour demand. As equilibrium quantities and wages are used here, what is being estimated is a mix of demand and supply forces, which not surprisingly delivers a nonsignificant coefficient on wages. The estimated effect of fertility rates is positive in Scandinavia and the Netherlands and negative elsewhere. On the one hand it can be argued that fertility is not exogenous with respect to participation. On the other hand, such international differences are likely to emerge from the interaction between fertility rates and women-oriented institutions like parental leave and the availability of subsidized childcare, which are not considered here. Part-time employment rates have the expected correlations with female participation, although little can be said about causality on aggregate data. Similarly for the correlation between the proportion of youths in education and youth participation. It should also be noted that if the aggregate unemployment rate is closely correlated to one of the groups considered, the presence of the aggregate unemployment rate on the right-hand side of the regression equation implies that other regressors mainly pick an employment effect. This may explain why union density and the generosity of the unemployment benefit system seem to have a negative impact on participation, which presumably works through their effects on equilibrium employment levels. Finally, it would be good to have a richer discussion on the purposes to which one is to put the estimates presented, in the light of recent participation trends and policy objectives. One may think of a few unexplored issues here which can provide directions for future work. First, the decline in male participation remains largely unexplained, and additional factors such as the role of disability benefit systems may be investigated (see Nickell and Quintini, 2002, for the UK). Second, there are substantial international differences in female participation, from around 50 per cent in Spain, Italy and Greece to 70 per cent in the UK and 77 per cent in Sweden. It would thus be good to discuss evidence of convergence, if any, in female participation across European countries, vis-à-vis the Lisbon 2010

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target for female employment rates. Third, declining youth participation can be driven by very different factors, from higher enrolment into postcompulsory education to more problematic labour market entry upon completing education, with record youth unemployment and temporary employment rates in southern Europe. Again, an effort to identify relevant causes and trends would considerably enrich the analysis of the chapter. Reference Nickell, S. and G. Quintini (2002), ‘The recent performance of the UK labour market’, Oxford Review of Economic Policy, 18 (2), 202–20.

6.

Hiring incentives and labour force participation in Italy1 Piero Cipollone, Corrado Di Maria and Anita Guelfi2

INTRODUCTION A long-standing economic tradition maintains that labour supply is sensitive to the tightness of the labour market. Increases in labour demand are not fully and immediately reflected in declines in the number of unemployed because of the positive elasticity of supply to labour demand. This empirical regularity is in part a statistical artefact because not all job seekers are included in the unemployment pool as defined by international standards. According to the generally accepted definition of unemployment, based on the ILO (1982) Resolution, a person is unemployed if, being above a specified age,3 he or she has no occupation in the reference period, is available to start to work and has actively looked for a job during the four weeks preceding the reference period. The ambiguity comes from the fact that many people might be willing to work but are not searching according to the ILO definition. Jones and Riddell (1999) have found that in Canada this group – which they call the ‘marginally attached’ – represents between 25 and 35 per cent of the unemployed. People belonging to this pool have a probability of transiting into employment which is lower than that of the unemployed, but much higher than that of those who do not want to work. Thus they constitute an intermediate category between job seekers and those who are out of the labour force. Brandolini et al. (2003) found similar results for the European countries: the marginally attached (whom they call ‘potential workers’) represent an important share of the working-age population, ranging from 1.2 per cent (in Ireland) to 3.5 per cent (in Finland). Moreover they found that, in the case of Italy, people who search with less intensity than required to be classified as unemployed display the same behaviour as those who are classified as job seekers. However, this phenomenon also has strong theoretical underpinnings. Shocks to labour demand change the opportunity cost of home production 222

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through the channels illustrated by the traditional models of labour supply (Becker, 1965) or by the modern theory of search (Pissarides, 2000, Chapter 7). Traditional theory points to market wages are the link between labour demand and participation. Recent theoretical developments have concentrated on workers’ search effort and on their decision to move into and out of the labour force. Less attention has been devoted to the reaction of labour supply to changes in the quality composition of labour demand. In a market where firms can hire workers with both temporary and openended contracts, do more people enter the market when the chances of finding a permanent job get higher? In this chapter we investigate this relationship within a framework of policy evaluation. In particular we look at how labour supply tends to react to subsidies which reduce firms’ labour costs. To this end we examine a programme introduced in Italy at the end of the year 2000, which provided a large subsidy to firms hiring workers with open-ended contracts. This programme, called ‘Credito d’Imposta’, was started in October 2000 and originally granted the eligible firms a tax credit of about €400 per month for each worker, at least 25 years old, hired with an open-ended contract, from the hiring moment until the end of December 2003. There are several recent theoretical contributions which relate participation to subsidies. Orszag and Snower (2003) study the optimal type of employment subsidies in an economy with heterogeneous workers. The central point in their analysis is that the subsidy does increase the value to the labour market of participating either as unemployed or as an employee.4 In the present chapter we propose a simple model showing that a subsidy to open-ended contracts brings about a similar increase in the value of participation through a change in the composition of the pool of available jobs, even in the absence of an increase in the overall employment level. This result builds on the findings of a previous analysis showing that the Italian tax credit was successful in reducing the share of temporary jobs in favour of permanent contracts, without changing total labour demand (Cipollone and Guelfi, 2003). The question we ask in this chapter is whether more people entered the labour force in response to this larger availability of better jobs, even in the presence of the same number of jobs. To address this question we look at how the transitions from inactivity to participation were affected by the subsidy; we resort to the micro-data of the April waves of the Italian Labour Force Survey for the period 1995–2002, using a difference-in-differences framework. Our results suggest that the subsidy did have a non-trivial effect on the participation rate. The tax credit appears to have increased labour force participation of eligible inactive individuals by about 1.4 per cent in 2001 and 2.1 per cent in 2002,

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relative to non-eligible individuals. This increase was rather heterogeneous across people. It seems indeed to have been mostly concentrated on individuals (especially males) aged 35–54, with a low or at most a secondary schooling level. Such characteristics of new labour market entrants look quite surprising since they do not precisely match with the composition by age and schooling level of the group of people who contributed to most of the growth in the Italian labour supply in the years preceding the introduction of the tax credit. To shed some light on this result, we investigate whether those who appear as new participants are in reality people who were previously working in the underground economy and took advantage of the subsidy to become regularly declared workers. The chapter is organized as follows. A section focusing on the tax credit by describing both its regulatory aspects and its actual usage is followed by a section which presents a simple theoretical framework illustrating a potential mechanism through which policy measures can influence individuals’ choices to enter the labour market. The next section describes our data set and the empirical results obtained on a simple econometric framework to evaluate the impact of the tax credit on the propensity to join the labour force. We next test the robustness of our estimates with respect to the potential regularization of previously hidden, irregular workers and a final section concludes.

THE TAX CREDIT5 Regulation The seventh paragraph of the Italian Finance Law for the year 2001 (issued at the end of the year 2000) introduced a new hiring incentive in the form of a general, automatic and quite generous tax credit to all firms hiring workers with open-ended contracts. In particular, the provision stated that, starting from October 2000, every firm (literally ‘employer’) hiring a new worker on a permanent basis would be rewarded with a tax credit of about €413 per month and per worker, from the moment of hiring until the end of December 2003. For workers in the southern regions of the country this monthly amount rises to about €620. Thus, for a worker hired in October 2000 in southern Italy and retained until December 2003, each firm would receive about €24 200.6 Eligibility criteria look quite mild. A worker is eligible if she or he is at least 25 years of age, provided she or he did not hold a permanent position in the 24 months preceding her or his hiring. A firm is eligible if the newly

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hired worker raises the overall level of permanent employment – at the firm level – above the average recorded between October 1999 and September 2000. The tax credit can be claimed against any kind of taxes, such as income tax, social security contributions, or value added tax. Furthermore it can be passed on to different fiscal years and can be cumulated with other existing incentives. Finally, unlike previous similar measures, no other restrictions apply (for example, disadvantaged areas, firm size thresholds, specific sectors and so on). Magnitude The contribution provided by the Italian subsidy looks quite generous. Figure 6.1 shows the percentage reduction in per-capita labour costs due to the tax credit (using data for the year 2000) by sector and geographical area. This reduction is variable because the tax credit is a fixed amount that only increases for southern workers, while the average labour cost differs across both sectors and geographical areas. The evidence shows a labour cost reduction which ranges from 9.3 per cent in the banking sector in the central and northern regions to almost 60 per cent in the agricultural sector in the south. On average, in the private non-farm sector the reduction amounts to about 30 per cent in the south and 16 per cent in the central and northern regions. These estimates understate the effect of the tax credit because labour cost data refer to an average worker, while the correct reference should be the labour cost of a new young worker, which is usually below the average. It should be mentioned, though, that national accounts also include estimates of the labour cost in the underground economy, which is very likely smaller than the legal labour cost for a new entrant; however, this effect only attenuates underestimation. Usage The new tax credit seems to have been very successful in 2001 and even more so in 2002. We have two sources of information about the actual usage of this instrument. The first source is the Italian Labour Force Survey (LFS), which provides data on the number of newly hired employees, distinguishing between open-ended and fixed-term contracts. Figure 6.2 reports the quarterly absolute growth in total employees by type of contract with respect to the corresponding period of the previous year. It suggests that in January 20017 – when the first survey since the new tax credit came into force – the rising trend in the use of fixed-term contracts came to an end, while it had been the only source of payroll employment growth since 1993. In October 2001 the number of fixed-term contracts

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Figure 6.1

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was smaller than one year earlier. However, a slow-down had already occurred in the year 2000, although most of it was due to a strong labour demand which turned the labour market into a seller’s market, especially in the northern regions, thereby allowing workers to negotiate hires with open-ended contracts. In 2001 open-ended employment increased and fully compensated for the slow-down in fixed-term employment. This was the largest increase in permanent employment since 1993 and looks quite remarkable given the sharp slow-down in economic activity in 2001 (the growth in value added in the private sector fell from 4.2 to 2.5 per cent between 2000 and 2001). The resurgence of open-ended contracts also characterized the year 2002, when they represented about 86 per cent of the overall increase in dependent employment. Nevertheless, the graph also shows that starting from April 2002 the progressive worsening of labour market conditions was accompanied by a reappearance of fixedterm contracts. The second source of information is represented by the figures collected by the Ministry of Finance (and reported by the Ministry of Labour8), to assess the amount of revenues lost through the tax credit. Figure 6.3 shows forgone revenues as a share of total social security contributions and the corresponding number of workers involved in 2001 and in the first five months of 2002. Between January and December 2001 the monthly flow of forgone revenues increased from zero to more than 0.7 per cent of the monthly flow of social security contributions. It involved almost 221 000 workers in November 2001, that is, about 1.4 per cent of total employees. The numbers are even higher in 2002: in May 2002 monthly flows of forgone revenues reached about 1.1 per cent of social security contributions, involving about 273 000 workers (1.8 per cent of total employees). These figures suggest that the tax credit has been a great success, involving far more than the 83 000 workers initially foreseen for the entire subsidized period, that is, from October 2000 to December 2003.9

CHOOSING TO PARTICIPATE: A THEORETICAL FRAMEWORK In this section we develop a simple model of the participating behaviour of the population. We aim at analysing the mechanism through which policy measures that improve the quality of the job pool can influence people’s choice to enter the labour market. Cipollone and Guelfi (2003) have modelled the effects of the tax credit on labour demand, showing thereby that fixed-term and open-ended contracts do coexist and the share of permanent jobs increases with the

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M

ar

2 -0

A

2 -0 pr

2 -0 ay M

number of workers hired as % of employees

Own calculations on Ministero del Lavoro e delle Politiche Sociali (2002) and Istat.

Revenue figures are flows, worker figures are stocks.

Figure 6.3

Source:

Note:

01

p-

Se

forgone revenues as share of social contributions

0 1 1 1 01 01 01 01 01 -0 -0 -0 ygnbnul ar pr e a u u J Ja J F A A M M

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

230

Factors affecting labour force participation

size of the tax credit. Here we take this composition effect as given and evaluate how workers react to the improved perspectives of getting a permanent job. Pissarides (2000) has shown how market tightness affects labour supply. There are at least two channels at work. First of all, there exists a mechanical effect due to the fact that in a tighter labour market the value of unemployment increases and raises the participation rate for a given distribution of reservation utility. The additional channel impinges on the positive correlation between worker search effort and labour market tightness. Higher labour demand enhances search productivity and thereby reduces its costs. These two channels are still at work in our case even though we have a different underlying force. In our model the value of participation increases because of the shift in the composition of labour demand in favour of better jobs, rather than because of the greater tightness of the labour market. Our model is a simple extension of Pissarides’s (2000) basic setting to the case of two types of jobs. We describe the behaviour of a labour market in which transitions between being out of the labour force and employment are always mediated by an intermediate step into unemployment, in line with the standard setting of search models. More recently Garibaldi and Wasmer (2005) developed a general equilibrium model where workers are allowed to move freely across the three labour market states. An additional characteristic of our model is that participating people supply an inelastic amount of work. This implies that labour supply can only vary at the extensive margin – because of a larger number of participating people – rather than at the intensive margin, where more hours are supplied by the same number of workers. We begin by illustrating the first channel through which labour demand affects participation. Assume that a non-participating individual has a reservation utility Uo o /r where o is the stream of real returns from non-participation and r is the discount rate. Uo can be thought of as the value of home production. We assume that o is a draw from a distribution with cumulative density function F(o) defined over the support [ , d]. ¯ if Let U be the utility of unemployment. An individual will participate U Uo. The size of the labour supply is then F(rU) which increases with the value of unemployment. We need to show that this quantity increases as the probability of being hired with an open-ended job increases. As a first step we need to show that U rises when the share of permanent contracts increases. To this end we write the value functions for unemployment and employment. As in Pissarides (2000) the return of being unemployed for a given worker endowed with a given level of productivity is

Hiring incentives and labour force participation in Italy

231

the sum of the flow of income and the expected value from moving into employment rU  b  (V
U)

(6.1)

where b denotes unemployment income, V is the present discounted value of employment and represents the probability of finding a job. We assume that is exogenous, since we are not interested in the effect of changes in the level of labour demand but in the effect of changes in its composition.10 The value of being at work V is an average between working with a fixed-term contract and an open-ended one: V   oeV oe   ftV ft

(6.2)

where oe and  ft are the probabilities of being hired with an open-ended and a fixed-term contract, respectively. The flow of value accruing to job holders is the sum of the wage and the expected gain (or loss) from moving into unemployment rV i  w  i(U
V i )
with i  oe, ft

(6.3)

where i is the exogenous probability of being fired, which we assume to be larger for fixed-term jobs because of the firing costs associated with termination of open-ended contracts. Open-ended contracts are more valuable than fixed-term contracts as long as the labour market is ‘viable’,11 that is, as long as w b. The first step of our reasoning requires proving that the value of unemployment rises when the quality of jobs improves; that is, we need to show that U 0 oe

(6.4)

holding constant r, w, b, , oe and ft. Using (6.3) into (6.2) in order to find an expression for V as a function of U, and then inserting it into (6.1), one can find the following expression for U: U

b(r  oe)(r   ft )  w[oe(r   ft )   ft(r  oe)] (r  )(r  oe)(r   ft )
[ oeoe(r   ft )   ft  ft(r  oe)]

(6.5)

from which one can verify that condition (6.4) holds for all workers if the market is viable, that is, if w b. Given this result, it can immediately be recognized that labour market participation rate F(rU ) increases with oe

232

Factors affecting labour force participation

because, by definition, F(rU ) 0. The economic intuition is straightforward. An individual who is indifferent between participating and staying out of the labour force will enter the market when the value of participation increases above that of inactivity. The magnitude of the rise in participation for a given increase in oe depends on the initial level of labour supply and on the distribution function. To summarize, the tax credit has a positive effect on labour supply even if it does not change the level of labour demand, thereby proving that our intuition holds true in a more formalized setting. Next we show that in our setting the second channel mentioned above is also at work, that is, that the tax credit increases labour force participation by raising worker search effort. Again we extend Pissarides’s (2000) model of search intensity to the case of two types of contract. The basic system of value functions looks like equations (6.1) – (6.3) with two modifications. The flow of income accruing to unemployed workers has changed into a function (c,b) (with c(c,b)0, cc(c,b) 0, b(c,b) 0) which describes the net income for a worker with a gross unemployment income b and a search intensity c. In addition we assume that the probability of finding a job is no longer constant but depends on the search intensity:

(c), 0. Therefore we modify equation (6.1) as: rU  (c, b)  (c)(V
U ).

(6.1 bis)

Pissarides (2000) shows that the optimal level of search is such that: c(c, b) 

(c) (V
U)  0. c

(6.6)

We want to show that (coe)  0; therefore we fully differentiate (6.6) with respect to c and oe and obtain



cc 







d (c) (
1)

(c) (V
U )

(c) (V
U ) (V
U)  dc 
doe c c c c c oe (6.7)

where   (d(c) c) (c d(c)) is between 0 and 1.12 In this equation the coefficient of doe is negative and those of dc are all negative except the last one. Therefore we can conclude that the condition (coe)  0 is satisfied if the value of the capital gain (V
U ) does not increase too rapidly with search effort.

Hiring incentives and labour force participation in Italy

233

THE EFFECT OF THE TAX CREDIT ON LABOUR SUPPLY: DATA DESCRIPTION AND EMPIRICAL RESULTS Empirical Specification and Identification Strategies In order to evaluate whether the tax credit has indeed encouraged inactive people to enter the labour force, we adopt a simple econometric framework. In particular we address two questions here, namely whether the introduction of the subsidy has raised the probability of entering the labour market for an average, but eligible, inactive person, and whether this increase has been homogeneous across individuals with different personal characteristics. To this end we estimate a probit model, where the probability of entering the labour force depends on a set of dummies which take value one if the person is eligible for the subsidy (there are two such variables, one for the year 2001 and one for the year 2002), some demographic characteristics including age and education (measured by the years of schooling), and year dummies. The two eligibility dummies are also interacted with the education level. Thus our empirical framework looks as follows: Pr(Entering the labour force)  (xit ) xit  0  1eligible 2001i  2eligible 2002i  3eligible 2001i * (educit
mean(educ))  4eligible 2002i * (educit
mean(educ))  g(educit, ageit)  other demographic characteristicsit  year dummies. (6.8) In this specification the marginal effects associated with the coefficients 1 and 2 provide an answer to the question whether the introduction of the tax credit has raised the probability of participating in the labour market in 2001 and 2002, respectively. The coefficients 3 and 4 measure the differential effect of a given level of education (different from the mean) on the probability that an inactive eligible person enters the labour force. Furthermore, the g(·) function is specified as a cubic in age, a quadratic in schooling and includes the interaction between the linear terms of these two variables. Finally other demographic characteristics include gender, marital status, regional dummies and the inactivity status ‘12 months before the interview’. A similar setting was adopted in Cipollone and Guelfi (2003) to evaluate the effect of the tax credit only on the composition of labour demand (not on the level) by type of contract (fixed-term and open-ended). In that case the conditioning population referred to newly hired workers, regardless of their previous labour market status. Here we address a completely different

234

Factors affecting labour force participation

issue. Our problem is to evaluate whether labour supply reacted to the improved quality of available jobs. Thus we look at the changes in the transition rate from out of the labour force to participation due to the tax credit, ignoring all moves from unemployment to employment. We carry out our estimation through a difference-in-differences strategy, which identifies the effect of the subsidy as the change occurred in 2001 and 2002 (that is, after the tax credit was introduced) with respect to a reference year in the difference between the share of labour force entrants eligible for the tax credit (the ‘eligible’ group) and the corresponding measure for those excluded from this provision (the ‘control’ group). According to the law that regulates the subsidy, all individuals older than 24 in either 2001 or 2002 are potentially eligible for the tax credit provided they did not hold a permanent position in the 24 months preceding their hiring. The pool of potentially eligible workers includes age cohorts with very different demographic and economic characteristics, and therefore cannot be easily compared with a control group of young individuals (up to 24 years of age). We did not have any particular way of identifying the maximum age of the eligible group that would make the comparison reasonable. For that reason we adopted a modular strategy which entails comparing two groups very close in age and then allowing for more heterogeneity by adding older individuals to the eligible group. Thus we started by restricting the analysis to around the eligibility threshold by focusing on the subsidy effects on individuals aged 25–26 (the ‘eligible’ group) compared with those aged 20–24 (the ‘control’ group). Keeping constant the control group13 we then added to the eligible group those aged 27 and re-estimated the usual equation. We repeated this procedure adding each time one year of age until the whole cohort of eligible workers (that is, those older than 24) was covered. This strategy embeds an implicit robustness check of our results since it secures them against the choice of a particular eligible group. Data Description We estimate our model using the Italian Labour Force Survey micro-data from 1995 to 2002. The sample includes all individuals present in the survey at the time of the interview who in the previous period were inactive for reasons other than disability or mandatory military service. The definition of labour market status (out of the labour force, unemployed, employed) adopted here is that provided by the Italian Statistical Institute (ISTAT), which fully reflects the ILO definitions. Unfortunately the Italian LFS questions interviewees about their previous labour market status only on a yearly basis, namely in the April wave, and restricts the enquiry to the status occupied in the same month of the

Hiring incentives and labour force participation in Italy

235

preceding year. For that reason our sample includes all respondents interviewed in the April wave surveys conducted between 1995 and 2002 who, in the same month of the year preceding the interview, were inactive (for reasons other than disability or mandatory military service). We exclude both agricultural workers (because of the specificities characterizing this sector) and public employees (who are not eligible for the subsidy). We are interested in evaluating how many of these individuals left inactivity because of the stimulus provided by the subsidy. These selection rules posit a preliminary question on the treatment of those who left inactivity but entered the labour market as self-employed. They were included in the sample but treated as if they were still out of the labour force. The reason behind this choice is that the decision of entering as self-employed should not be affected by the encouraging effect of the subsidy we are analysing. Tables 6.1a and b describe the basic characteristics of our sample in some detail. Our sample is representative of a large share of the Italian population: from about 15.5 million in 1995 to 14.9 in 2002. Among these individuals the number of those entering the labour force (excluding self-employed workers) ranges from a minimum of about 1.04 million individuals in 1995 to a maximum of about 1.26 in 2002 (Table 6.1b). The transition rate into the labour force ranges from 8 per cent in 1995 up to 10.8 per cent in 2002. In 2002, the majority of individuals entering the labour force were women (about 63 per cent) in the middle age bracket (56 per cent were between 25 and 44 years of age, 25.7 per cent younger than 25), with at least a high-school diploma (55 per cent), and who before entering the market were mainly housewives (40 per cent) or students (34.1 per cent). This composition changed significantly over the considered time-span. In 1995 the women’s share was about 70.2 per cent, entrants younger than 35 amounted to about 70 per cent (of which more than 43 per cent were between 15 and 24 years of age), highschool diploma holders were less than 50 per cent, while around 87 per cent were originally either housewives (44.3 per cent) or students (42.6 per cent). Most of these changes followed Italian demographic developments, such as the higher life expectancy of the population and the progressive improvement in educational attainment. Other changes however seem to coincide with the adoption of the subsidy, since they took place in 2001 and especially in 2002. For example, the large increase in the male share of new labour force entrants in 2001 and 2002 (5 and 8 percentage points with respect to the year 2000; Table 6.1b) is not matched by an analogous increase in their share of the reference population (1.4 and 1.6 percentage points with respect to the year 2000; Table 6.1a). Such a strong composition effect was not observed before. A similar striking change can be found by looking at the age composition: individuals older than 34 increased their share among the new entrants by about 9 percentage points between April 2000 and April 2002, while their

236

Factors affecting labour force participation

Table 6.1a Basic sample characteristics of the whole sample (percentage points) 1995

1996

1997

1998

1999

2000

2001

2002

By labour market status Employee Self-employed Persons looking for a job Inactive

2.2 1.4 4.5

2.5 1.6 4.4

2.8 1.7 4.5

2.7 1.9 4.7

3.0 1.8 4.4

2.8 1.3 4.7

3.5 2.1 3.8

4.5 2.3 3.9

91.9

91.5

91.0

90.6

90.8

91.2

90.5

89.2

By gender Male Female

28.4 71.7

28.9 71.1

29.1 70.9

29.5 70.5

29.9 70.1

30.1 69.9

31.5 68.5

31.6 68.4

By age 15–24 25–34 35–44 45–54 55 and over

31.9 14.1 11.3 15.1 27.7

30.8 14.2 11.2 15.5 28.3

30.4 14.4 11.6 15.4 28.2

29.7 14.6 11.5 15.5 28.8

29.3 14.7 11.6 15.2 29.3

28.7 14.9 11.6 15.3 29.6

28.3 14.6 12.0 14.9 30.2

28.4 14.4 12.4 14.9 29.8

By area North Centre South

47.4 39.4 13.1

47.5 39.6 12.9

47.3 39.5 13.2

47.2 39.4 13.4

47.6 39.4 13.0

47.1 39.8 13.1

47.1 39.9 13.0

46.2 39.9 13.9

By education Less than high school High school diploma College degree

75.0 22.8 2.2

74.1 23.5 2.5

72.9 24.5 2.6

71.5 25.7 2.8

70.9 26.0 3.1

70.3 26.6 3.2

69.7 26.8 3.5

68.6 27.6 3.8

By inactivity status 12 months before entering labour force Future activity to start 0.7 Housewife 45.4 Student 32.9 Retired 20.0 Others 1.1

0.7 44.8 32.3 20.9 1.3

0.7 44.8 32.2 20.9 1.4

0.7 44.5 32.0 21.8 1.0

0.7 43.9 31.9 22.0 1.5

0.9 43.2 31.8 21.7 2.4

0.6 43.2 31.4 22.3 2.4

0.7 42.8 31.0 22.9 2.6

Source: Own computations on Istat data.

share in the reference population stayed almost constant. Similarly it is quite surprising that labour force entry of housewives and retired workers rose considerably in the analysed period (2 and 9 percentage points, respectively), given that their weight in the population as a whole changed only marginally. Overall, this preliminary overview of our data suggests that some important changes actually occurred in the transition pattern between inactivity

237

29.8 70.2

43.2 29.6 16.4 7.6 3.2

47.0 41.0 12.0

50.2 41.6 8.3

By age 15–24 25–34 35–44 45–54 55 and over

By area North Centre South

By education Less than high school High school diploma College degree

1995

46.4 43.9 9.7

48.4 39.8 11.8

38.4 31.3 17.1 9.7 3.5

29.5 70.5

1996

47.6 43.0 9.4

50.5 37.3 12.2

36.3 30.7 19.2 10.7 3.1

28.9 71.1

1997

45.1 45.3 9.6

47.4 39.9 12.7

34.5 33.0 18.7 10.7 3.0

30.6 69.4

1998

43.7 45.7 10.6

52.1 36.8 11.1

33.6 33.2 19.8 9.9 3.6

30.2 69.8

1999

43.8 46.0 10.2

47.7 40.3 12.0

34.1 34.0 18.5 10.1 3.4

28.6 71.4

2000

43.4 45.8 10.8

49.5 38.5 12.0

29.5 33.3 22.5 11.5 3.2

33.5 66.5

2001

Basic sample characteristics of labour force entrants (excluding self-employed) (percentage points)

By gender Male Female

Table 6.1b

44.9 43.0 12.1

47.1 35.2 17.7

25.7 33.2 22.8 14.1 4.2

36.7 63.3

2002

238

(continued)

5.8 44.3 42.6 5.7 1.6

1995

Source:

Note:

Own computations on Istat data.

1Levels.

All labour force entrants1 1 042 240 (without self-employed) All inactive1 14 281 256 Whole sample people1 15 546 233 Whole sample observations1 55 434

By inactivity status 12 months before entering labour force Future activity to start Housewife Student Retired Other

Table 6.1b

1 126 066 14 039 769 15 423 872 54 589

14 215 343 15 536 241 55 109

4.9 44.4 38.3 8.6 3.8

1997

1 067 673

5.6 42.1 41.8 7.9 2.6

1996

13 920 539 15 362 962 54 486

1 144 433

6.1 41.5 40.8 9.9 1.8

1998

13 711 395 15 098 962 53 399

1 123 106

6.0 40.6 40.1 11.1 2.2

1999

13 615 035 14 931 626 53 058

1 123 315

7.8 38.1 43.4 2.8 7.9

2000

13 504 800 14 915 194 51 813

1 100 161

5.2 41.4 39.4 6.3 7.8

2001

13 320 997 14 926 567 50 789

1 255 737

5.8 40.0 34.1 11.7 8.3

2002

239

Hiring incentives and labour force participation in Italy

and activity in the labour market. The direction of these changes looks compatible with the possible effect of the subsidy. Results Estimation results on the effect of the tax credit in both 2001 and 2002 are reported in Table 6.2 and in Figure 6.4 (panel (a) describes the average effect, while panel (b) shows the interaction with education). Table entries represent marginal effects (M.E.) and their associated t-statistics (t-stat); columns 2 and 3 refer to the variable indicating whether workers were eligible for the subsidy in 2002; columns 4 and 5 report the interaction of the eligibility status with the schooling level; columns 6 and 7 refer to the eligibility for the tax credit in 2001 and columns 8 and 9 its interaction with schooling. Along the rows one can read the effect of the tax credit for several age groups. The numbers in the first row refer to the 20–26 age group Table 6.2 Probit estimates of the tax credit effect on the probability of entering the labour force a Eligible groups (1)

25–26 25–27 25–28 25–29 25–30 25–31 25–32 25–33 25–34 25–35 25–36 25–37 25–38 25–39 25–40 25–41 25–42 25–43

Eligible for tax credit in 2002

Eligible for tax credit in 2001

Average effect: 2

Average effect: 1

Interaction with schoolingb: 4

Interaction with schoolingb: 3

M.E. (2)

t-stat (3)

M.E. (4)

t-stat (5)

M.E. (6)

t-stat (7)

M.E. (8)

t-stat (9)

0.010 0.007 0.010 0.014 0.019 0.026 0.030 0.032 0.033 0.037 0.036 0.035 0.038 0.039 0.039 0.039 0.040 0.041

0.88 0.69 1.03 1.61 2.27 3.03 3.67 3.93 4.13 4.65 4.64 4.58 5.02 5.22 5.35 5.37 5.55 5.84


0.004
0.007
0.008
0.007
0.006
0.006
0.006
0.005
0.005
0.004
0.004
0.003
0.003
0.003
0.003
0.002
0.002
0.002


1.21
2.62
3.66
3.68
3.77
3.72
3.83
3.67
3.78
3.33
3.02
2.52
2.52
2.44
2.47
2.11
2.14
2.46

0.014 0.010 0.008 0.007 0.011 0.016 0.019 0.021 0.025 0.028 0.031 0.033 0.034 0.036 0.036 0.036 0.037 0.036

1.22 0.99 0.90 0.80 1.35 1.88 2.33 2.68 3.11 3.56 4.00 4.40 4.56 4.95 4.97 5.03 5.23 5.15

0.000 0.000
0.001
0.001 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.06
0.07
0.46
0.36
0.18
0.21 0.47 0.19
0.11
0.01
0.11 0.09 0.26
0.09 0.14 0.14 0.12 0.23

240

Table 6.2 Eligible groups (1)

25–44 25–45 25–46 25–47 25–48 25–49 25–50 25–51 25–52 25–53 25–54 25–55 25–56 25–57 25–58 25–59 25–60 25–61 25–62 25–63 25–64

Factors affecting labour force participation

(continued) Eligible for tax credit in 2002

Eligible for tax credit in 2001

Average effect: 2

Average effect: 1

Interaction with schoolingb: 4

M.E. (2)

t-stat (3)

M.E. (4)

t-stat (5)

M.E. (6)

t-stat (7)

0.042 0.042 0.044 0.045 0.045 0.045 0.046 0.045 0.045 0.045 0.043 0.041 0.039 0.037 0.034 0.032 0.030 0.028 0.026 0.024 0.021

6.02 6.06 6.42 6.70 6.83 7.00 7.25 7.28 7.49 7.60 7.62 7.53 7.56 7.43 7.36 7.31 7.31 7.39 7.40 7.30 7.14


0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000


2.46
2.20
2.26
2.13
2.25
2.14
2.47
2.13
1.99
2.32
2.02
1.96
1.69
1.55
1.48
1.52
1.23
1.43
1.45
1.40
1.39

0.037 0.036 0.037 0.037 0.037 0.038 0.036 0.035 0.035 0.033 0.032 0.030 0.028 0.027 0.025 0.023 0.022 0.020 0.018 0.016 0.014

5.40 5.29 5.44 5.53 5.70 5.89 5.76 5.77 5.94 5.86 5.88 5.75 5.60 5.58 5.55 5.51 5.47 5.40 5.25 5.15 4.96

Interaction with schoolingb: 3 M.E. (8) 0.000 0.001 0.001 0.001 0.001 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

t-stat (9) 0.36 0.68 0.82 0.79 0.63 0.56 0.58 0.76 0.51 0.60 0.58 0.67 0.71 0.64 0.47 0.34 0.19 0.30 0.37 0.39 0.49

Notes: a. Probit estimation of model (6.8) in the text. The dependent variable is a dummy that takes value one if the person i at time t is in the labour force. The sample ranges from 1995 to 2002 and includes all persons that at time t
1 were out of the labour force. The entries along the columns for a given row are marginal effects (M.E.) and t-statistics (t-stat) of the dummies that take value one if the individual is in the eligible group in the year 2002 (columns 2 and 3) or 2001 (columns 6 and 7) and their interaction with the level of schooling scaled by the mean (columns 4 and 5 for 2002, and 8 and 9 for 2001). These marginal effects are estimated in a separated regression for each of the age groups listed in the rows (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force). The model includes other variables not shown in the table (but shown in Table 6.3): a cubic in age, a quadratic in the years of education, the interaction between education and age, dummies for years, gender, marital status, regions, condition one year before the interview (such as student, housewife, retired). b. Scaled by the mean education for the eligible group. Source: Own calculations on Istat data.

Hiring incentives and labour force participation in Italy

241

(with the group 20–24 acting as a control and the group 25–26 acting as eligible). The second row refers to the 20–27 age group, and so on. Figure 6.4 contrasts these numbers for the years 2001 and 2002: panel (a) reports marginal effects associated with the eligibility dummies; panel (b) reports their interaction with the years of schooling. When we look at the marginal effect for individuals just above the cut-off point for eligibility (the row focusing on the eligible age group 25–26) we do not find any effect of the tax credit on labour market participation. Being eligible for the subsidy increased the probability of moving in the labour force by about one percentage point in 2002 and by 1.4 percentage points in 2001; the small t-stats suggest that neither of these effects is bound away from zero. When we include individuals in their late twenties, we still fail to detect any particular effect of the eligibility status on the transition probabilities. Indeed the average effect for both 2001 and 2002 appears to be zero for the youngest eligible workers. In particular, compared with the years before 2000, no encouragement effect seems to emerge for individuals up to around 29 years of age. However the lack of an average effect hides some heterogeneity among young workers. In particular in the year 2002 the marginal effect for the interaction between eligibility status and years of schooling is negative and statistically significant (starting from the eligible group 25–27), suggesting that only less educated individuals were affected by the subsidy. No sign of heterogeneity emerges in 2001. One question that might arise in this type of comparison is whether the subsidy induced firms to substitute non-eligible individuals (just below 25) for eligible ones (just above 24). If this were the case, the true net effect of the programme on the participation rate would be smaller than that implied by the size of the coefficient 1. We do not have a formal test to address this issue, but the small and statistically insignificant marginal effect for the group just above the critical threshold casts serious doubts on the presence of such a substitution process. As we progressively widen the eligible group, average effects appear to grow stronger both in magnitude and statistical significance. Panel (a) of Figure 6.4 indeed shows that, compared with the reference years, in both 2001 and 2002 labour force entry is higher the older the new participant, at least up to age 50. These results look quite stable over large age groups and the changes observed across age follow a rather smooth pattern after age 30. This regularity holds for the average effect (panel (a) of Figure 6.4) and for the additional effect of schooling (panel b) and allows us, for the sake of simplicity, to concentrate our comments on a few representative groups, without much loss of generality. In particular the columns of Table 6.3 report the results of the regressions referred to eight main age classes that we selected as representative: up to 29 years of age, up to 35, up

242

26

28

30

32

34

(a) Average effect on participation


0.025


0.015


0.005

0.005

0.015

0.025

0.035

0.045

0.055

0.065

36

38

40

42

44

46

48

50

52

Average effect 2002

54

56

58

60

Average effect 2001

62

64

243

26

28

30

32

34

36

38

40

42

44

46

48

Interaction with education 2002

50

52

54

56

Probability of entering the labour force

Table 6.2.

58

60

62

Interaction with education 2001

The lines over the bars represent confidence intervals defined as plus and minus two times the standart error.

Figure 6.4

Source:

Note:

(b) Interaction with education


0.012


0.010


0.008


0.006


0.004


0.002

0.000

0.002

0.004

0.006

64

244

Factors affecting labour force participation

Table 6.3 Probit estimates of the probability of entering the labour force for some specific age groups: all covariatesa Up to 29 M.E. t-stat Eligible in 2002 Average effect ( 2) Interaction with educationb ( 4) Eligible in 2001 Average effect ( 1) Interaction with educationb ( 3) Age Linear term Quadratic term Cubic term Education Linear term Quadratic term Interaction with age Female Male

Up to 35 M.E. t-stat

Up to 40 M.E. t-stat

0.014
0.007

1.61 0.037
3.68
0.004

4.65 0.039
3.33
0.003

0.007
0.001

0.80
0.36

3.56
0.01


0.992 0.039
0.001
0.039 0.001 0.001

0.036 0.000

4.97 0.14


7.18
0.263 6.86 0.010
6.65 0.000


7.65
0.072 7.59 0.003
7.59 0.000


4.58 4.85
5.1


9.06
0.012 14.47 0.001 6.93 0.000


4.08
0.004 15.52 0.001 1.71 0.000


1.73 15.22
0.86

0.028 9.21 Reference

0.028 0.000

5.35
2.47

0.018 6.11 Reference

0.010 3.66 Reference

Future activity Housewife Student Retired Other

Reference
0.173
2.2
0.439
32.3
0.117
14.4
0.090
8.81

Reference
0.233
28.7
0.347
38.1
0.108
15.3
0.089
1.9

Reference
0.271
34.4
0.303
42.2
0.107
17.8
0.092
13.3

1995 1996 1997 1998 1999 2000 2001 2002 Memorandum Mean education 01 Mean education 02 Number of observations


0.005
1
0.010
1.88
0.010
1.92
0.004
0.77
0.001
0.19 Reference
0.024
3.74
0.016
2.48


0.005
1.21
0.009
1.91
0.006
1.39
0.002
0.49
0.002
0.48 Reference
0.027
4.34
0.019
3.03


0.006
1.52
0.008
1.86
0.003
0.78 0.000
0.12 0.001 0.16 Reference
0.027
4.52
0.019
3.17

11.08 11.51 79 947

10.38 10.63 110 869

10.02 10.21 136 062

Notes: a. Probit estimation of model (6.8) in the text. The dependent variable is a dummy that takes value one if the person i at time t is in the labour force. The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force. The entries along the columns are marginal effects (M.E.) and t-statistics (t-stat) associated with indicated variables. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control

245

Hiring incentives and labour force participation in Italy

Up to 45 M.E. t-stat 0.042
0.002

Up to 50 M.E. t-stat

6.06 0.046
2.2
0.002

0.036 0.001

5.29 0.68

Up to 55 M.E. t-stat

7.25 0.041
2.47
0.001

0.036 0.000

5.76 0.58

Up to 60 M.E. t-stat

7.53 0.030
1.96
0.001

0.030 0.000

5.75 0.67

Up to 64 M.E. t-stat

7.31
1.23

0.021 0.000

7.14
1.39

0.022 0.000

5.47 0.19

0.014 0.000

4.96 0.49


0.029 0.001 0.000


3.44
0.019 4.19 0.001
4.93 0.000


3.77
0.014 5.39 0.001
7.11 0.000


4.35
0.003 7.05 0.000
1.2 0.000


1.38 4.8
9.24

0.002 0.000 0.000

1.64 2
7.16


0.001 0.001 0.000


0.6 14.64
2.07

0.67 14.79
4.19

0.95 15.48
5.28

1.35 15.67
6.19

0.001 0.000 0.000

1.58 15.65
6.65

0.001 0.001 0.000

0.001 0.001 0.000

0.001 0.000 0.000

0.001 0.32
0.009
3.55
0.011
5.55
0.010
6.85
0.008
7.75 Reference Reference Reference Reference Reference Reference
0.295
38.9
0.269
45
0.110
22.3
0.093
15.4

Reference
0.304
42.1
0.230
47.4
0.111
29.2
0.088
17.5

Reference
0.283
44.8
0.178
49.4
0.106
36.3
0.075
19.6

Reference
0.221
46.4
0.117
5.4
0.095
4.4
0.054
21.1

Reference
0.156
46.8
0.074
5.8
0.081
42
0.037
21.9


0.009
2.53
0.009
2.51
0.004
1.17
0.001
0.23 0.001 0.34 Reference
0.027
4.76
0.019
3.35


0.011
3.43
0.009
2.88
0.004
1.24 0.000 0.13 0.001 0.39 Reference
0.024
4.74
0.017
3.26


0.009
3.62
0.007
2.74
0.001
0.44 0.001 0.36 0.001 0.5 Reference
0.019
4.52
0.013
2.97


0.007
3.51
0.005
2.63
0.001
0.57 0.001 0.5 0.002 0.87 Reference
0.014
4.28
0.009
2.69


0.004
3.27
0.003
2.21 0.000
0.26 0.001 0.56 0.001 0.99 Reference
0.009
4.01
0.006
2.41

9.68 9.86 161 077

9.32 9.48 190 945

8.84 9.00 231 368

8.37 8.57 289 736

7.95 8.15 349 886

group which consists of individuals aged 20–24 who, at time t
1, were out of the labour force). The last three rows report average years of education of the eligible group in both 2001 and 2002, as well as the size of the sample used. b. Scaled by the mean education for each eligible group. Source: Own calculations on Istat data.

246

Factors affecting labour force participation

to 40 and so on, until the whole set of eligible individuals is included in the eligible group. Along with the average effect and its interaction with schooling for the years 2001 and 2002, the rows of the table report the marginal effects of all variables included in the regression. Our estimates indicate that in the period 2001–02 participation improved significantly for all individuals older than 29, though with relevant differences in magnitude. Indeed, labour force participation rose by about 2.8 percentage points in 2001 and a further 0.9 points in 2002 for eligible individuals up to 35 years of age, while the highest increases emerge when individuals up to 50 years of age are included in the eligible group. In this case participation rose by about 3.6 percentage points in 2001 and an additional 1.0 point in the following year. For the successive age groups, instead, labour force entry declines, though very slowly, remaining anyway positive and statistically significant even when the oldest cohort (that is, individuals aged up to 64) is included in the eligible group. As far as education is concerned, labour force entry was rather homogeneous across different schooling levels in 2001 in all age groups. Conversely, in 2002 changes in participation were higher among the less-educated. As panel (b) of Figure 6.4 shows (but it also emerges from the analysis of Table 6.2), this was particularly true when individuals aged between 30 and 36 were included in the eligible group. On the other hand, as labour force entrants grow older, this negative effect progressively fades away. Overall, this evidence suggests that most of the action took place among adult and older individuals. In particular it seems that the most strongly affected individuals were those aged 45–54. These results appear quite surprising since one would expect individuals to be making decisions on labour market participation in their early stages of life. The fact that only older workers seem to have been affected by the subsidy might therefore be hiding some confounding factor. In particular, one possible interpretation could be that what we are actually estimating is a decision to participate in the official labour market rather than in the black economy. In other words, the idea to be explored is that firms previously employing workers in the underground economy chose to take advantage of the subsidy to move these labour contracts ‘above the table’. We devote the next section to exploring this possibility. For the time being we just observe that the participation boost was stronger for adult and older male individuals who, before entering the labour force, were either retired or included in the residual ‘others’ category. The vagueness of this response is at least compatible with the idea that these individuals wanted to be rather ambiguous about their activity before entering the market. An additional reason behind the observed participation pattern could lie in the changes made in the legislation, which extended the possibility to

247

Hiring incentives and labour force participation in Italy

receive a pension while working. In the last ten years, the Italian pension system has undergone several modifications. New rules differentiate between the type of pension (old-age versus seniority pensions), the type of employment (employee or self-employed) and the year of retirement. In Table 6.4 we provide an overall view of the general regulation14 along with the eligibility criteria for both old-age and seniority retirement. From 1994 up to 2002 a retired person who entered employment as an employee would have to pay a tax rate of 100 per cent on the pension income that exceeds a minimum.15 This rate was reduced to 50 per cent in 1999 and then to zero from 2001 for workers in payroll employment and those retired under the seniority pension scheme with at least 40 years of contributions. Table 6.4 Retirement rules in Italy: eligibility criteria for retirement and tax rate applied to pension income of retired workers with a new job 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Seniority pensions (i) Eligibility rules for retirement (years) Private sector employees Age Contributions Only contributions Public sector employees Age Contributions Only contributions Self-employed Age Contributions Only contributions

52 35

54

55 35

56

36

52

57

37

53

54

55

56

35 35

36

37

56

57

58 35 40

35

(ii) Share of pension (above the minimum) withdrawn if workinga b, c As employee 100% 100% 100% 100% 100%/ 50%f 100%/ 0%f c As self-employed 0% 50% 100%d/ 50% 50% 30%/0%f 50%e Old-age pensions (i) Eligibility rules for retirement (years) Old-age retirement Age male 61 61/62 62 Age female 56 56/57 57 Contributions 16 17

63 58

63/64 58/59 18

64 59

65 60 19

20

0%g 0%g

248

Factors affecting labour force participation

Table 6.4 (continued) 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 (ii) Share of pension (above the minimum) withdrawn if workinga As employee As self-employed

0%

50% 50%

0%h 0%h

Notes: a. Numbers indicate the tax rate to be applied to the pension income, net of the minimum, if retired workers go back to work; in symbols let tx be the tax rate reported in the table, p be the pension income, mp the minimum pension and w the labour income: the take home (th) income is th  p w
( p
mp)*tx if w ( p
mp)*tx, th p if w  ( p
mp)*tx. These rules apply to individuals who become eligible for pensions in the indicated period. b. Until 30 September 1996. c. Workers retired in this period and already eligible for seniority pensions by 31 December 1994 follow the rules in force at 31 December 1994. d. It applies to the pension of individuals who worked as employees. e. It applies to the pension of individuals who previously worked as self-employed. f. For individuals who retired after working 40 years. g. It applies to individuals 58 years old with 37 years of contributions. h. Regardless of the requirements fulfilled to gain the eligibility for old-age pensions. Source: Information collected from Ministero del Lavore e delle Politiche Sociali (2003).

Further changes were made for retired workers who went back to work as self-employed. Before 1995 the tax rate on their pensions was 0 per cent. It was changed to 50 per cent in the years 1995–2000, and to 30 per cent in the period 2001–2002 (to zero for retired workers with 40 years of seniority). The most important change for workers retired with seniority schemes occurred in 2003, when the tax on pensions was abolished provided that workers had retired at age 58 or more with at least 37 years of contributions. Workers retired under old-age retirement rules bore a tax rate of 50 per cent for most of the 1994–2000 period. Thereafter the tax was eliminated. These frequent rule modifications do not seem to be driving our results as they mostly involved individuals aged at least 55 (from 1995) or with at least 40 years of seniority. For these individuals we do not detect any supply effect as most of the action seems to have been concentrated among individuals in their late thirties and forties. Some Robustness Checks This section is devoted to checking the robustness of our results. We chose to run two different types of tests. On the one hand we want to be sure that our results do not depend on the particular functional form we used to carry

Hiring incentives and labour force participation in Italy

249

out our estimation, and on the other, in order to attribute the registered increase in labour force participation to the newly issued tax credit, we need to exclude the possibility that an increasing trend was already in the data before the year 2001, that is before the subsidy was operating. Check on the functional form Do our results depend on the way we specified the functional form of our controls? In order to check the robustness of the evidence of Table 6.3 we estimated the models for the same age groups after substituting the (three) age and the (two) schooling variables with a full set of dummies for age (one dummy for each age) and for education (three dummies: up to eight years of schooling, high school diploma, college degree) and the interaction between these two sets of dummies. Results are presented in Table 6.5. Each pair of columns represents marginal effects and t-statistics that refer to the eligible group recorded in the heading. The usual eight age groups are reported on. We reported for 2001 and 2002 the effect of the eligibility status for the least educated among participants and the additional effect for those holding a high school diploma or a university degree. We did not report the effect of the age dummies and their interaction with the three schooling dummies. Overall the evidence confirms that the impact of the tax credit on the transition probabilities was small or null for young workers and stronger for older individuals. The table clarifies that the impact of the tax credit rises with age because the negative impact of education fades away with age. For example in 2002 the impact of the tax credit on the transition probabilities of the least educated was positive for all age groups and higher for those up to 29 years old (5.4 percentage points), and declined with age. However, the additional effect for those holding a high school diploma or a college degree was strongly negative among the youngest and declined toward zero with age. In the aggregate this second effect dominates the first and generates the rising pattern shown by Figure 6.4. Thus, this analysis confirms our previous finding that the tax credit appears to have mostly encouraged the participation of individuals belonging to the central and older age cohorts. In particular it confirms that individuals aged 45–54 experienced a significant rise in labour market participation. Check on the identification strategies The second check we ran is devoted to evaluating whether the positive trend in labour force participation for older workers was in the data even before the year 2001, that is, before the subsidy was operating. If this were the case, then we could not attribute the observed increase in labour supply to government intervention. To run such a test we included a dummy for all individuals at

250

Factors affecting labour force participation

Table 6.5 Probit estimates of the probability of entering the labour force: non-parametric specificationa Up to 29 M.E. t-stat Eligible in 2002 Effect for individuals with less than high school Additional effect for individuals with: High school College Eligible in 2001 Effect for individuals with less than high school Additional effect for individuals with: High school College Less than high school High school College Male Female 1995 1996 1997 1998 1999 2000 2001 2002

0.054

3.66

Up to 35 M.E. t-stat 0.050

4.92

Up to 40 M.E. t-stat 0.042

4.9


0.042
3.2
0.027
3
0.020
2.6
0.060
3.7
0.049
3.8
0.039
3.4 0.001

0.07

0.022

2.24

0.030

3.59

0.009 0.64 0.002 0.22
0.004
0.4
0.010
0.5
0.024
1.7
0.026
2.1 Reference Reference Reference 0.088 11.3 0.085 1.4 0.083 1.1
0.204
24
0.211
35
0.203
35 Reference 0.028 9.14
0.006
1.3
0.011
2.1
0.010
1.9
0.004
0.9
0.001
0.2 Reference
0.023
3.6
0.015
2.2

Reference 0.018 6.29
0.006
1.4
0.009
2
0.006
1.3
0.002
0.4
0.002
0.5 Reference
0.022
3.6
0.014
2.2

Reference 0.011 3.92
0.007
1.7
0.008
2.01
0.003
0.7 0.000
0.1 0.001 0.2 Reference
0.021
3.5
0.013
2

Note: a. Probit estimation of model (6.8) in the text, with a non-parametric specification for the effect of age and schooling. The dependent variable is a dummy that takes value one if the person i at time t is in the labour force. The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force. Entries along the columns are marginal effects (M.E.) and t-statistics (t-stats) associated with indicated variables. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force).

251

Hiring incentives and labour force participation in Italy

Up to 45 M.E. t-stat 0.043

5.53

Up to 50 M.E. t-stat 0.047

6.7

Up to 55 M.E. t-stat 0.040

6.74

Up to 60 M.E. t-stat 0.029

6.51

Up to 64 M.E. t-stat 0.021

6.48


0.019
2.8
0.018
3.2
0.012
2.6
0.007
1.9
0.005
1.9
0.035
3.3
0.031
3.2
0.023
2.9
0.015
2.6
0.010
2.6 0.029

3.73

0.030

4.41

0.025

4.36

0.019

4.37

0.012

4.07

0.001 0.21
0.001
0.2 0.002 0.44 0.001 0.23 0.001 0.53
0.025
2.2
0.022
2.2
0.021
2.6
0.016
2.9
0.011
2.8 Reference Reference Reference Reference Reference 0.080 9.92 0.075 9.84 0.065 9.8 0.051 9.73 0.038 9.75
0.190
35
0.168
35
0.133
32
0.093
24
0.062
24 Reference Reference Reference Reference Reference 0.002 0.6
0.008
3.3
0.010
5.2
0.010
6.5
0.008
7.5
0.010
2.8
0.010
2.7
0.004
1.1
0.001
0.2 0.001 0.36 Reference
0.021
3.7
0.013
2.2


0.011
3.7
0.010
3.1
0.004
1.2 0.001 0.19 0.001 0.41 Reference
0.019
3.7
0.011
2.1


0.010
3.9
0.008
2.9
0.001
0.4 0.001 0.41 0.001 0.54 Reference
0.015
3.5
0.008
1.8


0.007
3.8
0.005
2.8
0.001
0.5 0.001 0.55 0.002 0.9 Reference
0.011
3.4
0.006
1.7


0.005
3.6
0.003
2.5 0.000
0.3 0.001 0.58 0.001 1 Reference
0.008
3.3
0.004
1.6

The effect of age (not shown) is taken care of by including in the regression a dummy for each age. The variable ‘years of schooling’ is split into three categorical variables: less than high school (reference in the table), high school and college. The interaction between years of schooling and age is accounted for by a full interaction between age and school dummies. The regression also includes labour market conditions one year before the interview (not shown). Source: Own calculations on Istat data.

252

Factors affecting labour force participation

least 25 years old in every year of our sample, and for every considered year an interaction with education. Thus, the determinants of the probability of entering the labour force are xit  0 

2002

2002

t1995

t1995

 1t eligibleit*ti   2t eligibleit

*ti*(educit
mean(educt))  g(educit, ageit)  other demographic characteristicsit  year dummies

(6.9)

If the marginal effects associated with the coefficients 1t and 2t turn out to be equal over time, then we say that the rising trend in labour force participation was a feature of the Italian economy even before the introduction of the subsidy at the end of the year 2000. If instead the effect shows up only in 2001 and 2002, we can be confident in our results. Once more we estimated the model with the usual ‘robust against choosiness’ technique that starts with the comparison of individuals very close to the eligibility threshold (25–26 versus 20–24 years old) and progressively expands the sample until it includes everybody aged 20 to 64, that is, the entire potentially eligible cohort. Table 6.6 reports the results only for the usual representative groups. Overall our findings seem to be supportive of the claim that something actually happened in the biennium 2001–2002 and it was somewhat unprecedented, at least in its magnitude. In its upper part, Table 6.6 presents the 1t’s (average) effects which measure the difference in the probability of being in the labour force between individuals younger and older than 25. For all age groups these coefficients are larger in 2001 and especially in 2002 compared with any other previous year. The only exception is represented by the year 1998 when the so-called ‘Treu package’ – a series of active policy measures named after the Labour Minister Tiziano Treu – was put forward by the Italian government. One should note that this Treu package was mostly oriented towards young workers, and that data fully reflect this feature since the coefficients for the year 1998 are comparable in size to those of 2001 and 2002 only for individuals up to 35 years of age. Apart from this exception, before 2001 there was no advantage in being older than 25 with respect to labour force entry. In fact in most cases it seemed to be disadvantageous since the coefficients are negative. Then, in 2001 and 2002 emerged a clear premium, which rose with age and reached a maximum for individuals of 45–55, while it declined thereafter. The results of Table 6.3 are also confirmed as far as the interaction with schooling is concerned, even though the differences between the pre- and post-2000 periods are less clear-cut. For every year preceding 2001, better educated individuals tended to participate relatively more. For individuals

Hiring incentives and labour force participation in Italy

253

up to 50 years of age, the size of this larger propensity ranged between 1.0 and 1.2 percentage points for every additional year of education above the mean. It was smaller for older individuals. This pattern was present in both 2001 and 2002, but the higher participation propensity of more educated individuals, though still present, was much smaller than before. For example, for the age group up to 35 years of age in the year 2000 one more year of education increased the (differential) propensity to participate (of the eligible compared to the control group) by 1.6 percentage points. This additional effect declines to 1.2 in 2001 and to 0.8 in 2002. However, these differences in the pre- and post-2000 periods disappear as we widen the eligible group, which is consistent with the evidence shown in Table 6.3. Modes of Participation: Working or Searching for a Job? In this section we investigate what individuals do upon entering the labour force. This question is important because it allows us to purge the estimates of the previous section from possible confounding effects that arise because the boundaries between unemployment and inactivity are often weak, especially for marginally attached groups. Part of the effect we estimated is a pure demand effect if new participants were already searching for jobs in the previous period even if the intensity was below that required to be classified as unemployed. Indeed, this would be the case if new entrants were individuals belonging to the ‘marginally attached’ group. In our setting we would see a move from out of the labour force to participation while the true transition is from job search to employment. On the contrary, transitions from out of the labour force to unemployment would not suffer from this confounding effect and would reflect a pure increase in labour supply. However we cannot assume that all direct transitions from out of the labour force to employment are due to measurement errors. We look at transitions after one year; therefore there might be individuals who, during this interval, have transited first into unemployment and then into employment. There might also be individuals who entered the labour force because they found a job. In those cases we should count these transitions as a labour supply effect. Nevertheless, looking only at transitions into unemployment is still informative as it provides a lower bound to the effects of the tax credit on labour supply. In Table 6.7 we present the estimates of the tax credit effect on the probability of participating either as unemployed or as an employee (we also include the estimates for the overall probability of participation16). The statistical model is presented in equation (6.8) and the corresponding estimates are reported for the usual eight age groups. Restricting the analysis to the transition probabilities into unemployment does not alter the

254

Factors affecting labour force participation

Table 6.6 Probit estimates of the probability of entering the labour force: controls for the existence of a trend before the tax credita Up to 29

M.E.

Up to 35

Up to 40

Up to 45

M.E.

t-stat

M.E.

t-stat

M.E.

t-stat

0.039 0.027 0.005 0.000 0.019 0.001
0.003
0.021

4.14 2.95 0.56
0.04 2.12 0.07
0.3
2.51

0.045 0.040 0.008 0.008 0.024 0.009 0.002
0.017

5.03 4.52 1.02 1.02 2.89 1.16 0.25
2.15

0.045 0.038 0.008 0.009 0.021 0.007 0.000
0.019

5.32 4.52 1.09 1.19 2.72 0.96
0.02
2.65

Interaction with school b ( 2t) 2002 0.002 0.77 0.008 2001 0.008 3.12 0.012 2000 0.011 4.54 0.016 1999 0.011 4.34 0.014 1998 0.007 2.96 0.012 1997 0.011 4.24 0.014 1996 0.011 4.18 0.015 1995 0.008 3.32 0.015

4.5 6.72 8.49 7.8 6.84 7.67 8.18 7.9

0.009 0.011 0.014 0.013 0.011 0.013 0.014 0.014

5.66 7.36 8.96 8.19 7.48 8.45 9.17 8.78

0.008 0.010 0.012 0.011 0.011 0.011 0.013 0.012

5.89 7.73 8.64 7.98 8.07 8.47 9.11 8.92

t-stat

Average effect ( 1t) 2002 0.018 1.63 2001 0.007 0.64 2000 0.003 0.29 1999
0.002
0.19 1998 0.019 1.71 1997
0.009
0.88 1996
0.006
0.52 1995
0.025
2.47

Year effect 2002
0.013
1.85 2001
0.021
3.04 2000 Reference 1999 0.001 0.11 1998
0.011
1.53 1997
0.006
0.94 1996
0.008
1.22 1995 0.004 0.64


0.014
1.88
0.022
3.08 Reference 0.001 0.12
0.010
1.47
0.006
0.82
0.008
1.11 0.005 0.73


0.013
1.86
0.021
3.07 Reference 0.001 0.11
0.010
1.45
0.005
0.8
0.007
1.08 0.005 0.74


0.012
1.8
0.020
3.04 Reference 0.001 0.1
0.010
1.45
0.005
0.79
0.007
1.07 0.005 0.73

Notes: a. Probit estimation of model (6.9) in the text. The dependent variable is a dummy that takes value one if the person i at time t is in the labour force. The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force. Entries along the columns are marginal effects (M.E.) and t-statistics (t-stat) associated with indicated variables. The first block of rows under the heading ‘Average effect ( 1t)’ are the marginal effects associated with a set of dummy variables (one for each year) that take value one if the observation i in year t is at least 25 years old. We interact these variables with the years of schooling of the observation i at time t (scaled by the mean education of the group) to obtain a new set of variables whose marginal effects are reported in the second block of rows under the heading ‘Interaction with school ( 2t)’.

Hiring incentives and labour force participation in Italy

Up to 50

Up to 55 M.E. t-stat

Up to 60 M.E. t-stat

255

Up to 64 M.E. t-stat

M.E.

t-stat

0.043 0.032 0.003 0.003 0.015 0.001
0.007
0.025

5.5 4.19 0.4 0.43 2.11 0.16
1.01
3.73

0.033 0.022
0.003
0.002 0.008 0.000
0.010
0.024

4.97 3.45
0.47
0.39 1.33
0.08
1.65
4.39

0.018 0.010
0.008
0.007
0.001
0.007
0.013
0.022

3.66 2.06
1.77
1.53
0.11
1.65
3.02
5.51

0.009 0.002
0.009
0.008
0.004
0.008
0.012
0.018

2.48 0.65
2.97
2.76
1.44
2.73
4.03
6.5

0.007 0.009 0.010 0.010 0.010 0.010 0.012 0.011

6.13 8.04 8.52 8.5 8.48 8.69 9.89 9.27

0.006 0.008 0.008 0.008 0.008 0.008 0.010 0.009

6.56 8.12 8.36 8.56 8.48 8.28 1.28 9.01

0.005 0.005 0.005 0.006 0.006 0.006 0.007 0.006

7.24 7.98 8.07 8.48 8.78 8.79 1.56 8.81

0.003 0.004 0.004 0.004 0.004 0.004 0.005 0.004

7.24 8.26 8.06 8.59 9.08 8.77 1.32 8.75


0.011
1.7
0.019
2.99 Reference 0.001 0.08
0.009
1.46
0.005
0.81
0.007
1.11 0.004 0.69


0.009
1.59
0.015
2.94 Reference 0.000 0.08
0.008
1.47
0.004
0.84
0.006
1.14 0.003 0.65


0.006
1.51
0.011
2.9 Reference 0.000 0.08
0.006
1.48
0.003
0.83
0.004
1.12 0.003 0.65


0.004
1.48
0.008
2.88 Reference 0.000 0.08
0.004
1.48
0.002
0.81
0.003
1.09 0.002 0.66

The third block reports standard year dummies. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force). The model includes other variables not shown in the table: a cubic in age, a quadratic in the years of education, the interaction between education and age, dummies for gender, marital status, regions, condition one year before the interview (such as student, housewife, retired). b. Scaled by the mean education for each eligible group. Source: Own calculations on Istat data.

256

Factors affecting labour force participation

Table 6.7 Probit estimates of the probability of entering the labour force as unemployed or employee a Up to 29 M.E. t-stat Panel (a) Eligible in 2002 Average effect ( 2) Interaction with educationb ( 4) Eligible in 2001 Average effect ( 1) Interaction with educationb ( 3)

Up to 35 M.E. t-stat

Up to 40 M.E. t-stat

Probability of being unemployed

Panel (b) Eligible in 2002 Average effect ( 2) Interaction with educationb ( 4) Eligible in 2001 Average effect ( 1) Interaction with educationb ( 3)

0.0031 0.44 0.012
0.005
3.44
0.004

2.02 0.017
3.49
0.003

2.86
3.48

0.0085 1.18 0.020
0.003
1.67
0.002

3.09 0.026
1.50
0.001

4.26
1.29

4.41 0.025
2.97
0.002

5.03
2.39

Probability of holding a job 0.010
0.002

1.93 0.023
2.20
0.002


0.004 0.002


0.82 2.20

0.0046 0.0007

0.97 0.97

0.009 0.0001

1.97 0.15

Memorandum Panel (c) Eligible in 2002 Average effect ( 2) Interaction with educationb ( 4) Eligible in 2001 Average effect ( 1) Interaction with educationb ( 3)

Probability of participation 0.014
0.007

1.61 0.036
3.68
0.004

4.65 0.039
3.33
0.003

0.007
0.001

0.80 0.027
0.36
0.0

3.56
0.01

0.036 0.0001

5.35
2.47 4.97 0.14

Notes: a. Probit estimation of model (6.8) in the text. The dependent variable is a dummy that takes value one if the person i at time t is unemployed (Panel a) or is holding a job (Panel b) or is in the labour force (Panel c). The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force. Entries along the columns are marginal effects (M.E.) and t-statistics (t-stat) associated with indicated variables. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force).

message of the preceding section qualitatively. The supply effect is nil or very small for younger workers; it increases considerably for older workers reaching a maximum for those up to 50 years of age (2.6 and 1.9 percentage points in 2001 and 2002, respectively) and declines thereafter. For the whole 25–64 age range the effect is around 1 percentage point in both 2001 and 2002, about two-thirds of the total effect (including transition into employment). Those who enter unemployment from out of the labour force are the least educated among younger workers. Overall this check confirms the qualitative pattern of our results and sets the lower bound to the tax credit

257

Hiring incentives and labour force participation in Italy

Up to 45 M.E. t-stat

Up to 50 M.E. t-stat

Up to 55 M.E. t-stat

Up to 60 M.E. t-stat

Up to 64 M.E. t-stat

0.018
0.003

3.35
3.66

0.019
0.003

3.99
4.56

0.017
0.002

4.25 0.012
4.35
0.001

0.027
0.001

4.72
1.24

0.026
0.001

5.19
1.42

0.022
0.001

5.34 0.016 5.43 0.011 5.38
1.71
0.0006
2.01
0.0004
1.81

0.026
0.001

5.45
1.86

0.027 0.001

6.09 -0.073

0.022 -0.0002

5.81 -0.049

0.015 0.0000

4.90 0.12

0.008 0.0005

2.03 0.99

0.0039 0.0006

1.14 1.62

0.0005 0.0004

0.20
0.002
0.78 1.34 0.0004 1.83

0.008 0.0005

0.042
0.002 0.036 0.0006

1.86 0.78

6.06
2.20 5.29 0.68

0.046
0.002 0.036 0.0004

7.25
2.47 5.76 0.58

0.041
0.001 0.030 0.0004

4.29 0.009
4.34
0.001

0.009 0.0001

4.53
4.71

3.92 0.61

7.53 0.030 7.31 0.021 7.14
1.96
0.0005
1.23
0.0004
1.39 5.75 0.67

0.022 0.0001

5.47 0.19

0.014 0.0002

4.96 0.49

The model includes other variables not shown in the table: a cubic in age, a quadratic in the years of education, the interaction between education and age, dummies for the years, gender, marital status, regions, condition one year before the interview (such as student, housewife, retired). b. Scaled by the mean education for each eligible group. Source: Own calculations on Istat data.

effect to a one percentage point increase in the labour supply of the age group 25–64.

NEW PARTICIPANTS’ CHARACTERISTICS: MOVING OUT OF THE BLACK ECONOMY? The characteristics of the individuals who took advantage of the subsidy for the purpose of entering the labour market in the years 2001 and 2002

258

Factors affecting labour force participation

were rather surprising to us. We did not anticipate that the effects would have been stronger for adult and older males. In fact our guess was that the subsidy could further encourage groups whose participation rate was on the rise. Therefore we wondered whether these results could have been driven by the reduction of workers in the underground economy. The idea is that the strong labour cost reduction granted by the new subsidy could have made it more convenient for some workers and firms operating in the black economy to emerge and join the regular sector. Some assumptions on individual behaviour were needed in order to set up a statistical framework which allows us to check for the credibility of this idea. In particular we assumed that persons working in the black economy would answer the questions of the Labour Force Survey as if they were in the situation of being out of the labour force. If they are instead either selfemployed or working with a regular contract, or if they are active job searchers, we assume that all of them report their labour market status correctly. This assumption is very likely since it is supported by results found by several researchers that have studied this field more closely.17 If our identification assumption is correct and the effects of the subsidy were mostly due to the emergence of previously irregular workers, then stronger subsidy effects should be found in those areas and sectors characterized by larger shares of irregular work. Moreover, our story implies that the focus should be on new open-ended employment rather than on other forms of labour force participation (such as temporary work, self-employment or job search). A way to implement this idea is to augment our basic specification with a variable representing the share of employees in the underground economy on total payroll employment in the region/sector where the individual is currently working, and then studying the interaction of this new variable with the dummy for the eligible group. Formally one could add to the basic equation (6.8) the following term:

5 eligible 2001i * (blackit
mean(blackt))  6 eligible 2002i * (blackit
mean(blackt))  7 * blackit where the variable black is the incidence of the black economy, measured by the number of irregular employees over total payroll employment (regular and irregular) in the region/sector where individual i is working at time t. We scaled this variable by its mean, computed over those individuals who belong to the specific eligible group.18 The marginal effect associated with the coefficient 7 measures the average impact of the black economy on the participation rate while those related to the two slopes 5 and 6 indicate its additional effect for an eligible person in 2001 and 2002.

259

Hiring incentives and labour force participation in Italy

Despite its simplicity, several problems emerge when implementing this statistical model. First of all we only have limited information on the incidence of the black economy, namely either a regional or a sector breakdown from 1995 to 2000.19 To deal with this shortcoming we used the last available observed value for the years for which data are not yet available. This approximation should not deeply influence the results since the share of the underground economy changes only slowly over time. The second problem concerns the nature of the dependent variable. According to the black economy story, the interaction coefficient should be positive when looking at the number of workers hired under a permanent contract. Therefore we changed the dependent variable and estimated the probability of entering the market as a permanent worker, in contrast to the previous section where we looked at the chances of participating ‘tout-court’. We estimated this equation on the usual sample described above, which includes all individuals interviewed in the April wave of the LFS who, one year earlier, were out of the labour force (for reasons other than disability and mandatory military service). However, when using this sample we might misinterpret the results whenever the participation rate of a given group as a whole (that is, not only the share of individuals hired on open-ended contracts) rises in a region which happens to have a large incidence of irregular work. To control for this scale effect we also estimated our model after restricting the sample to individuals who actually entered the labour force, thus eliminating everybody who chose to remain inactive. An additional problem arises if we want to use sector information; in this case we need to restrict the analysis to employed individuals (job searchers and inactive persons do not have any sector affiliation). For the time being we confine ourselves to regional information on irregular work. Finally, the above statistical model does not allow us to evaluate whether the black economy also had an additional effect before 2001. This would be evidence contradicting our black economy story. To take all these problems into account we ended up specifying an equation in which the probability of entering the labour force as a permanent worker is related to the usual set of variables, a black economy indicator that varies at the regional level, a set of dummies for the eligible group interacted with the year dummies, and a set of interactions between eligible group, year dummies and the black economy indicator: xit  0 

2002

2002

t1995

t1995

 1t eligibleit * ti   2t eligibleit

* ti * (blackit
mean(blackt))  3 * blackit  g(educit,ageit)  other demographic characteristicsit  year dummies.

(6.10)

260

Factors affecting labour force participation

Ideal evidence in favour of our black economy story would call for a 2t (statistically) positive only for the years 2001 and 2002; otherwise our story could not be held since it would imply that workers older than 24 and living in regions characterized by larger shares of irregular work enjoyed a higher probability of being hired with an open-ended contract even before the introduction of the subsidy. A less strong but still valid evidence would be one in which 2t for t2001 is positive but (statistically) smaller than 2t for t  2000.20 We again adopted the ‘robust against choosiness’strategy to avoid selecting an unreasonable eligible group and estimated the above model recursively, adding each time one additional year of age. Table 6.8 reports the marginal effects associated with 1t and 2t. In the last row we present the marginal effect (and its t-stat) associated with the coefficient 3 that represents the effect of the black economy per se on the probability of entering the labour force as an open-ended worker. Results show that for the whole sample (ages 20–64) the probability of being hired on a permanent basis is strongly reduced by the share of the underground economy (a 10 percentage point share of underground economy decreases the probability by about 1 percentage point); this effect seems to be concentrated on individuals older than 45. For the years 2001 and 2002 both coefficients 1 and 2 accord in sign and magnitude with the underground economy interpretation of our previous results, with some variation across different age groups. In 2002 the average effect shows a pattern similar to that observed for the probability of participating discussed in the results section above: it is small and barely different from zero for individuals 25–29 years of age (about 1 percentage point increase in the probability of entering the market as an open-ended worker in 2002), it grows steadily in size as the eligible group becomes larger until it includes all individuals up to 50 years of age, and declines thereafter. In 2001 the pattern shown by the average effect looks very similar, though smaller in size. Interaction coefficients in 2002 are positive and statistically different from zero (except for individuals aged 25–29) and become stronger for older individuals (up to the age group 25–55). The magnitude of this effect is not trivial: for individuals aged 25–55, a 10 percentage point increase in the share of the underground economy above the average reduces its negative impact on participation by about 1 percentage point. For other age groups the impact is smaller but still relevant. The same pattern can be observed in 2001, though less pronounced. Can we conclude that we have enough evidence in favour of our underground economy story? Before answering this question we need to exclude the possibility that a similar pattern was present in the pre-2001 period, that is, before the subsidy was introduced. When we examine the 2

Hiring incentives and labour force participation in Italy

261

coefficients in the period 1995–2000 we find a U-shaped pattern. For the whole sample we find a very large effect in 1995 (about 1.1 percentage points for a 10 per cent increase of the black economy above the mean) which steadily declines in the following years (except for the year 1998) until it reaches its minimum in 2000 (0.27) and rises afterwards to a level significantly different from zero (about 0.6 points in 2002). This U-shaped pattern can be interpreted as mild evidence in favour of the fact that the subsidy revitalized a tendency to move out of the black economy, which was strong in the second half of the 1990s. Table 6.9 presents our last set of results, which refer to the estimation we carried out using the more restricted sample, that is, the one covering exclusively individuals who truly entered the labour force. These estimates are therefore purged by the scale effect due to the increases in labour force participation as a whole. The basic message does not change: the additional effect brought about by the existence of a black economy is positive, statistically different from zero, increasing in age, and stronger in 2002 than in 2001. However, the U-shaped pattern does not disappear.

CONCLUSIONS In this chapter we investigate how labour supply reacts to subsidies that reduce firms’labour costs. In particular we examine the effect of a recent programme introduced in Italy at the end of the year 2000, which provided a large subsidy to firms hiring workers with open-ended contracts. The question we ask is whether the subsidy encouraged more individuals to participate because of the improvement in labour market conditions. To address this question we look at how transitions from inactivity to participation were affected by the subsidy. We use the April waves of the Italian Labour Force Survey micro-data for the period 1995–2002 and adopt a difference-in-differences framework. Our results suggest that the subsidy did exert a non-trivial effect on the participation rate. The tax credit increased labour force participation of eligible inactive individuals by about 1.4 per cent in 2001 and 2.1 per cent in 2002 relative to non-eligible individuals. This increase was rather heterogeneous across people. It seems indeed to have been mostly concentrated among individuals aged 35–54, with a low or at most a secondary schooling level, who, before entering the labour market, were either retired or housewives. These demographic characteristics look quite surprising since they do not identically match with the composition by age and schooling level of the group of individuals who sustained the expansion of the Italian labour supply in the pre-2000 period.

262

Factors affecting labour force participation

Table 6.8 Probit estimates of the probability of entering the labour force as a permanent worker: the impact of the underground economya Up to 29 M.E. t-stat Average effect ( 1t) 2002 0.009 2.05 2001 0.004 0.88 2000 0.002 0.35 1999 0.008 1.77 1998 0.017 3.16 1997 0.003 0.68 1996 0.009 1.85 1995
0.004
1.01

Up to 35 M.E. t-stat 0.017 0.006 0.001 0.005 0.014 0.005 0.011 0.001

Up to 40 M.E. t-stat

Up to 45 M.E. t-stat

4.02 1.53 0.19 1.41 3.23 1.34 2.54 0.26

0.021 0.010 0.002 0.006 0.018 0.012 0.017 0.006

4.87 2.67 0.50 1.59 4.2 3.12 3.81 1.59

0.021 0.010 0.002 0.007 0.021 0.014 0.016 0.007

5.15 2.83 0.50 1.83 4.85 3.73 3.86 1.93

Interaction with black economyb ( 2t) 2002 0.041 1.08 0.063 2.04 2001 0.022 0.54 0.048 1.37 2000
0.006
0.13
0.034
0.93 1999 0.029 0.69 0.067 1.96 1998 0.059 1.49 0.097 3.03 1997 0.013 0.24 0.031 0.81 1996 0.062 1.14 0.079 1.92 1995
0.003
0.04 0.057 1.25

0.072 0.051
0.029 0.058 0.136 0.084 0.146 0.125

2.58 1.65
0.85 1.82 4.54 2.55 4.06 3.25

0.087 0.056
0.021 0.057 0.158 0.105 0.161 0.183

3.30 1.91
0.66 1.90 5.70 3.49 4.82 5.24

Black economy own effect ( 3) 0.031 0.29
0.066


0.030


0.31


0.105


1.19


0.64

Notes: a. Probit estimation of model (6.10) in the text. The dependent variable is a dummy that takes value one if the person i at time t is employed as a permanent worker. The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force. Entries along the columns are marginal effects (M.E.) and t-statistics (t-stat) associated with indicated variables. The first block of rows under the heading ‘Average effect ( 1t)’ are the marginal effects associated with a set of dummy variables (one for each year) that take value one if the observation i in year t is at least 25 years old. We interact these variables with the share of the black economy in the region of residence of the observation i at time t (scaled by the mean) to obtain a new set of variables whose marginal effects are reported in the second block of rows under the heading ‘Interaction with black economy ( 2t)’. The third variable is the share of the black economy in the

The fact that only adult and older workers seem to have been affected by the subsidy might therefore be hiding some other phenomenon. In particular, one possible story could be that what we are in fact estimating is the decision to participate in the regular labour market rather than remaining in the black economy. The idea is that some of the firms that employ workers in the underground economy took advantage of the subsidy in order

Hiring incentives and labour force participation in Italy

Up to 50 M.E. t-stat

Up to 55 M.E. t-stat

Up to 60 M.E. t-stat

263

Up to 64 M.E. t-stat

0.022 0.010 0.001 0.006 0.019 0.014 0.016 0.006

5.60 2.79 0.43 1.70 4.82 3.75 4.09 1.73

0.022 0.008 0.001 0.006 0.017 0.013 0.015 0.005

6.17 2.86 0.50 1.88 4.96 4.16 4.26 1.81

0.013 0.004
0.000 0.003 0.011 0.008 0.009 0.003

5.37 1.85
0.26 1.22 4.26 3.67 3.72 1.22

0.007 0.001
0.001 0.001 0.005 0.004 0.005 0.001

4.63 0.91
0.99 0.54 3.53 2.96 3.10 0.46

0.086 0.059 0.009 0.051 0.164 0.127 0.191 0.214

3.62 2.27 0.32 1.83 4.63 4.63 6.47 6.88

0.097 0.068 0.038 0.064 0.169 0.126 0.175 0.204

4.76 3.08 1.55 2.70 7.86 5.47 6.98 7.3

0.084 0.062 0.035 0.056 0.135 0.106 0.155 0.173

5.59 3.75 1.96 3.20 8.48 6.29 8.35 8.99

0.060 0.044 0.027 0.039 0.094 0.071 0.107 0.114

6.09 4.07 2.29 3.47 8.93 6.40 8.86 9.01


0.140


1.78


0.169


2.56


0.117


2.41


0.087


2.77

region of residence of observation i at time t. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force). The model includes other variables not shown in the table: a cubic in age, a quadratic in the years of education, the interaction between education and age, and dummies for years, gender, marital status, regions, condition one year before the interview (such as student, housewife, retired). b. Scaled by the mean education for each eligible group. Source: Own calculations on Istat data.

to move these labour contracts above the table. Our evidence seems indeed to confirm this assumption. However the phenomenon is not specific to the biennium 2001–02 but was quite evident also in the preceding years, especially between 1995 and 1998. We can therefore conclude that the new subsidy revitalized a tendency to move out of the black economy, which was strong in the second half of the 1990s.

264

Factors affecting labour force participation

Table 6.9 Probit estimates of the probability of finding a permanent job: the impact of the underground economy – sample restricted to labour force participantsa Up to 29 M.E. t-stat Average effect ( 1t) 2002 0.055 1.65 2001 0.020 0.61 2000 0.006 0.17 1999 0.061 1.75 1998 0.089 2.51 1997 0.026 0.76 1996 0.058 1.64 1995
0.011
0.31

Up to 35 M.E. t-stat 0.058
0.004
0.016 0.023 0.045 0.011 0.051 0.004

Up to 40 M.E. t-stat

Up to 45 M.E. t-stat

2.06
0.16
0.60 0.83 1.58 0.40 1.75 0.13

0.051
0.013
0.025 0.001 0.050 0.028 0.058 0.022

1.83
0.47
0.95 0.04 1.78 1.00 2.00 0.79

0.048
0.016
0.032
0.003 0.058 0.039 0.063 0.031

1.70
0.61
1.20
0.11 2.01 1.39 2.13 1.11

Interaction with black economyb ( 2t) 2002 0.327 1.07 0.638 2.72 2001 0.027 0.08 0.239 0.91 2000
0.155
0.47
0.264
0.97 1999 0.341 1.06 0.687 2.69 1998 0.217 0.72 0.465 1.88 1997 0.214 0.58 0.249 0.88 1996 0.412 1.03 0.561 1.85 1995 0.006 0.01 0.245 0.72

0.608 0.131
0.271 0.594 0.931 0.608 0.871 0.587

2.74 0.55
1.05 2.44 3.58 2.39 3.19 1.96

0.723 0.193
0.212 0.618 0.983 0.644 0.984 0.996

3.35 0.83
0.84 2.58 4.37 2.63 3.71 3.56

Black economy own effect ( 3)
0.321
0.38
1.00


0.761


1.03


1.21


1.70


1.28

Notes: a. Probit estimation of model (6.10) in the text. The dependent variable is a dummy that takes value one if the person i at time t is employed as a permanent worker. The sample ranges from 1995 to 2002 and includes all persons who, at time t
1, were out of the labour force and in year t are in the labour force. Entries along the columns are marginal effects (M.E.) and t-statistics (t-stat) associated with indicated variables. The first block of rows under the heading ‘Average effect ( 1t)’ are the marginal effects associated with a set of dummy variables (one for each year) that take value one if the observation i in year t is at least 25 years old. We interact these variables with the share of the black economy in the region of residence of the observation i at time t (scaled by the mean) to obtain a new set of variables whose marginal effects are reported in the second block of rows under the heading ‘Interaction with black economy ( 2t)’. The third variable is the share of the black economy in the

Hiring incentives and labour force participation in Italy

Up to 50 M.E. t-stat

Up to 55 M.E. t-stat

Up to 60 M.E. t-stat

265

Up to 64 M.E. t-stat

0.049
0.024
0.039
0.006 0.052 0.042 0.062 0.031

1.72
0.90
1.48
0.25 1.79 1.45 2.08 1.10

0.055
0.024
0.038
0.001 0.053 0.046 0.065 0.037

1.93
0.86
1.40
0.05 1.81 1.60 2.16 1.28

0.063
0.020
0.032 0.004 0.054 0.058 0.071 0.042

2.24
0.74
1.21 0.17 1.89 2.04 2.40 1.46

0.059
0.024
0.031 0.004 0.052 0.057 0.071 0.038

2.11
0.88
1.20 0.15 1.85 2.03 2.42 1.33

0.761 0.241
0.103 0.562 1.06 0.828 1.16 1.19

3.58 1.05
0.42 2.35 4.80 3.43 4.50 4.39

0.794 0.383 0.082 0.655 1.22 0.851 1.15 1.29

3.75 1.67 0.34 2.78 5.57 3.57 4.48 4.79

0.829 0.424 0.109 0.685 1.26 0.883 1.36 1.46

3.95 1.87 0.45 2.93 5.79 3.74 5.39 5.53

0.868 0.453 0.114 0.685 1.30 0.901 1.42 1.48

4.14 2.00 0.47 2.95 5.98 3.84 5.68 5.63


1.34


1.92


1.54


2.24


1.59


2.35


1.71


2.55

region of residence of observation i at time t. These marginal effects are estimated in a separated regression for each of the age groups listed in the top row (each regression also includes the control group, which consists of individuals aged 20–24 who, at time t
1, were out of the labour force). The model includes other variables not shown in the table: a cubic in age, a quadratic in the years of education, the interaction between education and age, and dummies for years, gender, marital status, regions, condition one year before the interview (such as student, housewife, retired). b. Scaled by the mean education for each eligible group. Source: Own calculations on Istat data.

266

Factors affecting labour force participation

NOTES 1. 2.

3. 4.

5. 6.

7. 8. 9. 10.

11. 12. 13.

14. 15. 16. 17. 18. 19. 20.

Reprinted from Giornale degli Economisti e Annali di Economia, ‘Hiring Incentives and Labour Force Participation in Italy’, by P. Cipollone, C. Di Maria and A. Guelfi, Copyright Università Commerciale ‘Luigi Bocconi’, Milan, 63 (2), November 2004. Piero Cipollone, Bank of Italy, Research Department, email: cipollone.piero@ bancaditalia.it; Corrado Di Maria, Tilburg University, Department of Economics, email: [email protected]; Anita Guelfi, Confindustria, Research Department, email: a.guelfi@ confindustria.it. Fifteen years old in the Italian case. Mortensen and Pissarides (2001) look at the same issue in a two-side search economy model. Although they do not directly address the issue of labour supply, they show that employment subsidies increase both employment and wages especially for low-skilled workers. This section heavily draws on Cipollone and Guelfi (2003). This general rule was in practice until a significant regulatory change was introduced unexpectedly in July 2002. The Italian government established a ceiling of about €652 million to serve as resources for the new employment bonus. Since this amount had already been spent at the beginning of July, the tax credit was suspended. At the end of September 2002 the government intervened again on this issue. It was decided that firms would receive a tax credit up to a given ceiling of employment growth and that all credits due for the period from July to December 2002 should be claimed in 2003 and by instalments. The regulation of the subsidy for the hires taking place during the year 2003 was instead intended to be solved by the new Financial Law, which simply extended the new September rules to the year 2003 for all firms already benefiting from the tax credit. Moreover it prolonged the functioning of the employment bonus up to 2006, although it significantly reduced the granted monthly amounts. Italian Labour Force Surveys are conducted in the first weeks of January, April, July and October. See Ministero del Lavoro e delle Politiche Sociali (2002). See Bank of Italy (2001). In equilibrium the probability is jointly determined with the wage. Our assumption that the probability of being hired is not affected by the size of the tax credit is also justified by the empirical findings that the tax credit had no effect on the level of labour demand (Cipollone and Guelfi, 2003). In the Pissarides (2000) model the condition of viable labour markets ensures that the value of employment is larger than the value of unemployment. See Pissarides (2000), p. 126, Equation 5.7. According to the age parameter, the group of non-eligible workers would consist of all people younger than 25, thus including also those aged 15–19. However, we chose to restrict the control group to the 20–24 cohort in all estimations under the assumption that the non-treatment effects would be more evident for people just below the eligibility cut-off point. There are several exceptions to the general rules for special worker categories. Up to a ceiling. The actual rule for the take home income is max ( p  w
( p
mp)*tx, p), where tx is the tax rate, p is the pension income, mp the minimum pension and w the labour income. In a linear setting the overall effect would be exactly the sum of ‘participating as unemployed’ plus ‘as a job holder’. See Busetta and Giovannini (1998). For example, the mean that we use in the interaction with the eligible group in 2002 is computed only among the observations belonging to the eligible group in 2002. Up to 1999 as far as the regional breakdown is concerned. This is the same argument we used to check for the existence of an average effect.

Hiring incentives and labour force participation in Italy

267

REFERENCES Bank of Italy (2001), Relazione Annuale, 2000, Rome. Becker, G. (1965), ‘A theory of allocation of time’, Economic Journal, 75 (September), 493–517. Brandolini, A., P. Cipollone and E. Viviano (2003), ‘Does ILO Definition Capture all Unemployment?’, CHILD Working Paper No. 22, www.child-centre.it. Busetta, P. and E. Giovannini (1998), Capire il Sommerso, Roma: Liguori Editore. Cipollone, P. and A. Guelfi (2003), ‘Tax credit policy and firms’ behaviour: the case of subsidies to open-end labour contracts in Italy’, paper presented at the XV Annual Conference of European Labour Economists (EALE). Garibaldi, P. and E. Wasmer (2005), ‘Equilibrium search unemployment, endogenous participation and labor market flows’, Journal of the European Economic Association, 3 (4). ILO (1982), ‘Statistics of labour force, employment, unemployment and underemployment’, conference paper, International Conference of Labour Statisticians, Geneva. Jones, S.R.G. and W.R. Riddell (1999), ‘The measurement of unemployment: an empirical approach’, Econometrica, 67, 147–62. Ministero del Lavoro e delle Politiche Sociali (2002), Rapporto di Monitoraggio sulle Politiche Occupazionali e del Lavoro, Roma. Ministero del Lavoro e delle Politiche Sociali (2003), Scelte Lavorative e di Pensionamento degli Anziani in Italia, realized by a temporary association of CEPR Centre for Research on Pensions and Welfare Policies and LABORatorio R. Revelli, CORIPE Piemonte, and R&P Ricerche e Progetti, Moncalieri, Italy; available at: http://cerp.unito.it/Pubblicazioni/altre_pubblicazioni.htm Rapporti. Mortensen, D.T. and C.A. Pissarides (2001), ‘Taxes, Subsidies and Equilibrium Labour Market Outcomes’, CEPR Discussion Paper No. 2989. Orszag, J.M. and D.J. Snower (2003), ‘Designing employment subsidies’, Labour Economics, 10 (5), October, 557–72. Pissarides, C.A. (2000), Equilibrium Unemployment Theory, 2nd edn., Cambridge, MA: MIT Press.

268

Factors affecting labour force participation

DISCUSSION Jan C. van Ours The chapter ‘Hiring incentives and labour force participation in Italy’ by Piero Cipollone, Corrado Di Maria and Anita Guelfi addresses the issue: to what extent marginally attached workers react to an increase in labour demand. The issue is policy relevant since marginally attached workers do not show up in unemployment statistics, but they do affect the way unemployment reacts to labour demand. If labour demand goes up, marginally attached workers enter the labour force and cause the decline in unemployment to be smaller than the increase in the number of jobs. To my knowledge there has not been a lot of research done on marginally attached workers. By nature it is difficult to investigate the behaviour of this type of worker; it is also not easy to distinguish the effect of a labour demand shock from the effect of other simultaneous changes in the economy. In their analysis the authors use information about the effects of an Italian policy measure where labour costs for one category of workers were reduced through a tax credit, while for other categories of workers there was no change in labour costs. They exploit this information as if it was a ‘natural experiment’. By studying the differences in labour force participation between the reduced labour costs category and the other categories, conclusions can be drawn about the effect of the tax credit on labour force participation. My comments focus on the empirical part of the chapter. First I will describe the main characteristics of the analysis performed. Then I will discuss to what extent a ‘natural experiment’ indeed happened and how the analysis could be improved. Finally I will evaluate the conclusions the authors draw from their analysis. The tax credit was introduced in 2001 and concerned a substantial reduction of labour costs for workers newly hired on a permanent basis. There were mild eligibility criteria: the newly hired workers had to be at least 25 years old, they were not allowed to have had a permanent position in the 24 months preceding hiring, and total employment at the firm had to increase compared to the level in the period from October 1999 to September 2000. The basic set-up of the analysis is the following. The authors distinguish between potentially eligible and potentially non-eligible individuals before and after the introduction of the tax credit. Thus they can apply a difference-in-differences strategy to identify the effects of the tax credit. Ignoring interaction terms of eligibility dummy variables with educational level, the main issue is the comparison between participation rates of the control group of individuals aged 20 to 24 and the treatment group of individuals aged 25 and older. The last group became eligible for the tax

Hiring incentives and labour force participation in Italy

269

credit in 2001. In the analysis the treatment group initially consists of workers aged 25 and 26. In additional analyses the treatment group is expanded gradually by introducing older workers. Of course the difference-in-differences methodology is most suitable for individuals close to the eligibility threshold of 25 years. After all, these individuals differ only slightly in age so any effect estimated must be largely due to the introduction of the tax credit. For workers above 30 years of age not only the tax credit but also changes in the age composition of labour demand caused by changes in, for example, technology or industrial structure may be important. Interestingly, when the treatment group consists of individuals aged 25 to 30 years, no significant effect is found. The estimates show that the participation rate of individuals aged 35 to 54 years is mostly affected by the tax credit. The authors explain this as a ‘black economy effect’, that is, workers who were previously involved in the informal sector now find it worthwhile to get a formal job. My main criticism is that the control group may also have been affected by the tax credit. Hiring a worker aged 25 instead of a worker aged 24 was beneficial for the employer. Therefore the participation rate of the control group was most likely reduced by the tax credit. This makes it even more puzzling why the change in the participation rate of the control group and the age group 25 to 29 years was very similar. A further puzzle is that the estimates presented show that the participation rate of workers aged 55 to 64 is reduced because of the introduction of the tax credit. The authors do not discuss this finding, but to me the most likely explanation is that the effect was due to a calendar-time-effect independent of the tax credit. Despite this criticism I think that the chapter is worthwhile reading. The authors provide evidence of individuals reacting to financial incentives and along the way they address a number of other interesting issues. They suggest that transitions of marginally attached workers to and from the labour market are to some extent related to transitions to and from the black economy. The chapter is a nice illustration of how changes in government policy can be used to learn about economic behaviour. It contributes to the difference-in-differences literature in an area where not much is known. All in all, it is an interesting study on the labour market position of marginally attached workers.

PART III

New Forms of Employment Relationships

7.

Recent developments in part-time work in EU countries: trends and policy1 Hielke Buddelmeyer, Gilles Mourre and Melanie Ward

INTRODUCTION Over the last 20 years, Europe has experienced a dramatic increase in the share of part-time employment. This increase is generally regarded as a positive trend, since the promotion of part-time work may be an important measure through which the flexibility of labour markets can be increased. On the labour supply side, part-time work may increase the labour market choices open to individuals, drawing people into the labour market who were previously unwilling or unable to work. On the labour demand side, it may allow employers to adjust hours worked to fluctuations in demand more easily, facilitating adjustment of production and labour costs. The functioning of labour markets and their ability to adjust flexibly to economic shocks are crucial for monetary policy. Helping inactive or unemployed persons to (re-)enter the labour market also increases potential output. Furthermore, the increase in part-time positions represents positive progress in achieving the employment targets of raising the EU employment rate to 70 per cent by 2010 and of increasing the female employment rate to more than 60 per cent by 2010,2 set up by the Lisbon European Council in March 2000. However, it is crucial to know more about the characteristics of this increase in the part-time employment share, such as which groups of the labour force are most likely to work part time and which jobs are most likely to be offered as part-time positions. Also important is to what extent parttime jobs offer career development opportunities. Furthermore, despite the generally positive trend in the incidence of part-time work, there are still remarkable differences in the relative sizes of the part-time employment share by country within the European Union. This suggests that policy, institutions and/or preferences toward part-time work are very different across different regions of the EU. 273

274

New forms of employment relationships

In recent decades, policies promoting flexible working arrangements have been implemented in several EU countries (OECD, 1998). Against this background, the objective of this chapter is to outline some stylized facts concerning the key characteristics of part-time employment in EU countries as well as the impact of policy and institutions. The first section briefly reviews the existing literature on part-time work and its determinants. The following section outlines the key differences in the incidence of part-time work and its development over time for each of the EU countries, by age group, gender, sector and occupation. We also consider the degree of involuntary part-time work for each of these categories. The second half of the chapter turns to consideration of the role that labour market policies have played in encouraging part-time work within EU countries. Here, we first outline some of the main regulations and institutions that have a direct or indirect impact on the possibility or incentive to work part-time. The next section then uses a panel econometric setting to analyse and quantify the effect of labour market policy, as measured by institutions and regulations, on the rate of part-time work. A final section concludes.

THE DETERMINANTS OF PART-TIME WORK – A SURVEY OF THE LITERATURE Broadly speaking, the literature on part-time job developments to date can be divided into two main branches. The first branch relies on microeconometric analysis based on surveys. The purpose of this branch is to identify the determinants of part-time work (Haskel et al., 1997; Houseman, 2001; Euwals and Hogerbrugge, 2004), to illustrate the role of part-time work as a flexible work arrangement (Haskel et al., 1997; Farber, 1999), or to explain the transition between part-time jobs and other labour market states (Blank, 1994; O’Reilly and Bothfeld, 2002). Studies by, for example, Long and Jones (1981), Nakamura and Nakamura (1983) and Blank (1988) have investigated the determinants of the choice to work part time at one point in time, finding that women with younger children, more children and higher levels of non-labour income were more likely to work part time. A larger number of studies examined wage differentials between full- and part-time staff, for example Nakamura and Nakamura (1983) and Blank (1990), which typically found that equivalent part-time workers earn less per hour than their full-time counterparts, although the size of the measured effect varies across occupations. A number of studies of part-time employment for individual countries exist. For example, Blank (1994) uses 14 years of data from the PSID to

Recent developments in part-time work in EU countries

275

explore dynamic labour supply choices in the US among adult women between full-time, part-time and no labour market work. Her results indicate that labour market history is a very important predictor of current labour supply. Furthermore, most women use part-time work as a temporary alternative to full-time work or to being out of the labour market. Only for a small proportion of her female sample is part-time work used as a stepping stone to full-time employment. Doris (1998) studies the determinants of the growth in part-time work in Ireland over the 1980s, concluding that the growth in part-time work was predominantly supply-led and that the growth of the services sector facilitated this increase. The second branch of literature considers part-time employment developments based on macro data and includes sociological and institutional qualitative evidence (Smith et al., 1998; Delsen, 1998; OECD, 1998; Walwei, 1998). In the same vein, some papers have focused on international comparisons (Lemaitre et al., 1997) and attempted to understand the reason why part-time work is so low in some countries such as Spain and Portugal (Ruivo et al., 1998), or so different across countries (Pfau-Effinger, 1998). The present chapter belongs to this second branch of the literature, whilst extending it to consider explicitly the effect of national policy on part-time work through the use of a country panel of time-varying institutions and part-time measures. This latter methodological approach can be associated with another branch of the labour market literature – initiated by Scarpetta (1996) and extended by Belot and van Ours (2000), Blanchard and Wolfers (2000) and Nickell et al. (2002) – which uses cross-sectional or pooled cross-sectional data on indicators of labour market performance and labour market institutions to account for unemployment differentials across countries.

TRENDS IN PART-TIME WORK IN EU COUNTRIES3 We begin our analysis with a description of the main trends in part-time employment. A growing part-time employment share has been a main feature of a number of industrialized countries over the past two decades. Figure 7.1 shows a significant increase in the share of part-time employment in total employment in the EU, Japan and Canada during the late 1980s and 1990s, while the US experienced a slight fall in the part-time employment share. In the EU, the part-time employment rate increased from 13 per cent in 1983 to 18 per cent in 2001. However, there are still remarkable differences in the relative sizes of the part-time employment share by country within the European Union. The Netherlands exhibits the highest rate of part-time work, with

276

New forms of employment relationships

EU

25

Canada

Euro area Japan

USA

20

15

10

5 1983 Sources:

1985

1987

1989

1991

1993

1995

1997

1999

2001

OECD, Eurostat (Labour Force Surveys).

Figure 7.1

Part-time employment rate (%)

42 per cent of its workers working part time in 2001. A second group of EU countries, that is, Denmark, Belgium, France, Germany, Ireland, Austria, Sweden and the UK, have relatively high part-time employment shares, between 16 and 25 per cent. Relatively low rates of part-time work are found in Finland, Luxembourg and Portugal at around 11 per cent, and in Italy and Spain at 8 per cent, and the rate of part-time work is lowest in Greece at 4 per cent (Figure 7.2). All EU Member States except Denmark, Sweden and Greece have experienced an increase in their part-time employment share over the past two decades (1983–2001), ranging from an average increase of 0.1–0.2 percentage point per year in Finland and Spain4 to 1.2 percentage points per year in the Netherlands. Figure 7.3 shows the development of part-time employment shares by country over the period 1983 to 2001. The part-time employment share is highest for 15–24 year olds, at nearly 23 per cent on average within the EU, and lowest for prime-age workers (those aged 25–49) at just over 16 per cent (Figure 7.4). Older workers have a part-time employment share of 21 per cent. Over the Union as a whole, only around 3 per cent of employed men of prime working age (25–49) were in part-time jobs. This contrasts with the figure of over 25 per cent for young men aged 15 to 24. This pattern of a high part-time employment rate for young and older workers, relative to prime-age workers, holds across

277

Part-time employment rate in 2001 (%)

OECD, Eurostat (Labour Force Surveys).

Figure 7.2

Sources:

The shaded bars indicate the non-EU countries.

e a ey in ly al rg nd A ea ce nd ria on da m ay rk ny nd en nd nd m an lia ds ec re a Ita ug ou la r S u o i n a re Ko urk Sp U o a ra rela ust Un ana lgi orw nm rma cela wed eala erla gd Jap usta rlan rt b in G F I r T I A n in S Z itz C Be N e Ge u A the Po xem F D E e w Sw d K ea u e p N L o te N i r n Eu U

Note:

0

5

10

15

20

25

30

35

40

45

278 45

New forms of employment relationships Netherlands

40

35

30 1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

1997

1999

2001

(a) High

30

Belgium France Sweden

Ireland Denmark United Kingdom

Austria Germany

25

20

15

10

5 1983

1985

1987

1989

1991

1993

(b) Medium

1995

279

Recent developments in part-time work in EU countries 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 1983

Finland Italy

1985

1987

Luxembourg Greece

1989

1991

Spain Portugal

1993

1995

1997

1999

2001

(c) Low Source: Eurostat (Labour Force Surveys).

Figure 7.3

Part-time employment rate (%)

countries, with the exception of Austria, Germany and Portugal which exhibit low rates of part-time work among the young relative to other age groups, and Luxembourg, where the share of part-time employment is similar across age groups. Young workers also experienced the fastest rate of growth in part-time work during the 1990s within the EU as a whole, although the growth rate has flattened out more recently. A breakdown of part-time work by gender reveals that part-time workers are more likely to be women (see Figure 7.5), with the EU average female part-time employment rate at 34 per cent and the male rate at 6.2 per cent in 2001. This pattern of a significantly higher rate of female part-time employment holds across time and for all EU countries. Since the early 1990s, growth in part-time work has also generally been strongest amongst female workers, although Denmark, Sweden, and more recently France have experienced declining female part-time employment rates. Rates have remained broadly unchanged for the UK over this period. A considerable variation in the rate of part-time work by occupation is also evident. The part-time employment share for the EU-15 (see Table 7.1, panel a) is high for elementary occupations, service workers, and shop and sales workers, at around 34 per cent. It is also relatively high for clerks at 24.3 per cent. On the other hand, craft and related trades workers, plant and machine operators, assemblers, and legislators/top officials all have

280

New forms of employment relationships

30

25–49 years

15–24 years

50 years and over

25 20 15 10 5 1987

1989

1991

1993

1995

1997

1999

2001

(a) In the EU

70

25–49 years

15–24 years

50 years and over

60 50 40 30 20 10

Be

lg D ium en m G ark er m an G y re ec Sp e a Fr in an Ir ce Lu ela xe nd m N bou et he rg rla nd s Ita A ly us Po tria rtu g Fi al nl an U d ni te Swe d K den in gd om

0

(b) In EU countries, 2001

Figure 7.4

Part-time employment rate by age group (%)

Recent developments in part-time work in EU countries

40

Total

Male

281

Female

35 30 25 20 15 10 5 0 1987

1989

1991

1993

1995

1997

1999

2001

(a) In the EU

80

Female

Male

Total

70 60 50 40 30 20 10

Be l D gium en m G ark er m an G y re ec e Sp ai Fr n an Ir ce ela nd Lu I t xe a m ly b N et our he g rla nd A s us Po tria rtu Fi gal nl an U ni Sw d te e d K den in gd om

0

(b) In EU countries, 2001

Figure 7.5

Part-time employment rate by gender (%)

282

Part-time employment rate

European Union (EU-15) Euro area Belgium Denmark Germany Greece Spain France Ireland Italy Luxemburg Netherlands Austria Portugal Finland Sweden United Kingdom

6.8

6.6 5.9 4.8 6.0 1.1 2.4 5.9 6.8 3.8 0.0 19.4 0.0 5.1 3.7 6.1 7.4

18.0

16.3 18.5 20.1 20.3 4.1 8.1 16.4 16.6 9.1 11.4 42.2 17.2 11.1 12.0 21.0 24.8

14.6 20.6 11.7 16.0 4.1 8.1 12.8 12.5 7.8 7.1 38.1 0.0 7.1 9.4 14.9 16.1

14.8 16.8 12.6 19.6 20.9 2.7 7.5 14.5 12.8 7.5 9.7 43.9 0.0 4.3 9.7 14.8 20.7

17.3 24.3 21.8 22.4 25.9 28.5 3.0 7.3 23.7 22.3 11.9 12.9 53.9 0.0 3.4 12.9 25.8

24.3 27.7 39.1 42.0 39.0 4.7 14.4 32.2 37.9 11.8 15.0 70.0 0.0 7.1 27.3 44.1 55.9

34.2 16.5 12.5 6.6 12.7 7.4 6.5 15.4 20.0 11.8 0.0 40.9 0.0 44.6 16.2 24.1 14.2

16.4 4.0 4.4 2.0 3.8 2.1 1.9 4.4 3.9 4.3 0.0 11.8 0.0 3.5 3.3 6.8 3.4

3.9

5.3 5.5 3.0 7.0 1.0 1.9 4.6 6.3 3.8 0.0 22.8 0.0 0.0 5.2 8.2 9.1

6.0

32.9 32.0 38.0 44.5 11.6 19.6 37.1 31.7 21.4 33.3 68.5 0.0 19.6 26.5 39.4 36.9

34.0

Total Legis- Profes- Technicians Clerks Service Skilled Craft and Plant and Elementary lators sionals and workers and agricultural related machine occupations associated shop and and fishery trades operators profesmarket sales workers workers and sionals workers assemblers

(a) Part-time employment rate by occupation (%)

Table 7.1

283

European Union 3.5 (EU-15) Euro area 4.5 Belgium 7.0 Denmark
2.4 Germany 6.0 Greece
0.7 Spain 2.3 France 3.7 Ireland 7.5 Italy 3.2 Luxembourg 4.6 Netherlands 7.7 Austria 17.2 Portugal 3.8 Finland 12.0 Sweden 21.0 United Kingdom 1.8

0.8 1.6 4.8
2.1 2.7 0.0 0.9 4.3 2.9 4.0 0.5 4.0 0.0 0.4 9.4 14.9
0.3


0.5

0.0 3.7
2.8 1.1
0.3 0.3
3.7 6.8 3.8
6.7 5.8 0.0
5.4 3.7 6.1
0.8

3.0 2.5
1.0 6.4
2.5 1.2 4.4 12.8 3.2 4.1 11.2 0.0 0.8 9.7 14.8 2.4

2.6 5.3 7.0
1.8 5.6
0.3 3.6 5.1 13.3 6.5 4.6 10.4 0.0 0.8 12.9 25.8 3.5

3.9 7.1 12.9
0.8 9.6
0.4 7.2 6.7 18.5 5.6 10.1 8.3 0.0
4.0 27.3 44.1 1.2

5.0 2.2 6.5
14.4 4.2
0.9 0.1 3.2 16.7
0.5 0.0 7.0 0.0 28.8 16.2 24.1 0.1

2.0


1.2
1.9 2.4
2.2 1.9
0.3 0.3 1.0 3.1 1.5
22.2 4.0 0.0 0.0 5.2 8.2 1.8


0.7
0.7 2.1 0.1 0.8
1.8
0.5 1.4 0.5 1.1 0.0 1.5 0.0
1.0 3.3 6.8
1.2

10.5 6.3
7.4 20.8 3.4 5.2 3.4 13.7 7.1 7.0 12.0 0.0 6.5 26.5 39.4
11.2

6.5

Total Legis- Profes- Technicians Clerks Service Skilled Craft and Plant and Elementary lators sionals and workers and agricultural related machine occupations associated shop and and fishery trades operators profesmarket sales workers workers and sionals workers assemblers

(b) Part-time employment rate by occupation (change between 1992 and 2001 in percentage points)

284

(continued)

Agriculture Mining and quarrying Manufacturing Electricity Construction Industry Wholesale and retail trade; repair of motor vehicles Hotels and restaurants Transport Financial intermediation Real estate

17.0 2.9

7.5 5.5 5.0 6.7 19.5

22.3

10.0 13.0

19.9

17.4 3.0

7.6 5.5 5.3 6.8 23.1

27.7

10.9 13.8

19.7

EU-15 Euro area

DK

DE

18.9 18.2 25.7

7.8 15.3 13.1 13.6 13.7 16.8

26.1 39.9 29.0

5.6 7.8 10.4 7.0 0.0 5.2 5.5 3.8 7.3 5.6 6.6 9.5 22.0 24.7 28.9

12.8 16.1 17.0 8.6 0.0 2.4

BE

(c) Part-time employment rate by sectors, 2001

Table 7.1

FR

IE

IT 7.8 0.0

LU

2.9 5.6 6.3 5.2 2.7 2.1 9.0 5.5 3.6 10.6 1.6 5.0 4.2 4.3 2.8 2.4 5.6 5.5 5.0 3.0 8.1 16.9 26.6 10.1 10.8

7.3 14.9 11.4 14.8 0.6 3.4 4.8 4.8

ES

3.7 10.0 9.1 4.3 11.6 11.4

4.6 7.7

6.5 9.0

3.4 14.8 15.5 14.6 13.8 13.8

1.2 1.6

5.5 13.3 22.9 33.9 15.0 13.7

1.6 0.0 2.4 1.8 3.2

7.6 0.0

GR

AT

PT

FI

SE

UK

35.1 28.4

30.6 9.9 28.8 17.5

63.4 21.4

23.1 8.1 17.5 4.0 10.2 5.3 19.0 7.2 44.7 24.3

9.9 16.9 13.0 5.5 14.3 16.2 9.7 13.6 16.9 19.8

2.5 6.2

7.5 27.3 32.8 56.0

3.0 4.2 9.0 7.9 0.0 2.4 3.5 6.2 2.3 4.0 8.4 6.4 2.7 4.1 8.6 7.3 8.0 20.6 23.4 38.6

36.8 17.8 42.2 15.6 35.6 18.1 9.2 5.9 0.3 3.7 0.0 3.8

NL

285

18.0

Total

2.9 17.5

8.4

7.3

9.1

5.5 14.6 26.6 29.5 14.6 21.0

7.9 14.4 20.4 21.6 10.1 20.5 2.8 9.3 28.5 31.1 10.4 25.7

0.9

0.0

5.4

18.5 20.1 20.3

23.1 25.1 26.0

5.9

9.1

9.9 18.6

8.4

4.0

4.1

8.2 16.4 16.5

9.1 11.3

4.1 11.3 20.5 22.1 10.7 13.7

n.a.

73.5 64.8 81.4 30.3 51.4 66.3 53.2 47.8 55.0

23.9 28.5 29.5

29.5 22.6 34.3 37. 37.5 32.2

20.4 11.7 15.1

5.8 13.7 18.0

7.0 12.3 24.6 38.2 3.0 13.1 40.7 41.6

1.8

n.a.

n.a.

Source:

Eurostat (Labour Force Surveys).

7.1

n.a. 64.3

41.0 17.2 11.1 11.9 22.3 24.8

8.8 14.8 26.4 30.9

3.8 10.6

47.5 21.7

n.a.

n.a. 73.4 51.6 56.5

54.6 25.0 10.5 20.7 32.6 34.4

55.6 16.5 72.8 30.6

26.9 11.6

Notes: Abbreviations: BE Belgium, DK Denmark, DE Germany, GR Greece, ES Spain, FR France, IE Ireland, IT Italy, LU Luxembourg, NL Netherlands, AT Austria, PT Portugal, FI Finland, SE Sweden, UK United Kingdom.

16.2

20.5

59.0

59.4

22.7

25.2

27.3

6.9

22.6 29.7

25.9 32.9

6.9

12.8

13.6

Public administration and defence; compulsory social security Education Health and social work Other community work Private households with employed persons Extra-territorial organizations and bodies Services

286

New forms of employment relationships

low shares of part-time employment: between 4 and 6 per cent in 2001. The first group experienced the strongest increase in the part-time rate between 1992 and 2001, while the second group saw the smallest growth (Table 7.1, panel b). These results by occupation may reflect the role of both sectors and skill levels. Indeed, elementary occupations, service workers, shop and sales workers are strongly concentrated in highly part-time-intensive sectors (that is, services as seen in Table 7.1, panel c) while craft and related trades workers and plant and machine operators and assemblers are likely to be more present in less part-time-intensive sectors (that is, industry). The part-time employment rate for high-skilled occupations such as legislators, professionals and technicians and associated professionals is below the average across all occupations, whilst it is significantly higher for lowskilled service occupations. Skill or educational level may well be negatively correlated with the part-time employment share. The majority of part-time work within the EU is described by its workers as voluntary. Nevertheless, 14.2 per cent of part-time workers were working part time involuntarily in the EU in 1992, with this share rising to 19.1 per cent in 1997, before falling back to 14.4 per cent in 2001 (Figure 7.6a). There is also considerable variation in the share of involuntary part-time work by country and over time (see Figure 7.7). For example, 44 per cent of part-time workers reported working involuntarily in Greece in 2001, compared to only 2.3 per cent in the Netherlands. It seems that the share of involuntary part-time work is negatively related to the rate of part-time employment (see Figure 7.6b). The share of involuntary part-time employment appears to be cyclical, that is, higher in troughs of economic activity and lower during peaks in the business cycle. This would suggest that part-time employment might follow a cyclical pattern as well. Investigation of the proportion of involuntary part-time work by age group and gender (Figures 7.8 and 7.9) highlights prime-age part-time workers as historically most likely to label their employment status as involuntary across all countries; 9.5 per cent of prime-age part-time workers in the EU in 2001 reported their part-time status as involuntary. Shares of involuntary part-time employment tend to be significantly lower amongst young and older age groups, with as little as 2.5 per cent of workers in these groups reporting involuntary part-time work in the EU in 2001.5 Involuntary part-time workers are also more likely to be male, with 20 per cent of male workers, compared to 13 per cent of female workers, labelling their part-time status as involuntary for the EU in 2001. This pattern of a higher incidence of involuntary part-time employment for men is generally found across countries, with the exception of Denmark, Portugal, Finland and Sweden, where the female rate is higher

287

Recent developments in part-time work in EU countries

20 18 16 14 12 10 1992

1993

Figure 7.6a

1994

1995

1996

1997

1998

1999

2000

2001

Share of involuntary part-time employment in the EU (%)

Part-time employment rate in 2001

45 NL y 
0.53x 27.01 R20.46

35 30 UK

25

DK

20

DE

15

AT

10

LU

SE BE

IE

FI

PT ES

5

FR

IT

GR

0 0

5

10

15 20 25 30 35 40 Share of involuntary part-time in 2001

45

50

Source: Eurostat (Labour Force Surveys).

Figure 7.6b Correlation of the share of involuntary part-time employment with overall part-time employment rate (EU-15)

288

New forms of employment relationships

40

25–49 years

15–24 years

50 years and over

35 30 25 20 15 10 5 k an G y re ec e Sp ai Fr n an c Ir e el an d N I et tal he y rla nd A s us t Po ria rt ug Fi al nl an U d ni S w te ed d K e in n gd om m

ar

er

G

m

en

D

Be

lg

iu

m

0

(a) By age groups, EU countries, 2001

60

Female

Male

Total

50 40 30 20 10

Be

lg

i D um en m G ark er m an G y re ec e Sp ai n Fr an c Ir e el an d N Ita et he ly rla n A ds us t Po ria rt ug Fi al nl an U d S ni w te e d d K en in gd om

0

(b) By gender, EU countries, 2001

Figure 7.7 Share of involuntary part-time employment as a % of all part-time employment

289

Recent developments in part-time work in EU countries

20

25–49 years

15–24 years

50 years and over

15

10

5

0 1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Source: Eurostat (Labour Force Surveys).

Figure 7.8 Share of involuntary part-time employment as a % of all part-time employment by age group in the EU

30

Total

Male

Female

25

20

15

10 1992

1993

1994

1995

1996

1997

1998

1999

2000

Source: Eurostat (Labour Force Surveys).

Figure 7.9 Share of involuntary part-time employment as a % of all part-time employment by gender in the EU

2001

290

New forms of employment relationships

than the male, and Spain, where the male and female rates are very close. Shares of both male and female involuntary part-time work exhibit variation over time.6 The stylized facts presented in this section support the view that parttime employment represents an important opportunity for particularly young, older and female workers to enter the labour markets of the European Union. The rate of part-time work is clearly higher for these groups relative to prime-aged males, a pattern found to be broadly consistent across countries. Furthermore, for the vast majority of workers in these groups, the decision to work part-time has been a voluntary one, which is all the more satisfactory in terms of welfare maximization. However, as these stylized facts have stressed, both the level of the parttime rate and its development over time vary strongly across the countries of the EU. This suggests that cross-country differences in policy setting and institutions, together with other national structures (sectoral, demographic and societal), play an important role in the incidence of part-time work. The remainder of the chapter focuses on an investigation of these latter influences.

REGULATIONS AFFECTING PART-TIME WORK IN EU COUNTRIES The regulatory and institutional framework varies considerably across countries of the EU and may represent an important influence on the cross-country development of part-time employment. A number of types of regulation may have a direct or indirect effect on the rate of part-time work. This section aims both to introduce the key regulations affecting working time across EU countries and to outline how such regulations differ across countries. A number of regulations affect the rate of part-time work directly. The first set of regulations that fall into this category are legally binding and limit the use of part-time work. For instance, labour laws may give the employer the right to deny an employee’s request to work part-time under certain circumstances. Certain types of regulation may, however, facilitate the conversion of full-time jobs into voluntary part-time employment in order to reconcile personal and professional lives (Genre et al., 2003). A second set of regulations affects part-time employment indirectly through shifting the financial incentives for firms to offer part-time opportunities and also for employees to accept them. For example, wage regulation (including the regulation of working time premia for shift-work or overtime), social protection systems and tax and benefit systems fall

Recent developments in part-time work in EU countries

291

into this group. In some countries, for example Portugal, hourly wages for part-time and full-time work are restricted by law to be equal (pro rata clause). Moreover, hours or earnings thresholds for social insurance contributions (above which contributions to social security schemes become payable and workers become eligible for social benefits) and special subsidies or tax rebates for firms hiring part-time staff may influence the structure of non-wage costs for employers and therefore also indirectly influence the rate of part-time work. For example, during the period 1993–2000, special social security contribution rebates and reductions were granted to firms hiring part-time workers in France. This coincided with a boom in part-time employment over the same period. Similarly, changes in hours and social security thresholds may also influence the supply of parttime work. For example, in Spain, a relaxation in working hours legislation, together with a reduction in social security contributions for part-time workers in 1994, resulted in a strong increase in the part-time employment rate between 1994 and 1995. However, lower pension entitlements may restrict the labour supply of part-time jobs (Ginn and Arber, 1998). Table 7.2 summarizes the main regulations affecting part-time work (through law or collective agreement) that have been adopted by some EU countries in recent years. A couple of interesting patterns emerge from this table. First, most of the reforms undertaken have been targeted at encouraging the use of part-time work by firms, either through a relaxation in the conditions of use of part-time jobs and/or through the granting of subsidies. Second, towards the end of the 1990s some countries adopted regulatory measures to address the overall job quality offered by part-time positions in terms of wages relative to full-timers, career development opportunities, access to training and so on. These measures followed the signature of a European framework agreement on part-time work on 6 June 1997 by the European Trade Union Confederation (ETUC), the Union of Industrial and Employers’ Confederations of Europe (UNICE) and the European Centre of Enterprises with Public Participation and of Enterprises of General Economic Interest (CEEP), in the presence of social affairs Commissioner Padraig Flynn.7 Consideration of Table 7.2 by country reveals fairly different situations across the EU. Four groups can be tentatively considered. First, in as early as 1982, the Netherlands adopted a tripartite agreement aimed at an efficiency-enhancing redistribution of existing employment through, among other measures, working time reduction and part-time work. A second group of countries (Belgium, Finland, France and Spain) adopted new regulations to foster the development of part-time work in the mid-1990s as a possible remedy to high unemployment. A third group (Austria, Germany and Italy) took regulatory steps in the late 1990s to help part-time work

292

Reduction of social security contributions by €929.60 per quarter per extra employee, for duration of agreement. Unemployed workers receiving full benefits may be hired to make up lost hours.

None

€74.37 to €123.95 per month for two years maximum depending on the extent of hours reduction.

Promotion de la remise au travail de chômeurs à l’aide de la redistribution du travail (promotion of vocational reintegration for the unemployed through the redistribution of employment): workers who opt for part-time work receive an incentive (Flemish region only; up to end 1995).

Reduction of social security contributions by a maximum of €929.60 per extra employee, for 13 quarters.

None

Plans d’entreprises de redistribution du travail (enterprise plans to redistribute employment; since 1994): firms create new jobs to compensate for reductions in individual working hours.

Accords pour l’emploi (employment agreements): firms introduce part-time working on a voluntary basis together with another measure designed to encourage hiring.

Temporary reduction in employers’ social security contributions for hiring unemployed person.

Monthly, flat-rate benefit between €59.82 and €149.55 depending on the size of the reduction.

Interruption de carrière à temps partiel (part-time career break; since 1996): a full-time worker reduces working time by between 80 and 50%, being replaced by a previously unemployed person.

Belgium

Incentives to employers

Measure

Country

Incentives to employees

Main regulatory measures impacting on working time adopted until 2001

Table 7.2

293 None

The 2001 decree on labour market reforms increased further the flexibility of part-time jobs: the decree allows working hours additional to the agreed part-time hours to be distributed over the year, with 7 days’ notice given to the worker.

None

The reform provides employees with social security benefits pro-rata to the number of hours worked.

The definition of part-time work was amended. Article 12.1 of the 1980 Workers’ Statute had laid down that a part-time worker was one who worked less than 2/3 of normal contractual hours. The 1994 reform removed this threshold, so that part-timers are now defined as those who work any number of hours less than the normal contract. In addition, it permits the calculation of hours on an annual basis.

Spain

None

In 1998 the Spanish government and labour trade unions signed an agreement on part-time employment contracts which seeks to improve the conditions of part-time workers.

Gesetz über Teilzeitarbeit und befristete Arbeitsverträge (act on part-time work): right for employees in firms with more than 15 employees to reduce their working time as long as no internal company reasons prevent such a reduction (since 1 January 2001).

Germany

Reduces the risk of overtime payment.

None

Reduction in social security contribution.

None

294

(continued)

Measure

Abattement de cotisations sociales patronales pour les emplois à temps partiel (reduction of employers’ social security contributions for part-time employment; since 1992): employers transform positions from full-time to part-time or hire part-time workers (with hours between 16 and 32 per week). The volume of employment in hours must be maintained. This rebate of social security contributions for part-time employment was abolished in 2000, following the implementation of the collective working time reduction (35 hours working week).

A 2000 legislative decree set out new regulations on part-time work towards more flexibility. Part-time work may be ‘horizontal’ (where the hours are reduced on a daily basis) or ‘vertical’ (where the work is executed full-time, but only in certain fixed periods during the week, month or year). Collective bargaining may permit the combination of vertical and horizontal part-time work. The decree repeals the law which allowed collective bargaining to set limits on the proportion of part-time workers in the full-time

Table 7.2

Country

France

Italy

The decree establishes the principle of ‘nondiscrimination’ between parttime and full-time workers (no difference as regards trade union rights, pay, annual holidays, parental leave, protection against workplace accidents and occupational illness and access to company training schemes).

None

Incentives to employees

A total of around 310 million euros has been allocated to finance over 3 years relief on the social security contributions paid by employers in respect of part-time permanent recruitments which, in the course of 2000, give rise to an increase in employment.

The reduction of social security contributions was 30%, as of April 1994, for jobs with duration between 16 and 32 hours per week. From late 1995, it could be combined with the abatement for low-paid workers.

Incentives to employers

295

Netherlands

The general principle of equal treatment, regardless of working time, was introduced in the Labour Law in November 1996.

The tripartite Agreement of Wassenaar (1982) introduced a long-term and collectivenegotiation-based approach aimed at redistributing existing employment more effectively, that is, an approach which encompasses several methods of redistributing employment, such as working time reduction and part-time work. This led to a strong increase in part-time jobs in firms in the 1980s and the early 1990s, especially for women.

workforce. The decree lifts the ban on extra hours (paid at the same rate as ordinary hours below a ceiling) and overtime for part-timers (in the case of vertical part-time work) and introduces the possibility of making the distribution of work laid down in the individual employment contract flexible. In the case of part-time vacancies, employees already working full-time may ask to change their employment relationship into a part-time arrangement.

Part-time employees can claim the same rights as full-time workers.

None

None

None

296

Sources:

OECD Employment Outlook (1998), EIRO website, national sources.

The government pays half of lost wages, up to 1.7 times the unemployment benefit.

From 1998, employees over 50 with family care responsibilities may reduce working time while maintaining employment rights, subject to employer’s agreement. This must be given if there are more than 10 employees and the employee is entitled to a part-time pension (Gleitpension).

Part-time supplement (since 1994): employees reduce their working time by 40% to 60% with the agreement of the employer for 1 year. An unemployed person must be recruited to the same position.

Employment rights are maintained.

Solidaritätsprämienmodell (voluntary reductions in individual working time): from 1998, collective or work agreements may permit reduction of normal working hours in order to recruit registered unemployed persons. Reductions have to be agreed between the employer and each employee.

Austria

Finland

Employees working less than normal hours receive a proportion of unemployment benefit; employment rights are maintained.

Measure

Country

Incentives to employees

(continued)

Table 7.2

None

None

None

Incentives to employers

Recent developments in part-time work in EU countries

297

developments. Finally, countries such as Greece, Ireland, Luxembourg, Portugal and Sweden have not adopted specific legislation to encourage part-time work. A third set of regulations and institutions influence employment in general, but may have an indirect bearing on part-time employment in particular. First, for mothers with young children it has been argued that the probability of (re-)entering the labour market, either in the form of full-time or part-time employment, is strongly linked with the system of child benefit/care arrangements in place. In general, the provision of child benefits can create an ‘unemployment trap’ if benefits are means tested.8 Moreover, some child benefit systems grant additional income to parents who renounce work in order to take care of their young children.9 On the other hand, the lack of affordable (subsidized) childcare may represent a major disincentive to taking up employment. Similarly, unemployment benefit systems (together with other benefits) may create an ‘unemployment trap’ through high net replacement rates and long benefit duration. Doudeijns (1998) studied the financial disincentive to work created by unemployment benefits, finding it had a significantly negative impact on the decision to take up part-time employment. However, the recent development of ‘in-work benefits’, the tightening of benefit eligibility criteria and the enhanced controls over their implementation should all help towards alleviating this disincentive. Tax systems, and in particular high income tax rates, may also deter parttime work through creating a financial disincentive for individuals to enter the labour market. Such a disincentive is reinforced when income tax is computed on the basis of a couple’s earnings, rather than on an individual’s, since in such a system the second earner in a couple may face a relatively high effective marginal rate. Dependent partner’s allowances may make the decision to take up a job even harder, especially a low-paid parttime job which would result in the loss of such a benefit. Relatively strict employment protection legislation (EPL) has become a more and more important reason for hiring part-time employees. This is because part-time jobs may fall outside the sphere of regular contract EPL and may therefore offer employers the opportunity to bypass such legislation. In the late 1990s, in a context of increasing competition and uncertainty, firms realized the advantages of ‘atypical jobs’, which were and continue to be considered more flexible and less expensive. Parttime employment was quickly acknowledged as offering these advantages, together with temporary jobs, help agency employment, shift work, on-call jobs and atypical working time jobs (nights and weekends). Bentolila and Dolado (1994) labelled this new search for flexible staffing arrangements as the ‘new reasons’ for hiring part-time workers.

298

New forms of employment relationships

This section has outlined both the number and type of labour market regulations and institutions that may affect the rate of part-time work. Some of these regulations affect part-time work directly and others indirectly. Furthermore there has been considerable variation in the extent and type of regulation applied across countries of the EU. The conclusions of this section therefore give good reason to think that part-time policies and other labour market institutions have contributed to the growth in parttime employment in some EU countries. In order to quantify such effects the next section moves on to a panel econometric setting.

THE EFFECT OF REGULATIONS AND LABOUR MARKET INSTITUTIONS ON PART-TIME EMPLOYMENT This section focuses on quantifying the effect of labour market policy, as measured by institutions and regulations, on developments in the part-time employment rate (PTR – that is, the share of those working part-time in total employment). Data and Estimation Strategy The part-time employment rate, as well as the share of involuntary parttime employment, is taken for each country from employment series reported in the annual European Community Labour Force Surveys, compiled by Eurostat. Data are generally available annually from 1983 to 200210 for most of the EU countries, except Portugal (from 1986), Spain (from 1987) and Austria, Finland and Sweden (from 1995).11 In order to capture the effect of part-time regulation on the part-time rate, we create a vector of country-specific trends which starts from the year in which regulations of part-time employment were relaxed in each country.12 The motivation for the creation of this variable is that we wish to capture the additional annual increase in the part-time employment rate that follows the change in part-time regulation.13 The starting date of each trend therefore varies by country and is identified by systematically exploiting the information reported in Table 7.3. For the countries where part-time regulation has not changed (Ireland, Portugal, Sweden and the United Kingdom), the value of the variable is set to zero for the complete period 1983 to 2001. To avoid an ‘outlier’ bias, we also construct the ‘policy’ variable excluding the trend for the Netherlands.14 Data on general labour market institutions are taken from the recent macroeconomic literature on labour markets (such as Nicoletti et al., 2000;

Recent developments in part-time work in EU countries

299

Blanchard and Wolfers, 2000; Nickell et al., 2002). Most of these data on national labour market institutions are available only until 1997–98. We include variables to alternatively capture overall employment protection legislation (EPL) and EPL on temporary and regular contracts. An indicator for the provision of child benefits (drawn from Eurostat) is also used. Unemployment benefit replacement ratios and unemployment benefit duration are included to capture possible disincentive effects on taking up part-time employment as a result of ‘unemployment traps’. We also add the total labour tax rate, computed by the OECD and reported by Nickell et al. (2002). Other variables capturing important characteristics of the labour market, and which are expected to influence the rate of part-time work, are also included in our specification. Such controls are the female participation rate, fertility rate, share of employment in services, schooling rate of 15–24 year olds and the share of temporary jobs. Finally we include the output gap taken from the OECD Economic Outlook database to capture the business cycle. Our model considers the level in the part-time employment rate, across countries and over time, that can be attributed to national policies and institutions. i are i country dummies and t are t time dummies. INS are k variables to capture national policies and institutions and C are j variables representing controls (demographic, structural, macroeconomic or societal factors). PTi t i t ˛

K

J

ak INSk i t  bj Cj i ti t    ˛

k 1

˛

˛

˛

(7.1)

˛

j 1

This specification is close to that used by Nickell et al. (2001) to explain OECD unemployment, but the variables for short-term macroeconomic shocks (labour demand shock, real import price shock, real interest rate and so on) are replaced by the output gap which is included as a control. Moreover, the lagged dependent variable is dropped as its presence introduces correlation between the errors and the regressors, which can bias the GLS estimate of parameters. However, as there is clear evidence of nonstationarity for many of the variables used in this equation, there is a risk of ‘spurious regression’. We use the Maddala–Wu test to check whether there are unit roots in the residuals of the equations. The test clearly rejects the hypothesis of the residuals having a unit root, which indicates the presence of cointegration relation between the variables. As some heteroskedasticity and first order autocorrelation are detected, all regressions are estimated by feasible generalized least squares. To tackle possible endogeneity problems and check for robustness, we also apply

300

Determinants of part-time employment rates

Controls (%) Female participation rate

Unemployment benefit replacement ratios (%) Unemployment benefit duration Total labour tax rate

EPL for temporary jobs

EPL for regular jobs

EPL

Change in part-time regulation (more favourable) excluding the Netherlands Change in part-time regulation (more favourable) including the Netherlands Child benefits (%)

0.381 (10.30)***


0.0145 (
1.39)
1.090 (
0.99)


1.679 (
4.53)***


1.437 (
3.79)*** 0.622 (1.73)*

0.401 (12.14)***

2.512 (5.44)*** 0.072 (0.42)
0.0188 (
2.09)**
0.790 (
0.80)

0.165 (2.32)**

(2)

0.165 (2.10)**

(1)

0.354 (12.25)***

2.207 (4.65)***
0.126 (
0.93)
0.0103 (
1.25) 0.099 (0.12)


1.045 (
3.10)***

0.349 (6.26)***

(3)

0.393 (12.12)***

2.732 (6.9)*** 0.069 (0.41)
3.571 (
2.79)**
1.488 (
1.44) 10.42 (2.07)**


2.01 (
5.19)***

0.131 (1.81)*

(4)

0.317 (9.43)***

2.004 (5.02)*** 0.171 (1.12)
0.0164 (
2.13)**
1.431 (
1.69)*


1.551 (
4.52)***

0.125 (1.94)*

(5)

Dependent variable: the part-time employment rate (%) 14 European countries 1983–98

Table 7.3

0.271 (7.84)***

1.961 (3.51)***
0.337 (
2.25)**
0.026 (
2.62)***
0.087 (
0.09)


0.881 (
2.42)**

0.375 (6.74)***

(6)

0.327 (5.18)***

4.600 (3.65)***
0.684 (
1.51)
0.0363 (
1.63) 5.340 (1.67)*


2.999 (
3.88)***

0.029 (0.25)

(7)

301


13.637 (
2.45)** 172 14 yes no


0.071 (
1.53) 4.886 (5.35)*** 0.033 (1.34)


14.382 (
3.24)*** 172 14 yes no


0.075 (
1.73)* 4.424 (5.45)*** 0.079 (3.41)***


11.933 (
2.75)*** 172 14 yes no


0.075 (
1.93)* 2.313 (3.29)*** 0.082 (3.79)***


32.8138 (
8.22)*** 163 14 yes no


0.084 (
2.05)** 4.791 (5.73)*** 0.0574 (2.51)**


0.025 (
0.65) 8.230 (6.96)*** 0.051 (2.32)** 0.0728 (2.52)** 0.229 (2.85)***
42.834 (
7.31)*** 154 14 yes no 0.010 (0.45) 0.04168 (1.19) 0.0777 (1.18)
2.079 (
0.78) 154 14 yes no


0.090 (
2.74)***


0.350 (
2.52)** 6.489 (2.86)*** 0.169 (3.11)*** 0.0156 (2.17)** 0.302 (1.67)*
43.724 (
4.04)*** 154 14 yes yes

Sources: Eurostat (Labour Force Surveys); OECD Economic Outlook; OECD labour market database; Nickell et al. (2002), Nickell and Nunziata database.

Notes: Countries included EU-15 Member States except Luxembourg and Greece (Belgium, Denmark, Germany, Spain, France, Ireland, Italy, the Netherlands, Austria, Portugal, Finland, Sweden and the UK). To increase the cross-section dimension, we have also added Norway in the regression, for which data available however display a very poor time-series dimension. Absolute value of t-statistics in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. The equations (columns (1)–(7)) are generally estimated by generalized least squares allowing for heteroskedastic errors and common-across-group first order serial correlation. Each equation contains country dummies, intercepts and a specific dummy for 1990–91 to take into account German reunification. When indicated (column (7)), the two-stage least squares within estimator (fixed-effects regression estimator with instrumental variables) was also used as a method to correct for possible endogeneity. The instruments are the variables lagged by two years. The panel is unbalanced as part-time employment series are starting later for some countries (Austria, Finland and Sweden).

Observations Number of countries Time dummies Instrumented variables

Share of employment in services Constant

Schooling rate of 15–25 year olds Share of temporary jobs

Fertility rate

Output gap

302

New forms of employment relationships

an instrumental variable method (the two-stage least squares within estimator). The presence of endogeneity may be particularly problematic in the case of the relationship between the part-time employment rate and participation, or between part-time employment and EPL, where the causality is likely to go both ways. Lacking alternatives, we instrument the structural and institutional variables by their values lagged by two years. Results As shown by Table 7.3, a number of findings for our group of European countries emerge from the regressions in columns (1) to (6), which are confirmed when using instrumental techniques – column (7). The change in part-time regulation turns out to have a very strong and significant impact on part-time employment developments. This still holds true when excluding the Netherlands, although the magnitude of the effect decreases somewhat. Thus we conclude that the relaxation of regulations towards part-time work in the EU countries has had a significantly positive effect on the development of the PTR. Child benefits exert a strong negative and highly significant effect on the PTR. Thus, child benefits act as a disincentive to working part time and make it less likely for individuals to (re-)enter the labour market through part-time employment. The reasoning behind this phenomenon may be that benefits alter the trade-off between work and leisure towards a more family-oriented life. While overall EPL is only significant at a 10 per cent level, EPL on regular jobs is highly significant and has a strong and positive effect on the PTR. Conversely, EPL for temporary jobs is either insignificant or negatively related to the PTR. Unemployment benefit replacement ratios are found to be negatively correlated with the PTR, but their statistical significance is not robust across the various equations estimated. Unemployment benefit duration seems to impact the PTR only modestly and its effect is estimated with little precision. Although total labour taxes appear to positively affect the PTR in Table 7.3, this result is not robust when using other specifications (Buddelmeyer et al., 2004). This reflects the theoretical uncertainty as regards the overall impact of labour taxes on employment (for example, income versus substitution effects). Moreover, the total labour tax rate remains too crude and aggregate an indicator to fully identify the specific effect on part-time employment, which would require knowing the relative tax burden borne by part-timers compared to full-timers. Most controls turn out to be significant and of expected sign. The female participation rate is positively related to the PTR. The rise in female

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303

participation has resulted in an increase in the part-time rate in many countries, which appears to have been used as the main way of integrating women into the labour market. Similarly the fertility rate and the schooling rate of 15–24 year olds are also found to affect the PTR positively and, particularly in the case of the fertility rate, strongly. The share of employment in the service sector is revealed as a particularly powerful determinant of the rate of part-time work. This ties in with our analysis above of the trends in part-time work which highlighted that a high proportion of parttime jobs are within the service sector. The impact of the output gap is negative and significant in most equations, confirming earlier results from Buddelmeyer et al. (2004). Conversely, the temporary employment rate is found to have a positive influence on the PTR, which suggests the absence of substitution between part-time employment and temporary employment, and that these two flexibility schemes are complementary in circumventing the rigidity of European labour markets. The coefficients provided by Table 7.3 allow us to say something about the relative size of the contribution made to the PTR by working time regulations and general labour market institutions. Although providing insightful order of magnitudes, these results should be interpreted with caution on the basis of previously outlined caveats regarding endogeneity; furthermore, their application to future periods is subject to the Lucas critique. Our results suggest that changes in part-time regulation of a similar type and applied at a similar rate to those recorded in the past (as captured by a time trend following the reform) would induce an additional rise in the PTR of between 0.16 and 0.35 percentage points on average each year – see columns (2) and (3) in Table 7.3 – which amounts to between 1.6 and 3.5 percentage points rise after ten years. It follows that more effective policies or a faster pace of reform could entail larger rates of increase. With regard to institutions having an indirect effect on the PTR, our results reveal that a switch from the extreme case of no EPL regulation on regular contracts to the opposite situation of extremely stringent EPL15 increases the PTR by around 11–15 percentage points. This implies that the future PTR will be sensitive to changes in EPL, although the effect might materialize with some lag. Furthermore, our results suggest that a decline in the proportion of child benefits in GDP by 1 percentage point could lead to an increase in the PTR of around 1.5 percentage points, while conversely a decline in unemployment benefit replacement ratios by 1 percentage point would result in a weak increase in the part-time rate (of around 0.02–0.03 percentage point). Our results reveal the potentially strong impacts that other factors, if they continue, may have on the PTR. For example, an increase of 10 percentage points in the female participation rate, or in the share of service employment,

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would increase the PTR by around 3–4 percentage points and 2–3 percentage points, respectively, while a strong rise in the fertility rate of 0.5 percentage point would contribute to increasing the PTR by around 2.5 percentage points.

POLICY CONCLUSIONS Our results indicate that the part-time employment rate is significantly affected by institutional and policy changes. In particular policy measures geared toward encouraging part-time work are found to be positively related to actual part-time developments. These measures include both the legal framework affecting part-time positions and the creation of financial incentives (subsidies and improvement of social protection) to take up a parttime job. Moreover, other labour market institutions, including benefit systems, are found to significantly but indirectly influence the proportion of part-time employed. For instance, the stringency of employment protection legislation for regular contracts favours part-time work, suggesting that part-time employment may be firms’ response to rigid labour markets. Furthermore, child benefits and, to a lesser extent unemployment benefit generosity and duration, may hinder the increase in the part-time rate through the disincentive to take up employment that they induce. Consequently, further reforms of labour market institutions can be expected to facilitate growth in part-time employment and thereby to pull more people into paid employment. Our analysis shows that such reforms can contribute to meeting the employment targets set for 2010 at the 2000 Lisbon Council meeting. Currently, the low female and total employment rates in some EU countries require considerable efforts in order to reach the Lisbon targets. Evidence from the broader economic literature to date has shown that the increase in female participation and the increase in part-time employment seem to go hand in hand. Our results indicate that targeted policy to facilitate part-time employment represents a promising avenue worthy of further exploration. The increase in the proportion of part-time employment can be expected to continue, because its main driving forces (employers’ desire for flexibility, service sector expansion, women’s increased labour market participation and so on) are likely to persist in the medium to long run. Part-time work developments are also expected to disproportionately benefit the relatively underemployed age groups of young and older workers. Keeping more older workers in the workforce by offering part-time work solutions would also help to reduce the financial burden of an ageing population.

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In addition to raising the employment rate in the European Union, part-time employment gives employers greater flexibility in adjusting working hours to business requirements while achieving higher productivity. If freely chosen and protected by law, part-time jobs also offer workers a good way of striking a balance between their professional and private lives. Furthermore, in some cases part-time work can be a stepping-stone towards full-time employment. We find that the share of involuntary parttime employment (that is, underemployment) is negatively related to the rate of part-time employment. However, albeit fairly low, the share of involuntary part-time employment remains non-negligible. Policies designed to promote part-time work by lowering its labour costs relative to those of full-time employment are likely to have the perverse effect of increasing further the proportion of involuntary part-time workers, with negative consequences for women and other workers already at a disadvantage in the labour market. This segmentation of the labour market may induce lower hourly remuneration, poverty traps, insufficient access to training and lack of career development, which in turn negatively affect part-time workers’ long-term productivity. Thus, in order to be fully effective, employment policy should also address the issue of the gap between the type of jobs actually offered by firms and workers’ relative preferences for part-time or full-time employment as well as that of equal opportunity between full-time and part-time employees.

NOTES 1.

2.

3. 4. 5. 6.

The views expressed in this paper are those of the authors and do not necessarily reflect those of the European Central Bank (ECB) nor those of the European Commission. Gilles Mourre was working at the ECB whilst this paper was written. Contact e-mail addresses are: [email protected]; [email protected]; [email protected]. We would like to thank Nadine Leiner-Killinger (ECB) who compiled some of the information on part-time policies. The Stockholm Council set intermediate targets for 2005 of 67 per cent for the total employment rate and 57 per cent for female employment and an additional target to increase the EU employment rate for older working-age people (aged 55–64) to 50 per cent by 2010. These objectives are meant to contribute to making the EU ‘the most dynamic knowledge based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion’. Here the EU refers to the EU-15; we therefore consider the EU prior to its enlargement to 25 Member States in May 2004. OECD data are used for Finland instead of LFS, as LFS series are not available. The period is 1987–2002 for Spain. It should be noted that the figures disaggregated by age group do not add up to the overall share of involuntary part-time employment due to missing values in age groups. The existence of involuntary part-time employment raises the question to what extent part-time jobs facilitate entry to the labour market and offer opportunities for career development. O’Reilly and Bothfeld (2002) and Buddelmeyer et al. (2005), exploiting

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7.

8.

9. 10. 11.

12.

13. 14.

15.

New forms of employment relationships micro data, find that the stepping-stone effect of part-time employment remains quite limited, which suggests that involuntary part-time employment contributes to the segmentation of European labour markets. The agreement seeks to establish a general framework for the elimination of discrimination against part-time workers, and hopes to contribute towards the development of opportunities for part-time working on a basis which is acceptable to employers and workers alike. If child care benefits are means tested, then additional income from work is partially (or completely) offset by a reduction in this benefit, thereby reducing the incentives to increase labour supply. Generally speaking, the net gains to family income may then be just too small to make the effort worthwhile. How large these disincentive effects are depends on the actual design of the benefit. Such a measure was in place in France in the 1990s (‘Allocation Parentale d’Education’). It has been shown to have a strong adverse effect on female participation (Laroque and Salanié, 2003). Since the preparation of this chapter data have been made available for 2002. The trends displayed earlier in this chapter used data until 2001 only. The econometric estimations used data up to 1998, as most institutional data were only available up to this date. For some countries, such as Finland and Sweden, data has been back-cast using OECD Annual Labour Force Statistics, when available. Data are based on declaration of individuals on their working status (part-timers/full-timers) rather than on a precise measure of hours worked. The data therefore represent the most harmonized existing sources on part-time work. Technically this vector corresponds to ‘pooled after-reform country trends’. For each country we create a trend variable that takes the value of 0 prior to regulatory reforms, equals 1 in the first year following a reform to part-time regulation, equals 2 in the second year following reform and equals n in the nth year following reform. We then stack the observations of this variable country by country to compose a vector. Given the relatively limited time-series dimension, we follow this stacking approach and restrict the coefficients to be the same across countries, rather than estimating each country block separately, in order to increase the efficiency of the estimate and to capture the overall effect of reforms. This impact on the change in the PTR is likely to disappear in the long run, as it corresponds to the dynamic toward the new steady state implied by the reform of part-time regulation. The Netherlands, which experienced a much sharper increase in part-time employment than the other EU countries, stood out clearly as an outlier within the EU (Euwals and Hogerbrugge, 2004). More technically speaking, as the change in the regulatory framework for part-time jobs occurred before the start of the sample (that is, 1982), the value of the ‘policy’ variable for the Netherlands cannot really measure the effect of the change in regulation. It would instead simply capture the time trend. This corresponds to level 0 and level 6, respectively, of the EPL variable computed by the OECD (Nicoletti et al., 2000).

REFERENCES Belot, M. and J. van Ours (2000), ‘Does the Recent Success of some OECD Countries in Lowering their Unemployment Rates Lie in the Clever Design of their Labour Market Reforms?’, CEPR Discussion Paper No. 2492. Bentolila, S. and J.J. Dolado (1994), ‘Labour Flexibility and Wages: Lessons from Spain’, Centro de Estudios Monetarios y Financieros: Documento de Trabajo No. 9404.

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Blanchard, O. and J. Wolfers (2000), ‘The role of shocks and institutions in the rise of European unemployment: the aggregate evidence’, Economic Journal, 78 (2), 182–7. Blank, R.M. (1988), ‘Simultaneously modelling the supply of weeks and hours of work among female household heads’, Journal of Labor Economics, 6 (2), 177–204. Blank, R.M. (1990), ‘Are part-time jobs bad jobs?’, in G. Burtless (ed.), A Future of Lousy Jobs? The Changing Structure of US Wages, Washington, DC: The Brookings Institute, pp. 123–55. Blank, R.M. (1994), ‘The Dynamics of Part-time Work’, NBER Working Paper No. 4911. Buddelmeyer, H., G. Mourre and M. Ward (2004), ‘The Determinants of Part-time Work in EU Countries: Empirical Investigations with Macro-panel Data’, European Commission Economic Paper No. 213 and IZA Discussion Paper No. 1361. Buddelmeyer, H., G. Mourre and M. Ward (2005), ‘Part-time Work in EU Countries: Labour Market Mobility, Entry and Exit’, ECB Working Paper, forthcoming. Delsen, L. (1998), ‘When do men work part-time?’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 57–76. Doris, A. (1998), ‘Married women in the Irish part-time labour market’, Economic and Social Review, 29 (2), 157–78. Doudeijns, M. (1998), ‘Are benefits a disincentive to work part-time?’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Parttime Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 116–36. Euwals, R. and M. Hogerbrugge (2004), ‘Explaining the Growth of Part-time Employment: Factors of Supply and Demand’, IZA Discussion Paper No. 1124. Farber, H.S. (1999), ‘Alternative and part-time employment arrangements as a response to job loss’, Journal of Labor Economics, 17, S142–69. Genre, V., R. Gómez-Salvador, N. Leiner-Killinger and G. Mourre (2003), ‘A widening scope for non-wage components in collective bargaining in the EU?’, in G. Fagan, J. Morgan and F.P. Mongelli (eds), Institutions and Wage Formation in the New Europe, Cheltenham, UK and Northhampton, MA, US: Edward Elgar, pp. 134–73. Ginn, J. and S. Arber (1998), ‘How does part-time work lead to low pension income’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 156–74. Haskel, J., B. Kersley and C. Martin (1997), ‘Labour market flexibility and employment adjustment: micro evidence from UK establishments’, Oxford Economic Papers, New Series, 49 (3), 362–79. Houseman, S. (2001), ‘Why employers use flexible staffing arrangements: evidence from an establishment survey’, Industrial and Labor Relations Review, 55 (1), 149–69. Laroque, G. and B. Salanié (2003), ‘Participation, fertility and financial incentives in France’, presented at ECB/CEPR Labour Market Workshop ‘What Explains the Pattern of Labour Supply in Europe?’, 23–24 June, Frankfurt am Main. Lemaitre, G., P. Marianna and A. Van Bastelaer (1997), ‘International comparisons of part-time work’, OECD Economic Studies, 29, 139–52.

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Long, J.E. and E.B. Jones (1981), ‘Married women in part-time employment’, Industrial and Labor Relations Review, 34, 314–25. Nakamura, A. and M. Nakamura (1983), ‘Part-time and full-time work behaviour of married women’, Canadian Journal of Economics, 16, 229–57. Nickell, S., L. Nunziata, W. Ochel and G. Quintini (2001), ‘The Beveridge Curve, Unemployment and Wages in the OECD from the 1960s to the 1990s’, prelim. version, CEP Discussion Papers No. 502, Centre for Economic Performance, London School of Economics. Nickell, S., L. Nunziata and W. Ochel (2002), ‘Unemployment in the OECD Since the 1960s. What Do We Know?’, Bank of England Discussion Papers, May. Nicoletti, G., S. Scarpetta and O. Boylaud (2000), ‘Summary Indicators of Product Market Regulation with an Extension to Employment Protection Legislation’, OECD Working Papers No. 266. OECD (1998), ‘Working hours: latest trends and policy initiatives’, Employment Outlook, 173–88. O’Reilly, J. and S. Bothfeld (2002), ‘What happens after working part-time? Integration, maintenance or exclusionary transitions in Britain and western Germany’, Cambridge Journal of Economics, 6, 409–39. Pfau-Effinger, B. (1998), ‘Culture or structure as explanations for differences in part-time work in Germany, Finland and the Netherlands’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 177–98. Ruivo, M., M.P. Gonzalez and J.M. Varejao (1998), ‘Part-time work in Portugal and Spain’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 199–213. Scarpetta, S. (1996), ‘Assessing the role of labour market policies and institutional settings on unemployment: a cross-country study’, OECD Economic Studies, 2 (26), 43–82. Smith, L., C. Fagan and J. Rubery (1998), ‘Where and why is part-time work growing in Europe?’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 35–56. Walwei, U. (1998), ‘Part-time work: a threat to labour standards?’, in J. O’Reilly and C. Fagan (eds), Part-time Prospects: An International Comparision of Part-time Work in Europe, North America and the Pacific Rim, London and New York: Routledge, pp. 96–115.

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DISCUSSION Rob W. Euwals The international policy debate on part-time employment changed considerably in the last few decades. Before the 1990s many economic observers highlighted the underutilization of labour and discussed the incidence of involuntary part-time employment. On the other hand, already at the beginning of the 1980s the OECD (1983) concluded that most parttime employment is voluntary. Moreover, authors like Hart (1987) and Hamermesh (1993) consider part-time employment as an outcome that can be in the interest of both firms and workers. In recent years, economic observers started to open up their minds to part-time employment as a potential advantage rather than a trap for workers. Bollé (1997) concludes that, if it is chosen freely and protected by law, part-time employment offers a good way of striking a balance between time to earn a living and time to devote to other activities. The OECD (2001) discusses part-time employment as a way of helping parents into paid employment and to balance work and family life. The literature on social policies and employment distinguishes two models for combining paid employment and private care responsibilities. Social democratic countries like Sweden, Norway and France rely on government intervention, while liberal countries like the USA, Canada and Australia leave the opportunity for combining work and family life to the market. Both models are successful, as female employment rates are high and fertility rates are reasonably high in these countries. Part-time employment may be interpreted as an alternative model to combine work and private care responsibilities. Box 4.1 of OECD (2001) shows that the country with the highest part-time employment rate, the Netherlands, experienced a rather moderate decline in fertility despite the strong increase in female employment. In this chapter, Buddelmeyer, Mourre and Ward try to measure the impact of policy on the rate of part-time employment. They take a kind of difference-in-differences approach: they compare the growth of part-time employment in countries before and after implementation of policy on part-time employment and compare this to the growth in countries without such policy. The authors use data on countries that are part of the EU. This is unfortunate as they neglect interesting examples like Australia, Switzerland and the USA. The authors find a significant impact of policy on the rate of part-time employment. I discuss two issues that remain open despite the clear result:

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(1)

(2)

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Why does a country with a relatively competitive labour market like the UK have such a high part-time employment rate? The same holds for Australia. In the econometric model this should be explained by other exogenous variables, but the impact of institutional factors like employment protection and unemployment benefits does not seem to work in the right direction. Is the impact of policy really a causal one? The country with the highest part-time employment rate, the Netherlands, implemented laws on part-time employment in the 1990s while the growth of part-time employment started much earlier.1 The general view in the Netherlands is that policy facilitates part-time employment, but it does not necessarily lead to more growth. Both policy and the growth of part-time employment may be caused by a latent preference within society.

In this chapter the authors support the general view that part-time employment contributes to the economic performance of the EU countries as it allows parents with young children and in particular mothers to participate in the labour market. Part-time employment may even help countries to reach the employment targets for 2010 of the 2000 Lisbon Council meeting. Is part-time employment good news only? One fact may be worrying: part-time employment is a women’s business. On the basis of the British Census of Population, Joshi (1998) states, ‘the role sharing couple both doing part-time jobs while they bring up children did become more common over the decade but only from the level of 2 to 3 per thousand’. Furthermore, Booth and Coles (2004) suggest that part-time employment may distort human capital investment, leading to a trap for part-time workers. They claim that child care policy provides more efficient incentives. In this respect it is interesting to see that countries like Sweden and Norway have a reasonably high part-time rate but currently experience a decline in female part-time employment. The growth of part-time employment in the EU may therefore be a temporary phenomenon: young generations of women may be participating in the labour market at a higher rate than older generations because of the opportunity to work part time, but future generations of women may decide to work full time. Note 1. The tripartite agreement of 1982 included agreements on working time reduction, but not on part-time working. The agreement may have triggered economic growth, which in turn may have led to more part-time employment.

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References Bollé, P. (1997), ‘Part-time work: solution or trap?’, International Labour Review, 136 (4), 557–79. Booth, A. and M. Coles (2004), ‘Part-time employment traps and childcare policy’, mimeo, Australian National University. Hamermesh, D. (1993), Labor Demand, Princeton: Princeton University Press. Hart, R. (1987), Working Time and Employment, Winchester, MA: Allen and Unwin. Joshi, H. (1998), ‘The opportunity costs of childbearing: more than mothers’ business’, Journal of Population Economics, 11, 161–83. OECD (1983, 2001), Employment Outlook, Paris: OECD Publications Service.

8.

Matching workers to jobs in the fast lane: the operation of fixed-term contracts José M. Varejão and Pedro Portugal*

1

INTRODUCTION

Typically, fixed-term contracts give employers the opportunity to lay off workers at lower cost and with fewer restrictions than is the case for regular (permanent) workers. These two features are expected to make fixed-term contracts a useful option for employers, particularly in labour markets where the costs of terminating a permanent contract are high. As such, fixed-term contracts become a component of labour market flexibility, one that seems particularly appropriate to adjust the level of the workforce to fluctuations of labour supply and demand, expected or not. Many European governments have tried to reform the labour market, leaving existing contracts unchanged but reducing (or eliminating) firing restrictions on new contracts. The focus on the flexibility role of fixed-term contracts has fuelled a debate about the quality of jobs and the opportunities for career advancement offered to temporary workers. The concern is that workers with fixedterm contracts are used as buffer stocks, insulating permanent workers from fluctuations in labour demand. Should this be the case, temporary jobs should be expected to result in job loss, and workers should suffer from poor job security and receive little training, which would further harm their prospects of subsequently finding a better job. Another consequence of using fixed-term contracts to avoid higher prospective firing costs is that employment adjustment to shocks should be speeded up. However, in a study of the speed of employment adjustment in German manufacturing over a period of 15 years, Hunt (2000) found no evidence that the 1985 law aimed at facilitating the use of fixedterm contracts resulted in faster adjustment. Restrictions on rolling over fixed-term contracts were identified as one of the reasons behind this puzzling result. 312

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The fact that fixed-term contracts cannot last forever and must be, at some point, either made permanent or terminated, severely reduces the usefulness of their availability as an instrument of flexibility. Restrictive roll-over clauses may however create an extra reason to use fixed-term contracts. Churning, that is, match replacement at continuing positions, may be the employer’s optimal response to the prohibition of contract roll-over, although being an unintended result of those restrictions (Varejão, 2000; Blanchard and Landier, 2002). If, at the end of a given duration, the employer decides to keep a temporary worker, his contract becomes permanent and subject to regular firing costs. To avoid this consequence, the employer has an incentive to destroy this (otherwise) good match and hire a replacement match of an uncertain value. The result is unproductive churning. If such use of fixed-term contracts dominates, workers, especially those entering the labour market, will have to go through a succession of low productivity temporary jobs and many spells of unemployment before obtaining a regular job. However, fixed-term contracts may also serve in a third role. In high firing cost settings, investments in pre-hiring screening are the employers’ optimal response to the costs of having bad regular matches terminated. The availability of low firing cost contracts changes the preferred mix of pre-hiring and on-the-job screening, increasing the demand for the latter. With fixed-term contracts, employers have the incentive to hire workers for a trial period, observe them on the job, gaining information about the true value of the match, and only then make the decision about keeping or laying off a worker, holding the option of a low-cost dismissal throughout. If fixed-term contracts are used as screening devices, for workers they act as stepping-stones to regular positions. By definition, not all newly formed matches will survive and become regular matches. Some will not meet the employers’ reservation match value and will end up as layoffs followed by replacement hirings. Others will not offer the worker’s utility reservation level and will end up as quits. Others still will be successful matches and will result in the fixed-term contract being converted into a regular contract. As a result, the productivity value of the surviving matches is enhanced and employment relationships are more stable, both facts fostering investments in human capital and productivity growth. The consequences of fixed-term contracts in terms of the employment and wage dynamics of workers, as well as the corresponding policy implications, are closely related to the objectives of the employers’ staffing policies. In a companion paper (Varejão and Portugal, 2003), we found evidence of fixed-term contracts being used in the Portuguese labour market mostly for the purpose of screening workers for regular positions.1 In this chapter, we look at the worker’s side and, after establishing the profile of the typical

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temporary worker, we examine the impact of fixed-term contracts on wage profiles of temporary and regular workers, and the patterns of transitions across labour market states. The chapter is organized as follows. Section 2 describes the institutional framework. In Section 3 the data set used in empirical work is described. Section 4 describes the most salient features of fixed-term employment in Portugal, while Section 5 compares wages of temporary and regular workers, and Section 6 looks at transitions across labour market states. Section 7 concludes.

2

INSTITUTIONAL FRAMEWORK

Rules governing the termination of permanent contracts in Portugal are widely perceived as very restrictive, placing the country high in all international rankings of labour market rigidities (for example OECD, 1999). Fixed-term contracts, which were first regulated in 1976, were conceived as an instrument of flexibilization at the margin. The mushrooming of these contracts eventually led to major changes in the legal framework governing both regular (permanent) and fixed-term contracts in 1989, when most rules currently in force were established. As a result, fixed-term contracts are permitted for objective reasons (replacement of temporarily absent permanent workers, exceptional workload, seasonal work, and specific projects). They are also permitted for business start-ups, the launching of new activities of uncertain duration, and recruiting long-term unemployed workers and first-time job seekers. Fixed-term contracts have a minimum duration of six months unless they are justified on objective grounds, in which case no minimum applies. Their maximum duration is set at three years (two in the cases of business start-ups and the launching of new activities). Three successive renewals is the maximum permitted. The contract expires only if the employer notifies the worker eight days in advance that he does not intend to renew it; otherwise it is automatically renewed. If the maximum duration of the contract is exceeded, the contract automatically becomes permanent. When the end of a temporary contract is reached and the worker is not offered a regular position, he or she becomes entitled to a terminal bonus equal to two days’ pay for each complete month of service. If the employer terminates the contract before its term, and the termination is unlawful, the worker is entitled to compensation equal to the pay loss from dismissal to the date of the court’s decision or the term of the contract (whichever occurs first). He or she is also entitled to reinstatement if the term of the contract has not yet been reached. If during the period of the contract

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a vacancy for a permanent position opens at the firm, employees with fixed-term contracts at that moment who may qualify for the job are given priority over other applicants. Contracts surviving for 12 months that are terminated for reasons not attributable to the worker cannot be replaced within a period of three months. Hence, fixed-term contracts offer Portuguese employers the opportunity of hiring new workers on a much less stringent basis than regular contracts. However, fixed-term contracts are also subject to a number of restrictions that may limit their practical usefulness. As mentioned above, clauses prohibiting the roll-over of contracts may give rise to unproductive churning, but the prohibition on replacement of temporary workers that stay with the firm for one year actually makes churning a less interesting option.

3

DATA

The Household Employment Survey is a quarterly employment survey run by the Instituto Nacional de Estatística (INE). In every quarter it randomly surveys approximately 40 000 individuals. One-sixth of the sample is rotated out every quarter. Seven spells of the survey, from the first quarter of 1998 to the third quarter of 2000, were available to this study, covering 56 140 individuals. Individual identification numbers permit the tracking of individuals over consecutive spells of the survey (5/6 from quarter to quarter). This makes computation of quarterly transition rates across labour market states (employment, unemployment, and not in the labour force) possible. Because information on the type of contract held by the employed is available, it is possible to compare the pattern of transitions for workers on fixed-term and open-ended contracts. The survey also contains detailed information on the individual’s demographic characteristics, his or her labour market background, current job characteristics, current wage, and job-search efforts. This allows for a full characterization of the temporary workforce and extensive control for other relevant job and workers’ characteristics in regression analysis.

4

BASIC FACTS ABOUT FTCS

Between 1991 and 1998, fixed-term contracts represented an average of 14 per cent of total employment (Figure 8.1a), but an average of 62 per cent of all accessions and 43 per cent of all separations (Figure 8.1c and d).

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%

1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q 91 991 992 992 993 993 994 994 995 995 996 996 997 997 998 998 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (a) Share of FTCs in total employment

20 18 16 14 12 10 8 6 4 2 0

Open-ended

FTCs

1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q 91 991 992 992 993 993 994 994 995 995 996 996 997 997 998 998 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (b) Total employment, by type of contract

0

100 000

200 000

300 000

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317 20 10 0

20

10

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Figure 8.1

Proportion of fixed-term contracts in total employment and worker flows

1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1Q 91Q 92Q 92Q 3Q 93Q 94Q 94Q 95Q 95Q 96Q 96Q 97Q 97Q 98Q 98Q 9 9 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 (d) Share of FTCs in total separations

30

40

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80

30

40

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1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1Q 91Q 92Q 92Q 93Q 93Q 4Q 94Q 95Q 95Q 96Q 96Q 97Q 97Q 98Q 98Q 9 9 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 (c) Share of FTCs in total accessions

%

80

%

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New forms of employment relationships

That the importance of fixed-term contracts is under-evaluated by pointin-time stock counts is confirmed by comparing the number of workers with a fixed-term contract at the end of each calendar year with the number of those who, at any point within the year, were employed with those contracts. For firms with at least 100 employees, point-in-time measures capture only 54 per cent of the total number of individuals with fixed-term contracts at any point within the period (Social Audit).2 Across plants there is an enormous diversity in the intensity of use of temporary workers in total employment. The distribution of the share of temporary employment has a large mass point at zero that represents 75 per cent of all establishments. However, for the remaining 25 per cent, the intensity of use of temporary workers spans the entire range. Nearly 2 per cent of the establishments, presumably newly created establishments, employ temporary workers only. Excluding units that do not employ temporary workers, the mode of the distribution occurs in the interval corresponding to 15 to 20 per cent of total employment (Establishment Employment Survey, establishments of all sizes, in all sectors considered). The incidence of fixed-term contracts varies dramatically across occupational categories. Temporary contracts account for only 4 per cent of total employment of managers, but this share varies inversely with the skill level, reaching 68 per cent for apprentices at the lower end of the scale.3 This pattern of variation of the incidence of fixed-term contracts across skill levels indicates that firms use staffing policies that differ enormously across occupational categories. At the top of the occupational scale, workers are either recruited as permanent or they are offered an open-ended contract after a short trial period. For lower skill levels, individuals are predominantly hired under fixed-term contracts, which, on average, have longer duration. Workers with fixed-term contracts are predominantly women (54.1 per cent of total employment with fixed-term contracts) and are younger and better educated than workers with open-ended contracts (Table 8.1). The average age of workers with fixed-term contracts is 30.5 years (38.8 for permanent workers) and their average number of years of education is 8.2 (7.2 for permanent workers). The higher educational attainment of temporary workers is not independent of the fact that they are younger, because the nine years’ mandatory schooling is still recent in Portugal.

5

WAGE PROFILES

Raw differences between monthly wages of temporary and permanent workers indicate that fixed-term contracts are clearly associated with lower pay – the average hourly wage of employees with fixed-term contracts is

Matching workers to jobs in the fast lane

319

Table 8.1 Structure of total employment, by type of contract and individual attributes (percentage)

By gender Male Female By age group 16–20 21–35 36–55 55 By schooling level 4 years 4 years 9 years 12 years Technical school College

Fixed-term contract

Open-ended contract

All

45.9 54.1

54.7 45.3

53.6 46.4

14.8 57.2 25.5 2.5

4.3 36.7 50.3 8.7

5.6 39.4 47.1 7.9

3.7 49.4 21.1 15.4 2.4 8.0

5.2 59.0 14.6 11.8 3.3 6.1

5.1 57.7 15.4 12.3 3.2 6.3

82.6 per cent of the corresponding measure for employees with open-ended contracts. To fully control for the individual observed characteristics of both the worker and the employer, Mincerian-type wage equations for workers on fixed-term and open-ended contracts were estimated. The set of regressors includes human capital variables (age, schooling and vocational training), two dummies indicating if the worker is in his first job and if he is in a part-time job, and controls for the type of employer (public administration, state-owned company and other). A dummy variable indicating the type of contract the individual has (fixed-term or open-ended) is added, multiplied by the age variable (49*2 coefficients, corresponding to ages between 16 and 64 and the two contract types). The dependent variable is the log of the monthly wage. For our purpose it is essential to compare the wages received by workers with temporary and permanent contracts at different points of their working lives. To do that we represent in Figure 8.2 the estimated log of the monthly wage corresponding to each type of contract–age pair. Two third-degree polynomial regression curves were added to the figure. The figure shows that both curves have the usual shape. Workers in the early years of their careers receive similar pay, independently of the type of contract they have. If anything, workers with fixed-term contracts receive slightly higher wages than similar workers with open-ended contracts, which could indicate the presence of a compensating differential for reduced job

320

New forms of employment relationships

11.2

11.1

11.0 open-ended contracts

Monthly wage (log)

10.9

10.8

fixed-term contracts

10.7

10.6

10.5

10.4

10.3

15

Figure 8.2

20

25

30

35 40 Age

45

Age–earnings profile, by type of contract

50

55

60

321

Matching workers to jobs in the fast lane

security associated with temporary contracts. However, the situation is completely reversed as we look at older workers – at the age of 30, the penalty is about 10 per cent of the wage received by a permanent worker. The gap grows wider with the workers’ age, reaching 20 per cent for workers aged 50.

6 6.1

WORKER FLOWS Empirical Transition Rates

If firms use fixed-term contracts predominantly for screening, for workers these contracts are stepping-stones to a permanent form of employment. At the same time, if fixed-term contracts are used for churning or to deal with workload fluctuations they are a transitory form of employment with poor prospects of career advancement. Employment by fixed-term contract is a transient state by design. Several destination states are admissible. Here we consider transitions out of temporary employment into unemployment, inactivity and another job, as well as transitions from temporary to permanent positions with the same employer. As a benchmark, the corresponding transition rates originating in regular employment were also computed. Results are in Table 8.2.4 Transitions out of employment are less frequent in Portugal than in other countries.5 However, because of the very nature of those contracts, we expect that workers with fixed-term contracts change their status more frequently, which is exactly what we see in Table 8.2. Workers with fixedterm contracts are about five times as likely to lose their jobs, and nine times more likely to become unemployed as are workers on open-ended contracts. This is, of course, what one could expect whatever the reasons firms use fixed-term contracts. The second point is that about 5.1 per cent of all workers on a fixed-term contract are likely to be given an open-ended contract every quarter. This is suggestive of the importance of screening as a motivation for offering fixed-term contracts to newly admitted workers. Table 8.2 Quarterly transition rates from employment, by type of contract (percentage)

From OEC (1) From FTC (2) (2)/(1)

To unemployment

To inactivity

To another job

To an OEC

0.36 3.21 8.9

0.78 3.08 3.9

0.75 3.36 4.5

5.05

To an FTC 0.32

322

6.2

New forms of employment relationships

Estimation Procedure

Duration analysis provides a convenient statistical framework to study worker flows. The estimation of the hazard function, as applied to the context of job duration, starts with the definition of the duration variable (t) that measures the workers’ tenure in the job. All workers are observed over a fixed time interval of one quarter. A useful concept in statistical analysis of a duration phenomenon is the hazard function. In the study of job duration, the hazard function gives the instantaneous probability of moving out of a job (or ending a fixed-term contract) at t, given that a worker stayed in the job (or with a fixed-term contract) until t: h(t)  lim

t →0

(P(t  T  t  t | T  t) f (t) f(t)   t 1
F(t) S(t)

(8.1)

where f(t) is the probability density function, F(t) is the distribution function and S(t) is the survival function. A useful function is the integrated hazard function t

Λ(t) 

 h(u)du

(8.2)

0

which relates to the survivor function simply by






t



S(t)  exp
h(u)du  exp(
Λ(t)). 0

(8.3)

In this chapter we employ a conventional piecewise-constant hazard model with five segments:

  

1,
if 0  t  6, 2,
if 6  t  12, h(t) 
3,
if 12  t  24, 4,
if 24  t  36, 5,
if 36  t  .

(8.4)

We shall also distinguish between three exit modes from a job (unemployment, inactivity, and other job) and three destinations out of the fixed-term contract regime (permanent contract, other fixed-term job, and

Matching workers to jobs in the fast lane

323

non-employment). Thus, we define cause-specific hazard functions to destination j (P(t  T  t  t, R  r | T  t) (8.5) h(t)r  lim t→0 t which yields the aggregate hazard function h(t) 

3

 hj(t)

(8.6)

j1

and the survivor function S(t) 

3

Sj(t). ! j1

(8.7)

The model has a conventional competing risks interpretation. In this framework, a latent duration (Tj) attaches to each exit mode. We only observe the minimum of each latent variable. If risks are assumed to be independent, with continuous duration, this model simplifies to three separate single-cause hazard models. The same simplification can be obtained if we assume that transitions can solely occur at the limits of the intervals, which, for the sake of simplicity, we will do. A common way to accommodate the presence of observed individual heterogeneity is to specify a proportional hazards model h(t | x)  h0j (t) exp(x j)

(8.8)

where h0j(t) denotes the baseline specific hazard function, that is, the hazard function corresponding to zero values for the covariates x. In this case, the covariates affect the hazard function proportionally – that is, (dh(x)/dxk) kh(x). An implication of this assumption is that the impact of the covariates does not change (in relative terms) with the progression of the spell of inaction. Our information on the elapsed duration is grouped into quarterly intervals (while transitions can only be identified over a fixed interval of one quarter). Let Mm denote the occurrence of an exit in a given month [ct
1, ct], where m is the realization of a discrete random job duration variable M  (1, . . ., K). The probability that an event occurs in the mth interval (that is, that an exit occurs over the course of the three-month window), and that such an exit is to destination r, will be given (neglecting, for the sake of parsimony, the t and x variables) by the complementary log-log function hj (m |x)  1
exp[
exp(mj  x j)]

(8.9)

324

New forms of employment relationships

where t condenses the value of the cumulative hazard function over the interval m. With our sampling plan, the contribution of observation i for the likelihood function is simply L( ,  | t, j, x) 

K
1

3

! ! m1 j1

[Sj (m
1)
Sj (m)]mj

! K

m1

Sj(m)



1
mj

(8.10)

where mj is an indicator that assumes the value 1 if the individual exits to destination j during the mth interval, and 0 otherwise. Finally, since individual observations can be repeated (up to six times) the model has to accommodate the presence of individual-specific error terms. 6.3

Estimation Results

The data set used covers ten potential transitions (11 quarters). Overall, it contains 98 325 observations, 13 per cent of which (12 847) correspond to workers with fixed-term contracts. Table 8.3 presents the results for transitions out of employment into unemployment (E-U), out-of-labour-force (E-I) or another job (E-E). These results clearly indicate that workers with fixed-term contracts face higher probabilities of transition to all the destination states considered, as indicated by the parameters of the baseline hazard functions for openended and temporary contracts. It is also clear that transition rates decline with job duration.6 Transitions out of employment are significantly influenced by human capital variables. Workers with more years of education are less likely to leave their jobs to become unemployed or inactive, but are more likely to leave for another job. Higher wages are also associated with fewer transitions to other jobs (E-E), which is consistent with a matching interpretation. Things are less clear-cut with labour market experience. The positive sign in the employment to out-of-the-labour force (E-I) equation captures retirements. The negative sign in the employment-to-employment equation has an obvious matching interpretation – more experienced workers are more likely to have found good matches and, for that reason, they are less likely to switch jobs voluntarily. However, a matching interpretation could imply a negative sign in the employment to unemployment, which is contrary to the result we obtained. The fact that experience enhances the probability of becoming unemployed could be the result of mass layoffs, associated or not with plant closings, that imply the termination of good and bad matches alike (hence, more flows of regular workers into unemployment) and that hit disproportionately more experienced workers.

325

Matching workers to jobs in the fast lane

Table 8.3

Transitions out of employment E-U estimate

Sex
0.1687** Training 0.2651** Schooling
0.0009 First job 0.0426 Experience 0.0029 Left because Dismissal 0.2127*** End FTC 0.4323 Quit 0.0155

E-I

E-E

std error

estimate

0.0783 0.1212 0.0121 0.1329 0.0039


0.4738* 0.0501
0.0550* 0.5181* 0.0244*

0.0663 0.3629* 0.1202 0.0504 0.0105 0.0123 0.0982
0.4471* 0.0030
0.0280*

0.0649 0.1074 0.0098 0.1018 0.0036

0.1260 0.1166 0.1330


0.2991*
0.0032
0.2094**

0.1065
0.0858 0.1010 0.0608 0.1058 0.0745

0.1037 0.0988 0.0953


0.0661 0.1545
0.1525

0.0977
0.6690* 0.1217
0.4559* 0.0928
0.4490*

0.1269 0.1636 0.1523

Type of employer Public adm.
0.2364*** 0.1253 Public firm
0.3133*** 0.1855 Hourly wage
0.5049* 0.1189 (log)

std error

estimate

std error

Baseline-hazard Open-ended contracts 1 0.0352 2
0.6532 3
0.8581 4
1.0775 5
2.2101**

0.8607 0.8664 0.8625 0.8704 0.8746


2.0740*
2.7707*
2.8962*
3.0804*
3.7878*

0.6746 0.3491 0.6800 0.0209 0.6727
0.3890 0.6781
0.8153 0.6793
1.3544***

0.6943 0.6953 0.6952 0.7035 0.7043

Baseline-hazard Fixed-term contracts 0.7810 1 2 0.5720 3 0.0295 4
0.3520 5
0.2717

0.8509 0.8533 0.8587 0.8825 0.8867


1.4999**
1.6094**
2.3958*
2.3391*
2.9043*

0.6640 0.5299 0.6642 0.4651 0.6721 0.0561 0.6892
0.1213 0.7084
0.6707

0.6899 0.6915 0.6966 0.7166 0.7422

N 98 325 Log likelihood
3662.6

98 325
5295.0

98 325
5271.6

Note: Piecewise-constant hazard model with random effects. The model also includes ten time dummies. *, ** and *** denote statistical significance of the regression coefficients at 1, 5 and 10 per cent, respectively.

326

New forms of employment relationships

In the three equations, the type of contract was interacted with tenure. The results show that the conditional probabilities of making all the transitions considered decrease with tenure. The slope of the hazard is higher in the case of workers with open-ended contracts than it is for workers with fixed-term contracts. For workers with open-ended contracts, the probability of becoming inactive and of switching jobs is close to its baseline level after 12 months of tenure. The focus on the effects of temporary employment episodes on workers’ career prospects highlights the importance of looking at transitions originating in temporary employment. The empirical transition rates from temporary employment to permanent employment and to unemployment are depicted in Figure 8.3. Transitions to permanent positions peak at 12 and at 36 months. Conversely, transitions to unemployment are highest for very short durations, although they have local peaks at 12 and 24 months. The pattern of transitions observed corresponds to the one implied by a matching process – bad matches are quickly undone and good matches become permanent within a short period (12 months), but also as the legal maximum duration of these contracts (36 months for a number of cases) is reached. Local peaks at 12 and 24 months in transitions to unemployment may however indicate that these contracts also serve (as they are expected to) to fill temporary positions and are therefore terminated at their terminal date. To analyse this issue further, transitions originating in temporary positions were studied in the context of multivariate analysis. Three destination states were considered – from a temporary position to a permanent one with the same employer (FTC-OEC in Table 8.4), from one temporary position to another temporary position with a different employer (FTCFTC), and from a temporary position to non-employment (FTC-NE). The equation for transitions from fixed-term to open-ended contracts highlights the role of some human capital variables. Workers with more schooling and training, but not with more experience, are more likely to receive a permanent contract. Wages are inversely related to the probability of moving from a fixed-term to an open-ended contract, which means that there is a wage differential compensating workers for job insecurity, but not necessarily for having a fixed-term contract. Wages are also inversely related to the probability of moving to another temporary job or to non-employment. The shape of the baseline hazard function on transitions to permanent employment confirms the results already mentioned – bad temporary matches are quickly undone, but those surviving for longer periods are more likely to become permanent.

327

Figure 8.3

Quarterly transition rate

0

6

12

18 24 Job duration (in months)

to unemployment

to an open-ended contract

Transition rates from fixed-term contracts

0

1

2

3

4

5

6

7

8

9

10

30

36

328

Table 8.4

New forms of employment relationships

Transitions originating in temporary employment FTC-OEC

FTC-FTC

estimate

std error

estimate

0.2636* 0.0719 0.0328*
0.1973
0.0076***

0.0815 0.1292 0.0119 0.1388 0.0045

0.1090
0.0523 0.0331**
0.2739
0.0210*

0.1199
0.4466* 0.1917 0.2080** 0.0176
0.0469 0.2051 0.4786* 0.0072 0.0062

0.0761 0.1149 0.0112 0.1297 0.0038

0.1352 0.1258 0.1331


0.0909 0.1709
0.0334

0.2181
0.0175 0.1766 0.3306* 0.2107
0.0517

0.1324 0.1097 0.1398

Type of employer Public adm.
0.4502* Public firm
0.3678** Hourly wage
0.2109** (log)

0.1273 0.1791 0.1037


0.4888**
0.1166
0.3086

0.2027 0.1125 0.2505
0.0001 0.1713
0.1926***

0.0992 0.1507 0.1105

Baseline-hazard 1
2.3979* 2
1.7868** 3
1.6655** 4
1.1507*** 5
1.3755***

0.8086 0.8085 0.8102 0.8171 0.8265


0.9154
1.0156
1.5358
1.5777
2.7941**

1.2294 1.2332 1.2399 1.2574 1.3170


0.3897
0.5603
1.1990
1.3871***
1.6685**

0.7311 0.7329 0.7365 0.7496 0.7604

Sex Training Schooling First job Experience Left because Dismissal End of FTC Quit

0.1393 0.0086 0.1925

N 19 298 Log likelihood
2525.5

12 928
1370.4

std error

FTC-NE estimate

std error

12 928
2860.9

Note: Piecewise-constant hazard model with random effects. The model also includes ten time dummies. *, ** and *** denote statistical significance of the regression co-efficients at 1, 5 and 10 per cent, respectively.

Less experienced workers are more likely to engage in on-the-job search and, therefore, switch jobs. Controlling for the same worker’s characteristics as before, as well as for other relevant factors (such as part-time status, work at unusual hours, and over-education), we find that having a fixedterm contract significantly raises the probability that the worker searches for another job (Table 8.5). This indicates that fixed-term contracts are search devices also for workers. Their advantage as such is that they permit searching for the adequate match while employed, thereby reducing the costs of search, with the likely additional advantage of giving workers (especially those with reduced labour market experience) access to better wage distributions (Connolly and Gottschalk, 2001).

329

Matching workers to jobs in the fast lane

Table 8.5

On-the-job search behaviour – probit results Estimate

std error

mg effect

Sex Schooling Training Experience Tenure First job FTC Part-time Unusual hours Hourly wage (log) Over-education

0.0283 0.0355* 0.2201*
0.0022
0.0036*
0.1150** 0.2294* 0.6755* 0.0546
0.4076* 0.5643*

0.0451 0.0061 0.0606 0.0018 0.0004 0.0540 0.0481 0.0697 0.0441 0.0567 0.0609

0.0006 0.0007 0.0056
0.00004
0.0001
0.0019 0.0059 0.0300 0.0011
0.0078 0.0219

Type of employer Public administration State-owned Constant

0.0277
0.0987 0.4382

0.0684 0.0956 0.3961

0.0005
0.0017

N Log likelihood

99 115
3206.1

Note: * and ** denote statistical significance of the regression coefficients at 1 and 5 per cent, respectively.

In the on-the job search equation, schooling, vocational training and over-education (a dummy variable equal to 1 if the employed worker believes that his or her qualifications exceed those necessary at his or her current position) are positively and significantly associated with search efforts.

7

CONCLUSIONS

Fixed-term contracts are, by design, associated with more transitions out of employment to all destination states. The results in the employment to unemployment and employment to inactivity equations could indicate that fixed-term contracts are ‘dead-end’ jobs. However, in a previous paper we found that employers use fixed-term contracts essentially for screening workers for permanent positions. We argue that the contradiction is only apparent. More transitions from fixed-term contracts to other jobs or nonemployment status is not contradictory with these contracts being used as mechanisms of search by employers and workers.

330

New forms of employment relationships

The survival rate of newly-formed matches is endogenous to the amount of pre-hiring search effort. From the standpoint of firms, the availability of low termination cost contracts may stimulate less pre-hiring screening and thereby reduce the proportion of new temporary matches that become permanent. The quarterly rate of transition from a temporary to a regular job indicates that a non-trivial share of all fixed-term contracts eventually become permanent. This result unequivocally means screening. In this context, fixed-term contracts become stepping-stones to regular forms of employment. The age–earnings profile of workers with fixed-term and regular contracts compared indicates that temporary employment is costly for older workers with more cumulated labour market experience if, for some reason (for example, job loss due to plant closings), they lose their regular jobs. Temporary employment implies the loss of returns to experience and age reduces the probability of obtaining a new job. A particularly strong result indicates that, over the life cycle, workers with fixed-term contracts receive lower returns to both experience and tenure.

NOTES * 1. 2.

3. 4.

5. 6.

We would like to thank participants at the EALE Conference (Paris, 2002). The authors also gratefully acknowledge the partial financial support from Fundação para a Ciência e a Tecnologia. The usual disclaimer applies. Results supporting the importance of screening as a motivation for the use of fixed-term contracts were also obtained by Adam and Canziani (1998), Booth et al. (2002), and Guell-Rotlan and Petrongolo (2000). A similar result was also obtained for the US labour market (Houseman, 2001). There is, however, a difference of degree. Where Houseman reports a five-to-one relationship between the point-in-time and accumulated stock counts of short-term hirings for the US, here a two-to-one relationship was obtained. Even though the two contractual arrangements are not strictly comparable, this suggests a longer average duration of temporary contracts in Portugal (about six months). All these figures are from the Social Audit and refer to firms with at least 100 employees. In this data set, fixed-term contracts represent 17.2 per cent of total employment (which exceeds the 14 per cent average in Figure 8.1a). Spanning from the first quarter of 1998 to the third quarter of 2000, the sample period covers an upturn only. This could, of course, influence the results reported here. However, a comparison with similar evidence for the 1992–97 period (Portugal, 2000), is reassuring. Except for transitions to unemployment, which are, as expected, higher (0.61 and 4.70 for open-ended contracts and fixed-term contracts), the magnitude of transition rates is remarkably similar for the two periods. More important, for all types of transitions, the differential between the two types of contract is about the same in the two periods. Blanchard and Portugal (2001) estimate that the magnitude of worker flows out of employment for Portugal is about one-fourth of the corresponding figure for the United States. Note that the baseline hazard function is defined for a benchmark where all covariates take zero values.

Matching workers to jobs in the fast lane

331

REFERENCES Adam, P. and P. Canziani (1998), ‘Partial De-regulation: Fixed-term Contracts in Italy and Spain’, CEP Discussion Paper No. 386. Blanchard, O. and A. Landier (2002), ‘The perverse effects of partial labor market reform: fixed-term contracts in France’, Economic Journal, 112, F214–44. Blanchard, O. and P. Portugal (2001), ‘What hides behind an unemployment rate?’, American Economic Review, 91, 187–207. Booth, A., M. Francesconi and J. Frank (2002), ‘Temporary jobs: stepping stones or dead ends?’, Economic Journal, 112, F189–213. Connolly, H. and P. Gottschalk (2001), ‘Dead-end and stepping-stone jobs: theory and evidence’, mimeo, Boston College. Guell-Rotlan, M. and B. Petrongolo (2000), ‘Workers Transitions from Temporary to Permanent Employment: The Spanish Case’, CEP Discussion Paper No. 438. Houseman, S.N. (2001), ‘Why employers use flexible staffing arrangements: evidence from an establishment survey’, Industrial and Labor Relations Review, 55, 149–70. Hunt, J. (2000), ‘Firing costs, employment fluctuations and average employment: an examination of Germany’, Economica, 67, 177–202. OECD (1999), Employment Outlook, Paris: OECD Publications Service. Portugal, P. (2000), ‘Volatilidade do emprego, protecção ao emprego e desemprego’, Banco de Portugal, mimeo. Varejão, J. (2000), ‘Three Essays on Labor Demand’, Doctoral Dissertation, Universidade do Porto. Varejão, J. and P. Portugal (2003), ‘Why Do Employers Use Fixed-term Contracts?’, CETE Discussion Papers No. 2003-10.

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DISCUSSION Julián Messina In this chapter José Varejão and Pedro Portugal study the consequences of employers’ use of fixed-term contracts for workers’ employment and wage dynamics in the Portuguese economy. The analysis relies on data from a representative household survey spanning from the first quarter of 1998 to the third quarter of 2000. The topic is of major relevance in the current European policy debate, which is characterized by the opposing views of those who consider fixedterm contracts as ‘stepping stones’ to more stable employment relationships, and those who view fixed-term contracts as synonyms of low quality jobs or ‘dead-ends’, characterized by high insecurity and little training. The chapter is well written and focused and the analysis is well executed. My discussion will be divided into three parts. First I will briefly put into an international context the Portuguese situation regarding the regulation of fixed-term and open-ended contracts. Second I will describe the characteristics of the analysis performed by the authors and discuss their main findings. Finally I will discuss their empirical strategy and suggest a number of directions in which the analysis could be extended and possibly improved. Fixed-term contracts might be used by employers for a variety of reasons: to engage workers in seasonal or casual work duties, as a buffer stock to economic fluctuations or as a screening device. The first type of jobs may not easily lend themselves to permanent contracts, while in principle the second and third set of motivations could be achieved either with fixed-term or permanent workers. In the latter cases, the set of rules and regulations, together with the social norms governing the labour market, will determine the employers’ choice of a specific type of contract. The rules and regulations governing the termination of contracts in Portugal are very restrictive when compared with other OECD countries. According to the OECD (2004), Portugal ranked second regarding the stringency of employment protection regulations during the period of analysis considered by the authors, only surpassed by Turkey. However, it is probably not the overall stringency of employment protection that matters for employers at the time of deciding whether to hire a new worker on a fixed-term versus a regular contract, but instead the relative stringency of regulation of dismissals for fixed-term versus open-ended contracts. The OECD indicators allow distinguishing between regulations governing both contractual figures. These cardinal measures assess the overall stringency of regular and fixed-term contracts on a scale from 0 to 6 according to

333

0

1

2

3

4

0

HUN

NLD SWE

JPN KOR FIN

AUT

USA 1

DEU

BEL

NOR

ESP

ITA

FRA

2 3 4 Employment protection – fixed-term contracts

AUS DNK CAN NZL CHE GBR

IRL

POL

CZE

SVK

PRT

MEX

5

TUR GRC

Figure 8D.1

The regulation of fixed-term and open-ended contracts in OECD countries

Note: Abbreviations: AUS Australia, AUT Austria, BEL Belgium, CAN Canada, CZE Czech Republic, DNK Denmark, FIN Finland, FRA France, DEU Germany, GRC Greece, HUN Hungary, IRL Ireland, ITA Italy, JPN Japan, KOR Korea, MEX Mexico, NLD Netherlands, NZL New Zealand, NOR Norway, POL Poland, PRT Portugal, SVK Slovakia, ESP Spain, SWE Sweden, CHE Switzerland, TUR Turkey, GBR United Kingdom, USA United States.

Employment protection – regular contracts

334

New forms of employment relationships

the increasing strictness of regulatory provisions. Figure 8D.1 presents a cross-plot of the regulation of fixed-term and regular contracts in OECD countries. It clearly shows a positive association between both measures, suggesting complementarities between regulations of fixed-term and openended contracts. Interestingly, Portugal (PRT) appears as a clear outlier in the graph, presenting the highest score on regulations for regular contracts and a just above average score on regulations for fixed-term contracts. To some extent, this difference might reflect the fact that the OECD indicators do not include information about severance payments for fixed-term contracts. Nevertheless, this suggest that the incentives for substituting regular for fixed-term contracts in Portugal are high, making the Portuguese case an interesting case study. Varejão and Portugal discuss the different reasons for employers deciding whether to offer a fixed-term or a regular contract to a worker. They argue that, if fixed-term contracts are used as a buffer stock to fluctuations in labour demand, they should be associated with more instability and uncertainty and lower training, harming the prospects of the worker to find a better job. Instead, if fixed-term contracts are used as a screening device, they might act as ‘stepping stones’ to regular positions. In order to test for these two conflicting hypotheses, the analysis proceeds in two directions. First the authors examine the wage profiles of workers, conditional on the type of contract, following the estimation of Mincer-type wage equations. Second they study the transitions of workers holding any type of job to three labour market states (unemployment, inactivity and another job) and the transition of workers holding a temporary contract to three additional exits: another fixed-term contract, an open-ended contract and non-employment. The analysis of wage profiles suggests a clear distinction in the effects of holding a temporary contract depending on the timing of the worker’s career. Young workers at early stages of their career have similar wages irrespective of the type of contract. However, older workers holding a fixed-term contract suffer from important wage penalties, which increase from 10 per cent at the age of 30 to 20 per cent for workers aged 50. Unfortunately the short span of the data does not allow for properly identifying long-run wage effects of holding temporary contracts, but the cross-sectional evidence suggests important differences for different types of workers. As expected, the analysis of workers’ transitions shows that workers holding temporary contracts have a higher unconditional probability of changing labour market status. Interestingly, the duration analysis shows that the transitions of fixed-term contracts to open-ended contracts increase with tenure. This is interpreted as evidence of employers and workers’ use of temporary contracts as a screening device. While bad

Matching workers to jobs in the fast lane

335

matches are quickly destroyed, those workers surviving longer periods with temporary contracts are more likely to obtain a fixed-term contract. This is one possible interpretation of the result. Alternatively, it might be argued that individuals holding contracts for longer periods might signal themselves as high ability individuals, having a higher probability of obtaining a permanent job outside the firm. Unfortunately, the authors cannot distinguish between workers obtaining a permanent contract within the same firm and workers moving to another firm with a permanent contract after a fixed-term contract spell. There are a number of issues that could complement the current analysis. In general, a more detailed discussion of the characteristics of the sample would have been desirable. I found surprising some choices in the set of control variables included, and I suppose that some simply reflect the characteristics of the data set. An important control variable that is missing is the size of the firm, since it is well known (and the authors discuss this point in the chapter) that firms with different sizes have different attitudes towards flexible forms of employment. Also, since the analysis cannot distinguish between seasonal and short-term jobs, it would have been important to clarify if the sample includes workers in the agricultural sector, which is characterized by the employment of a high number of seasonal workers. At first sight it is surprising that the authors pool all types of workers in their analysis, making no distinctions across genders. One would expect that the consequences of holding a fixed-term contract are different for male and female workers, given different labour market attachment and human capital decisions (see for instance Booth et al., 2002). However, the high participation of female workers in the Portuguese labour market might justify this choice. Finally, the analysis would greatly benefit from the expansion of the period of study. As the authors point out, the short period of analysis represents an overall expansion. Not having a full cycle will affect the estimated transitions between labour market states and might also affect the conclusions obtained from the analysis. During a period of expansion we are expected to observe more transitions from fixed-term to open-ended contracts and fewer transitions from the former to unemployment, reducing the estimated costs of these flexible working arrangements. References OECD (2004), Employment Outlook, Paris: OECD Publications Service. Booth, A., M. Francesconi and J. Frank (2002), ‘Temporary jobs: stepping stones or dead ends?’, Economic Journal, 112, F189–213.

PART IV

Panel Discussion – Labour Supply in an Enlarged Europe

LABOUR SUPPLY IN A NEW EUROPE Francesco Paolo Mongelli To open the panel discussion, I have assembled some ‘early’ empirical evidence on labour market dimensions since the start of EMU (that is, Stage Three of EMU and the run up to it). Some views that had been put forward on the possible effects of EMU were also reviewed. First of all we might ask why should EMU have any effects? What forces might monetary integration unleash or catalyse that did not exist before? There are diverse market-based forces. Some costs will disappear or fall. The introduction of the euro will contribute, amongst other effects, to reducing trading costs both directly and indirectly; for example, by removing exchange rate risks and the cost of currency hedging (although we know that there are different views on this cost). Some information costs will be reduced. The euro is expected to have a catalysing role for the Single Market Programme by enhancing price transparency and discouraging price discrimination. This should contribute to reducing market segmentation and fostering competition. Such effects are likely to be displayed more fully following the physical introduction of the euro in January 2002. Some sources of cross-country uncertainties/risks will decline or disappear. A common currency among partner countries is seen as ‘a much more serious and durable commitment’ (McCallum, 1995). A new effect is that one single currency is more efficient than multiple currencies in performing the roles of medium of exchange by eliminating transaction costs, and unit of account by promoting convergence in social conventions with potentially far-reaching legal, contractual and accounting implications (García Herrero et al., 2001). There are also institutional forces at play, and EMU might have a catalysing role in combination with some ongoing institutional reforms, as for example those fostered and monitored by the EU Commission and including the Financial Services Action Plan (FSAP), the Lamfalussy Report and its follow-ups, the Giovannini Report and its follow-ups, and others (see for example the EU Commission Scoreboard). For example, Artis and Zhang (1998) and Buti and Suardi (2000) argue that the European process of economic and monetary integration might have had a ‘disciplining effect’. Other authors have mentioned that political institutions might also be endogenous to some extent (De Grauwe and Mongelli, 2004). Hence, we expect that over the coming years all these forces will gradually display their effects and show up in the data. In the rest of this discussion I inquire if there are already any visible differences in labour market 339

340

Panel discussion

developments of the euro area vis-à-vis non-euro area countries. That is, I search for some effects of EMU by looking at different points of view. First Point of View: Looking at Quantities A noteworthy development is the increase in employment rates across the euro area, which hold by age (Figure P.1), by gender (Figure P.2) and for the euro area as a whole (Figure P.3). Figures P.1–3 show that between 1995 90

1995

80

1996

1997

1998

1999

2000

2001

2002

70 60 50 40 30 20 10 0 15–24

25–29

30–34

35–39

40–44

45–49

50–54

55–59

60–64

Figure P.1 Euro area employment rate by age, 1995–2002 (% of the population) 80

Total

Males

Females

70 60 50 40 30 20 10 0 1995

1996

1997

1998

1999

2000

2001

2002

Figure P.2 Euro area employment rate by gender, 1995–2002 (% of the working age population)

341

Labour supply in a new Europe 90

1995

1996

1997

1998

1999

2000

2001

2002

80 70 60 50 40 30 20 10 0 Euro area

UK

DK

SE

Figure P.3 Euro area employment rates, 1995–2002 (% of the working age population) and 2002 the euro area improvement in employment was about 4.6 percentage points, while the UK improved by 3.4, Denmark by 2.5 and Sweden by 3.3 percentage points (Eurostat, second quarter Labour Force Survey). Hence, the euro area employment rate has been simply catching up since 1995 (especially since 1998). However, the lower progress in the latter EU non-euro area countries is understandable as the UK, Denmark and Sweden started the period with a significantly higher level of employment. In summary, from the standpoint of aggregate quantity measures the euro area countries still have a long way to go to close the employment gap and reach the Lisbon target of 70 per cent employment. In fact, employment rates in most euro area countries (EAeuro area weighted average) remain below the Lisbon target, while non-euro area EU countries have already achieved 70 per cent and the USA is well above this threshold. Figure P.4 provides a similar result by adding unemployment rates. We can gauge some further indications on the effects of EMU from some very recent studies. Mourre (2004) shows that the pattern of labour demand might have changed in the euro area. In particular, there has been more employment creation for the following reasons: (a)

a weak growth of direct and indirect labour costs, that is, wage moderation (which I examine next), (b) changes in the sectoral composition of employment toward sectors with higher employment intensity (such as trade, repairs and financially related services), (c) the strong development of part-time jobs and fixed-term jobs,

342

Panel discussion

(d) lower labour tax rates, and (e) the increase in active labour market policies (ALMPs) such as subsidies to private employment (which I will examine below). 90

Employment rate

Unemployment rate

80 70 60 50 40 30 20 10 0 DE BE GR ES FR IE IT LU NL AT PT FI EA

UK DK SE

Figure P.4 European Union employment and unemployment rates in 2002 (as percentage) Genre and Gómez-Salvador (2002) find a gradual increase in the labour force of the euro area since the 1980s. This phenomenon is due to a gradual increase in the participation rate, with only modest and temporary setbacks during periods of slow growth (the so-called ‘discouragement effect’) across most age groups and both genders, that is more than compensating for the slowdown in working age population. This positive trend in the labour force captures diverse factors including sociological changes in female participation and also the adoption of labour market policies encouraging the participation of the youngest and a reduction in early retirement schemes in the case of the oldest. An insightful analysis of women’s participation in the labour force and a discussion of several policy options is given in Pissarides et al. (2005). In summary, all experts in this area agree that a very significant gap still exists in comparison with the USA (which has a participation rate of about 77 per cent vis-à-vis 67.5 per cent in the euro area). There is also a need to strengthen policy actions to meet the objectives set at the Lisbon European Council (calling for a 70 per cent participation rate by 2010 in the EU as a whole). Over the coming years, labour force developments, and in particular sustained growth in labour supply (that subsumes both labour force and hours worked) will play an important role in raising potential output growth (see also OECD, 1999, 2000, 2001 and 2002).

343

Labour supply in a new Europe

Second Point of View: Looking at Wages A quite visible phenomenon of recent years has been an impressive progress toward nominal stabilization in the run-up to EMU and since the start of Stage Three of EMU. Calmfors (2001) and Pichelmann (2003) note that such wage moderation coincided with a reappearance of national income policies, a strengthening of national wage coordination in some countries, and longer contract periods in some others (as a result also of lower negotiation costs and a higher predictability of real wages). Figures P.5 and P.6 show that wage rate inflation has declined across the euro area and so has wage dispersion. It is reassuring that other indicators, such as unit labour costs and compensation per employee, provide a similar picture. However, although the 1996–2000 period has been characterized by widespread wage moderation in the euro area, wages have subsequently risen at a somewhat higher rate and within international averages in 2001–02. From the Labour Cost Index for the euro area one can see a persistently low growth rate in non-wage costs (mostly employers’ social security contributions) in the period between mid-1997 and early 2001, in contrast to the growth rate of wages. Thereafter non-wage costs swung back and wage costs have risen as well (see Figure P.7). Hence, wage moderation in the euro area might have somewhat weakened in recent years.

25

Euro area

Non-EU countries

EU non-euro area countries

20 15 10 5

Figure P.5 Unit labour costs (weighted averages)

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1980


5

1981

0

344

Panel discussion

12

Euro area

Non-EU countries

EU non-euro area countries

10 8 6 4 2

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

0

Figure P.6 Unit labour costs (weighted standard deviation with respect to the euro area) 5

4

Wage cost growth

3

2

1

Non-wage cost growth

Figure P.7

2003Q1

2002Q3

2002Q1

2001Q3

2001Q1

2000Q3

2000Q1

1999Q3

1999Q1

1998Q3

1998Q1

1997Q3

1997Q1

1996Q3

1996Q1

0

Wage versus non-wage costs

However, there are other factors at play; will they still foster wage moderation over time?: (a)

Monetary unification and now EMU may also be affecting wage bargaining more generally by enhancing price transparency and fostering competition in product and service markets. This reduces the potential rent to be shared by workers and firms and encourages a

Labour supply in a new Europe

345

decentralization of wage bargaining. (b) Boeri et al. (2001) note that there is a process favouring deunionization due to a variety of factors including the relative shift in employment and the growing importance of part-time and temporary contracts. (c) Snower (2003) notes that decentralization is also favoured by changes in the way we work due to the diffusion of information technologies and more decentralized decision-making within flatter hierarchical business organizations with less standardized pay systems and more pay differentiation. In summary, some of the gloomy forecasts announced by some researchers prior to the start of Stage Three of EMU have not materialized. Soskice and Iversen (2001) and Coricelli et al. (2001) were concerned that with EMU the ‘deterrence argument’ might be weakened, or at least diluted, and incentives for real wage restraints could be diminished. But we don’t see that, as both average and dispersion fall. Very importantly, Calmfors (2001) postulates that the current resurgence of national bargaining coordination through national income policies, social pacts and consensual norms might represent a transitional phase that could be exhausted over the next 10–15 years. There is instead a long-run shift towards decentralized bargaining. Third Point of View: Looking at Labour Market Reforms and Policies Is EMU encouraging or hindering labour market reforms? There are diverse complementary points of view ranging from ‘macroeconomic policy games’to approaches looking at microeconomic interactions. Saint-Paul and Bentolila (2000) note in their seminal paper ‘Will EMU Increase Eurosclerosis?’ that ‘EMU creates incentives to alter the economy’s structure to improve its response to shocks. But, such incentives depend on the characteristics of the economy.’ Hence, labour markets might become more or less flexible depending on a country’s preferences and welfare functions. However, the loss of monetary policy discretion at the country level lowers the incentive to undertake large-scale reform of labour markets as it precludes a ‘two-handed’ approach according to which macroeconomic stimulus should facilitate structural reforms. Hence, EMU increases the likelihood of having gradual reforms and coordination of reform across countries. Some authors focus on the optimal timing of reforms in terms of the overall cyclical stance of the economy that depends also on aggregate shocks (for example, Saint-Paul, 2002, and Saint-Paul and Bentolila, 2000, who state that ‘an economic boom would create a window of opportunity for [labour market] reform’). Other authors, for example Bertola (2000),

346

Panel discussion

indicate instead that the framework to discuss the design of institutional reforms could be one in which the economy is hit by reallocative shocks, that is, shocks that force the economy to undertake a process of microeconomic reallocation and adjustment. Such reallocative shocks are opposed to aggregate shocks, which cannot be insured against. Blanchard and Giavazzi (2001) postulate a slightly different argument but with a similar outcome. Product market deregulation and enhanced competition decrease total rents to be shared and the incentives for workers to appropriate such rents would then decrease, making labour unions weaker, reducing insider power and leading to labour market deregulation. Very importantly, Jean and Nicoletti (2001) find a significant relationship between product market regulation in several sectors and wage premia. But which reforms and/or labour market policies do we actually see? Bertola and Boeri (2003) conduct an insightful experiment: they take stock of reforms carried out in Europe, amongst others, in the field of employment protection and non-employment benefits. In a first step they look at the broad orientation of reforms: in the case of employment protection whether it is becoming more or less stringent, and in the case of nonemployment benefits whether their ‘reward’ would increase or decrease. Non-employment benefits include a variety of rewards. The most important are unemployment benefits, but various other cash transfers are also included, as well as pensions and some forms of employment protection. The second step in their exercise is articulated in two distinct stages. In a first stage they classify reforms as marginal or radical depending on whether the reforms are comprehensive, involve existing entitlements and reduce replacement rates of the average production worker by 10 per cent or more. The second stage is a validation procedure to verify the actual behaviour of the series. This requires the collecting of a number of successive observations to confirm the initial qualitative assessment (and to exclude that a reform has been reverted). An important working assumption by the authors is that they choose a relatively early EMU break, that is, 1995, presuming that the convergence process led by the Maastricht Treaty Criteria and expectational effects of EMU even preceding 1997 were at work. They then report reform frequencies on a per-country and per-year basis for 1987 to 2002 for euro area and non-euro area EU countries. The impact of EMU on reforms is visible since the mid-1990s and particularly for reforms of non-employment benefits. The data indicate an acceleration of reforms especially in the euro area and in the field of non-employment benefits. Bertola and Boeri (2003) caution against any over-interpretation of these results as it will take more time to understand the joint effect of many reforms (several of which are marginal or are offset or compensated by measures relating to specific interest groups).

347

AUT 1.2 0.9 0.8 0.3 0.1 0.1 0.0 0.0 0.0 0.0 – 0.0 AUT 1.3 1.0 0.8 0.3 0.1 0.1 0.0 0.1

Total Passive measures Unemployment compensation Active measures PES and administration Labour market training Youth measures Subsidized employment of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

1990

Total Passive measures Unemployment compensation Active measures PES and administration Labour market training Youth measures Subsidized employment

3.8 2.6 1.9 1.2 0.2 0.2 – 0.7

BEL

4.7 3.4 2.5 1.3 0.2 0.2 0.0 0.8 0.0 0.8 – 0.2

BEL

2.1 1.1 1.1 1.0 0.2 0.4 0.0 0.2

GER

2.2 1.4 0.3 0.8 0.2 0.2 0.0 0.2 0.1 0.1 – 0.2

GER

5.4 4.3 3.1 1.1 0.1 0.3 0.3 0.3

DNK

– – – – – – – – – – – –

DNK

3.1 2.3 2.3 0.8 0.1 0.2 0.1 0.4

ESP

3.1 2.8 1.4 0.3 0.1 0.0 – 0.2 0.1 0.1 0.0 0.0

ESP

2.1 1.1 0.6 1.0 0.1 0.3 0.1 0.5

FIN

2.2 1.3 – 0.9 0.1 0.3 0.1 0.4 0.1 0.4 – 0.1

FIN

2.6 1.8 1.3 0.8 0.1 0.3 0.2 0.1

FRA

3.0 2.4 2.8 0.7 0.1 0.3 0.2 0.1 0.0 – 0.0 0.1

FRA

Structure of public expenditure in labour market programmes (as a percentage of GDP)

1985

Table P.1

1.6 1.0 1.0 0.6 0.2 0.2 0.2 0.0

UK

2.9 2.2 0.9 0.8 0.2 0.1 0.3 0.2 – 0.2 0.0 0.0

UK

0.8 0.5 0.5 0.4 0.1 0.2 0.0 0.1

GRC

0.5 0.4 1.2 0.2 0.1 0.0 0.0 0.1 0.0 0.0 – –

GRC

348

(continued)

AUT 1.6 1.1 1.0 0.5 0.1 0.2 0.0 0.1 0.1 0.0 0.0 0.1 IRE 4.9 3.4 3.4

Total Passive measures Unemployment compensation Active measures PES and administration Labour market training Youth measures Subsidized employment of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

1985

Total Passive measures Unemployment compensation

0.0 0.0 – 0.1

1999/2000

of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

Table P.1

– – –

ITA

3.7 2.3 1.8 1.4 0.2 0.3 – 0.8 – – – 0.1

BEL

0.1 0.6 0.0 0.2

1.4 0.9 0.3

LUX

3.1 1.9 1.9 1.2 0.2 0.3 0.1 0.3 0.0 0.25 0.0 0.3

GER

0.1 0.1 – 0.2

4.6 3.4 3.4

NE

4.5 3.0 1.3 1.5 0.1 0.8 0.1 0.2 0.0 0.2 – 0.3

DNK

0.0 0.2 0.1 0.2

– – –

POR

2.2 1.3 1.3 0.9 0.1 0.2 0.1 0.4 0.3 0.1 0.0 0.0

ESP

0.1 0.1 0.2 0.0

3.0 0.9 0.8

SWE

3.3 2.2 1.8 1.1 0.1 0.4 0.2 0.3 0.2 0.1 0.0 0.1

FIN

0.0 0.4 0.0 0.1

– – –

USA

3.1 1.8 1.5 1.4 0.2 0.3 0.4 0.4 – – – 0.1

FRA

0.1 0.0 0.0 0.1

2.8 2.1 1.4

EU

0.9 0.6 0.6 0.4 0.1 0.1 0.2 0.0 – – – 0.0

UK

– – 0.0 0.0

0.8 0.5 0.5 0.3 0.1 0.2 0.1 – – – – 0.0

GRC

0.1 – 0.0 0.0

349 4.0 2.6 2.5 1.4 0.1 0.5 0.4 0.3 0.0 0.2 0.0 0.1

IRE

1990

Total Passive measures Unemployment compensation Active measures PES and administration Labour market training Youth measures Subsidized employment of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

1.5 0.2 0.6 0.5 0.2 0.0 0.1 0.1 0.0

Active measures PES and administration Labour market training Youth measures Subsidized employment of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

– – – – – – – – – – – –

ITA

– – – – – – – – –

0.9 0.6 0.1 0.3 0.0 0.0 0.1 0.1 0.0 0.0 – 0.1

LUX

0.5 0.0 – 0.1 0.1 – – – 0.3

3.7 2.4 2.4 1.3 0.3 0.2 0.1 0.0 0.0 0.0 – 0.6

NE

1.3 0.3 0.1 0.0 0.1 0.0 0.0 – 0.7

1.0 0.4 0.3 0.6 0.1 0.1 0.3 – – 0.0 0.0 0.1

POR

– – – – – – – – –

2.6 0.9 0.8 1.7 0.2 0.5 0.1 0.0 0.0 0.1 0.0 0.8

SWE

2.1 0.3 0.5 0.2 0.4 0.1 0.3 0.0 0.7

0.7 0.4 0.4 0.2 0.06 0.09 0.03 – – 0.0 – 0.0

USA

– – – – – – – – –

2.4 1.5 1.3 0.9 0.2 0.3 0.1 0.1 0.1 0.1 0.1 0.1

EU

0.8 0.2 0.2 0.1 0.2 0.0 0.2 0.0 0.1

350

4.0 2.3 2.2 1.6 0.2 0.2 0.2 0.9 – – – 0.1

Total Passive measures Unemployment compensation Active measures PES and administration Labour market training Youth measures Subsidized employment of which: subsidized to regular employment direct job creation sub. to entrepreneurship Measures for disabled

– 0.6 0.6 – – 0.1 0.3 0.3 – – – –

ITA 0.9 0.6 0.4 0.3 0.0 0.0 0.1 0.1 – – – 0.0

LUX 3.7 2.1 2.1 1.6 0.3 0.3 0.0 0.4 0.1 0.4 – 0.6

NE – – – – – – – – – – – –

POR 2.7 1.3 1.3 1.4 0.3 0.3 0.0 0.3 0.2 0.1 0.1 0.5

SWE 0.4 0.2 0.2 0.2 0.0 0.0 0.0 0.0 – 0.0 – 0.0

USA

2.6 1.4 1.3 1.0 0.2 0.2 0.2 0.3 0.1 0.2 0.0 0.2

EU

Sources:

OECD Employment Outlook (2002) and Morgan and Mourougane (2003).

Notes: Latest year available is 2000 for most of the countries. The exceptions are: 1999 for BEL, FRA, UK, ITA; 1998 for GRC; 1997 for LUX; 1996 for IRE. – means no data are available; the EU average is computed as a weighted average of countries for which data exist.

IRE

(continued)

1999/2000

Table P.1

Labour supply in a new Europe

351

A very different approach is pursued by Morgan and Mourougane (2003) who show an increasing relevance of active labour market measures across all European countries during 1985 and 2000 (Table P.1). As a percentage of GDP, ALMMs grew to about 1 per cent in 1999–2000. Boone and van Ours (2003) have put forward some evidence supporting the view that active labour market measures and policies can effectively contribute to the improvement of labour market performance. In summary, there is not much ‘early’ and firm empirical evidence on the possible effects of EMU on euro area labour markets. In terms of quantities there has been some catching up in the levels of employment but still the gap is considerable with respect to non-euro area EU countries and the USA. In terms of wage developments, wage moderation stood out in the run-up to EMU and in the early EMU period, but wage developments are now in line with those of the main partner countries. In terms of labour market reforms there is some encouraging evidence but it is still early to herald some clear effects for euro area countries. Fortunately, and as already said, some of the gloomy forecasts made by some researchers prior to the start of Stage Three of EMU have not materialized. References Artis, M.J. and W. Zhang (1998), ‘The linkage of interest rates within the EMS’, Weltwirtschaftliches Archiv, 132 (1), 117–32. Bertola, G. (2000), ‘Labor markets in the European Union’, Ifo-Studien, 46 (1), 99–122. Bertola, G. and T. Boeri (2003), ‘Product market integrations, institutions and the labour markets’, mimeo, Universitá di Torino and Bocconi University. Blanchard, O. and F. Giavazzi (2001), ‘Macroeconomic Effects of Regulation and Deregulation in Goods and Labour Markets’, CEPR Discussion Paper Series No. 2713. Boeri, T., A. Brugiavini and L. Calmfors (2001), The Role of Unions in the TwentyFirst Century: A Report for the Fondazione Rodolfo Debenedetti, Oxford: Oxford University Press. Boone, J. and J.C. van Ours (2003), ‘How Active Labour Market Policies can Bring Unemployed Back to Work’, paper presented at CEPR/ECB workshop ‘What Explains the Pattern of Labour Supply in Europe?’, 23–24 June, Frankfurt am Main. Buti, M. and M. Suardi (2000), ‘Cyclical convergence or differentiation? Insights from the first year of EMU’, Bank- en Financiewezen/Revue de la Banque, 2–3, 164–72. Calmfors, L. (2001), ‘Unemployment, labour market reform, and monetary union’, Journal of Labour Economics, 19, 265–89. Coricelli, F., A. Cukierman and A. Dalmazzo (2001), ‘Economic Performance and Stabilization Policy in a Monetary Union with Imperfect Labour and Goods Markets’, CEPR Discussion Paper Series No. 2745.

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De Grauwe, P. and F.P. Mongelli (2004), ‘Endogeneities of OCAs’, mimeo, ECB. García Herrero, A., V. Gaspar, L. Hoogduin, J. Morgan and B. Winkler (eds) (2001), ‘Why Price Stability?’ Proceedings of First ECB Central Banking Conference, Frankfurt am Main: European Central Bank. Genre, V. and R.Gómez-Salvador (2002), ‘Labour Force Developments in the Euro Area since the 1980s’, ECB Occasional Paper No. 4. Jean, S. and G. Nicoletti (2001), ‘Product Market Regulation and Wage Premia in Europe and North America: An Empirical Investigation’, OECD Working Paper No. 318. McCallum, J. (1995), ‘National borders matter: Canada–US regional trade patterns’, American Economic Review, 85 (3), 615–23. Morgan, J. and A. Mourougane (2003), ‘The impact of active labour market policies in Europe’, in G. Fagan, F.P. Mongelli and J. Morgan (eds), Institutions and Wage Formation in the New Europe, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Mourre, G. (2004), ‘Did the Pattern of Aggregate Employment Growth Change in the Euro Area in the Late 1990s?’, ECB Working Paper Series No. 358. OECD (1999), EMU: Facts, Challenges and Policies, Paris: OECD Publications Service. OECD (2000), EMU One Year On, Paris: OECD Publications Service. OECD (2000, 2001 and 2002), Economic Outlook, Paris: OECD Publications Service. OECD (2002), Employment Outlook, Paris: OECD Publications Service. Pichelmann, K. (2003), ‘Wage developments in the early years of EMU’, in G. Fagan, F.P. Mongelli and J. Morgan (eds), Institutions and Wage Formation in the New Europe, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Pissarides, C., P. Garibaldi, C. Olivetti, B. Petrongolo and E. Wasmer (2005), ‘Women in the labour force: how well is Europe doing?’, in European Women at Work, Part A, Oxford: Oxford University Press and Milano: Fondazione Rodolfo Debenedetti (forthcoming). Saint-Paul, G. (2002), ‘The political economy of employment protection’, Journal of Political Economy, 110 (3), 672–701. Saint-Paul, G. and S. Bentolila (2000), ‘Will EMU Increase Eurosclerosis?’, CEMFI Working Paper No. 0004, March. Snower, D. (2003), ‘Centralized bargaining and the organization of work’, in G. Fagan, F.P. Mongelli and J. Morgan (eds), Institutions and Wage Formation in the New Europe, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Soskice, D. and T. Iversen (2001), ‘Multiple wage bargaining systems in the single European currency area’, Empirica, 28 (4), 435–56.

CHALLENGES FOR LABOUR MARKETS IN THE NEW EUROPE Juan J. Dolado Introduction With an aggregate employment rate of 64.7 per cent in 2003 the European Union (EU) still has some ground to cover in order to achieve the 70 per cent Lisbon Summit target for 2010. The gap is even larger in comparison to the current 72 per cent employment rate in the USA. Over the last halfdecade the employment rate increased by 2 percentage points but the rate of growth has slowed down to less than 0.5 percentage points since the turn of the century. It is therefore not surprising that European public opinion (for example through the Eurobarometer) consistently places ‘increasing employment rates’ at the top of policy priorities. Under these circumstances it seems reasonable to expect that the EU’s success or failure in achieving such a goal will depend to a large extent on whether the functioning of European labour markets in the past can be substantially improved upon in the future. A return to the negative performance of the early 1990s, when employment rates got stacked around 60 per cent, would mean a serious threat to the success of the new EU. The difficulties may be even more serious when one takes into account that the new Member States (with employment rates in the range of 50–55 per cent, except in the Czech Republic) fare worse than the EU-15 in this dimension. When thinking about labour markets (LM) in the future, a useful starting point is to recognize that LM regulations are not imposed exogenously. They do serve a useful purpose to the extent that there are realistic market imperfections and that competition fails to address relevant distributive considerations. Thus it is quite misleading to suppose that all LM institutions are just rent-seeking devices, as advocates of across-the-board reforms often make us think. This should of course not prevent us from acknowledging that drastic reforms are urgent in some areas where the negative effects of some existing institutions have outweighed the advantages foreseen at the time they were introduced. After all, the introduction of such institutions in the 1950s did not prevent the EU performing better than the USA up to the 1970s when the world was quiet and the EU economies were engaged in a catching-up process. However, later, when the environment became more turbulent, the search for a more compressed wage structure in the EU came at the cost of a much lower employment rate than in the USA. To affect the competitive outcome, price restrictions (for example minimum wages, unemployment benefits (UB), collective bargaining and 353

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so on) or quantity restrictions (employment protection legislation (EPL), working time restrictions and so on) can be imposed. Both instruments are interrelated in obvious ways. For example, firing restrictions could hardly be binding if wages were completely unrestrained and therefore could decrease so as to make stable employment profitable or to induce voluntary quits. And compressed wage structures would not offer much protection to workers if they resulted in more frequent dismissals under a weaker EPL. Thus, wage inequality is lower in those countries where EPL is stricter. However, to the extent that both instruments lead to higher unemployment, some countries prefer to have less job protection and more unemployment insurance. These choices have led in the past to a rather heterogeneous set of institutions across the EU giving rise to at least four different modes of ‘Social Europe(s)’: Scandinavian (with strong unions, tax wedges and active labour market policies (ALMPs)), Anglo-Saxon (with weak unions, wage dispersion and low-paid jobs), Continental (with generous UB systems) and Mediterranean (with strong EPL). Thus, despite the common usage of ‘EU LM institutions’, there is a wide diversity of models within the EU which will be helpful in extrapolating useful lessons for the future. With those considerations in mind I will proceed to briefly discuss the future of social policies in the EU and the effects that the recent Eastern enlargement may have upon them. I will refer to demand for social protection as expressed by private agents (workers, firms), and supply of social protection as offered by institutions and governments. The Demand for Social Protection: Competition and Discipline As is well known, one of the expected effects of a larger EU is to further increase trade, and labour and capital mobility within Europe, fostering in this fashion greater competition in the goods and factors markets. As firms respond to increasing competition, they will seek all possible means of reducing costs and boosting efficiency. Indeed, labour costs and flexibility in adapting to changing conditions are likely to remain the last opportunity to become competitive. Hence, the first implication is that labour market reforms are becoming more urgent and hence that the demand for social protection is likely to decrease. On the other hand the social insurance benefits of some of those institutions will probably increase. In effect, openness to trade cannot make a country as a whole worse off, but will hurt the income of factors that were in relatively scarce supply before integration, namely, labour in richer countries and capital in poorer countries. Thus, income distribution is likely to worsen in the richer economies whereas it will improve in less developed new Member States. It is therefore likely that, in contrast to the forces

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discussed above leading to a reduction, increasing integration will hurt the poorest in the richer countries, an effect which leads to strong pressures towards a higher demand for social protection. In sum, although the costs of current market institutions will tend to be more pronounced in a larger trade area, their benefits as instruments of social insurance will probably increase. Because of greater exposure to the uncertainties of the international market place, the decisive voter might be perfectly willing to pay a higher price for any given labour market distortion. The insurance argument points towards a more rigid labour market whilst the cost argument points in the opposite direction. The idea that increased openness and integration increases the demand for social protection seems to be supported by the data (see for example Bertola, 2000, and Agell, 2003). Typically, a strong positive correlation is often found between openness and measures of the scope of social protection. In this sense it is fairly illustrative to notice that the most important steps in the construction of social policies across the EU have taken place in those situations where there has been an enlargement (like the Single Act in 1986 at the time of the accession of Portugal and Spain, the Social Chapter of the Maastricht Treaty in 1992, and the Amsterdam Treaty in 1997 in view of future Eastern enlargement, and so on). The Supply of Social Protection: Coordination or Subsidiarity? I have argued before that integration is on the whole more likely to raise demand for social protection in richer countries and that this force tends to be greater the more unequal are the countries being integrated. But how does integration affect the supply of social protection? Here we again find two conflicting arguments, one suggesting coordination and another leading to competition or subsidiarity. The argument for coordination is a familiar one and relies upon the idea that there are important international spill-over effects from social policies. A simple example of the need for coordination to ensure effectiveness of social policies is provided by taxes on capital. Individually, countries have an incentive to levy low taxes on capital (and other mobile factors) to encourage FDI (foreign direct investment), with offsetting tax revenues stemming from tax raised on immobile factors or government spending cuts, in order to compensate for the rising deficit. Individual countries acting on their own can improve a lot this way; indeed complementarities may mean that even immobile factors may end up better off. However, other countries are likely to retaliate, leading to a typical race to the bottom in tax rates with undesirable consequences for social provision. In the face of unavoidable market failures, such as asymmetric information or adverse

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selection, this situation leads to an inefficient equilibrium where all countries end up being worse off. A similar scenario is bound to prevail with social protection. In this type of world, coordination is clearly good while subsidiarity is bad. As regards the arguments for competition, subsidiarity is clearly preferable when countries have differences in preferences, for cultural or other socio-economic reasons, as seems to be the case in the EU, given the diversity of social Europe(s). It is clear that a diversity of welfare systems allows countries to discover what works and what does not, leading to efficient ‘benchmarking’ when adopting new policies. For example, the role played by the relative success of ‘flexicurity’ in countries like the Netherlands or Denmark in the policy discussions underlying LM reforms in more rigid economies like France, Germany or Italy is fairly illustrative in this respect. How important are those race to the bottom pressures? The structure of US federal and state budgets tells us that they are important. So, we can find low state sales taxes (0 per cent in several cases), large differences in state minimum wages or no state introducing its own health system unilaterally, possibly because of difficulties of financing it through state taxes. As pointed out by Bean et al. (1998), a nice illustration of the tension arising from those different shifts in demand and supply for social protection was the ‘Posted Workers Directive’ following the arrival of British, Portuguese and Italian firms on building sites in East Germany in 1996 by winning tenders under the Single Market provisions through their cheaper foreign workers. As a consequence, 0.4 million German construction workers (out of 1.3 million) were unemployed and received generous unemployment benefits, which further underpinned German wages, while those foreign firms were subject to the lower wage levels prevailing in their countries. In principle this situation was efficient: Germany was getting cheaper buildings since the supply price of foreign workers was lower than that of German construction workers. But there was a fiscal externality since the extra unemployment benefits raised the tax burden on German taxpayers. The final resolution was that there was a cut in benefits (race to the bottom element) coupled with a new law introducing a minimum wage of 18 DM per hour in the construction industry, applying to all workers irrespective of their origin (social clause element). The foreign firms faced a large increase in their labour unit costs and contested the decision at the EU level. However, their claim was turned down on the basis of the introduction of a new social clause preventing the ‘exploitation’ of foreign workers. While this anecdotal evidence may just correspond to an isolated case, it does provide a good example of the type of tensions that may arise in the near future.

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What Have We Learned from the Past? As discussed in the introduction, across the EU economies there is a wide variety of labour market institutions some of which have significantly changed in the past. Consequently, we have a fair bit of information on what works and what does not work, both with regard to participation, employment and unemployment, and with respect to fostering productivity. My purpose here is to summarize what are the lessons for the future, in order to improve the trade-off between efficiency and distributive goals. In my opinion, the following changes should be advocated: (i) Improve the workings of the unemployment benefit system so as to ensure that: (a) benefits are time limited and the insurance and assistance roles of benefits are not mixed up; (b) there is a strict work test as in the Nordic countries; (c) the government job placement services are located in the same place as the benefit office; and (d) targeted negative income tax schemes are employed when poverty traps exist (single parents or parents with large families). (ii) The government should not have a monopoly on job placement, and temporary help agencies should be allowed to operate subject to the same labour laws as other workers in the companies where they work. (iii) Ensure that laws governing trade unions encourage responsiveness to the wishes of the entire membership, ensure there are secret ballots for all officials, for strikes and so on. Union power should be derived from the membership, not from political position. (iv) Avoid extension laws whereby wages agreed in union firms are extended to non-union firms. Indeed, a two-layer level of negotiation should be introduced. Nationwide bargaining should agree on general aspects of industrial relations, such as establishing floors on severance payments, general rules concerning functional and occupational mobility, setting average wage growth and so on, while bargaining at the firm level should focus on wages, employment and productivity clauses. It is crucial to have this type of coordinated bargaining system since de facto all labour negotiations will become more decentralized as we move from the national level to the EU as a whole. According to the well-known hump-shaped relationship between degree of centralization in wage bargaining and unemployment, two effects may follow. If initially labour bargaining is at the industry level, like in Spain or Germany, a larger single market improves the situation. However, if we start from a very centralized system, like in Austria or the Nordic countries, the new EU represents

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(v)

(vi)

(vii)

(viii)

(ix)

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a move from a good extreme to the inefficient middle, where unions have more incentive for leapfrogging. This line of reasoning suggests that the ability to undertake reforms will vary across countries depending on their initial position. Moving to a two-layer system like the one proposed above may therefore prove useful in achieving the benefits without paying the costs. Encourage product market competition. It is often assumed that a single currency and a single market will bring the EU closer to perfect competition. However, as is well known, intra-industry trade dominates across EU countries and that type of trade is predicated upon increasing returns to scale and local monopoly power, so it would seem that perfect competition is the wrong paradigm to expect, unless further deregulation in the goods market takes place. Moreover, by enhancing openness and competition, wage resistance to the tax wedge may decrease since firms will not be able to shift taxes onto prices without suffering large reductions in profitability. Furthermore, greater competition is the best recipe to fight gender discrimination. Minimum wages rules should always set lower rules for young people or low-income regions, particularly when there are high payroll taxes which cannot be shifted onto workers because of the floor imposed by the minimum wage. Avoid superimposing minimum wages agreed in the collective bargaining on top of the statutory ones. Avoid when possible two-tier reforms. In particular, the evidence for Spain points out very clearly that the combination of strict employment protection laws for permanent employees with encouragement to hire workers on fixed-term contracts leads to a permanent dual labour market. Better to make employment protection less strict and let firing costs be set at the firm level in combination with a ‘layoff tax’ as recently advocated by Blanchard and Tirole (2004). Never introduce a scheme to reduce labour supply. Yet make it more flexible. Discourage early retirement through an experience-rating (bonus malus) system: it does not improve the employability of young workers. Foster the creation of flexible part-time jobs where the total amount of working hours is set at the annual level, to increase female and old-age workers’ labour market participation. Implement targeted ALMPs on specific disadvantaged groups. Displacement, deadweight and substitution effects are much larger when such policies are supported by discriminatory temporary subsidies. Devote a proportion of the total budget of each ALMP to conducting randomized experiments in order to evaluate their net return.

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(x) Improve vocational training at the level of the firm rather than purely school-based training with little attachment to the need of firms. In particular it should be much more innovative with respect to new job opportunities, for instance in the information and telecommunication industries. (xi) Improve the portability of pension rights and educational degrees. At present the available laws are still definitely rigid and harm labour mobility much more than the lack of a common language. What Will Happen in the Future? Over the past we have seen how national levels of social protection have risen, indicating that the demand effect has dominated. Nonetheless, in the future, supply issues will become more important as integration proceeds, and race to the bottom pressures increase. Although demand pressures may increase because of restructuring in the face of a larger market, overall I expect current and future enlargements of the EU to lead to less regulation at the national level, a move in the right direction if it is not pushed too far. Yet, because of the larger spillover effects, there will be increased pressures for EU-wide action on social policies to prevent social dumping and to ensure that what remains of social policy is effective. As a result, EU decisions are likely to act as a constraint on national governments. All this points to the presumption that extensive harmonization of a set of minimal standards in the labour market will be more likely to occur. This harmonization will be more appropriate when it takes place among countries at a similar standard of development. Moreover, a second presumption is that harmonization is desirable when policies themselves are desirable and undesirable when the policies themselves are undesirable. Abundant illustrations of both types of policies have been discussed above. These lessons from the past point out that it is advisable to open up EU markets as soon as possible, but allow the incoming member countries to postpone the introduction of the full acquis communautaire until income levels have converged significantly with western standards. Yet, EU governments and some other social groups can be expected to resist this. In particular the imposition of minimum wages or unemployment benefits would be disastrous (although not on the agenda now, note what happened under the Posted Workers Directive!). The outcome of all these forces is likely to be a political equilibrium involving some increase in social clause protection and therefore less than full exploitation of gains of integration and lower levels of social protection all round. Overall it is safe to predict that the labour markets of the

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new Europe will not resemble their own past selves or their American counterparts as closely as may be feared or advocated. References Agell, J. (2003), ‘On the determinants of labour market institutions: rent seeking vs. social insurance’, German Economic Review, 3 (2), 107–35. Bean, C.R., S. Bentolila, G. Bertola and J.J. Dolado (1998), Social Europe: One for All, Monitoring European Integration 8, London: CEPR. Bertola, G. (2000), ‘Labour markets in the European Union’, CESifo Forum 1/2000, 9–11. Blanchard, O. and J. Tirole (2004), ‘Redesigning the employment protection system’, De Economist, 152, 1–20.

CAUSES AND CONSEQUENCES OF INTERNATIONAL LABOUR MOBILITY IN AN ENLARGED EU Herbert Brücker On 1 May 2004, the European Union (EU) admitted ten new Member States1 with a joint population of 74.1 million. Two more, Bulgaria and Romania, are scheduled to join in 2006. These two enlargement rounds will increase the total population of the EU by 103.6 million and the European labour force by some 45 million (Eurostat, 2004). In 2002 the per-capita GDP of the ten Central and Eastern European accession countries (CEEC-10) was approximately 20 per cent of the EU-15 average at current exchange rates and approximately 45 per cent in purchasing power parities (Eurostat, 2003). Against this background, two questions have attracted increasing attention in the public debate on the economic consequences of Eastern enlargement: first, will removing migration barriers result in mass migration from Eastern to Western Europe? Second, how will increasing migration affect labour markets in an enlarged EU? In this contribution I will discuss these two questions in the context of recent research findings that reassess the estimates of migration potentials and evaluate the potential effects of migration on European labour markets. The Scale of Migration: What Do We Know? What Don’t We Know? Starting with the seminal contribution of Layard et al. (1992), numerous studies have tried to estimate East–West migration potential.2 Basically we can distinguish three approaches in the literature: representative surveys, extrapolations of South–North migration to East–West migration, and forecasts based on multivariate econometric studies. Representative surveys of the Eastern European population have three disadvantages, which make it hard to draw quantitative inferences from them. First, it is difficult to assess the extent to which the migration intentions revealed in surveys later materialize into actual movements of individuals or households. Second, surveys capture only the supply side and ignore demand-side factors such as job opportunities and the availability of housing. Third, surveys cannot appropriately capture the temporary dimension of migration. Only a minority of migrants stay in a foreign country permanently. A large number of persons that experience a migration episode once in their lifetime may coincide with a small number of persons that actually live abroad at that certain point of time. Thus, surveys 361

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asking for the intention to move may overstate the total number of migrants which are observed at a given point of time. As a consequence, findings from surveys of the CEECs’ population vary widely: individuals intending to migrate range from 2 to 30 per cent of the population, depending on the questionnaires employed and the interpretation of the answers (see Fassmann and Hintermann, 1996; Krieger, 2003). Note that the correlation between migration intentions and actual migration is rather weak: for example, according to the German Socio-Economic Panel (GSOEP) more than 10 per cent of the East German population intended to migrate to Western Germany in 1991, but only 5 per cent of those who expressed the intention to migrate had actually moved five years later. A number of studies have simply extrapolated the number of South– North migrants in the 1960s and early 1970s to East–West migration. Note that the income gap between the Southern and the Northern European countries in the 1960s was similar to the gap between the EU-15 and the accession countries today.3 Moreover, although the Southern European countries were not EU members at that time, bilateral guest-worker agreements led to the de facto opening of Northern European labour markets and supported labour migration until the first oil price shock of 1973. In general, these extrapolation studies find a long-run migration potential of around 3 per cent of the population (see Layard et al., 1992; Lundborg, 1997; Bauer and Zimmermann, 1999). However, in stark contrast to the conditions for South–North migration in the early 1960s and 1970s, the conditions for East–West migration today are affected by imbalances in the labour markets of both the receiving and sending countries, incomplete recovery from the transition shock, and close geographical proximity. Thus, extrapolation studies can provide no more than a hint at plausible orders of magnitude. The majority of the forecasts of East–West migration are based on econometric estimates which usually explain migration stocks or flows using variables such as the income differential, (un)employment rates and some institutional variables. Although most studies employ the same set of explanatory variables, the estimates of the parameters, and hence of migration potentials, differ considerably in the literature. These differences in the econometric studies can be largely traced back to the way those studies dealt with country-specific effects. Economic intuition suggests that countryspecific effects such as culture, language, history, geography, and so on, affect the costs and benefits of migration and, consequently, the migration propensity across countries. Many studies however ignore those effects. As is discussed in further detail in the Appendix, the omission of country-specific factors can yield heavily biased estimates of the other parameters, which in turn results in an overestimation of migration potentials. Moreover, it can

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363

be shown that the forecasting performance of fixed effects models, which allow country-specific effects to differ, yield much better forecasts than pooled effects models which assume that unobserved country-specific effects have no impact on migration. Another aspect that it is important to understand is the dynamics of migration. Most models in the empirical literature explain migration flows by a set of explanatory variables such as the income differential. Consequently, those models assume that for a given income differential a certain migration flow persists forever. This type of model implicitly assumes that individuals are homogeneous with regard to preferences and human capital characteristics which affect the costs and benefits of migration. If we assume however that individuals are heterogeneous, then an equilibrium between migration stocks and explanatory variables such as the income differential emerges. Statistical tests of the time series properties of the data indeed suggest that an equilibrium – or, in more technical terms, a cointegrating relationship – between migration stocks and the explanatory variables exists. Note that this point may have important policy consequences. In the case of Southern enlargement of the EU, net emigration flows from Greece, Portugal and Spain into the EU were negligible or even negative although a substantial income differential existed at that time. In terms of the stock model, this episode can be explained by the fact that migration stocks were already at their equilibrium when the free movement was introduced. However, in the case of Eastern enlargement, migration stocks might be further away from their equilibrium levels, such that the introduction of the free movement may yield higher migration flows initially (see Appendix). Thus, relying on a fixed effects model which explains migration stocks by explanatory variables such as per capita GDP levels, employment rates and a set of institutional variables in the host and home countries, we estimate the migration potential for Germany at between 2 per cent and 2.8 per cent of the population of the CEEC-10 in the long run, that is, approximately 25 years after free movement is introduced. Shortly after the introduction of free movement, a peak in annual net immigration will be reached at between 190 000 and 240 000 persons, which will then decline to less than 100 000 persons within seven years (Figure P.8). Approximately 45 per cent of the immigrant population participate in the labour force in the receiving countries. An extrapolation of these estimates to the EU-15, based on the present distribution of migrants from the CEECs across the EU, yields a long-run migration potential of between 3.1 and 4.2 million, and a peak of annual net immigration after the introduction of free movement of between 285 000 and 360 000 persons (Table P.2). However, a good deal of uncertainty still surrounds these estimates. They should be understood as a hint at the plausible magnitude of migration potential, not as accurate forecasts.

364

low scenario

baseline scenario

high scenario

net immigration (left axis)

foreign population (right axis)

low scenario

baseline scenario

high scenario

500 000

1 000 000

1 500 000

2 000 000

2 500 000

3 000 000

Migration scenario for Germany

Alvarez-Plata et al. (2003), own calculations.

0 04 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

0

50 000

100 000

150 000

200 000

250 000

Figure P.8

Sources:

Net increase of foreign population from CEEC-10 (persons)

300 000

Foreign population from CEEC-10 (persons)

365

346 186 256 000 90 186

286 879 213 168 73 711

247 253 185 780 61 472

1 301 585 1 009 955 291 630

CEEC-10 CEEC-8 CEEC-2

CEEC-10 CEEC-8 CEEC-2

CEEC-10 CEEC-8 CEEC-2

2004

1 734 198 1 351 322 382 876

308 253 247 299 60 955

357 918 284 241 73 677

432 613 341 366 91 247

2005

2007

2008

2009

2010

2 162 657 1 694 359 468 298

302 384 246 536 55 848

352 983 284 708 68 275

41 513 30 483 11 030

3 983 158 3 096 588 886 570

147 268 118 173 29 095

Foreign population (persons) High scenario 2 548 732 2 878 961 3 152 768 3 375 514 2 003 816 2 266 819 2 482 344 2 654 852 544 916 612 142 670 424 720 661

56 735 39 601 17 134

Low scenario 227 136 185 014 185 427 149 997 41 709 35 017

269 382 220 498 48 884

316 587 255 971 60 616

178 839 140 531 38 308

75 862 51 209 24 653

2015

Baseline scenario 269 250 221 699 216 646 176 645 52 604 45 054

Net increase of foreign population p.a. (persons) High scenario 428 460 386 075 330 228 273 808 222 745 343 038 309 457 263 003 215 525 172 508 85 422 76 618 67 226 58 282 50 238

2006

Migration scenario from the CEECs to the EU-15, 2004–30

CEEC-10 CEEC-8 CEEC-2

Table P.2

4 216 739 3 243 563 973 176

14 868 10 655 4 212

23 728 15 333 8 395

34 889 21 164 13 725

2020

4 442 273 3 375 532 1 066 741


15 594
17 531 1 938


11 685
15 616 3 930


5 076
12 270 7 194

2030

366

1 202 652 939 736 262 916

1 242 278 967 123 275 155

2004

2005

1 510 905 1 187 034 323 871

1 600 197 1 251 364 348 832

(continued)

1 813 290 1 433 571 379 719

1 953 180 1 536 072 417 108

2006

2 082 672 1 654 068 428 603

2 269 766 1 792 043 477 724

2007

2009

Low scenario 2 309 808 2 494 822 1 839 495 1 989 492 470 312 505 330

Baseline scenario 2 539 017 2 760 715 2 008 689 2 185 333 530 328 575 382

2008

2 642 090 2 107 665 534 425

2 939 554 2 325 864 613 690

2010

3 011 768 2 392 680 619 089

3 412 153 2 677 698 734 455

2015

3 122 580 2 471 713 650 867

3 577 684 2 787 055 790 629

2020

Sources:

Authors’ calculations. Based on Alvarez-Plata et al. (2003).

Notes: CEEC-10: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia; CEEC-8: CEEC-10 without Bulgaria and Romania; CEEC-2: Bulgaria, Romania.

CEEC-10 CEEC-8 CEEC-2

CEEC-10 CEEC-8 CEEC-2

Table P.2

3 209 642 2 534 795 674 847

3 721 788 2 878 422 843 367

2030

Causes and consequences of international labour mobility

367

Transitional periods, which allow the postponement of free movement for a maximum of seven years, have only a minor impact on the migration potential. They postpone migration, but both net immigration flows and the long-run migration potential are reduced by only a few thousand persons. This can be traced back to the rather slow convergence of per capita income levels. However, transitional periods can lead to a reallocation of migration across individual EU countries. While some EU members (Denmark, Italy, Portugal, Sweden, Spain and the UK) plan to open their labour markets at least partially right from the beginning, the main immigration countries (for example, Austria and Germany) will use the transitional periods to restrict immigration from the CEECs. Although geographical factors, network effects and prospective labour market opening will mitigate the diversion of migrants, even in those countries that start with migration restrictions, it is still possible that the transitional periods will affect the future distribution of migrants from the CEECs across the EU. At the present state of research, however, it is not possible to quantify this effect. Impact on Labour Markets Labour migration increases the efficiency of resource allocation. Against the background of the large difference in per capita income levels, these efficiency gains can be substantial. Calculations based on Computable General Equilibrium (CGE) models find that the emigration of 1 per cent of the CEECs’ population will increase the joint GDP of the receiving and sending countries by between 0.15 per cent and 0.3 per cent.4 If we also assume that the receiving countries are characterized by regional differences in income levels and employment opportunities, and that migrants will tend to move into prosperous regions, then the GDP gain may be even higher (see Borjas, 2001). The size of the GDP gains depends, however, on the flexibility of labour markets, that is, whether and to what extent the receiving economies are able to absorb the additional labour supply. The gains from international migration are not, however, equally distributed between the receiving and sending countries and among the different production factors. The assessment of the impact of migration on income distribution depends on the framework of analysis. Most empirical studies rely on a closed economy framework, where the labour market bears the whole burden of adjustment. In a nutshell, these models predict that production factors which are (net) complements to immigrant labour win, while those which are (net) substitutes lose. If labour markets clear, the aggregate income of natives in the receiving countries increases, while the aggregate income of the natives left behind in the sending countries

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decreases. However, if labour markets do not clear, natives in the receiving countries may lose and natives in the sending countries may win, depending on the relative increase or decrease in the unemployment rate in the respective location. Calibrations based on computable general equilibrium models indicate that most of the income gains from international migration accrue to the migrants themselves: in fact, more than 80 per cent. If we consider remittances, the income of private households in the sending countries increases through migration. With a 1 per cent increase in the labour force through migration, wages fall by 0.4 per cent, capital income increases by 0.5 per cent and, given reasonable assumptions regarding wage flexibility, the unemployment rate increases by 0.2 percentage points in the receiving countries. Analogously, wages increase, capital income decreases and the unemployment rate falls in the sending countries (see Bauer and Zimmermann, 1997; Brücker, 2002, 2003). Econometric studies that try to estimate the impact of migration on native wages and employment opportunities by using the variance of foreigner shares across regions or sectors often find lower elasticities.5 Many of the recent studies find no measurable impact of migration on wages or employment at all (for example, Bauer, 1997; Gavasto et al., 1999; Trabold and Trübswetter, 2003). Beyond estimation and measurement problems these findings can be interpreted to suggest that the closed economy framework might be inappropriate. In contrast to closed economy labour market models, the standard foreign trade theory model – the Heckscher–Ohlin–Samuelson (HOS) model – states that factor prices depend on prices in the international goods markets, not on domestic factor endowments. An influx of foreign workers is thus absorbed by reduced imports of labour-intensive goods, reduced exports of capitalintensive goods and increased production of labour-intensive goods (Rybczynski effect). Wage and interest rates remain unaffected. The results of the HOS model rely, however, on a set of strong assumptions regarding technologies, specialization patterns, mobility of factors across sectors, and so on, such that migration may still have an impact on wages and employment opportunities in open economies (see Venables, 1999 for a detailed discussion). Nevertheless, some empirical evidence suggests that Rybczynski effects matter, and that the actual impact of migration on wages and employment thus might be considerably smaller than is predicted by closed economy models (Hanson and Slaughter, 2002). According to our estimates, the number of foreign residents from the CEECs will reach between 3.1 and 4.2 million persons in the long run, that is, between 0.8 per cent and 1.1 per cent of the EU-15 population. One million citizens from the CEECs already reside in the EU-15, such that

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the increase in the population and the labour force can be estimated at between 0.56 per cent and 0.85 per cent of the EU-15 labour force. Following our simulations, this may reduce wages in the EU-15 by between 0.25 per cent and 0.4 per cent, and increase the unemployment rate by between 0.1 and 0.2 percentage points. In contrast, the evidence from econometric studies indicates that the wage and employment impact of this scale of immigration will tend to zero. However, the migrants from the CEECs are not equally distributed across the EU-15. Germany and Austria absorb around 75 per cent of the total migrants from the CEEC-10. Our projections indicate that the share of residents from the accession countries will increase from 0.7 per cent to between 2.6 per cent and 3.4 per cent of the German population. This would imply that, according to the simulations above, wages in Germany will fall by between 0.9 per cent and 1.3 per cent, while the unemployment rate will increase by between 0.3 and 0.5 percentage points. According to most econometric studies, the effects of migration would again be negligible. It is worth noting in this context that both the simulation studies and the econometric estimates refer to the short-run adjustment of labour markets. The actual impact of East–West migration is therefore presumably smaller, since the migration potential is reached over a 20-year time period, such that capital accumulation mitigates its impact on labour markets. Altogether, the labour market effects of migration are probably much smaller than is widely expected in the public debate. This holds true especially if we assume that the open economy models are more appropriate for analysing the adjustment of the economies to changing labour supplies than closed-economy models. For an assessment of the labour market impact of East–West migration it is also important to note that human capital endowments of the Central and Eastern European countries differ significantly from the traditional source countries of European immigration. Formal education indicators – for example, average years of schooling – show that education levels in the accession countries are only slightly below the EU average and well above southern EU levels. Almost all workers in the CEECs possess at least a vocational training degree, although qualifications have been made regarding the quality of training in some cases. Moreover, migrants from the CEECs currently have higher formal education levels than natives in the receiving countries. This has important policy implications: although migrants from the CEECs will compete with other foreign workers for relatively low-skilled jobs in the beginning, it is reasonable to expect that they will assimilate relatively quickly into the labour markets of the receiving countries and begin to climb the job ladder. This will mitigate possible adverse effects of labour migration from the CEECs on low-skilled workers in the receiving countries.

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Policy Conclusions The discussion in the preceding sections has shown that (i) estimates of East–West migration potential are rather moderate, at between 3.1 per cent and 4.2 per cent of the CEEC-10 population, and at between 0.9 per cent and 1.1 per cent of the EU-15 population, and that (ii) migration of this scale will yield substantial income gains for the enlarged EU, but will have only relatively minor effects on wages and employment in the labour markets of the receiving countries. However, both the estimates of migration potentials and the estimates of the wage and employment impact of migration are surrounded by considerable uncertainty. It is this uncertainty that may justify the use of transitional periods for the free movement of labour. The introduction of free movement is an irreversible decision. Since its outcome is uncertain, it might be rational to wait for further information. If transitional periods are introduced, the two central questions are how they should be applied, and who should decide on their length. Regarding the first question, migration quotas or safeguard clauses have two advantages relative to a policy of zero immigration. First, they minimize the welfare losses that result from postponing immigration. Second, they provide information on the actual migration potential. If quotas are not filled, they can be lifted ahead of time without risking a wave of mass migration. As to the second question, the delegation of the decision on transitional periods from the EU to individual Member States’ governments has resulted in a lack of coordination. While many EU members originally planned to open their labour markets immediately after Eastern enlargement, they increased barriers to immigration after the two main receiving countries, Austria and Germany, announced they would close their labour markets. Smaller countries in particular fear the diversion of migration flows from large receiving countries if they open their labour markets immediately. Consequently the immigration policies of the individual Member States are more restrictive now than if there had been policy coordination at the EU level. An EU-wide migration quota could reduce the risk of a wave of mass migration, minimize the costs of migration restrictions, and at the same time avoid an economically inefficient diversion of migration flows. Finally it is important to note that the main winners from migration – the migrants themselves – are not represented in the decision-making process. This may explain why the liberalization of international labour markets lags so far behind the liberalization of international goods and capital markets. The EU, which in the Treaty of Rome already defined free

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labour mobility as one of the four fundamental freedoms in the Common Market, makes a notable exception here. Although the transitional periods now in place in the EU will certainly create a temporary welfare loss, the CEECs’ citizens will eventually benefit from this fundamental freedom. Appendix: Why do Estimates of Potential East–West Migration Differ? Econometric forecasts of the migration potential from Central and Eastern Europe into the old EU Member States (EU-15) differ substantially. Two aspects are important in order to understand why the results of econometric studies vary. First, country-specific effects such as culture, language, history, geography and so on affect the costs and benefits of migration and consequently the migration propensity across countries. These factors are only partly observable and therefore cannot be included completely in migration models (Alecke et al., 2001; Brücker, 2001). Basically we can distinguish three types of models, which address this heterogeneity across countries in different ways. First, pooled ordinary least squares (OLS) models assume that both the intercept term and the slope parameters are homogeneous across countries. Second, fixed and random effects models assume that the intercept term differs across countries, while the slope parameters are homogeneous. Third, heterogeneous estimators assume that both the intercept term and the slope parameter differ across countries. Pooled OLS models yield biased and inconsistent results if omitted variables are correlated with the explanatory variables. The fixed and random effects models avoid this estimation bias, but can still result in biased and inconsistent estimates if the slope parameters differ across countries. Moreover, with dynamic fixed effects models, simultaneous equation bias can affect the estimation results.6 Heterogeneous estimators can be based on individual regressions. For out-of-country forecasts, averages of the estimated parameters can be used.7 However, in samples with a limited group and time dimension, heterogeneous estimators can yield unstable results, such that homogeneous panel estimators might outperform heterogeneous estimators (see, for example, Baltagi et al., 2000). It can be shown that the quantitative differences between the migration estimates can be traced back to different parameter estimates of the pooled OLS and fixed effects models: most pooled OLS regressions estimate the parameter for the lagged dependent variable close to unity, resulting in large long-run elasticities between migration stocks and explanatory variables such as the income differential. It can be shown that the estimates for the long-run parameters in pooled OLS models are around ten times higher than in fixed effects models (Alvarez-Plata et al., 2003). Consequently, the projections of long-run migration potentials differ widely. For example,

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while Sinn et al. (2001) and Flaig (2001), using a pooled OLS model, find that Germany has a long-run migration potential of between 6.5 per cent and 8.5 per cent of the population of the CEECs, Boeri and Brücker (2001) and Brücker (2001), using a fixed effects model and a similar data set, find a migration potential of between 2 per cent and 2.8 per cent. Since specification tests clearly show the joint significance of fixed effects, the high estimates of migration potentials by pooled OLS models are probably just the result of biased and inconsistent estimates of the parameters. In recent studies, Alvarez-Plata et al. (2003) and Brücker and Siliverstovs (2004) test the forecasting performance of more than 20 different estimators that are applied in the empirical migration and panel literature. These studies show that (i) standard fixed estimators outperform all other estimators, (ii) the forecasting error of pooled OLS estimators is around twice as high as that of fixed OLS estimators, and (iii) the average of individual regressions yields the worst forecasting performance of all estimators. Thus, country-specific effects have an important impact on estimates of migration potentials, but standard panel models seem to have an advantage relative to estimators that consider heterogeneity in the slope parameters. Note that this latter result confirms findings in other contexts, such as cigarette demand, gasoline consumption, energy demand, and so on (see Baltagi et al., 2000). An important caveat must be made however regarding the projections based on fixed effects models: due to the Iron Curtain, the accession countries cannot be included in the sample. Consequently fixed effects cannot be estimated for the accession countries. For the out-of-sample projections, many studies therefore ‘explain’ the fixed effects in a second regression with time-invariant variables (language, distance, and so on).8 Although between 40 per cent and 60 per cent of the variance in the fixed effects can be ‘explained’ by these regressions, a considerable amount of uncertainty still surrounds these estimates of the fixed effects. A second aspect of empirical migration model specification carries important implications for the estimation of migration potentials. Based on the traditional expected income hypothesis of Harris and Todaro (1970) most studies regress gross or net migration rates against employment rates and per capita GDP as a proxy for the wage rates in the receiving and sending countries. Moreover, many studies consider lagged migration stocks as a proxy for network effects. In terms of modern time series econometrics, this type of migration model implicitly assumes that a cointegrating relation exists between the migration rate and the explanatory variables. This involves a theoretical and an empirical problem. Theoretically, a contradiction may arise since the migration rate is (almost) the first difference of the migration stock. Thus, if the migration stock is a non-stationary variable, then the

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migration rate and the explanatory variables follow different stochastic processes and cannot form a cointegrated set. Empirically, employment rates and per capita GDP variables are usually conceived as non-stationary variables, or, more precisely, integrated of order one (I(1) variables). However, panel unit root tests conducted using German migration data show that migration rates are stationary while migration stocks tend to be I(1) (Boeri and Brücker, 2001; Brücker et al., 2003). Although this evidence is not unequivocally shared by other countries discussed in the literature9 it can be shown that a cointegrating relation exists between migration stocks – instead of migration flows – and the explanatory variables, at least for the main European destinations of migrants from the CEECs. The use of the stock model can be also be supported by theoretical considerations. If individuals differ with regard to their preferences and human capital characteristics, the stock of migrants eventually reaches equilibrium for a given income differential, while net migration ceases. This does not deny that network effects have an important impact on migration. It only states that the decreasing costs of migration, which result from existing migration networks, are eventually dominated by a declining propensity to migrate in the home population. The question as to whether a cointegrating relation exists between migration stocks or flows and the explanatory variables in migration models is not purely of academic interest. In the case of Southern enlargement, the introduction of free movement has not resulted in increasing migration stocks, although the income of the southern Member States amounted to no more than 65–70 per cent of the EU average at that time. In terms of the stock model, this phenomenon can be explained by the fact that migration stocks had already achieved their equilibrium levels when the free movement was introduced. Note that guest worker recruitment and later family reunification opened EU labour markets at least partially before free movement was introduced for the southern members. In the case of the CEECs, however, the Iron Curtain and later immigration barriers within the EU-15 effectively prevented the development of migration stocks similar to those of the southern EU members. This suggests that there will be more immigration following Eastern enlargement than following Southern enlargement. Moreover, the stock model predicts that net migration will eventually come to a halt before per capita income levels are equalized, while the flow models suggest that net migration will continue until the expected income levels are equalized. Thus, we conclude that (i) fixed effects models lead to better predictions than pooled OLS models and heterogeneous estimators, and that (ii) the dynamics of migration tends – at least in the long run – to decline with the share of the population already living abroad.

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Notes 1. Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia. 2. Over 30 studies now exist, among them Alvarez-Plata et al. (2003), Bauer and Zimmermann (1999), Boeri and Brücker (2001), Brücker (2001), Dustmann et al. (2003), Fassmann and Hintermann (1996), Fertig (2001), Fertig and Schmidt (2001), Krieger (2003) and Sinn et al. (2001). For a survey, see Straubhaar (2002). 3. For a historical comparision of per capita GDP levels, see Maddison (1995). 4. It is assumed that the receiving countries have the same population size as the sending countries. 5. The results rely critically on the way it is controlled for possible endogeneity. For surveys of the literature see Borjas (1995), Friedberg and Hunt (1995), Brücker (2002) and Brücker (2003). 6. The simultaneous equation biases the estimated parameters for both the fixed effects and pooled OLS models, since the errors are correlated with the lagged dependent variable. It disappears however with the time dimension of the panel. See Nickell (1981) and Kiviet (1995). 7. It can be shown that the estimated parameter means are consistent and unbiased, if the group and time dimension of panels tends to infinity (Pesaran and Smith, 1995). 8. See Fertig (2001); a similar approach is applied by Boeri and Brücker (2001) and AlvarezPlata et al. (2003). 9. Hatton (1995) finds a cointegrating relation between migration flows and the explanatory variables for UK–US migration in the period 1870–1913, and Mitchell and Pain (2003) for UK migration in the post-war period.

References Alecke, B., P. Huber and G. Untiedt (2001), ‘What a difference a constant makes: how predictable are international migration flows?’, in OECD (ed.), Migration Policies and EU Enlargement. The Case of Central and Eastern Europe, Paris: OECD, pp. 63–78. Alvarez-Plata, P., H. Brücker and B. Siliverstovs (2003), Potential Migration from the Central and Eastern European Accession Countries into the EU-15 – An Update, Study for the European Commission, DG Employment and Social Affairs, Berlin: DIW. Baltagi, B., J.M. Griffin and W. Xiong (2000), ‘To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand’, Review of Economics and Statistics, 82 (1), 117–26. Bauer, T. (1997), ‘Do Immigrants Reduce Native Wages? Evidence from Germany’, Münchner Wirtschaftswissenschaftliche Beiträge 97, University of Munich. Bauer, T. and K.F. Zimmermann (1997), ‘Looking South and East, labour market implications of migration in Europe and LDCs’, in O. Memedovic, A. Kuyvenhoven and W. Molle (eds), Globalization and Labour Markets. Challenges, Adjustment and Policy Responses in the EU and LDCs, Dordrecht: Kluwer, pp. 75–103. Bauer, T. and K.F. Zimmermann (1999), ‘Assessment of Possible Migration Pressure and its Labour Market Impact Following EU Enlargement to Central and Eastern Europe’, IZA Research Report No. 3. Boeri, T. and H. Brücker (main authors) (2001), The Impact of Eastern Enlargement on Employment and Labour Markets in the EU Member States, Brussels: European Commission, DG Employment and Social Affairs.

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Borjas, G.J. (1995), ‘The economic benefits from immigration’, Journal of Economic Perspectives, 9, 3–22. Borjas, G.J. (2001), ‘Does immigration grease the wheels of the labor market?’, Brookings Papers on Economic Activity, 1, 69–133. Brücker, H. (2001), ‘Die Folgen der Freizügigkeit für die Ost-West-Migration. Schlussfolgerungen aus einer Zeitreihenanalyse der Migration nach Deutschland’, Konjunkturpolitik – Applied Economics Quarterly, Supplement 52, 17–54. Brücker, H. (2002), ‘Can international migration solve the problems of European labour markets?’, UNECE Economic Survey of Europe, 2, 109–42. Brücker, H. (2003), ‘Die Arbeitsmarkteffekte der Ost-West-Migration: Theoretische Überlegungen, Simulationen und empirische Befunde’, Vierteljahreshefte zur Wirtschaftsforschung, 69 (4), 579–93. Brücker, H. and B. Siliverstovs (2004), ‘On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?’, mimeo, DIW, Berlin. Brücker, H., B. Siliverstovs and P. Trübswetter (2003), ‘International Migration to Germany: Estimation of a Time-series Model and Inference in Panel Cointegration’, DIW Discussion Paper No. 391. Dustmann, C., M. Casanova, M. Fertig, I. Preston and C.M. Schmidt (2003), ‘The Impact of EU Enlargement on Migration Flows’, Home Office Online Report 25/03, at http://www.homeoffice.gov.uk/rds/pdfs 2/rdsolr 2503.pdf. Eurostat (2003), ‘Kaufkraftparitäten und abgeleitete Wirtschaftsindikatoren für EU, beitretende Länder, Beitrittskandidaten und EFTA’, Statistik kurz gefasst No. 64. Eurostat (2004), ‘Erste Bevölkerungsschätzungen für 2003’, Statistik kurz gefasst No. 1. Fassmann, R. and C. Hintermann (1997), ‘Migrationspotential Ostmitteleuropa. Struktur und Motivation potentieller Migranten aus Polen, der Slowakei, Tschechien und Ungarn’, ISR-Forschungsbericht 15, Vienna: Institut für Stadt- und Regionalforschung, Verlag der Österreichischen Akademie der Wissenschaften. Fertig, M. (2001), ‘The economic impact of EU enlargement: assessing the migration potential’, Empirical Economics, 26, 707–20. Fertig, M. and C.M. Schmidt (2001), ‘Aggregate-Level Migration Studies as a Tool for Forecasting Future Migration Streams’, University of Heidelberg Economics Department Discussion Paper No. 324. Flaig, G. (2001), ‘Die Abschätzung der Migrationspotentiale der osteuropäischen EU-Beitrittsländer’, Konjunkturpolitik – Applied Economics Quarterly, Supplement 52, 55–76. Friedberg, R. and J. Hunt (1995), ‘The impact of immigration on host countries wages, employment and growth’, Journal of Economic Perspectives, 9, 23–44. Gavasto, A., A. Venturini and C. Villosio (1999), ‘Do immigrants compete with natives?’, Labour, 13, 603–22. Hanson, G.H. and M.J. Slaughter (2002), ‘Labor market adjustment in open economies: evidence from US states’, Journal of International Economics, 57, 3–29. Harris, J.R. and M.P. Todaro (1970), ‘Migration, unemployment and development: a two-sector analysis’, American Economic Review, 60, 126–42. Hatton, T.J. (1995), ‘A model of UK emigration, 1870–1913’, Review of Economics and Statistics, 77, 407–15.

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Kiviet, J. (1995), ‘On bias, inconsistency and efficiency of various estimators in dynamic panel data models’, Journal of Econometrics, 68, 53–78. Krieger, H. (2003), Migration Trends in an Enlarged EU, Dublin: European Foundation for the Improvement of Working and Living Conditions. Layard, R., O. Blanchard, R. Dornbusch and P. Krugman (1992), East-West Migration: The Alternatives, Boston: MIT Press. Lundborg, P. (1997), ‘Determinants of migration in the Nordic labor market’, Scandinavian Journal of Economics, 93 (3), 363–75. Mitchell, J. and H. Pain (2003), The Determinants of International Migration into the UK: A Panel Based Modelling Approach, London: NIESR. Nickell, S. (1981), ‘Biases in dynamic models with fixed effects’, Econometrica, 49, 1417–26. Pesaran, M.H. and R.P. Smith (1995), ‘Estimating long-run relationships from dynamic heterogeneous panels’, Journal of Econometrics, 68, 79–113. Sinn, H.-W., G. Flaig, M. Werding, S. Münz, N. Düll and H. Hoffmann (2001), EU-Erweiterung und Arbeitskräftemigration. Wege zu einer schrittweisen Annäherung der Arbeitsmärkte, Munich: Ifo – Institut für Wirtschaftsforschung. Straubhaar, T. (2002), ‘Ost-West Migrationspotenzial: Wie groß ist es?’, Jahrbücher für Nationalökonomie und Statistik, 222, 20–41. Trabold, H. and P. Trübswetter (2003), ‘Schätzung der Lohn- und Beschäftigungseffekte der Zuwanderung’, in H. Brücker, H. Trabold, P. Trübswetter and C. Weise (eds), Migration: Potential und Effekte für den deutschen Arbeitsmarkt, Baden-Baden: Nomos Verlagsgesellschaft. Venables, A. (1999), ‘Trade liberalization and factor mobility: an overview’, in R. Faini, J. DeMelo and K.F. Zimmermann (eds), Migration. The Controversies and the Evidence, Cambridge: Cambridge University Press.

DOES EU ENLARGEMENT IMPLY A ‘RACE TO THE BOTTOM’ IN EU LABOUR MARKETS? Dennis J. Snower The ‘race to the bottom’ thesis is that EU enlargement will make labour markets more flexible by reducing wages. The underlying reasoning is well known: the more generous the welfare benefits provided by a country, the greater will be the inflow of immigrants to that country. The immigrants, furthermore, will self-select, that is, the people with the largest incentives to immigrate will be those who are most likely to be or to become dependent on welfare benefits. To finance these benefits, the government will need to raise taxes, thereby reducing the incentives to work and invest. As a result, there will be an outflow of capital from the country. In these ways, countries will be punished for providing generous welfare benefits and thus will have an incentive to reduce them. Consequently, real wages will fall and labour markets will become more flexible. A necessary condition for this to happen is that labour, capital, or goods and services are sufficiently mobile across countries. In addition, such mobility must not generate a significant increase in rent-seeking activities by incumbent firms and employees with vested interests in the status quo, thereby preventing the ‘race to the bottom’ through the political process. In practice, however, there are good reasons to believe that these conditions are highly unlikely to be met. Let us begin by considering labour mobility. Although income differentials between the EU and the CEECs are large, labour mobility from the latter area to the former may be expected to remain low and concentrated on a few EU Member States. At present, Germany and Austria together receive about 80 per cent of all the migrants from the CEECs. Differences in housing costs, language and culture are likely to pose significant obstacles to migration. By way of analogy it is worth noting that mobility rates within the EU have remained very low (between 0.1 and 0.2 per cent of the destination countries’ population), even though most remaining restrictions on labour mobility within the EU were dismantled in 1993. The US experience is also relevant in this regard. Here geographic wage differentials have persisted for decades, despite the absence of differences in language and culture (see, for example, Brown and Medoff, 1989; Krueger and Summers, 1988). Furthermore, inter-US migration rates do not appear to be related to differences in welfare benefit generosity (see, for example, Blank, 1988). Next, capital mobility may be expected to remain low as well. Current annual capital flows from the EU to the CEECs are small, amounting to 377

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0.2 per cent of EU GDP and 0.8 per cent of gross fixed investment in the EU. Furthermore, only 20 per cent of foreign investment from the EU goes to industries with relatively low labour costs. It appears that market access, rather than labour costs, provides the main incentive for foreign investment. As for mobility in goods and services, trade volumes between the EU and the CEECs are very small. In particular, EU exports to and imports from CEECs are about 1 per cent of EU GDP. Most of these are concentrated in the border countries, such as Germany and Austria. This is so even though most trade restrictions for goods between the EU and the CEECs have already been abolished and thus trade flows have probably already adjusted. Of course the Single Market Programme has been implemented only to a limited degree in services, and service trade flows are likely to remain low. For all these reasons it appears unlikely that mobility in labour, capital or goods and services is likely to be the driving force that leads to a ‘race to the bottom’ in EU labour markets. Lastly, it is important to note that political considerations in the EU are also likely to prevent such a ‘race to the bottom’. Increased openness of advanced industrialized countries does indeed create more risk for employees, particularly unskilled ones. This increased risk characteristically leads to greater demands for social protection and the greater the voting constituency that becomes dependent on such protection, the more likely it is that this protection becomes more generous and comes to be financed by the general tax payer. On this account, it is perhaps not surprising that various economists (for example, Agell, 1999 and Rodrik, 1997) have found an empirically significant positive relation between the openness of an economy and its welfare benefit generosity. References Agell, J. (1999), ‘On the benefits from rigid labour markets: norms, market failures, and social insurance’, Economic Journal, 109, F143–64. Blank, R. (1988), ‘The effect of welfare and wage levels on the location decisions of female-headed households’, Journal of Urban Economics, 24 (2), September, 186–211. Brown, C. and J. Medoff (1989), ‘The employer size wage effect’, Journal of Political Economy, 97 (50), 1027–59. Krueger, A.B. and L.H. Summers (1988), ‘Efficiency wages and interindustry wage structure’, Econometrica, 56, 259–93. Rodrik, D. (1997), ‘Trade, Social Insurance, and the Limits to Globalization’, NBER Working Paper No. 5905.

THE EFFECTS OF ENLARGEMENT IN IMPERFECT LABOUR MARKETS Etienne Wasmer A panel discussion shares some of the features of a Nash equilibrium, in the sense that panellists need to prepare their speeches taking into account what others will do. To avoid repetition and to deal in advance with some of the emphasis put on the gains provided by enlargement, and on the minimization of its costs, it is tempting to take a view slightly different from the consensus (a very clear exposition can be found in Boeri and Brücker, 2001). Let us first notice that enlargement was primarily and almost exclusively a political decision. This decision was reached in the years following the fall of the Berlin Wall for geo-strategic reasons: (a) to bring political stability to the eastern part of the continent and (b) as a symbol of the political and economic victory of democracies and market economies over the former Eastern bloc. Twelve applications (ten from former Communist countries, plus those of Malta and Cyprus) led to negotiations. Very rapidly, negotiations became de facto understood as leading to entry into the Union in a more or less near future. The criteria for entry were adopted in 1993 in Copenhagen. The decisions to present an application, to open negotiations, to set criteria and to approve formally, being mostly political, may also be associated with positive economic consequences for both the 15 European Union Member States (prior to the 2004 enlargement; we shall call them ‘incumbents’ hereafter) and for the newcomers (we shall call them ‘accession countries’). However, it would be a coincidence if these decisions led at the same time to both political and economic gains. Further, given the large costs associated with this enlargement (dilution of powers within the Union, larger coordination costs, renegotiation of treaties, political uncertainty and sometimes anxiety from citizens in several regions of Western Europe), economic reasons alone were not sufficient to justify the process. In fact, the bottom line of my discussion will be that, even if long-run gains of enlargement are certain, there are at the same time short-term costs associated with the transition. Such short-term costs are often mentioned by experts, but rarely in detail, and it is often argued that they are not as large as citizens perceive them to be. In my view, those short-term costs are likely to be large and difficult to cope with. The reasons for anticipating troubles are numerous. There are very large GDP/capita differences between the accession countries and the incumbents, which imply waves of reallocation of labour from traditional industries in which comparative advantages disappear in favour of new sectors. Many labour markets 379

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are not functioning very well, which means difficulties in adjustment. Populations are ageing all over Europe, meaning lower reactivity and adaptability to social and economic changes. Further, ageing in Western Europe puts social security and pensions systems under pressure so that it might be more difficult for the usual social pillars to smooth out the transition. Finally, and perhaps most importantly, European transfers across regions or countries will remain very limited. This being said, a few preliminary remarks are needed. In the debates about enlargement, there is a need to clarify what the enlargement process will imply in terms of economic integration. To dismiss the idea of adverse consequences of enlargement, it has often been claimed that trade flows have already grown considerably since the collapse of the Berlin Wall, thus long before the formal enlargement of the European Union. However, such trade links would never have developed so fast and so strongly if the enlargement had not been a credible horizon. So, in assessing the impact of enlargement one needs to add up two dimensions: first, the post-enlargement economic impact and second, the pre-enlargement economic impact due to (rational) expectations that Central and Eastern European Countries (CEECs) would join the European Union. In other words, even if a large part of the impact of enlargement has already been anticipated, this doesn’t mean that the process per se has not had many economic impacts in the short run; adding up both dimensions is precisely what economic analysis is about. A second remark is that one should not underestimate the first of these dimensions, that is, the direct impact of enlargement at the time of accession of ten countries in 2004. One argument often made is that trade costs have already been drastically reduced through several bilateral agreements between the Union and accession countries before 2004. However, trade costs are not only pecuniary. There are dozens of dimensions along which the enlargement will make a significant difference. These dimensions include expectations of stability of the trade area, the emergence of a common set of laws protecting economic agents, political lobbying through European institutions to promote free trade and some more anecdotal evidence such as the much welcome reduction in waiting time at customs offices of airports for busy businessmen (or academics) between the East and the West. For all these reasons, the enlargement process will be less innocuous and minor than in an ideal world. It will have some consequences, which I will detail now. Long-run versus Short-run Gains The enlargement process, as a reduction of barriers to trade and mobility costs, will have unambiguously large long-term aggregate gains. This

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process will generate, consistently with economic theory, further trade, migrations of skilled labour and industry specialization where countries have their comparative advantage, inducing additional economies of scale. Enlargement is also a political process with economic consequences, notably bringing political stability and democracy to the newcomers and incentives towards better political and economic governance for the remaining candidates to European integration. The expected price stability as well as competition policy should be beneficial to a majority of economic agents. Beyond this broad consensus among economists, let us focus on shortterm costs in both incumbent and accession countries. While most analyses in ‘international economics’ textbooks are devoted to long-run perspectives, they also acknowledge short-term costs and transitional difficulties, including costly reallocation of labour across sectors and industries, loss of specialized skills for older workers, and migration costs. Indeed, workers are not as mobile as free trade models predict, especially when they are older. In traditional sectors, workers have many skills, but most of them are likely to be specific, which further reduces mobility, meaning costly job reallocations. The important point is that such reallocations are unavoidable given the scale of differences in GDP per capita across countries. Vidovic (2002) indicates that, even controlling for PPP, the Czech Republic had in 2000 a GDP per head of 59 per cent of the EU-15 average, Hungary 53 per cent, Poland and Estonia 41 per cent and Latvia and Lithuania 30 per cent. As a matter of comparison, in Spain in 1986, at the time of admission to the EU, GDP per capita was about 75 per cent of the PPP-adjusted European Union in 1986, with a GDP per worker above 90 per cent of the European Union (again after PPP-adjustment), according to Jimeno (2002). So far, in Europe the welfare states have played a major role in attenuating the costly reallocation of labour. One can think for instance of the way the coal mining and agricultural industries have been reduced: pre-pension schemes have been extensively offered to older workers in industries, ambitious and costly policies such as the Common Agricultural Policy have been designed and, simultaneously, massive regional aid schemes such as Objectives 1 and 2 and the Cohesion Fund for Ireland, Portugal, Greece and Spain have been implemented with great success. To give an idea of the quantitative importance of such schemes, Jimeno (2002) gives very interesting figures for Spain. In 1990 Spain received 0.52 per cent of its GDP in regional funds and European social funds. In 1995 it received no less than 1.28 per cent of its GDP from various assistance funds (0.71 per cent of GDP in regional funds, 0.27 per cent in European social funds, 0.26 per cent with the newly created Cohesion funds, plus some additional structural funds). This figure was still 1.15 per cent in 1996, and in 1995 and 1996 the

382

Panel discussion

funds represented about 4 per cent of total investment. According to Guérin (2004), the average funds for the four Cohesion countries between 1994 and 1999 were even larger, constituting about 2 per cent of total GDP and between 5.5 and 8.9 per cent of total investment. In the ongoing process of enlargement, none of these dimensions of cross-country and within-country solidarity will have the same magnitude. As pensions systems are currently under considerable pressure, the easy solution of seeking recourse from pre-pensions is no longer viable; incumbent countries, notably France, Germany and the United Kingdom, have made it clear that Cohesion budgets will remain very limited and, if anything, the subsidies of the Common Agricultural Policy are going to be sharply reduced, not increased. According to the European Commission, cited by Guérin (2004), net transfers between 2004 and 2006 should represent 0.3 per cent of GDP for the Czech Republic, 0.8 per cent for Slovakia, 0.6 per cent for Hungary, 1.2 per cent for Poland and 0.3 per cent for Slovenia. Implicit comparisons made with previous enlargements of the European Union thus neglect the scale difference of the redistribution process across countries and can be misleading. Short-term labour reallocation costs throughout the process of enlargement will not be particularly easy to smooth out. Specific Skills It is not necessarily easy to quantify these reallocation costs. Labour economics theory has only recently devised analytical tools designed to capture the short-term factors and the transitions to the long run, and this perhaps to a greater extent than the New International Trade Theory and the New Economic Geography, which are, however, the dominant paradigms in the analysis of the enlargement. For instance, search matching models of the labour market (Diamond, 1982; Mortensen and Pissarides, 1999) develop direct or indirect measures of the costs of reallocating labour in imperfect labour markets. Among others, Diamond (1980) has developed a notion of the cost of the transition from one steady state to another steady state along a transitional path in a very simple and insightful paper. Another older and relevant theoretical tool is the concept of specific human capital (Becker, 1964; more recently, Lazear, 2003), that is, human capital lost in the displacement of workers as it only applies to a job or a sector. There are also various theoretical results concerning the shape and specificity of human capital investments in imperfect labour markets. One major finding is that the more imperfect the labour markets are, the more likely it is that workers will wish to invest in human capital specific to their job or, in declining sectors, to their sector of activity. Among other things,

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383

search and matching frictions, layoff costs and unemployment benefits reinforce the specificity of human capital investments (Wasmer, 2002). This indicates that for countries in which for a very long time labour market regulations were as high as in communist countries in which workers were quite immobile across firms, sectors or regions, the amount of general skills which ease the transitions is likely to be very low. The same can be said to a lesser extent about many workers in Western Europe, at least in countries where employment protection is strong. It is important to note that the mechanisms of human capital accumulation described above have in principle the elements of a virtuous circle: labour market institutions induce human capital investments by protecting them, a feature that is fairly well understood in contract theory (Malcolmson, 1999, for instance). However, in addition it can be shown that the greater the specificity of human capital investments, the larger the costs of transition from one steady state to another. In accession countries as well as in the 15 incumbent countries, the prevalence of labour market institutions has driven the accumulation of a large amount of specific skills. Waves of labour reallocations, leading to the obsolescence of some sectors and of their associated skills, should therefore be costly to society as a whole (since specific skills cannot be exported towards other sectors), and especially for workers for whom welfare losses are likely. The current situation of older workers living under poverty standards in CEECs, which is widely documented in the media, is partly due to the lack of generous pension systems, but is also to a large extent symptomatic of situations in which, due to loss of skills during the transition process, older workers have not been able to work in modern sectors. We also have empirical evidence that workers in CEECs were heavily concentrated in agriculture and that the industrial sector was underdeveloped. Table P.3 indicates how Eastern Germany, Czechoslovakia, Hungary and Poland compared in the late 1980s to the OECD, subdivided into three categories of countries (ranked per quantile of the income per capita distribution). The bottom third corresponds broadly speaking to Southern European countries such as Spain, Portugal and Greece and to Ireland, the four ‘Cohesion Fund countries’. The table illustrates that CEECs and East Germany have a very large industrial sector by comparison with the richest OECD countries. The situation is somewhat different from the previous enlargements to Southern Europe, where agriculture held first place; and only Poland can be compared to Southern Europe in terms of the sectoral composition of employment. Table P.4 provides an update of these figures and an idea of the transition speed (comparing 1993 and 2000). Significant movement towards transition has already occurred, notably in the decline of agriculture, but the

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Panel discussion

Table P.3 Comparisons between four Eastern European countries and OECD countries, late 1980s Employment OECD (top third, 1991) OECD (middle third, 1991) OECD (bottom third, 1991) East Germany (1989) Czechoslovakia (1989) Hungary (1989) Poland (1989)

Agriculture (%)

Industry (%)

Service (%)

5.5 5.8 17.9 10.0 11.6 17.5 27.2

29.8 30.4 29.5 44.1 46.8 36.1 36.3

64.7 63.9 52.6 45.9 41.6 46.4 36.4

Source: Roland (2000).

Table P.4

Country comparisons, 1993 and 2000

Employment Czech Republic (1993/2000) Hungary (1993/2000) Poland (1994/2000) Germany (2000) Italy (2000) Spain (2000) Sources:

Agriculture (%)

Industry (%)

Service (%)

6.9/5.0 9.1/6.5 23.9/18.7 2.7 5.2 6.2

42.1/39.5 33.8/33.7 25.9/23.9 24.6 25.0 20.4

49.4/55.4 57.1/59.7 50.1/57.4 72.8 69.8 73.4

ILO Labor Statistics and author’s calculations.

share of services is still low compared to three major Western economies. Poland’s agriculture is still very large in terms of employment, although its share of GDP is considerably lower than its employment share: it was 4.9 per cent in 1998, according to the World Bank, indicating a major productivity gap. There is also evidence that more workers in the East were employed in large firms, and fewer in small businesses; this implies less turnover and more specialization (Boeri, 2000). Now, what happens during a transition? Transitions are always very costly as the relative position of individuals, groups and sectors rapidly changes along with the economic conditions. Such is the case for workers with specific skills. From an individual point of view, there is evidence that in former communist countries workers who had received vocational education (as measured by technical and vocational secondary education) fared much worse in labour markets, as compared to those with similar general skills: according to Boeri (2000), these workers had, between 1994 and 1997, a 10 per cent

The effects of enlargement in imperfect labour markets

385

higher probability of job loss in the Czech Republic, 9 per cent in Bulgaria, and 1 to 3 per cent in other countries. They also had a lower probability of finding a job:
6.5 per cent in Bulgaria and Hungary,
13.5 per cent in Poland,
3 per cent in Czech Republic and
5 per cent in Slovakia and Slovenia. Finally, they had lower wages: general secondary education pays 20 per cent more than technical and vocational secondary education. Of course, given these numbers, young workers have strong incentives to develop general skills, especially as episodes of deregulation of labour markets tend to discourage specific skills, as noted above. A first concern is of course whether education systems will rapidly adapt to the new economic context in Eastern countries. Even if one ignores this issue, one cannot ignore the fact that workers who have been out of schooling for years are going to find it costly to re-invest in new skills: there is an age above which learning is difficult, especially for those with low initial education levels. Demography A look at the age structure is crucial to understanding the three following economic aspects of enlargement: order, mobility and human capital structure, and the ability to cope with structural changes. Indeed, transitions are costly and require changes in habits, a reorganization of society and new investments in skills. All this is easier if the population is young and adaptable.1 European countries have instead an ageing population. In 2000, according to the ILO, the median age was 39, 38 and 38 years in Western, Northern and Southern Europe respectively, and around 37 in Eastern Europe. For the sake of comparison, the median age was between 31 and 33 in 1970, and will be between 39 and 42.5 in 2010. The oldest part of Europe will be Western Europe six years from now, with a predicted median age of 42.5, which Figure P.9 illustrates. Some more detailed data by country is displayed in Figure P.10, which also displays some average age figures by country (horizontal lines) for Europe and for the accession countries, the latter being calculated with and without Turkey. The age difference between Turkey and all other countries is rather spectacular. Can an ageing population smoothly adapt to deep structural changes such as those implied by the process of enlargement? Nobody has the answer. In terms of migration, these demographic figures would lead us to conclude that one should not anticipate massive waves of geographical reallocation of labour from the East to the West. Indeed, workers who migrate for economic reasons are mostly from the younger age bracket. Brücker draws a similar conclusion in this panel discussion. Is this necessarily good news? It is from a political perspective, in the sense that the fears of massive migration waves expressed by citizens are exaggerated and economically

386

Panel discussion

43 41 39 37 35 33 31 29 27

2010

25

2000 Eastern Europe

Northern Europe

Southern Europe

1970 Western Europe

Source: UN Population Data and author’s calculations.

Figure P.9

Median age of the population

unjustified. Economic reasoning and previous empirical work (for example Blanchard and Katz, 1992) teach us however that the news is not so good in the sense that, in the absence of geographical labour mobility (and as argued above, in the absence of occupational mobility given specific skills), adjustments occurring in the labour markets are mostly inefficient, through higher unemployment or declining participation in labour markets. Concluding Words Changes implied by the enlargement are both necessary and inevitable, and according to economic theory profitable in the long run. According to the few indicators discussed in this discussion (demography, the specificity of skills investments and the distribution of jobs by broad sectors), it seems however that short-term costs of enlargement and the changes in the geography of economic activity are not going to be innocuous. This should not come as a surprise, as costs of transitions are usually large, since they imply a shift in the distribution of economic and political power. In addition,

387

Median age of population in 2000

UN Population Data and author’s calculations.

Horizontal lines  average.

Figure P.10

Source:

Note:

a k d e y d y s n n us ia ry ia ia ta d lic ia ia ey tri ium ar an nc an an tal nd ai de UK USA ypr ton nga atv uan al olan ub ven vak urk M P ep lo lo T us e l g n m i n l F r a r m I r e l I e r l a S p w e L th C Es Hu A B e F e S R S S h Li G D h et N ec z C

20

25

30

35

40

45

388

Panel discussion

conventional policies designed to address the impact on welfare of displacing older workers from traditional industries have been in the past a combination of massive pre-retirement schemes, country-specific subsidies for these industries and structural and cohesion funds from the European Union. Such conventional policies will be extremely difficult to sustain due to huge tensions on the financing of Western European social security systems, competition policies and the self-imposed limitation on the EU budget. Note 1. Many societal changes (such as in May 1968 in France or the drastic increase in female participation in the late 1960s and early 1970s in the USA) occur when an unusually large cohort reaches the age of 20–25.

References Becker, G. (1964), Human Capital, 3rd edn, Chicago: The University of Chicago Press. Blanchard, O. and L. Katz (1992), ‘Regional evolutions’, Brookings Papers on Economic Activity, 1, 1–61. Boeri, T. (2000), Structural Change, Welfare Systems and Labour Reallocation, Oxford: Oxford University Press. Boeri, T. and H. Brücker (2001), ‘Eastern Enlargement and EU-Labour Markets: Perceptions, Challenges and Opportunities’, IZA Discussion Paper No. 256. Diamond, P. (1980), ‘An alternative to steady state comparison’, Economic Letters, 5 (1), 7–9. Diamond P. (1982), ‘Aggregate demand management in search equilibrium’, Journal of Political Economy, 89, 798–812. Guérin, J.-L. (2004), ‘Pays d’Europe de l’Est: Point de Conjoncture’, Document de travail, May, Rexecode. ILO Labor Statistics, http://laborsta.ilo.org/. Jimeno, J. (2002), ‘Employment policies in Spain (1975–2000): Lessons from central European countries acceding to the European Union’, in B. Funck and L. Pizzatti (eds), Labor, Employment and Social Policies in the EU Enlargement Process, Washington: The World Bank and IBRD. Lazear, E. (2003). ‘Firm-Specific Human Capital: A Skill-Weights Approach’, IZA Discussion Paper No. 813. Malcolmson, J.M. (1999), ‘Individual employment contracts’, in D. Card and O. Ashenfelter (eds), Handbook of Labor Economics, Vol. 3, Amsterdam: NorthHolland. Mortensen, D.T. and C.A. Pissarides (1999), ‘Job reallocation, employment fluctuations and unemployment’, in J. Woodford and M. Taylor (eds), Handbook of Macroeconomics, Vol. 1, Amsterdam: Elsevier Science, pp. 1171–228. Roland, G. (2000), Transitions and Economics: Politics, Markets and Firms, Cambridge, MA: The MIT Press.

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389

UN Population Data, http://www.un.org/popin/data.html.UN Population Data, http://www.un.org/popin/data.html. Vidovic, H. (2002), ‘Labor market trends in Central and Eastern European countries’, in B. Funck and L. Pizzatti (eds), Labor, Employment and Social Policies in the EU Enlargement Process, Washington, DC: World Bank and IBRD. Wasmer, E. (2002), ‘Interpreting Europe and US Labour Markets Differences: The Specificity of Human Capital Investments’, CEPR Working Paper No. 3780, August, under revision.

Index Aassve, A. 149 absolute advantage, tax rates and 49, 51–4, 89 Active Labour Market Policies (ALMPs) 2, 342, 351, 358–9 activity rates in the Euro area 341–2 Adam, P. 330 advertising costs 14, 16, 19, 20, 21, 25 Agell, J. 355, 378 aggregate shocks 345–6 Agreement of Wassenaar (1982) 295 agricultural sector Common Agricultural Policy in 381, 382 employment share of 381, 383–4 labour cost reduction induced by tax credit in 225, 226 part-time working in 284 seasonal workers in 335 Aguiar, M. 89 Alecke, B. 371 Alesina, A. 100 Alvarez-Plata, P. 364, 366, 371, 372, 374 Amsterdam Treaty (1997) 355 Anglo-Saxon model 354 apprentices, fixed-term contracts for 318 Arber, S. 291 Arellano, M. 203 Aronson, T. 99 Artis, M.J. 339 Auerbach, A. 100 Australia combining work and family life in 309 part-time working in 277, 310 participation rates in 116 regulation of fixed-term and openended contracts in 333 tax rates in 64, 94

Austria Active Labour Market Policies (ALMPs) in 347–8 median age of population of 387 migration potential for 367, 369, 370, 377 part-time working in 276, 277, 278, 279, 280, 281, 282, 283, 284–5, 288 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 96 shadow economy in 47 tax rates in 64, 71, 72, 73, 74, 75, 76 average personal tax rates, by country 63, 64 Baltagi, B. 371, 372 Bank of Italy 266 banking and finance sector 225, 226 Barrell, R. 217 Bauer, T. 362, 368, 374 BCS, see British Cohort Study (BCS) Bean, C.R. 356 Becker, G.S. 44, 223, 382 Belgium Active Labour Market Policies (ALMPs) in 347–8 employment rate in 109 inactivity rate in 10, 11 median age of population of 387 part-time working in 276, 277, 278, 280, 281, 282, 283, 284–5, 288 participation rates in 199, 200, 201 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 73, 96

391

392

Index

sectoral value added shares in 74, 76, 96 shadow economy in 47, 81 social security system in 292 start-up costs in 109, 110 tax rates in 64, 71, 73, 74, 76, 81, 88 unemployment rate in 10, 11 working hours in 94, 110 see also Belgium, part-time working in Belot, M. 196, 275 Bentolila, S. 21, 22, 39, 297, 345 Berger, L.M. 191 Bertola, G. 21, 22, 39, 196, 202, 345, 346, 355 Bertrand, M. 84 bilateral trade agreements 380 black economy, see shadow economy black race 134, 141 Blanchard, O. 48, 83, 84, 99, 196, 275, 299, 313, 330, 346, 358, 386 Blank, R.M. 274–5, 377 Blau, F.D. 83, 86 Blöndal, S. 196, 209, 216 Blundell, R. 107 Blundell, S. 39 Boeri, T. 345, 346, 372, 373, 374, 379, 384 Bollé, P. 309 Boone, J. 351 Booth, A. 310, 330, 335 Borjas, G.J. 367, 374 Boskin, M.J. 99 Bothfeld, S. 274, 305 Bover, O. 203 Brandolini, A. 222 Brennan, H.G. 44 Brigden, A. 38 Britain, see United Kingdom British Census of Population 310 British Cohort Study (BCS) 133, 146 Brittan, S. 37 Brown, C. 100, 377 Brücker, H. 371, 372, 373, 374, 379 Buchanan, J.M. 44 Buddelmeyer, H. 302, 303, 305 Bulgaria 361, 385 Bundeserziehungsgeldgesetz (BerzGG) 168, 172 Bundesstatistik Erziehungsgeld 169

Busetta, P. 266 business cycles 201, 203–4, 206, 207–8, 209, 210, 211, 286, 299; see also output gap business start-ups 314; see also start-up costs Buti, M. 339 Canada combining work and family life in 309 employment rate in 109 marginally attached individuals in 222 participation rates in 116 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 73, 96 sectoral value added shares in 74, 76, 77, 96 start-up costs in 109, 110 tax rates in 64, 71, 73, 74, 76, 81 working hours in 110, 275, 276, 277 Cagan, P. 59 Calmfors, L. 343, 345 Canziani, P. 330 capital income business-level taxes on 51, 52 impact of migration on 368 capital inputs, choice of production sector with 50–53, 54–5 capital mobility, in enlarged EU 6, 377–8 capital services, taxes on 51, 52, 355–6 capital utilization 52–3, 54 Caucutt, E.M. 149 Central and Eastern European countries (CEECs) average years of schooling in 369 capital flows from EU to 6, 377–8 GDP per capita in 361 labour migration to EU from 6, 361–73 impact on labour markets 367–9, 370, 377 scale of 6, 361–7, 370, 371–3, 377, 385–6 transitional periods for 367, 370–71

Index trade with EU 378, 380 see also under names of individual CEECs, e.g. Czech Republic; Hungary Centre for Economic Policy Research (CEPR) 1 child benefits and female participation rates 207, 215 and part-time employment rates 297, 299, 300, 302, 303, 304 child care availability of 132, 152, 153, 191, 192, 206, 220, 297, 310 tax sensitivity of 55 children, labour supply of mothers of, see mothers, labour supply of churning 5, 313, 315, 321 Cipollone, P. 223, 228, 233, 266 clerks, part-time working by 279, 282, 283 closed-economy labour market models 367–8 coal mining 381 Cohesion Funds 381–2, 383, 388 Coles, M. 310 Common Agricultural Policy 381, 382 comparative advantage 379, 381 tax rates and 49–50, 53–4, 89 competition, argument for 355, 356 computable general equilibrium (CGE) models 367, 368 Connolly, H. 328 construction sector 226, 284, 356 consumption smoothing 89 Continental model 354 contract theory 383 coordination, argument for 355 Copenhagen criteria (1993) 379 Coricelli, F. 345 country-specific effects 362–3, 371–2, 377 country-specific subsidies 388 craft and related trades workers, parttime working by 279–86 Credito d’Imposta programme 223 cultural differences 362, 371, 377 Currency Demand Method 59–60, 61, 78–82, 94 currency hedging 339

393

Cyprus 374, 379, 387 Czech Republic accession to EU 374 EU funding to 382 GDP per capita in 381 median age of population of 387 sectoral employment distribution in 384 vocational education in 384, 385 Czechoslovakia 383, 384; see also Czech Republic; Slovakia Davis, S.J. 50, 53, 79, 83, 101 De Grauwe, P. 339 de Koning, J. 12, 37 decentralization of wage bargaining 344–5, 357–8 Del Boca, D. 217 Delsen, L. 275 demographic factors 195, 235, 380, 385–6, 387 Denmark Active Labour Market Policies (ALMPs) in 347–8 employment rates in 109, 341 fertility rate in 206 inactivity rate in 10, 11 median age of population of 387 migration potential for 367 part-time working in 276, 277, 278, 279, 280, 281, 282, 283, 284–5, 286, 288 participation rates in 199, 200, 201 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 96 shadow economy in 81 social security system in 356 start-up costs in 109, 110 tax rates in 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 9, 10, 11 working hours in 110 see also Denmark, part-time working in Di Maria C. 266 Diamond, P. 382

394

Index

difference-in-differences method 192, 223, 234, 268–9 disability insurance 44, 45, 90–91, 191, 208, 220 disabled, measures for 347, 348, 349, 350 distribution function 322 divorced women, see widowed and divorced women Dolado, J.J. 86, 297 Doris, A. 275 Doudeijns, M. 297 duration analysis 322–4 duration of unemployment 23, 24, 30 duration of unemployment benefit, see unemployment benefit duration duration of vacancies, expected 25 Dustmann, C. 374 early retirement 24, 203, 342, 358, 388; see also retirement programmes earnings, see wage dispersion; wage pressure; wages East Germany 383, 384 eating and drinking establishments employment and value added shares in 54, 110 labour skill measures for 79 see also eating, drinking and lodging establishments; hotels and restaurants sector; trade, repair services, eating, drinking and lodging eating, drinking and lodging establishments employment and value added shares in 59, 61, 62, 69, 70, 73, 76, 77, 84, 85, 86, 87, 90, 96 labour skill measures for 78, 79 see also eating and drinking establishments; hotels and restaurants sector; trade, repair services, eating, drinking and lodging economies of scale 381 education enrolment in, and participation decisions 205, 210, 211, 213–14, 220, 221

level of, see educational attainment level returns to 152 education sector, part-time working in 285 educational attainment level in CEECs 369 and part-time employment share 286 and participation rates 164, 189, 224, 233, 235, 236, 237, 239–46, 249, 250–51, 252–3, 254–5, 256–7, 261 percentage of females attaining tertiary education 151 of temporary workers 318, 319 and transitions from fixed-term contracts 324, 325, 326, 328, 329 and women’s labour supply 3, 122–45, 151–2, 164, 189, 192, 235 data used in analysis 146, 147, 148 see also schooling, years of Eisner, R. 46 Ejrnaes, M. 194 elasticity of labour supply 44, 55, 90–91, 105, 107–8, 222, 230 elderly, care of 55 Electricity Method 59, 60, 61, 80, 82, 86, 94 electricity sector, part-time working in 284 elementary occupations, part-time working by 279, 282, 283, 286 Elmeskov, J. 196 employment transition rates of H-types and Ltypes into 23–5, 30, 42, 43 transitions from fixed-term contracts into 321, 322, 324, 325, 326, 327, 328–9, 330, 334–5 value of 231 employment adjustment, speed of 312 employment protection legislation and fixed-term contracts 5, 297, 299, 300, 302, 314, 332–4, 358 international rankings of stringency of 314, 332–4 and labour specificity 383

Index and part-time employment rate 297, 299, 300, 302, 303, 304 and participation rates 4, 197, 202–3, 205, 207, 209, 214, 216, 219–20 reforms in 346 tax effects affected by 83, 85, 86, 90 wages and 353–4 employment rates by age 195 aggregate EU 353 EMU and 340–41, 351 female 88, 154, 156, 158, 195, 221, 273, 304, 305, 309, 340 impact of migration on 368–9, 370 Lisbon and Stockholm targets for 6, 195, 220–21, 273, 304, 305, 310, 341, 342, 353 male 195, 340 and migration potential 362, 363, 372–3 part-time, see part-time employment product of participation rate and (extensive margin) 154, 156, 158, 161, 191–2, 193, 194, 230 start-up costs and 108, 109, 110 tax rates and 2, 44–5, 55–6, 59, 61, 65–7, 69, 87, 89, 94, 109, 110 in US 109, 341, 353 employment shares, sectoral country comparisons 379, 381, 383–4 tax rates and 2, 44, 54–5, 59, 62, 69, 70, 71, 73, 77–8, 84, 85, 86, 87, 89, 90, 94–6, 110 Enste, D.H. 47, 59, 94 equal spending-response condition 58, 98, 99 Erziehungsgeld 168 Estonia accession to EU 374 GDP per capita in 381 median age of population of 387 Euro area activity rates in 341–2 employment rates in 340–41, 351 part-time 276, 277, 282, 283, 284–5 forces unleashed by formation of 339 labour market reforms in 345–51

395

participation rate in 342 wages in 343–5, 351 Eurobarometer 353 European Central Bank (ECB) 1, 198, 199, 215, 216, 305 European Centre of Enterprises with Public Participation and of Enterprises of General Economic Interest (CEEP) 291 European Commission 214, 339, 382 European Community Labour Force Surveys 214, 215, 298, 301 European Monetary Unification (EMU) 339–51 and activity rates 341–2 and employment rates 5–6, 340–41, 351 forces unleashed by 339 and labour market reforms 345–51 and wages 5–6, 343–5, 351 European Social Funds 381 European Trade Union Confederation (ETUC) 291 European Union age structure of population of 195, 380, 385–6, 387 aggregate employment rate in 353 aggregate part-time employment rates in 275, 276, 277, 279, 280, 281, 282, 283, 284–5, 286, 287, 289 determinants of labour force participation in 3–4, Ch. 5 enlargement of and capital flows 6, 377–8 and East–West labour migration 6, 361–73, 377, 385–6 effects in imperfect labour markets 379–88 reasons for 379 and social protection 6, 354–60 and trade flows 378, 380 monetary unification in, see European Monetary Unification (EMU) total labour force of 361 total population of 361 see also under names of individual EU countries, e.g. France; Germany

396

Index

Eurostat 12, 195, 198, 199, 215, 217, 276, 277, 279, 285, 287, 289, 298, 299, 301, 341, 361 Euwals, R. 274, 306 exchange rates 339 expected income hypothesis 372 experience, returns to 5, 324, 325, 326, 328, 329, 330 extensive margin 154, 156, 158, 161, 191–2, 193, 194, 230 extra-territorial organizations and bodies sector, part-time employment in 285 Family and Medical Leave Act (1993) 191 family composition 201 Farber, H.S. 274 Fassmann, R. 362, 374 fathers, parental leave for 168 females educational attainment levels of 3, 122–45, 151–2, 164, 189, 192, 235 measurement of 146, 147, 148 employment rates for 88, 154, 156, 158, 309, 340 Lisbon and Stockholm targets for 195, 220–21, 273, 304, 305 on fixed-term contracts 318, 319 hours worked by 88, 117, 121, 122–9, 153–89, 274–5, 279, 281, 288, 289, 290, 310 parental leave and 3, 153, 154, 155, 161, 164–89, 191–4 parliamentary seats occupied by 201, 204, 206–7, 215 participation rates of 116, 117, 119, 153, 154, 156, 158, 159, 164–7, 188, 189, 191–2, 196–201, 202, 204–7, 208–9, 211, 212, 213–14, 219, 220–21, 235, 236, 237, 244, 250–51, 299, 300, 302–4, 342, 388 private returns to education for 152 retirement age for 247 unemployment rates for 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142 data on 147, 148

see also married women; mothers, labour supply of; single women; widowed and divorced women Fertig, M. 374 fertility rates definition of 215 and female participation 151, 153, 201, 204, 206, 214, 220 and part-time employment rate 299, 301, 303, 304, 309 policies to increase 191 financial intermediation sector, parttime working in 284 Financial Services Action Plan (FSAP) 339 Finland Active Labour Market Policies (ALMPs) in 347–8 inactivity rate in 10, 11 marginally attached individuals in 222 median age of population of 387 part-time working in 276, 277, 279, 280, 281, 282, 283, 284–5, 286, 288 regulations and institutions affecting 291, 296 participation rates in 199, 200 regulation of fixed-term and openended contracts in 333 tax rates in 64 unemployment rate in 10, 11 firing costs for fixed-term and open-ended contracts 231, 312–13, 358 for L- and H-types 16, 19, 21, 22, 26, 32, 33, 34–5, 36 firm size 335 Fisher test 207 fixed effects models 363, 371–2, 373 fixed-term contracts and de-unionization 344–5 employment protection legislation and 5, 297, 299, 300, 302, 314, 332–4, 358 firm size and 335 flexibility role of 297, 312–13, 314, 334 in Portugal 5, Ch. 8 data 315

Index institutional framework governing 314–15 institutional framework in international context 332–4 share of FTC in total employment and worker flows 315–18 transitions from 321–9, 334–5 variation across establishments 318 variation across occupations 318 variation by age, educational attainment level and gender 318, 319 wages of employees on 318–21, 324, 325, 326, 328, 329, 330, 334 regulation of, in OECD countries 314, 332–3 restrictions on rolling over 312–13, 315 as screening devices 5, 313, 321, 329–30, 332, 334–5 as search devices 328–30 seasonal 332, 335 share of 5, 299, 301, 303, 315–18 tax credits and 223, 225–8 Flaig, G. 372 flexible working hours 181, 183, 185, 187, 210 Flynn, Padraig 291 foreign direct investment (FDI) 355, 377–8 France Active Labour Market Policies (ALMPs) in 347–8 child benefit system in 306 combining work and family life in 309 employment rate in 109 inactivity rate in 10, 11 median age of population of 387 part-time working in 276, 277, 278, 279, 280, 281, 282, 283, 284–5, 288 regulations and institutions affecting 291, 294 participation rates in 116, 199, 200, 201 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 95, 96

397

sectoral value added shares in 74, 75, 76, 96 shadow economy in 81 social security system in 291, 294 start-up costs in 109, 110 tax rates in 45, 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 9, 10, 11 working hours in 110, 294 see also France, part-time working in Freeman, R.B. 88, 110 Friedberg, R. 374 functional form, checks on 249, 250–51 García Herrero, A. 339 Garibaldi, P. 13, 110, 230 Gateway programmes 34 Gavasto, A. 368 gender discrimination 201; see also parliamentary seats occupied by females Genre, V. 195, 217, 290, 342 German Federal Statistical Office (Statistisches Bundesamt) 154, 155, 169, 171, 172, 188 German Socio-Economic Panel (GSOEP) 189, 193, 362 Germany Active Labour Market Policies (ALMPs) in 347–8 construction industry in 356 employment adjustment speed in manufacturing in 312 employment rate in 109 fertility rate in 153, 191 inactivity rate in 10, 11 maternity leave in 161, 167–8, 191, 192, 193, 194 median age of population in 387 migration to 156, 363, 364, 367, 369, 370, 372, 373, 377 parental leave in 3, 153, 154, 155, 161, 164–89, 191–4 parental subsidies in 168, 169–71, 172–89, 191, 193 part-time working in 161, 168, 169, 171–89, 193, 194, 276, 277, 278, 279, 280, 281, 282, 283, 284–5, 288

398

Index

regulations and institutions affecting 291, 293 participation rates in female 116, 153, 154, 156, 158, 159, 164–7, 188, 189, 191–2, 199 male 116, 158, 159, 167, 199, 200 older persons 199 younger persons 199 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96, 384 sectoral value added shares in 74, 75, 76, 96 shadow economy in 81 start-up costs in 109, 110 tax rates in 64, 71, 72, 73, 74, 75, 76, 81, 88, 192 unemployment rate in 9, 10, 11 working hours in 110 female 3, 88, 153–89, 191–4, 281, 288 male 156–8, 160, 167, 288 see also Germany, part-time working in see also East Germany Gersbach, H. 84 Giavazzi, F. 83, 84, 346 Giles, D.E.A. 47, 59, 89 Ginn, J. 291 Giovannini, E. 266 Giovannini Report 339 Glyn, A. 86 Gómez-Salvador, R. 195, 342 Gottschalk, P. 328 government welfare spending, tax rates and 44, 45, 57–8, 90–91, 97–9 Greece Active Labour Market Policies (ALMPs) in 347–8 EU funding to 381–2 part-time working in 276, 277, 279, 280, 281, 282, 283, 284–5, 286, 288 regulations and institutions affecting 297 participation rates in 220 regulation of fixed-term and openended contracts in 333 shadow economy in 47

Greenwood, J. 47 Gregg, P. 37 Guelfi, A. 223, 228, 233, 266 Guell-Rotlan, M. 330 Guérin, J.-L. 382 guest workers 156, 362, 373 Guttiérrez-Domènech, M. 149 H-types (high effort searchers) 10, 14, 41 non-employment rate of 18, 19, 23–5, 26, 27, 28, 29, 30–35, 36 rental value of a job to 16 rental value of non-employment to 16 rental value to firm of a job filled by 16 search effort of 2, 10, 14–15, 17, 18, 19–20, 21, 23, 25, 26, 27, 28, 29, 35–6 transition rates of 23–5, 30, 43 unemployment benefits available to 12, 14–15, 21, 25, 32, 33, 35, 36 wage rate of 15–16, 17, 18, 19, 20, 25, 26–30, 35–6, 41, 42 Hamermesh, D. 22, 25, 309 Hanson, G.H. 368 harmonization of labour market standards 359 Harris, J.R. 372 Hart, R. 309 Haskel, J. 274 Hatton, T.J. 374 hazard function 322 aggregate 323 baseline 323, 324, 325, 326, 328 cause-specific 323 health and social work sector, parttime working in 285 Heckman, J.J. 132, 144, 145, 149 Heckscher–Ohlin–Samuelson (HOS) model 368 Henrekson, M. 50, 79, 83, 99 heterogeneous estimators 371, 372, 373 Hintermann, C. 362, 374 hiring costs 11, 15, 17, 19, 21, 22, 26, 31, 32, 33, 34 hiring subsidies 4, 29–30, 31, Ch. 6; see also tax credits and labour force participation in Italy

Index Hispanic ethnicity 134, 141 Hogerbrugge, M. 274, 306 hotels and restaurants sector 226, 284; see also eating and drinking establishments; eating, drinking and lodging establishments hourly wages, skill measured by 78, 79 hours worked, see working hours household production education and 144, 145 tax rates and 46–7, 48–50, 60, 77, 78 with capital inputs 50–53, 54–5 empirical implications 53–6 with endogenous labour supply 107–8 evidence on 88–9, 91 graphical representation of 105–7 with team production 52, 53, 54 Houseman, S. 274, 330 housewives entering the labour force 235, 236, 238, 244, 261 Hungary accession to EU 374 EU funding to 382 GDP per capita in 381 median age of population in 387 regulation of fixed-term and openended contracts in 333 sectoral employment distribution in 383, 384 vocational education in 385 Hunt, J. 312, 374 Hurst, E. 89 HUS (HUShållens ekonomiska levnadsförhållanden) 147, 148 IAB Employment Sample 193 identification strategies, checks on 249–53, 254–5 ILO, see International Labour Office (ILO) in-work benefits 297 inactives 195; see also L-types (low effort searchers) inactivity, transitions from fixed-term contracts into 321, 322, 324, 325, 326, 329 inactivity rates, relationship between unemployment, wage pressure and 9–12, 30–35, 41–3; see also

399

H-types (high effort searchers); L-types (low effort searchers) income differentials, inter-country impact of migration on 367–8 and migration potentials 362, 363, 367, 371–3 and sectoral employment distribution 379, 381 income distribution, within countries 354–5 income effect 202, 302 income velocity 59–60, 80 insider–outsider models 201–2, 208, 214 Instituto Nacional de Estatística (INE) 315 instrumental variable methods 302 integrated hazard function 322 intensive margin 154, 158, 161, 189, 191–2, 194, 230 interest rates 368 International Labour Office (ILO) 168, 214, 217, 222, 384, 385 intra-industry trade 358 Ireland Active Labour Market Policies (ALMPs) in 348–50 employment rate in 109 EU funding to 381–2 inactivity rate in 10, 11 marginally attached individuals in 222 median age of population of 387 part-time working in 275, 276, 277, 278, 280, 281, 282, 283, 284–5, 288 regulations and institutions affecting 297, 298 participation rates in 199, 200, 201 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 73, 96 sectoral value added shares in 74, 76, 96 shadow economy in 81 start-up costs in 109, 110 tax rates in 64, 71, 73, 74, 76, 81 unemployment rate in 9, 10, 11 working hours in 94, 110

400

Index

see also Ireland, part-time working in ISTAT, see Italian Statistical Institute (ISTAT) Italian Finance Law for the year 2001 (issued 2000) 224 Italian Labour Force Survey 4, 223, 225, 234–5, 258, 259, 261 Italian Statistical Institute (ISTAT) 234, 236, 238, 240, 243, 251, 255, 257, 263, 265 Italy Active Labour Market Policies (ALMPs) in 348–50 employment rate in 109 fertility rate in 153 inactivity rate in 10, 11 marginally attached individuals in 222 median age of population of 387 migration potential for 367 part-time working in 276, 277, 279, 280, 281, 282, 283, 284–5, 288 regulations and institutions affecting 291, 294–5 participation rates in 116, 199, 200, 220 tax credits and 4, Ch. 6 pension system in 246–8 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96, 384 sectoral value added shares in 74, 75, 76, 96 shadow economy in 47, 81 social security system in 294 start-up costs in 109, 110 tax rates in 63, 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 9, 10, 11 working hours in 110, 153 see also Italy, part-time working in Iversen, T. 345 Japan part-time working in 275, 276, 277 participation rates in 116 regulation of fixed-term and openended contracts in 333 tax rates in 63, 64

Jean, S. 346 Jimeno, J.F. 196, 381 job churning 5, 313, 315, 321 job destruction rate 16, 18, 21, 22, 29 job-matching process 12–20, 41 Jobseeker’s Allowance (JSA) 33–4, 36 Johnson, S. 100 Jones, E.B. 274 Jones, L. 153 Jones, S.R.G. 222 Joshi, H. 310 Juster, F.T. 47, 88 Kahn, L.M. 83, 86 Karoleff, V. 88 Katz, L. 386 Kiviet, J. 374 Kleven, H.J. 99 Korea 277, 333 Kramarz, F. 84 Krieger, H. 362, 374 Krueger, A.B. 90, 99, 377 Krusell, P. 44 Kunze, A. 194 L-types (low effort searchers) 10, 14, 41 non-employment rate of 12, 17, 18, 21, 23–5, 26, 27, 28, 29, 30–35 rental value of a job to 16 rental value of non-employment to 16 rental value to firm of a job filled by 16 search effort of 2, 10, 14–15, 17, 18, 19–20, 21, 23, 25, 26, 27, 28, 29, 35–6 state benefits available to 10, 14–15, 16, 19, 21, 25, 28–9, 30, 31, 32, 35–6 transition rates of 23–5, 30, 43 wage rate of 15–16, 17, 18, 19, 20, 25, 26–30, 35–6, 41, 42 Labour Cost Index 343 labour cost reductions, induced by tax credit 225, 226, 268 labour demand, participation affected by 222, 230–32, 233–4, 268 labour force entrants, characteristics of, in Italy 235–9 labour force participation rates

Index definition of 214 determinants in the EU 3–4, Ch. 5 conclusions 211–14 data sources and definitions 214–16 empirical evidence 203–11 likely determinants 201–3, 219–20 educational attainment level and 164, 189, 224, 233, 235, 236, 237, 239–46, 249, 250–51, 252–3, 254–5, 256–7, 261 employment subsidies and 4, Ch. 6 female 116, 117, 119, 153, 154, 156, 158, 159, 164–7, 188, 189, 191, 196–201, 202, 204–7, 208–9, 211, 212, 213–14, 219, 220–21, 235, 236, 237, 244, 250–51, 299, 300, 302–4, 342, 388 male 116, 117, 118, 158, 159, 167, 196–201, 202, 204–5, 207–8, 209, 211, 212, 214, 220, 224, 235, 236, 237, 244, 246, 258 older persons 196, 199, 200–201, 202, 204–5, 208–9, 211, 212, 213, 214, 219, 236, 237, 238, 244, 246, 249–52, 253, 255, 256, 258, 260, 261, 262, 263, 269, 342 product of employment rate and (extensive margin) 154, 156, 158, 161, 191–2, 193, 194, 230 recent trends in the EU 197–201, 342 transitions from the black economy 224, 246, 257–61, 262–5, 269 younger persons 196, 197–200, 202, 204–5, 210, 211, 213–14, 220, 221, 235, 236, 237, 241, 249, 250, 252–3, 254, 256, 260, 262, 268–9, 342 labour laws 290, 295 labour market deregulation 346 labour market experience, returns to 5, 324, 325, 326, 328, 329, 330 labour market rigidities, international rankings of 314, 332–4 labour market tightness, labour supply affected by 222, 230 labour migration in the EU 156 East–West 6, 361–73 impact on labour markets 367–9, 370, 377

401

scale of 6, 361–7, 370, 371–3, 377, 385–6 transitional periods for 367, 370–71 South–North 362, 363, 373 labour mobility, see labour migration in the EU; specific skills, workers with labour supply elasticity 44, 55, 90–91, 105, 107–8, 222, 230 Lamfalussy Report 339 Landier, A. 313 language differences 362, 371, 372, 377 Laroque, G. 306 Latvia 374, 381, 387 Layard, R. 86, 361, 362 Lazear, E. 382 legislators, part-time working by 279–86 Leibowitz, A. 144 leisure function 15, 20–21 Lemaitre, G. 275 life-cycle models 144–5, 194 life expectancy 235 likelihood function 324 Lindbeck, A. 47 Lindert, P. 99 Lisbon Council targets 6, 195, 220–21, 273, 304, 310, 341, 342, 353 Lithuania 374, 381, 387 Ljungqvist, L. 48 lodging establishments 54, 79; see also eating, drinking and lodging establishments; trade, repair services, eating, drinking and lodging lone parents 34 Long, J.E. 274 long-term unemployment ratio 208, 212, 215 Lopez Garcia, P. 108 Lucas critique 303 Lundborg, P. 362 Luxembourg Active Labour Market Policies (ALMPs) in 348–50 part-time working in 276, 277, 279, 280, 281, 282, 283, 284–5 regulations and institutions affecting 297

402

Index

Maastricht Treaty, Social Chapter of 355 Maastricht Treaty Criteria 346 MaCurdy, T. 107 Maddala, G.S. 207 Maddala–Wu test 299 Malcolmson, J.M. 383 males employment rates for 340 Lisbon and Stockholm targets for 195 on fixed-term contracts 319 hours worked by 117–22, 156–8, 160, 167, 279, 286–90 participation rates of 116, 117, 118, 158, 159, 167, 196–201, 202, 204–5, 207–8, 209, 211, 212, 214, 220, 224, 235, 236, 237, 244, 246, 258 retirement age for 247 Malta 374, 379, 389 managers, fixed-term contracts for 318 manufacturing industry employment adjustment speed in 312 employment share of 70, 77–8, 95, 96, 383–4 labour cost reduction due to tax credit in 226 part-time working in 284, 286 marginal tax rates 49, 50, 60, 90, 93, 106, 152 market production education and 144, 145 tax rates and 45–6, 48–50, 60, 77, 78 with capital inputs 50–53, 54–5 empirical implications 53–6 with endogenous labour supply 107–8 evidence on 88–9, 91 graphical representation of 105–7 with team production 52, 53, 54 married women hours worked by 161–4, 189 actual hours compared to normal hours 171–88 labour force participation of 164, 165, 188 see also mothers, labour supply of matching function 12, 13–14, 20, 22–3, 42

maternity leave 152, 161, 167–8, 191, 192, 193, 194 maternity pay 167, 191 McCallum, J. 339 McGrattan, E. 153 means testing 171, 191, 193, 297 Medicare Hospital Insurance (HI) 92 Mediterranean model 354 Medoff, J. 377 men, see males metal industry, weekly hours in 158 Mexico 333 Meyer, B.D. 90, 99 migration flows, see labour migration in the EU migration quotas 370 Millard, S.P. 22, 23, 25 Mincerian wage equations 319, 334 minimum wage legislation 82, 83, 84, 86, 90, 210, 216, 356, 358 mining and quarrying sector, part-time working in 284 Ministero del Lavoro e delle Politiche Sociali 228, 229, 248 ‘missing data’ view 12–13 Mitchell, J. 374 Mongelli, F.P. 339 Morgan, J. 350, 351 Mortensen, D.T. 22, 23, 25, 29, 37, 266, 382 mothers, labour supply of 3, 122, 125–30, 151–2, 161–4, 188 datasets used in analysis 146–8 determinants of part-time working 274 institutional differences affecting 144, 152, 297 parental leave and 3, 153, 154, 155, 161, 164–89, 191–4 time to first birth 3, 129–35, 136–45 time to re-entry after first birth 3, 129–30, 135–42, 143–5 Mourougane, A. 350, 351 Mourre, G. 341 Mulligan, C.B. 44 Nakamura, A. 274 Nakamura, M. 274 Nash equilibrium 379

Index National Child Development Study (NCDS) 133, 146 National Longitudinal Survey of Youth (NLSY) 134, 141, 147 NCDS, see National Child Development Study (NCDS) negative income tax 357 Neiss, K. 23 Nelson, E. 23 Netherlands Active Labour Market Policies (ALMPs) in 348–50 employment rate in 109 fertility rate in 204, 206, 220 inactivity rate in 10, 11 median age of population of 387 part-time working in 275–6, 277, 278, 280, 281, 282, 283, 284–5, 286, 288 regulations and institutions affecting 291, 295, 298, 310 participation rates in 116, 197, 199, 200, 201, 208, 220 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 96 shadow economy in 81 social security system in 356 start-up costs in 109, 110 tax rates in 63, 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 9, 10, 11 working hours in 110 see also Netherlands, part-time working in network effects 367, 372–3 New Deal for Young People (NDYP) 34, 36 New Economic Geography 382 New International Trade Theory 382 New Zealand part-time working in 277 regulation of fixed-term and openended contracts in 333 shadow economy in 94 tax rates in 64, 94 Nickell, S.J. 9, 33, 37, 60, 61, 62, 63, 64,

403

65, 67, 68, 69, 80, 82, 86, 87, 90, 93, 101, 105, 108, 109, 196, 215, 216, 220, 275, 299, 301, 374 Nicoletti, G. 83, 86, 298, 306, 346 NLSY, see National Longitudinal Survey of Youth (NLSY) non-employment rate for H-types 18, 19, 23–5, 26, 27, 28, 29, 30–35, 36 rate for L-types 12, 17, 18, 21, 23–5, 26, 27, 28, 29, 30–35 transitions from fixed-tem contracts to 326, 328 non-employment benefits, reforms of 346 non-stationary variables 373 non-wage costs 343, 344 Norway combining work and family life in 309 employment rate in 109 inactivity rate in 10, 11 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 73, 96 sectoral value added shares in 74, 76, 96 shadow economy in 81 start-up costs in 109, 110 tax rates in 64, 71, 73, 74, 76, 81 unemployment rate in 10, 11 working hours in 110, 277, 310 Nunziata, L. 60, 61, 62, 63, 64, 65, 67, 68, 69, 80, 82, 87, 90, 93, 108, 109, 196, 215, 216, 301 O’Reilly, J. 274, 305 Objectives 1 and 2 programmes 381 OECD 59, 83, 86, 92, 93, 94, 95, 96, 108, 109, 148, 152, 198, 199, 214, 216, 274, 275, 276, 277, 296, 299, 301, 305, 309, 314, 332, 342, 350 OECD countries, sectoral employment distribution in 383, 384 Office for National Statistics (ONS) 147 oil price shock (1973) 362 old-age pensions 247–8 Old-Age, Survivors and Disability Insurance (OASDI) 92

404

Index

older persons employment rates for 340 Lisbon and Stockholm targets for 195 expenditure on social protection for 203, 205, 208, 209, 215 on fixed-term contracts 319 wages of 321, 330, 334 mobility of 381, 383, 385, 388 part-time working by 276–9, 280, 286, 288, 289, 290, 296, 304 participation rates of 196, 199, 200–201, 202, 204–5, 208–9, 211, 212, 213, 214, 219, 236, 237, 238, 244, 246, 249–52, 253, 255, 256, 258, 260, 261, 262, 263, 269, 342 pension systems for 246–8 Olivetti, C. 153 Olovsson, C. 88, 91–2, 99 on-the-job search 328–9 open-ended contracts regulation of, in OECD countries 314, 332–4 share of 41, 223, 227, 228–9, 230–32 subsidies to firms hiring workers with 4, Ch. 6 see also tax credits and labour force participation in Italy Orszag, J.M. 223 other community services sector, parttime working in 285 output gap 204, 208, 299, 301, 303 definition of 214 Pain, H. 374 Palda, F. 100 parental leave and female participation rate 206, 220 and female working hours 3, 153, 154, 155, 161, 164–89, 191–4 and re-entry into paid work 140–42 and time to first birth 132 parental subsidies 168, 169–71, 172–89, 191, 193 parliamentary regimes 58 parliamentary seats occupied by females 201, 204, 206–7, 215 part-time employment 4–5, Ch. 7

by age group 276–9, 280, 286, 288, 289 availability of, and participation decisions 152, 196, 197, 201, 204, 205, 206, 210, 211, 213, 219, 220 in Canada 275, 276, 277 determinants of, in literature 274–5 and de-unionization 344–5 equal treatment of 291, 294, 295, 305 European framework agreement on (1997) 291 by gender 279, 281, 286–90 involuntary 286–90, 305, 309 in Japan 275, 276, 277 by occupation 279, 286 parental leave and 161, 168, 169, 171–89, 193, 194 policy recommendations 304–5, 358 regulatory and institutional framework in EU countries 290–98, 310 quantifying effect on part-time rate 298–304, 309 by sector 284–5, 286 of students 34, 210 trends in EU countries 275–90 see also under names of individual EU countries, e.g. France; Germany in US 152, 274–5, 276, 277 wages of part-time workers 274, 290–91, 294 participation rates, see labour force participation rates payroll taxes 49, 50, 60, 63, 93, 106, 107 pensions 246–8, 291, 296, 346, 359, 382, 383 perfect competition 358 Persson, M. 102 Persson, T. 44, 58, 100 Pesaran, M.H. 374 Petrongolo, B. 20, 22, 330 Pfau-Effinger, B. 275 Phillips curves 31 Phillips Perron test 207 Pichelmann, K. 343 Piggott, J. 88, 99

Index Pissarides, C.A. 9, 13, 15, 17, 20, 22, 29, 35, 37, 38, 41, 108, 217, 223, 230, 232, 266, 342, 382 plant and machine operators and assemblers, part-time working by 279–86 plant closures 324, 330 point-in-time measures 318 Poland accession to EU 374 EU funding to 382 GDP per capita in 381 median age of population of 387 regulation of fixed-term and openended contracts in 333 sectoral employment distribution in 383, 384 vocational education in 385 political institutions, endogeneity of 339 pooled OLS models 363, 371–2, 373 population age structures 195, 235, 380, 385–6, 387 Portugal accession to EU 355 Active Labour Market Policies (ALMPs) in 348–50 EU funding to 381–2 fixed-term contracts in, see fixedterm contracts inactivity rate in 10, 11 labour market rigidities in 314 migration potential for 367 part-time working in 275, 276, 277, 279, 280, 281, 282, 283, 284–5, 286, 288 regulations and institutions affecting 291, 297, 298 participation rates in 199, 200 shadow economy in 47 tax rates in 63, 64 unemployment rate in 10, 11 wage regulation in 291 Portugal, P. 313, 330 Posted Workers Directive 356, 359 potential earnings, participation rates affected by 201, 203, 204, 206, 208, 211, 220 potential workers 222 Prescott, E. 45, 91, 105 price discrimination 339

405

price transparency 339, 344 private households with employed persons sector, part-time working in 285 private non-farm sector, labour cost reduction induced by tax credit in 225, 226 private sector employees, seniority pension rules for 247 probability density function 322 probit models 233 product market competition 358 product market deregulation 346 product market regulations 83–4, 86, 87, 90, 108–10 productivity rises 11, 26–8, 30, 31 professionals, part-time working by 282, 283, 286 proportional elections 58 proportional hazards model 323–4 Pryor, F.L. 83, 86 PSID 274–5 public administration and defence; compulsory social security sector, part-time working in 285 public sector employees, seniority pension rules for 247 Quintini, G. 220 race-to-the-bottom thesis 6, 355–6, 359, 377–8 real estate sector, part-time working in 284 real wage definition of 214 participation rates affected by 201, 203, 204, 206, 208, 211, 220 reallocative shocks 345–6 regional aid programmes 381–2, 388 rents 344, 346, 353, 377 repair services labour skill measures for 78, 79 see also retail trade and repair services; trade, repair services, eating, drinking and lodging reservation utility 230 reservation wage 19–20, 27, 30, 35 retail and wholesale trade 226; see also retail trade; wholesale trade

406

Index

retail trade employment and value added shares in 54–5, 84, 85, 86 labour skill measures for 78, 79 product market regulations in 84 tax evasion in 89 see also retail and wholesale trade; retail trade and repair services; trade, repair services, eating, drinking and lodging retail trade and repair services employment and value added shares in 59, 61, 62, 69, 70, 72, 75, 77, 90, 96 labour skill measures for 78, 79 part-time working in 284 retired workers entering labour force 236, 238, 244, 246, 247–8, 261 retirement, transitions into 324 retirement age, official 156, 203, 205, 208, 209, 216, 247 retirement decisions 196 retirement programmes 196, 203, 219, 388; see also early retirement revenue requirements, differences in 56–7, 98 revenues lost through tax credit 228, 229 Riddell, W.R. 222 Riley, R. 39 Rodriguez-Palenzuela, D. 196 Rodrik, D. 378 Rogerson, R. 55, 153 Roland, G. 384 Romania 361 Rosen, S. 55, 99 Ruivo, M. 275 Rybczynski effect 368 safeguard clauses 370 Saint-Paul, G. 345 Salanié, B. 306 sales taxes 356 Sandmo, A. 99 Sargent, T.J. 48 Sattinger, M. 53 Scandinavian model 354 Scarpetta, S. 196, 209, 216, 275 Schettkat, R. 88, 110 Schmidt, C.M. 374

Schneider, F. 47, 59, 60, 61, 62, 65, 66, 70, 71, 72, 73, 74, 75, 76, 78, 80, 81, 82, 86, 87, 90, 92, 93, 94, 109, 110 Schniewind, A. 84 schooling, years of CEEC average 369 and participation rates 233, 239–46, 249, 250–51, 252–3, 254–5 of temporary workers 318, 319 and transitions from fixed-term contracts 324, 325, 326, 328, 329 of workers in selected industries 78, 79 schooling rate of 15–25 year olds 299, 301, 303 Schweitzer, M. 23, 37 scientific use files 155–6 search costs 328, 382 search intensity fixed-term contracts and 328–9 tax credits and 230, 232, 253 variable 2, 10, 14–15, 17–21, 23, 25, 26, 27, 28, 29, 35–6 see also H-types (high effort searchers); L-types (low effort searchers) search productivity 230 seasonal workers 332, 335 second-earner model 192 sectoral employment shares, see employment shares, sectoral sectoral value added shares, see value added shares, sectoral self-employment 215, 235, 236, 247, 248, 258 seniority pensions 247–8 service workers and shop and market sales workers, part-time working by 279, 282, 283, 286 services sector employment in 205, 206, 215, 299, 301, 303–4, 383–4 part-time working in 285, 286, 303 services to firms sector, labour cost reduction induced by tax credit in 226 shadow economy definition of 46

Index labour cost in 225 methods for estimating size of 59–60, 61, 78–82, 86, 94 tax credits and transitions from 224, 246, 257–61, 262–5, 269 tax rates and size of 2, 44, 45, 46, 47, 59–60, 61, 78–82, 84, 85, 86, 87, 88, 89–90, 91, 94 shift working 297 sick leave assistance 45 sickness, non-employment due to 24 Siliverstovs, B. 372 Single Act (1986) 355 single women hours worked by 161, 162, 163, 189 labour force participation of 164, 165 as percentage of total female employment 164 Sinn, H.-W. 372, 374 skilled agricultural and fishery workers, part-time working by 282, 283 skilled workers, demand for 44, 55, 78, 79, 89, 90 Skolka, J. 89 Slaughter, M.J. 368 Slovakia 374, 382, 385, 387 Slovenia 374, 382, 385, 387 Smith, L. 275 Smith, R.P. 374 Snower, D.J. 223, 345 social dumping 359 social norms 47–8, 191 social protection demand for 6, 354–5, 378 future prospects for 359–60 improvements in 290–91, 292, 293, 294, 304 level of, and immigration 377–8 policy recommendations for 357–9 supply of 6, 355–6 Sørensen, P.B. 99 Soskice, D. 345 Spain accession to EU 355 Active Labour Market Policies (ALMPs) in 347–8 employment rate in 109 EU funding to 381–2 fertility rate in 153

407

GDP per capita in 381 inactivity rate in 10, 11 median age of population of 387 migration potential for 367 part-time working in 275, 276, 277, 279, 280, 281, 282, 283, 284–5, 286, 288 regulations and institutions affecting 291, 293 participation rates in 199, 200, 220 regulation of fixed-term and openended contracts in 333, 358 sectoral employment shares in 384 shadow economy in 47, 81 social security system in 291, 293 start-up costs in 109, 110 tax rates in 63, 64, 81 unemployment rate in 9, 10, 11 working hours in 110, 153 legislation on 291, 293 see also Spain, part-time working in specific skills, workers with 381, 382–5 Spiro, P. 88 Stafford, F.P. 47, 88 start-up costs 105, 108–10 state benefits to the inactive 10, 14–15, 16, 19, 21, 25, 28–9, 30, 31, 32, 35–6 Statistics Sweden 117 Statistisches Bundesamt 154, 155, 169, 171, 172, 188 Stockholm Council targets 195, 305 Straubhaar, T. 374 students entering employment 13, 24, 34, 41, 235, 236, 238, 244 number of 12, 34, 36 Suardi, M. 339 subsidiarity, argument for 355, 366 substitution effect 202, 302, 358 sum of tax rates 62, 63–5, 67, 69, 70, 86, 87, 90, 93, 216 Summers, L.H. 377 survivor function 322, 323 Sweden Active Labour Market Policies (ALMPs) in 348–50 combining work and family life in 309 employment rates in 109, 341

408

Index

female educational attainment levels in 151 fertility rate in 151, 206 inactivity rate in 10, 11 labour supply of mothers in 3 datasets used in analysis 147–8 hazard of (first) motherhood 131–2, 135 hazard of working for pay after first childbirth 136, 137, 142, 143, 144 median age of population of 387 migration potential for 367 part-time working in 152, 276, 277, 278, 279, 280, 281, 282, 283, 284–5, 288, 310 regulations and institutions affecting 297, 298 participation rates in 116, 117, 118, 119, 199, 200, 201, 220 private returns to higher education for females in 152 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 73, 96 sectoral value added shares in 74, 76, 96 shadow economy in 81 start-up costs in 109, 110 tax rates in 63, 64, 71, 73, 74, 76, 81, 152 unemployment rate in 10, 11 working hours in 88, 110 see also Sweden, part-time working in Switzerland employment rate in 109 inactivity rate in 10 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 96 shadow economy in 47, 81 start-up costs in 109, 110 tax rates in 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 10 working hours in 110 synthetic fertility index 151

Tabellini, G. 44, 58, 100 Tanzi, V. 59 tax avoidance 46 tax credits and labour force participation in Italy 4, Ch. 6 data description 234–9 empirical results 223–4, 239–48, 261 empirical specification and identification strategies 233–4 checks on 234, 248–53 magnitude of tax credit 225, 226 model of participating behaviour 228–32 regulatory aspects of tax credit 224–5, 234, 268 theoretical underpinnings 222–3 transitions from the black economy 224, 246, 257–61, 262–5, 269 transitions to employment or unemployment 253–7 usage of tax credit 225–8, 229 tax evasion 46, 59, 89 tax factor 49, 60, 62, 64–5, 69, 70, 86, 87, 90, 92–3, 106 tax rates Ch. 2 and choice of production sector 48–50, 60, 77, 78 with capital inputs 50–53, 54–5 empirical implications 53–6 with endogenous labour supply 167–8 evidence on 88–9, 91 graphical representation of 105 with team production 52, 53, 54 cross-country investigation on effects of personal tax rates 56–89 controls for policies and institutions affecting work activity 82–7, 90, 108 data 59–63, 92–7 evidence of effects 63–91 identification 56–8, 82, 97–9 delayed responses to 47–8 and employment rates 2, 44–5, 55–6, 59, 61, 65–7, 69, 87, 89, 94, 109, 110 and government welfare spending 44, 45, 57–8, 90–91, 97–9 and leisure 46, 91 negative 357

Index and part-time employment rates 290–91, 297, 299, 300, 302 and participation rates 201, 202, 203, 205, 207, 209, 216, 220, 247–8 on pensions 247–8 race to the bottom in 355–6 and sectoral employment and value added shares 2, 44, 54–5, 59, 61–2, 69–78, 79, 84, 85, 86, 87, 89, 90, 91, 94–6, 110 and shadow economy size 2, 44, 45, 46, 47, 59–60, 61, 78–82, 84, 85, 86, 87, 88, 89–90, 91, 94 sum of 62, 63–5, 67, 69, 70, 86, 87, 90, 93, 216 and working hours 2, 44–5, 55–8, 59, 61, 65–9, 84, 85, 86, 87, 88, 89, 91–2, 94, 97–9, 109, 110, 153, 192 tax wedge 62, 63–5, 67, 69, 70, 86, 87, 90, 93, 216 taxing capacity, differences in 56 team production methods 52, 53, 54 technicians and associated professionals, part-time working by 282, 283, 286 Tedds, L.M. 47, 59 temporary employment, see fixed-term contracts temporary employment agencies 357 tenure 326, 329, 334–5 tertiary education 151, 152 Thomas, J. 38 Todaro, M.P. 372 Tirole, J. 358 total labour tax rate 299, 300, 302 Trabold, H. 368 trade flows in enlarged EU 378, 380 trade, repair services, eating, drinking and lodging employment and value added shares in 59, 61, 62, 69, 70, 71, 74, 77, 78, 79, 84, 85, 86, 87, 90, 96 see also eating and drinking establishments; eating, drinking and lodging establishments; hotels and restaurants sector training costs, see hiring costs training programmes 347, 348, 349, 350; see also vocational education and training

409

transaction costs 339 transfer payments to new parents 168, 169–71, 172–89, 191, 193 transition costs, in EU enlargement process 6, 379–88 transition rates into employment for H-types and L-types 23–5, 30, 42, 43 transport and communication sector 226, 284 Treaty of Amsterdam (1997) 355 Treaty of Rome 370–71 Treu, Tiziano 252 Treu package 252 Troske, K. 100 Trübswetter, P. 368 Turkey employment protection legislation in 332, 333 median age of population of 385, 387 part-time working in 277 UK Labour Force Survey 13, 20, 22, 23, 30, 34 UN Population Data 386, 387 underground economy, see shadow economy unemployment duration of 23, 24, 30 ILO definition of 222 transitions into 235–7 from fixed-term contracts 321, 322, 324, 325, 326, 327, 329, 335 value of 230–32 unemployment benefit duration definition of 215 and part-time employment rate 297, 299, 300, 302, 304 and participation rates 205, 207, 209 unemployment benefit replacement ratio definition of 215 and part-time employment rate 297, 299, 300, 302, 303, 304 and participation rates 205, 207, 208, 209 value of 25 unemployment benefits abuse of 48

410

Index

minimum standards for 359 and participation rates 4, 197, 201, 202–3, 205, 207, 208, 209, 214, 219, 220 reforms of 346, 347, 348, 349, 350, 357 and search effort 2, 12, 14–15, 21, 25, 32, 33, 35, 36 tax rates and 33, 45, 90–91 see also unemployment benefit duration; unemployment benefit replacement ratio unemployment insurance system, US 92 unemployment rates definition of 214 female 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142 data on 147, 148 impact of migration on 368–9, 370 and participation rates 203–4, 206, 207, 209, 210, 211, 220 relationship between inactivity, wage pressure and 9–12, 30–35, 41–3 see also H-types (high effort searchers); L-types (low effort searchers) unemployment traps 297, 299 union density decreases in 344–5 definition of 216 and firing costs 35 and participation rates 4, 196, 201, 202, 203, 205, 207, 209, 214, 219, 220 Union of Industrial and Employers’ Confederations of Europe (UNICE) 291 unit labour costs 343, 356 unit root tests 299, 373 United Kingdom Active Labour Market Policies (ALMPs) in 347–8 employment rates in 109, 341 female educational attainment levels in 151 fertility rate in 151 inactivity rate in 10, 11, 30–35, 41 labour supply of mothers in 3 datasets used in analysis 146–7

hazard of (first) motherhood 132, 133, 135 hazard of working for pay after first childbirth 136–9, 142, 143, 145 median age of population of 387 migration potential for 367 part-time working in 152, 276, 277, 278, 280, 281, 282, 283, 284–5, 288, 310 regulation and institutions affecting 298 participation rates in 116, 197–200, 201, 220 private returns to higher education for females in 152 regulation of fixed-term and openended contracts in 333 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 96 shadow economy in 81, 94 start-up costs in 109, 110 tax rates in 64, 71, 72, 73, 74, 75, 76, 81 unemployment rate in 9, 10, 11, 30–35, 41 working hours in 110 see also United Kingdom, parttime working in United States Active Labour Market Policies (ALMPs) in 348–50 combining work and family life in 309 employer social security contributions in 92 employment rates in 109, 341, 353 federal state budgets in 356 female educational attainment levels in 151 fertility rate in 151, 191 fixed-term contracts in 330 regulation of fixed-term and open-ended contracts 333 geographic wage differentials in 377 household production sector in 46–7 labour supply of mothers in 3 data sets used in analysis 147

Index hazard of (first) motherhood 132–5 hazard of working for pay after first childbirth 140–42, 143, 144 maternity leave and payments in 191 median age of population of 387 participation rates in 116, 342, 388 private returns to higher education for females in 152 sectoral employment shares in 71, 72, 73, 96 sectoral value added shares in 74, 75, 76, 77, 96 shadow economy in 81 start-up costs in 109, 110 tax rates in 45, 64, 71, 72, 73, 74, 75, 76, 81, 88, 92–3 unpaid family leave in 140 working hours in 88, 110, 117–29 part-time working 152, 274–5, 276, 277 unmarried mothers, parental leave for 168 unobserved differences 192, 219 US Bureau of Labour Statistics 116, 147 US Current Population Survey (CPS) 117, 155 US House of Representatives 92 vacancies advertising cost of 14, 16, 19, 20, 21, 25 expected duration of 25 rental value to firm of 16–17 vacancy rate 26, 27, 28, 29, 30, 32, 33, 206, 208 definition of 214 value added shares, sectoral 54–5, 59, 61, 70, 74, 75, 76, 77, 84, 85, 86, 87, 89, 90, 94–6 value added tax 49, 50, 51, 52, 88, 92, 106 van Ours, J.C. 9, 33, 196, 275, 351 Varejão, J. 313 Venables, A. 368 Vidovic, H. 381 vocational education and training 329, 359, 384–5

411

Wadsworth, J. 37 wage dispersion 343, 345, 354 wage pressure, relationship between inactivity, unemployment and 9–12, 30–35, 41–3; see also H-types (high effort searchers); Ltypes (low effort searchers) wages employment protection legislation and 353–4 EMU and 343–5, 351 of H-types and L-types 15–16, 17, 18, 19, 20, 25, 26–30, 35–6, 41, 42 impact of migration on 368–9, 370, 377 of part-time workers 274, 290–91, 294 real, see real wage skill measured by 78, 79 of temporary workers 318–21, 324, 325, 326, 328, 329, 330, 334 vocational education and 385 see also minimum wage legislation Waldfogel, J. 191 Walker, J.R. 99, 132, 145, 149 Walwei, U. 275 Wasmer, E. 13, 110, 230, 383 welfare state preferences, differences in 56, 58 Whalley, J. 88, 99 wholesale trade 79, 284; see also retail and wholesale trade widowed and divorced women hours worked by 161, 162, 163, 189 labour force participation of 164, 165, 188 as percentage of total female employment 164 Williams, C. 92 Wirtschaftswunder 158 Wolfers, J. 196, 275, 299 women, see females work tests 357 worker compensation 90–91, 92 working hours decomposition into intensive and extensive margin 154, 156, 158, 161, 191–2, 193, 194, 230

412

Index

female 88, 117, 121, 122–9, 153–89, 274–5, 279, 281, 288, 289, 290, 310 parental leave and 3, 153, 154, 155, 161, 164–89, 191–4 legislation on 291, 293 length of working week 158–61, 294 male 117–22, 156–8, 160, 167, 279, 286–90 part-time working, see part-time employment start-up costs and 108–10 tax rates and 2, 44–5, 55–8, 59, 61, 65–9, 84, 85, 86, 87, 88, 89, 91–2, 94, 97–9, 109, 110, 153, 192 working time premia 290–91, 293, 295 working week, length of 158–61, 294 Wu, S. 207

Young, G. 39 younger persons Active Labour Market Policies (ALMPs) for 347, 348, 349, 350 employment rates for 340 on fixed-term contracts 319 wages of 319–21, 334 minimum wages for 358 part-time working by 276–9, 280, 286, 288, 289, 290 participation rates of 196, 197–200, 202, 204–5, 210, 211, 213–14, 220, 221, 235, 236, 237, 241, 249, 250, 252–3, 254, 256, 260, 262, 268–9, 342 schooling rate of 299, 301, 303 youth programmes 347, 348, 349, 350 Zhang, W. 339 Zimmermann, K.F. 362, 368, 374