Method and Substance in Macrocomparative Analysis

Method and Substance in Macrocomparative Analysis Edited by Lane Kenworthy and Alexander Hicks Method and Substance i...

Author: Lane Kenworthy | Alexander Hicks

8 downloads 719 Views 2MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

Method and Substance in Macrocomparative Analysis Edited by

Lane Kenworthy and Alexander Hicks

Method and Substance in Macrocomparative Analysis

Research Methods Series General Editors: Bernhard Kittel, Professor of Social Science Methodology, Department of Social Sciences, Carl von Ossietzky Universität Oldenburg, Germany and Benoît Rihoux, Professor of Political Science, Université catholique de Louvain (UCL), Belgium. In association with the European Consortium for Political Research (ECPR), Palgrave Macmillan is delighted to announce the launch of a new book series dedicated to producing cutting-edge titles in Research Methods. While political science currently tends to import methods developed in neighbouring disciplines, the series contributes to developing a methodological apparatus focusing on those methods which are appropriate in dealing with the specific research problems of the discipline. The series provides students and scholars with state-of-the-art scholarship on methodology, methods and techniques. It comprises innovative and intellectually rigorous monographs and edited collections which bridge schools of thought and cross the boundaries of conventional approaches. The series covers both empirical-analytical and interpretive approaches, micro and macro studies, and quantitative and qualitative methods. Titles include: Audie Klotz and Deepa Prakash (editors) QUALITATIVE METHODS IN INTERNATIONAL RELATIONS A Pluralist Guide Lane Kenworthy and Alexander Hicks (editors) METHOD AND SUBSTANCE IN MACROCOMPARATIVE ANALYSIS

Research Methods Series Series Standing Order ISBN 978–0230–20679–3–hardcover Series Standing Order ISBN 978–0230–20680–9–paperback (outside North America only) You can receive future titles in this series as they are published by placing a standing order. Please contact your bookseller or, in case of difficulty, write to us at the address below with your name and address, the title of the series and one of the ISBNs quoted above. Customer Services Department, Macmillan Distribution Ltd, Houndmills, Basingstoke, Hampshire RG21 6XS, England

Method and Substance in Macrocomparative Analysis Edited by

Lane Kenworthy Professor of Sociology and Political Science University of Arizona, USA

and Alexander Hicks Professor of Sociology Emory University, USA

Editorial matter, selection, and introduction © Lane Kenworthy and Alexander Hicks 2008 All remaining chapters © respective authors 2008 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1T 4LP. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2008 by PALGRAVE MACMILLAN Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N.Y. 10010 Companies and representatives throughout the world PALGRAVE MACMILLAN is the global academic imprint of the Palgrave Macmillan division of St. Martin’s Press, LLC and of Palgrave Macmillan Ltd. Macmillan® is a registered trademark in the United States, United Kingdom and other countries. Palgrave is a registered trademark in the European Union and other countries. ISBN-13: 978 0 230 20257 3 ISBN-10: 0 230 20257 8

hardback hardback

This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin. A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Method and substance in macrocomparative analysis / edited by Lane Kenworthy and Alexander Hicks. p. cm. (Research methods series) Includes index. ISBN 0 230 20257 8 (alk. paper) 1. Social sciences Comparative methods. 2. Employment (Economic theory) I. Kenworthy, Lane. II. Hicks, Alexander M. H61.M49155 2008 2008016151 300.72 dc22 10 17

9 16

8 15

7 14

6 13

5 12

4 11

3 10

2 09

Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne

1 08

To the memory of Michael Wallerstein – friend, colleague, exemplary macrocomparativist

This page intentionally left blank

Contents List of Tables List of Figures Notes on Contributors

ix xii xiv

1

Introduction Lane Kenworthy and Alexander Hicks

2

Statistical Narratives and the Properties of Macro-Level Variables: Labor Market Institutions and Employment Performance in Macrocomparative Research Bernhard Kittel

3

4

5

6

7

Comparative Employment Performance: A Fuzzy-Set Analysis Jessica Epstein, Daniel Duerr, Lane Kenworthy, and Charles Ragin Do Family Policies Shape Women’s Employment? A Comparative Historical Analysis of France and the Netherlands Joya Misra and Lucian Jude The Welfare State, Family Policies, and Women’s Labor Force Participation: Combining Fuzzy-Set and Statistical Methods to Assess Causal Relations and Estimate Causal Effects Scott R. Eliason, Robin Stryker, and Eric Tranby Family Policies and Women’s Employment: A Regression Analysis Alexander Hicks and Lane Kenworthy Part-Time Work and the Legacy of Breadwinner Welfare States: A Panel Study of Women’s Employment Patterns in Germany, the United Kingdom, and the Netherlands, 1992–2002 Jelle Visser and Mara Yerkes vii

1

29

67

91

135

196

221

viii Contents

8

9

Comparative Regime Analysis: Early Exit from Work in Europe, Japan, and the USA Bernhard Ebbinghaus

260

Identifying the Causal Effect of Political Regimes on Employment Adam Przeworski

290

Index

315

List of Tables 1.1 1.2 2.1

Analytical strengths of the three methodological approaches Summary of the contributions Cross-sectional and time variance components of main variables 2.2 Autoregression 2.3 Nonstationarity: Maddala and Wu’s Fisher test 2.4 Service employment and replacement rate: panel models 2.5 Employment in private sector consumer services, cross-sectional analysis model 1: cross-section model, 1981 2.6 Controlling for other factors 2.7 Explaining variation in long-term change of private service employment, 1981–91 2.8 Pooled model, first differences 2.9 Scores for replacement rate 2.10 Reanalysis using new data for replacement rate 3.1 Truth table from analysis of all six causal conditions 3.2 Examples of solution sets 3.3 Five causal pathways 3A.1 Fuzzy-set scores 4.1 Values regarding work and family, 1990 and 1999 5.1 Decade means and standard deviations for cumulative left cabinet incumbency 5.2 Decade means and standard deviations for percentage civilian government employment 5.3 Decade means and standard deviations for maternity leave index 5.4 Decade means and standard deviations for public day care index, children ages 0–2 5.5 Decade means and standard deviations for public day care index, children ages 3–School age 5.6 Decade means and standard deviations for weeks of extended leave 5.7 Decade means and standard deviations for Cash/Tax Family/Child Benefits Index 5.8 Decade means and standard deviations for female labor force participation rates

7 19 40 40 41 45 49 53 55 58 59 60 75 78 82 87 117 148 149 150 151 152 153 154 154

x

List of Tables

5.9

5.10

5.11

5.12

5.13

6.1 6.2 6.3 6.4 7.1 7.2 7.3A 7.3B 7.3C 7.4A 7.4B 7.5 7.6 7.7

Select five-way and four-way partition tests of goodness-offit F statistics for the outcome “High Female Labor Force Participation – Subsequent Year” Goodness-of-fit for relationships between hypothesized causal condition “High Level of Cumulative Left Cabinet Incumbency” and select outcomes Goodness-of-fit for relationships between outcome “High Female Labor Force Participation – Subsequent Year” and select hypothesized causal conditions Bootstrapped EDF estimates of compliers average causal effects on female labor force participation rates, with strong left political tradition as the instrument in the intention-totreat analysis Bootstrapped EDF estimates of compliers average causal effects on female labor force participation rates, with all other non-left political traditions as the instrument in the intention-to-treat analysis Correlations between family policy measures Principal components analysis of the four family policy measures Regression results Regression results: change in women’s employment on level of family policy generosity Employment rates by presence of children, 2000 Distribution of women aged 15–64 years by labor market status in 1992–2002, by birth cohort Determinants of labor market status of women – inactive (Ref: full-time) Determinants of labor market status of women – short part-time, 1–19 hrs (Ref: full-time) Determinants of labor market status of women – long part-time, 20–34 hrs (Ref: full-time) Determinants of labor market transitions into inactivity Determinants of labor market transitions to job from inactive status Preferred and actual working hours of women with children under six, living in a couple, 1998 Preferences for more or less working hours and chances to realize preferences, 1992–2002 Determinants of women’s preferences for more or less working hours

157

160

162

163

165 204 204 206 217 222 236 238 239 240 243 245 247 248 251

List of Tables xi

8.1 8.2 8.3 8.4 9.1 9.2

Conceptual map of protection, production, and partnership regimes Relative exit rates, men and women aged 60–64, 1970–2003 Index of pathways ranked by exit opportunities Early exit from work and regime configurations Different estimates of the average treatment effect Estimates of the effect of the treatment on the treated and on the control group

268 274 279 283 308 308

List of Figures

1.1 1.2 1.3 1.4 1.5 1.6

1.7 2.1 2.2 2.3 2.4 2.5 2.6 2.7

2.8

3.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2

Employment levels and employment change, 1979 to 2005 Women’s and men’s employment levels, 2005 Illustration of causal sufficiency and necessity Illustration of “nearly always sufficient” Illustration of small-N ordinal comparison Mahoney’s argument for use of small-N analysis to eliminate a hypothesized sufficient or necessary condition Summary of analysis in Schettkat (2005) Main variables Replacement rate and service sector employment Annual changes in service employment Long-term changes, 1982–91 Employment regulation and private service employment Private service employment: replacement rate effect conditional on employment regulations Change in private service employment, 1981–91: replacement rate effect conditional on employment regulations Change in private service employment, 1981–91: replacement rate effect conditional on employment regulations, new data for replacement rate Employment change in low-end private sector services, 1979 to 1995 Employment change fuzzy-set scores by employment change raw values Causal condition fuzzy-set scores by raw values Consistency and coverage Poor employment change performance by causal configurations 1–4 from Table 3.3 Poor employment change performance by causal configuration 5 from Table 3.3 Employment rate for women as a proportion of all women, 15–64 Employment rate for women between 25 and 54, 1968–2004

3 5 10 11 15

16 17 39 43 47 48 50 51

56

61 70 71 73 79 83 84 97 98

List of Figures

4.3 4.4 4.5 4.6 5.1 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 7.1 7.2 8.1 8.2 8.3 8.4 9.1 9.2 9.3 9.4 9.5 9.6

Full-time and part-time employment rates for women aged between 25 and 54, 1983–2004 Involuntary part-time work as a proportion of part-time work for women 25–54, 1992–2004 Percentage of respondents who believe that work is “very important” in their life Proposed model explaining women’s employment Hypothesized causal chain, including variables used in the empirical analysis Men’s and women’s employment, 2000–2005 Women’s employment rates, 1960–2000 Women’s employment by family policies, decade averages, 1960s–1990s Women’s employment by women’s preference for employment and women’s education Women’s educational attainment by family policies, Nordic countries, 1960s–1990s Public child care, age 0–2, 1960s–1990s Public child care, age 3–5, 1960s–1990s Maternity leave, 1960s–1990s Public employment, 1960s–1990s Family policy factor scores, 1960s–1990s Women’s employment by family policies and by women’s education: over-time within-country patterns Incidence of part-time employment among women Employment population ratios and unemployment rates, women, aged 15–64 years Employment rates for men aged 55–59/60–64/15–64, 1970–2000 Employment rates for women aged 55–59/60–64/ 15–64, 1970–2000 Relative exit rates for men aged 60–64, 1970–2003 Relative exit rates for women aged 60–64, 1970–2003 Average rate of growth of employment in the world, 1950–1990 Average difference between productivity and wage growths Labor share as a function of product per worker, by regime Growth of employment as a function of per capita income, by regime Density of per capita income, by regime Density of average world employment growth, by regime

xiii

99 100 118 120 155 198 199 200 201 203 210 211 212 213 214 216 226 230 269 271 273 275 292 293 294 302 306 307

Notes on Contributors Daniel Duerr is a PhD candidate in sociology at the University of Arizona. His research focuses on the development of the welfare state, stratification and income equality, and the causes of poverty in affluent countries. Bernhard Ebbinghaus is professor of sociology and director, Mannheim Centre for European Social Research (MZES), University of Mannheim, Germany. His recent publications include Reforming Early Retirement in Europe, Japan and the USA (2006). His research interests are welfare state analysis, industrial relations, institutional theory, and comparative methods. Scott Eliason is associate professor of sociology, and faculty affiliate of the Minnesota Population Center, at the University of Minnesota. He is also a faculty fellow at the Center for the Study of Poverty and Inequality at Stanford University. His research interests and publications span the areas of quantitative methodology and statistics, sociology of labor markets, stratification, law and organizations, the welfare state, and the life course. Jessica Epstein is a PhD candidate in sociology at the University of Arizona. Her research interests are the political economy of food and agriculture, political ecology, and research methods. Alexander Hicks is Winship Distinguished Research Professor of Sociology at Emory University. His publications include Social Democracy and Welfare Capitalism (1999) and articles in leading sociology and political science journals. He has twice served as editorial board member for the American Sociological Review and was inaugural co-editor of Socio-Economic Review. Lucian Jude is a former doctoral student in sociology at the University of Massachusetts – Amherst. Lane Kenworthy is professor of sociology and political science at the University of Arizona. He studies the causes and consequences of poverty, inequality, mobility, employment, economic growth, and social policy

Notes on Contributors

xv

in affluent countries. He is author of In Search of National Economic Success (1995), Egalitarian Capitalism (2004), and Jobs with Equality (2008). His current book project is tentatively titled Rethinking Inequality and Poverty. Bernhard Kittel is full professor of social science methodology at the University of Oldenburg, Germany. His research interests include methodology of cross-national research, in particular of the welfare state and industrial relations, and the experimental study of political decision making. Recent publications include “A Crazy Methodology? On the Limits of Macro-quantitative Social Science Research”, International Sociology (2006); “European Rigidity vs. American Flexibility? The Institutional Adaptability of Collective Bargaining,” Work and Occupations (2005, with Bernhard Ebbinghaus). Joya Misra is associate professor of sociology and public policy at the University of Massachusetts, Amherst. Her research focuses on comparative welfare states and the intersection of race/ethnicity, nationality, gender, and class in labor markets and social policy. She has published articles in journals such as American Sociological Review, American Journal of Sociology, Socio-Economic Review, and Social Problems. Adam Przeworski is the Carroll and Milton Petrie Professor of Politics at New York University. His books include The Logic of Comparative Social Inquiry (1970), Capitalism and Social Democracy (1985), Paper Stones (1986), Democracy and the Market (1991), Democracy and Development (2000), and States and Markets (2003). Recent relevant publications on comparative work–family policy and women’s employment include articles in the Journal of Comparative Policy Analysis (2007) and Gender & Society (2007). Charles C. Ragin is professor of sociology and political science at the University of Arizona. In 2000/01 he was a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University, and before that he was professor of sociology and political science at Northwestern University. His main interests are methodology, political sociology, and comparative-historical research, with a special focus on such topics as the welfare state, ethnic political mobilization, and international political economy. His books include Redesigning Social Inquiry: Fuzzy Sets and Beyond (2008), Fuzzy-Set Social Science (2000), Constructing Social Research: The Unity and Diversity of Method (1994), What is a Case? Exploring the Foundations of Social Research (with Howard

xvi

Notes on Contributors

S. Becker, 1992), Issues and Alternatives in Comparative Social Research (1991), and The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies (1987). Robin Stryker is professor of sociology and affiliated professor of law at the University of Minnesota. Among her recent publications are “Half Empty, Half Full, or Neither?: Law, Inequality and Social Change in Capitalist Democracies,” Annual Review of Law & Social Science (2007); “A Sociological Approach to Law and the Economy” (with Lauren Edelman), in the Handbook of Economic Sociology (2005); “The Strength of a Weak Agency: Title VII of the 1964 Civil Rights Act and the Expansion of State Capacity, 1965–1971” (with Nicholas Pedriana), American Journal of Sociology (2004); and “Redefining Compassion to Reform Welfare: How Supporters of 1990s US Federal Welfare Reform Aimed for the Moral High Ground” (with Pamela Wald), Social Politics (2008). Her current research on the politics of social science in government regulation of equal employment opportunity is supported by the National Science Foundation. Eric Tranby is a PhD candidate in sociology at the University of Minnesota. His research interests include gender and racial inequality in contemporary social life, comparative public policy, and life course research. His dissertation research examines the effect of family policies on women’s employment outcomes in the United States, Germany, and Sweden. Ongoing projects include research on gendered labor markets, public policy, and female labor force participation; the diverse experiences of young adulthood; and understandings of racial and religious diversity in the United States. His work has been published in journals such as Social Problems and Research in Social Stratification and Mobility. Jelle Visser is professor of sociology at the University of Amsterdam, where he directs the Amsterdam Institute of Advanced Labour Studies (AIAS). His main work is on labor relations, welfare states, social policies, employment, and trade unions. With Bernhard Ebbinghaus he edited the Trade Unions in the Societies of Europe series (published by Palgrave Macmillan) and with Anton Hemerijck he wrote “A Dutch Miracle” (1997). Mara Yerkes is a post-doctoral research fellow in sociology at the Erasmus University, Rotterdam. She is the author of What Women Want: Individual Preferences, Heterogeneous Patterns? Her research interests include industrial relations, labor policy, work and care issues, and comparative welfare state research.

1 Introduction Lane Kenworthy and Alexander Hicks

Macrocomparative researchers use a variety of methodological approaches. This book features analyses of a single substantive topic using several of the most common. The topic is comparative employment performance in affluent countries. The chief methodological approaches are pooled cross-section time-series regression, qualitative comparative analysis (QCA), and small-N analysis. The aim of the volume is to illustrate in a practical fashion the advantages and drawbacks of these analytical strategies. Instruction and advice is available in numerous monographs, articles, and edited volumes (for example, Greene, 2003; Ragin, 1987, 2000; King, Keohane, and Verba, 1994; Mahoney and Rueschemeyer, 2003; Brady and Collier, 2004; George and Bennett, 2004). But often that advice is provided at a general level. Commonly, substantive illustrations are offered with reference to a single methodological approach. A key question for researchers is when and why to use one methodological approach rather than, or in addition to, another, and what are the payoffs and sacrifices entailed by a particular choice. Although general advice is helpful, the best way to understand the tradeoffs involved is via practical application. This book was conceived partly as a follow-up to the 1994 volume The Comparative Political Economy of the Welfare State, edited by Thomas Janoski and Alexander Hicks. That book was aimed at comparativists interested in the welfare state and in comparative political economy more generally. It included methodological and substantive chapters covering time-series regression, pooled cross-section time-series regression, event history analysis, and qualitative comparative analysis (“Boolean analysis”). This volume differs in three main respects. First, it covers a different set of methodological approaches, focusing exclusively on those that involve macro-comparison – that is, comparison 1

2


across countries (or regions). The techniques explored in this book are pooled regression, qualitative comparative analysis, and small-N analysis. Secondly, this book includes no strictly methodological chapters; the methodological techniques are described and discussed in substantive chapters. Thirdly, the substantive chapters in the Janoski–Hicks book examined disparate issues: economic growth, wage trends, active labor market policy, and pension systems. The analyses here for the most part address the same substantive question: What are the determinants of variation in employment performance across affluent countries? Our hope is that the common substantive focus helps to reveal as clearly as possible the advantages and drawbacks of the methodological strategies.

The substantive issue: comparative employment performance Macrocomparativists engage a wide array of substantive issues. We chose comparative employment performance as the outcome for the analyses in this book. A country’s employment rate is measured as the number of people with paying jobs divided by the population age 15 to 64 (the working-age population). Employment is a useful barometer of labor market performance in a country. For most of the past half-century, unemployment rates have been considered the main indicator of labor market outcomes, but the fact that unemployment can be hidden in various ways – low labor market participation, active labor market programs, and so on – has encouraged a shift toward employment rates. Employment has intrinsic merit (Jahoda, 1982; Wilson, 1996; Phelps, 1997). With heightened geographical mobility, later marriage, and increased divorce, neighborhood and family ties have dissipated somewhat. As a result, work is an increasingly important site of social interaction. Employment imposes regularity and discipline on people’s lives. It can be a source of mental stimulation. It helps to fulfill the widespread desire to contribute to, and be integrated with, the larger society. For many individuals, work is inextricably bound up with identity and self-esteem. In addition, an increasingly common view is that high employment is critical to maintenance of low or moderate levels of income inequality (Esping-Andersen, 1999; Ferrera et al., 2000; Scharpf and Schmidt, 2000; Esping-Andersen et al., 2002; Kok et al., 2003; Kenworthy, 2004, 2008; OECD, 2005, 2006). Meeting pension and health care commitments for an ageing population will require greater government funds

Lane Kenworthy and Alexander Hicks Employment levels, 1979 and 2005 Swi Den Nor NZ Ja UK Nth Can Swe US Asl Aus Fin Ire Ger Fr Sp Bel It 20

Employment change, 1979 to 2005 Nth NZ Swi Ire Sp Can Asl Aus Ja US UK Nor It Den Ger Bel Fr Fin Swe

1979 2005

0

3

40

60

Employment (%)

80

⫺10

0

10

20

Employment change, 2005 minus 1979 (%)

Figure 1.1 Employment levels and employment change, 1979 to 2005 Note: Employment = employed persons as a share of the population age 15 to 64. Portugal is omitted due to lack of employment data for 1979.

in coming decades. Yet governments increasingly find it difficult to raise tax rates, due to capital mobility. This makes it difficult to maintain generous transfers for the working-age population and their children. A rising employment rate helps to increase tax revenues without raising tax rates. And by bringing former benefit recipients into the workforce, it reduces government transfer payments. Figure 1.1 shows employment rates in 1979 and 2005, and changes in employment during that period, for the group of affluent countries examined in this book: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States. As of the mid-2000s, employment rates ranged from less than 60 percent of the working-age population in Italy to more than 80 percent in Switzerland. The variation in change between the late 1970s and the mid-2000s was equally large, with the employment rate falling by more than 5 percentage points in Sweden and increasing by 20 points in the Netherlands. How can countries achieve a high and/or rising employment rate? There are two principal debates around this issue. The first concerns the determinants of overall employment performance and focuses on

4


the impact of labor market institutions and policies. These include wage levels at the low end of the distribution, employment protection regulations, government benefits, and taxation. If wage levels at the low end of the labor market are high relative to productivity levels – due to a statutory minimum wage, a collectively bargained minimum, or a tight labor market – employer demand for low-end workers may diminish, reducing the employment rate. If it is difficult for employers to fire workers when the economy is bad or the firm’s sales are slumping – because they must get the approval of a works council or provide a generous severance package or pay for extensive retraining and job placement – employers may hire fewer workers when times are good. If government benefits – social assistance, unemployment insurance, sickness or disability compensation, pensions, and so on – are fairly generous, easy to access, and of lengthy duration, workers at the low end of the job market may be less likely to search for and accept employment. A high tax burden can reduce the net benefit to a worker from employment and/or increase the cost to an employer of hiring, thereby potentially producing less supply of and demand for labor. High low-end wages, strict employment protection regulations, generous government benefits, and high taxes are sometimes referred to as “labor market rigidities.” The notion that such rigidities impede high and/or rising employment has been around for a long time, but it has been especially prominent since publication of The OECD Jobs Study in 1994. The Jobs Study was a clear and systematic statement of the rigidities ➔ poor employment performance hypothesis, and it was published at a time when unemployment in a number of western European countries had been high for roughly a decade and showed no signs of imminent decline. Since 1994 dozens of comparative empirical studies have examined the hypothesis. (Some recent studies, which include citations to earlier ones, include Blau and Kahn, 2002; Kenworthy 2004, 2008; Howell, 2005; Nickell, Nunziata, and Ochel, 2005; Bassanini and Duval, 2006; OECD, 2006; Baccaro and Rei, 2007.) Despite this extensive research, there is nothing close to a consensus regarding the merit of the hypothesis. The second debate is about the impact of so-called family policies (also variously referred to as work–family reconciliation policies and womenfriendly policies) on female employment. In countries with employment deficits, the problem consists chiefly of a shortage of women’s employment. This can be seen clearly in Figure 1.2, which shows employment rates for men and women as of 2005. A critical task – perhaps the critical task – for low-employment countries, therefore, is to identify and


Swi Nor Swe Den Can US Fin NZ Nth UK Por Asl Aus Ja Ger Fr Ire Bel Sp It

5

Women Men

0

20

40

60

80

100

Employment (%) Figure 1.2 Women’s and men’s employment levels, 2005 Note: Employment = female or male employed persons as a share of the female or male population age 15 to 64.

implement institutional or policy changes that can substantially increase female employment. A number of studies have suggested that the key is generous family policies (Winegarden and Bracy, 1995; Ruhm, 1998; Meyers, Gornick, and Ross, 1999; Plantenga and Hansen, 1999; Rubery, Smith, and Fagan, 1999; Sainsbury, 1999; Daly, 2000; Korpi, 2000; Dingeldey, 2002; OECD, 2001; Stier, Lewin-Epstein, and Braun, 2001; Esping-Andersen et al., 2002, ch. 3; Orloff, 2002; Pettit and Hook, 2002; Ferrarini, 2003; Gornick and Meyers, 2003; Jaumotte, 2003; Morgan and Zippel, 2003; Mandel and Semyonov, 2006; Kenworthy, 2008). One such policy is public provision or financing of child care. Lack of affordable child care can pose a significant obstacle to employment for women with preschool-age children. A second is paid parental/care leave. The expectation is that if women know they can take a reasonably long break from work without losing their job and without foregoing all of their earnings, more will choose to enter the labor market in the first place and more will return after having a child. A third is government

6


provision of public sector jobs, which may be more attractive to women than private sector jobs because they are more likely to be available at reduced hours (part-time), to be secure (governments are less likely than private employers to fire employees during economic downturns), and to accommodate family needs such as illness. A fourth is promotion of or support for part-time employment, which women may prefer because the shorter hours facilitate work–family balance. A fifth is the structure of the tax system. Of particular relevance is the degree to which a couple with two earners is penalized relative to a couple with one earner; the greater the tax penalty, the stronger the disincentive for a woman with an employed husband to get a job. A sixth is anti-discrimination and affirmative action laws. To the extent that women’s employment is impeded by discriminatory action by employers, such policies are likely to help. The book’s chapters focus on these two substantive questions: What has been the impact of labor market institutions and policies on overall employment performance? What has been the effect of family policies on women’s employment?

Methodological approaches In attempting to answer a question such as what determines employment performance, various analytical strategies can be pursued. One is to examine individual behavior. Another is to consider patterns across firms or industries within countries. A third is to look at developments over time in a single country. A fourth approach is macrocomparative. Countries are the unit of analysis. The causal factors of interest (policies and institutions) and the outcomes are measured at the level of the nation. Analytical leverage is gained at least in part, and often primarily, by comparison across countries. Macrocomparative analysis can be conducted using a variety of techniques. In this book the focus is on three: regression, qualitative comparative analysis, and small-N analysis. In this introductory chapter we offer a brief outline of the main distinguishing features, advantages, and disadvantages of these three techniques. They are summarized in Table 1.1. A key point we wish to stress at the outset is that these approaches are best viewed as complementary rather than competing and overlapping rather than mutually exclusive (Lieberman, 2005; Ragin, 2005). Each is capable of contributing to macro-level analysis in different ways.1 Overlaps between the three approaches are possible. For example, QCA is not


7

Table 1.1 Analytical strengths of the three methodological approaches Pooled Regression Designed to assess tendential relationships, manifested in patterns of co-variation among variables Useful for assessing the net effect of a variable on the outcome Enables variation across units (countries) and over time to be analyzed together Allows formal estimation of the magnitude of impact of a cause Qualitative Comparative Analysis Designed to assess deterministic causal relationships (logically conceived): sufficiency and necessity Useful for exploring causal configurations (combinations of causes) Useful for examining multiple causal paths to the same outcome Allows formal estimation of the magnitude of impact of a cause Small-N Analysis Useful for assessment of causal mechanisms via process tracing Useful for elimination of hypothesized (“always”) sufficient or necessary conditions Ordinal cross-country comparison can be used to assess hypothesized tendential or quasi-deterministic relationships, but generalization beyond the studied cases is problematic Possibility of considering variables that cannot be included in a large-N analysis because data are available for only a few countries Possibility of better measurement of variables due to case knowledge Possibility of more nuanced attention to interaction among causal factors than is possible with regression

uncommonly used for small Ns of 10 or fewer, and an analysis that treats a few cross-sectionally differentiated units in the “small-N” style might include a time-series regression of years encompassed by a cross-sectional slice or QCA analysis of subunits (e.g., of states). Pooled regression Regression is the most commonly used analytical technique in macrocomparative analysis. It is a correlational technique, although causal interpretation of regression slope estimates backed by special statistical care (for example, fastidiously specified lag structures) as well as theoretical argument sometimes accompany regression analyses. The aim is to identify statistical associations between hypothesized causes and outcomes. Such associations are based on co-variation. In regression, an independent variable is associated with a dependent variable when levels of the two variables correspond to one another.

8


The relationships assessed in regression are “tendential” rather than “deterministic.” A particular level of a hypothesized cause is thought to make it more likely that the outcome will have a certain level, but it is assumed that there may be exceptions. Regression predictions are made with allowance for error (for example, residuals, confidence intervals). For analysts interested in understanding differences across the rich countries, a fundamental analytical obstacle is the small number of cases. Depending upon one’s definition of “affluent,” there are approximately 15 to 25 nations to study. This inhibits estimation of regressions with more than a few independent variables. Another limitation of standard regression in macrocomparative analysis is that it frequently is confined to cross-country variation, ignoring variation over time within countries. Time-series regression does the opposite. A pooled cross-section time-series regression combines information about variation across countries with information about variation over time within countries. The unit of analysis is the country-year or countryperiod, rather than either the country or the time period. This not only combines the two kinds of variation; it also substantially increases the number of observations, thereby helping to address the small-N problem. For these reasons, pooled regression has, as Janoski and Hicks (1994) envisioned, become the dominant technique for large-N analysis in macrocomparative research. Yet pooled regression has important limitations, to which practitioners do not always pay sufficient heed. One is that the determinants of cross-country variation may not correspond to those of over-time variation within countries (Griffin et al., 1986; Kittel, 1999; Kenworthy, 2006, 2007; Shalev, 2007). Over a very long period, we would expect such correspondence, but most analysts do not have lengthy time-series data that are comparable across more than a few countries. One way to partially address this is to pool periods of years rather than individual years (Hicks and Kenworthy, 1998, 2003, this volume; Barro, 2000). A second limitation concerns time lags in causal effects. Many hypotheses about determinants of change in macrocomparative analysis either implicitly or explicitly refer to relatively long-term effects. Yet most pooled regression analyses use the country-year as the unit. This is likely a function of researchers’ desire to significantly boost the number of observations and thereby facilitate estimation of models with a large number of regressors. Sometimes analyses with annual data can pick up medium-term or long-term effects, but that hinges on getting the lag structure correct. More often than not, using annual data to examine


9

hypothesized medium-run or long-run associations will obscure rather than clarify. Finally, successful estimation of pooled regressions requires meeting, or addressing, various technical requirements. Bernhard Kittel discusses these issues extensively in chapter 2, so we will do no more than mention them here (see also Kittel, 1999; Ebbinghaus, 2005; Wilson and Butler, 2007). They include independence of observations, non-trending (stationarity), and assessment of the cross-sectional or longitudinal dominance of the data array (the proportions of the variable variances accounted for by the longitudinal and cross-sectional dimensions). Qualitative comparative analysis (QCA) Qualitative comparative analysis is a technique for systematically exploring relations between explanatory factors and outcomes (Ragin, 1987, 2000; Ragin and Rihoux, 2004). QCA was conceived originally with the aim of formalizing the analytical process often pursued by small-N qualitative researchers, to enable the process to be applied more systematically and to a larger number of cases. This process is articulated in terms of the logical language of set theory. There are two variants of QCA. One, crisp-set QCA (Boolean), uses dichotomous codings of causal conditions and outcomes. The other, fuzzy-set QCA, uses pseudo-continuous codings that vary between zero and one. In both versions, the aim is to identify hypothesized causal factors, or combinations of those factors, that are related to the outcome in a pattern consistent with that of a sufficient or necessary condition – the two main types of deterministic causal relationship. When plotted in a scattergram, a relationship of sufficiency is suggested if the data points all fall above and to the left of a 45-degree line running from the lowerleft corner to the upper-right corner. When the hypothesized cause is absent or low, the outcome may be absent/low or present/high; but when the cause is present/high, the outcome is always present/high. A relationship of necessity is indicated by the data points being located below and to the right of the 45-degree line. When the cause is present/high, the outcome may be absent/low or present/high. But the outcome is never present/high unless the cause is present/high. These two patterns are illustrated in Figure 1.3. “Cause” is the preferred term for sufficient and necessary “conditions” in the QCA literature. Although some have cautioned against identification of the logical conditions of formal languages such as QCA’s Boolean logic with real world causes (Ayer, 1956, pp. 170–5; Passmore, 1967, pp. 355–60; Manicas, 2006), we adopt the QCA usage here.

10 Method and Substance in Macrocomparative Analysis

High

Necessity

Low

Low

Outcome

Outcome

High

Sufficiency

Low

High

Low

Cause

High Cause

Figure 1.3 Illustration of causal sufficiency and necessity Note: The lines in the charts are 45-degree lines, not regression lines.

Charles Ragin and others have suggested that a deterministic relationship may have empirical exceptions, due to the randomness of social processes, measurement error, or other reasons. A cause need not be always sufficient or necessary; it may be “nearly always” or “usually” sufficient or necessary (Ragin, 1987, ch. 7; Ragin, 2000, pp. 107–16; see also Goertz and Starr, 2003). For some, the idea of introducing a tendential element into an otherwise deterministic notion of causality is oxymoronic. Why not instead simply refer to the relationship as tendential? Consider the pattern in Figure 1.4. Except for one data point, it is consistent with a hypothesis of sufficiency: in cases where the cause is present/high, the outcome is also present/high. One could conceptualize the relationship as tendential; the two variables correlate at .54. But “almost always sufficient” seems a more accurate description of the empirical pattern. Which interpretation is more sensible depends heavily on what the substantive issue is and whether a tendential or deterministic understanding is more compelling on theoretical grounds. For macrocomparative analysis, QCA has several potential advantages relative to regression. One is its focus on deterministic relationships. As noted earlier, regression is designed to assess tendential causal (or, more simply, explanatory) relationships. When a cause is hypothesized to be sufficient or necessary for an outcome, QCA may thus be a more appropriate method. Secondly, QCA is adept at exploring causal configurations – situations in which variables have an impact only in combination with a high or


11

Low

Outcome

High

Sufficiency

Low

High Cause

Figure 1.4 Illustration of “nearly always sufficient” Note: The line in the chart is a 45-degree line.

low degree of one or more other factors. In regression analysis, causal configurations are assessed via interaction terms. But with the small or moderate number of cases that is common to macrocomparative analysis of affluent countries, the number of interactions terms that can be included in a regression model tends to be limited. Pooling the crosscountry and over-time variation can alleviate this problem, but the collinearity produced by interaction terms that involve more than two or three variables and the difficulty in interpreting the results makes modeling complex interactions problematic. Moreover, while assessing interactions in regression requires that variables demonstrate product terms, QCA treats any aspects of cases that appear together systematically – in any quantity – as potentially interdependent. Thirdly, QCA facilitates identification of multiple pathways to an outcome. Many social phenomena have causes that are relevant to only a fraction of the cases. With a correlational technique such as regression, when the dependent variable is high/present but the independent variable is low/absent, this weakens the estimate of the effect of the independent variable (or interacted combination of independent variables). QCA, by contrast, is designed to reveal patterns of association that differ across subsets of cases. It thereby enables discovery of more complex causal patterns than are generally recognizable via regression. In the view of some, an additional advantage of QCA is that the deterministic relationships it identifies are more likely to be causal than the


tendential associations identified by regression (Mahoney and Goertz, 2006). This view, however, has been contested and criticized – by logical positivists such as Ayer (1956, pp. 170–5) as well as by realists like Manicas (2006) and interpretivists like Wittgenstein (Passmore, 1967, pp. 355–60) – for a conflation of logical and causal relations (but see Pruss, 2006). Like any technique, qualitative comparative analysis has limitations. First, if a causal factor is suspected to have a tendential relationship with the outcomes, rather than a deterministic one, QCA is of little use. Secondly, a QCA analysis is, in a key respect, bivariate. Multiple causal factors are considered, and the “solution set” of causal configurations yielded by a QCA analysis will vary with the set of causal factors entered into the analysis. Specification of additional explanatory conditions, like entry of new control variables in regression, will often modify analytical results. Yet QCA examines the relationship between the outcome and each single hypothesized cause or combination of causes without controlling for – that is, without “holding constant” – any other causes. As Aaron Katz, Matthias vom Hau, and James Mahoney (2005, p. 568; see also Seawright, 2005) point out: Fuzzy-set analysis is only a multivariate method in the sense that the technique can explore if combinations of variables represent sufficient causes. However, since each combination is reduced to a single value, each combination is, in effect, treated as a single cause. Jason Seawright (2002, p. 181) has argued that given the assumption that the causal relationship is a deterministic one, this is appropriate: . . . claims of necessary and/or sufficient causation are fundamentally bivariate in nature. The hypothesis entailed in this idea of causation is that no other variable or combination of variables can overcome the effects of the necessary and/or sufficient cause. Therefore, controlling for other variables cannot alter the conclusion of the bivariate analysis, and a bivariate focus is fully appropriate. However, positing that a causal relationship is deterministic and finding a bivariate pattern consistent with that hypothesis does not rule out the possibility of spuriousness (omitted variable bias). Although regression is scarcely immune to this concern, it is designed to estimate the “net” effect of each variable. Charles Ragin (2005, p. 35) sums up this point effectively: “Regression analysis is a preeminent tool for estimating


13

net effects; QCA’s primary analytic focus is on the different ways causal conditions combine.” Thirdly, to this point there is no QCA counterpart to the combining of cross-country and over-time variation that is possible with pooled regression. Regression has several tools for assessing the magnitude of a variable’s causal effect. One is the variable’s coefficient, which estimates the change in the dependent variable given a one-unit increase in an independent variable (net of other independent variables). Another is the R2 (coefficient of determination), which measures the precision or “goodness of fit” of the coefficient estimates. Others include multiple partial coefficients of determination for subsets of regressors, standardized coefficients, and various techniques of exogeneity and Granger causality assessment. In QCA the principal tools for assessing the strength of relationships are “consistency” and “coverage.” Consistency refers to the degree to which the empirical pattern corresponds to that of sufficiency or necessity. An “always” sufficient relationship can be considered stronger than a “nearly always” or “usually” sufficient one. Coverage refers to the share of cases having a particular outcome that feature a particular causal factor or causal configuration. Consistency and coverage are discussed in greater detail in chapter 3 of this volume and in Ragin (2006).

Small-N analysis By “small-N analysis” we refer to macrocomparative analyses in which the number of countries (cases) studied is ten or fewer. This is, of course, an arbitrary cutoff; there is no number of nations that objectively demarcates “small” from “large.” The most common number of nations studied in small-N analyses is one, two, or three.2 One of the most important contributions of small-N analysis is descriptive. Studying a small number of countries allows the researcher to learn, and convey to the reader, a level of detailed knowledge that is beyond the reach of an analyst committed to comparing a large number of countries. The small-N researcher typically examines variables, events, actors, and other aspects of the national context in extensive detail. This type of information is inherently interesting to comparativists. It also helps large-N researchers to check their coding of variables, to consider additional causal factors, to think about interactions among causal conditions, and to judge the general plausibility of their causal hypotheses.


What can small-N analyses contribute in terms of theory testing (hypothesis testing)? One contribution is process tracing (Mahoney, 2000, 2003; Hall, 2003; George and Bennett, 2004). Process tracing consists of examining theoretically-specified causal pathways (causal mechanisms) in the context of developments in a single country. This is particularly useful where a large-N analysis has suggested support for a particular causal story but where, often due to lack of data, the large-N analysis is unable to examine the causal mechanisms. Of course, any finding of an association or “solution set” in a large-N analysis should be supported by a plausible theoretical story. But investigating the causal paths empirically is equally critical, and small-N analysis is a useful way to do that. When the small-N analysis is of a single country, it is often referred to as a “case study.” Small-N analyses of multiple countries sometimes are actually multiple case studies, rather than cross-country comparisons. A good example of a case study of employment performance is Jelle Visser and Anton Hemerijck’s (1997) book-length analysis of developments in the Netherlands, which was revisited in an article by Visser (2002). These two works consider a number of possible determinants of the Dutch “employment miracle” since the early 1980s (see Figure 1.1 above). Visser and Hemerijck carefully trace developments in public policy, economic institutions, and employment patterns in the 1980s and 1990s. They conclude that wage restraint and increased female educational attainment played critical roles, but also that a variety of conjunctural factors, such a growing preference for part-time workers among public sector employers and reactions by employers’ associations to an early-1980s agreement to reduce the standard work week, were important. Family policy and union strategies are found to have played distinctly secondary roles. Small-N analyses of more than one country sometimes are simply multiple case studies. Frequently, however, they are comparative: they attempt to gain analytical leverage from cross-country comparison. How so? In an insightful paper, James Mahoney (2000, pp. 399–406; also Mahoney, 2003) points out that small-N analyses often engage in a simple form of correlational analysis, which he refers to as ordinal comparison. The countries are rank-ordered on the outcome and on a hypothesized causal variable, and the analyst draws inferences about causal impact based on the consistency of the rankings for the two variables. Suppose a researcher compares employment performance in Sweden, Italy, and the United States during the 1990s. The analyst might argue that performance was strongest in the United States, followed by Sweden and then Italy, and that non-market institutions and policies were weakest in the


15

US

Employment performance

Good

United States and strongest in Italy, with Sweden in between. Among these three countries, the ranked positions on the two variables are consistent, which supports the notion that “rigidities” had an adverse effect on employment outcomes. The analysis is depicted in Figure 1.5. The strength of such an analysis is likely to lie in the coding of the variables and perhaps the attention to interactions among them. However, this type of implicit correlational analysis is problematic with a small number of cases. It is difficult to take very many factors into account (to control for them). And while an inference based on well-done analysis of this type can certainly be suggestive of a tendential causal relationship, it leaves open the possibility that the countries analyzed are atypical. Of course, large-N correlational analyses are never definitive; they too can do no more than suggest a causal relationship. Still, all else equal, the larger the number of countries analyzed, the less reason there is to worry about representativeness or generality of findings (Geddes, 1990; King, Keohane, and Verba, 1994). Mahoney (2000, pp. 391–8) argues that small-N comparison offers analytical leverage chiefly via its ability to eliminate a hypothesized sufficient or necessary condition. A hypothesized sufficient condition, he suggests, can be tested using John Stuart Mill’s “method of difference.” Here cases are selected that differ on the outcome; in at least one country the outcome is present and in another it is absent. Since “sufficiency” implies that where the cause is present the outcome will occur, any cause that is present in the country in which the outcome is absent can be ruled out as a sufficient condition. A hypothesized necessary condition can

Swe

Bad

It Low

Labor market “rigidities”

Figure 1.5 Illustration of small-N ordinal comparison

High


be assessed using Mill’s “method of agreement,” in which the outcome is present in all countries selected for analysis. Any cause that is absent in any of the countries can be ruled out as a candidate for “necessity.” Findings in these types of analyses that are consistent with a hypothesis of sufficiency or necessity can be treated as supportive, but by no means definitive, since there may be other nations – possibly many of them – for which the hypothesis is contradicted. Mahoney’s point is illustrated in Figure 1.6. We find Mahoney’s observation illuminating. However, a causal factor can be eliminated as sufficient or necessary based on a single case; there is no need for cross-country comparison. Indeed, if the aim is to eliminate a hypothesized sufficient or necessary condition, comparison across countries is irrelevant. Comparison implies variation, and in testing a hypothesized deterministic relationship there is no need for variation. To test a hypothesized sufficient condition, one should select only cases in which the condition is present/high; countries in which it is absent/low offer no analytical leverage. To test a hypothesized necessary condition, a researcher should select only cases in which the outcome is present/high. For the purpose of eliminating a hypothesized sufficient or necessary condition, then, the utility of studying more than one country is not that it enables comparison across the countries. Rather, the gain is simply that examining multiple countries increases the opportunity for elimination. Given this, does a small-N comparative analysis have any advantage over a large-N analysis for eliminating a hypothesized sufficient or necessary condition? After all, the larger the number of countries, the greater the opportunity for elimination – and the greater the confidence in the hypothesis if it is not eliminated. There are two potential advantages to keeping the N small. One is that the likelihood of measurement error may be reduced because of the researcher’s greater case knowledge. Another is that the researcher may be able to consider variables for which data are not available for a larger set of countries. A. Eliminating a hypothesized sufficient condition

B. Eliminating a hypothesized necessary condition

Country 1

Cause Outcome ——————————— Present Present

Country 1

Cause Outcome ——————————— Present Present

Country 2

Present

Country 2

Absent

Absent

Present

Figure 1.6 Mahoney’s argument for use of small-N analysis to eliminate a hypothesized sufficient or necessary condition


17

For a tendential or quasi-deterministic theory, small-N analysis is more limited in its analytical utility. A single nonconforming case, and perhaps even two or three, cannot eliminate a hypothesized tendential, quasi-sufficient, or quasi-necessary cause. Then again, a nonconforming case increase grounds for skepticism. This is particularly true if the case is a “most favorable” one – one that there is reason to suspect will be highly likely to support the theory, or one that proponents of the theory frequently refer to as an illustration (Eckstein, 1975). A recent small-N macrocomparative analysis of employment performance is Ronald Schettkat’s (2005) study of Germany and the Netherlands. Schettkat provides qualitative and quantitative information to suggest that these two countries have been similar in their degree of institutional and policy “rigidities.” According to the tradeoff hypothesis, therefore, both should have had poor employment performance during the 1980s and 1990s. Yet the Netherlands arguably had very good employment performance. Schettkat provides extensive detail to support this coding decision. Schettkat does not say whether he considers the hypothesis he is assessing to be deterministic, quasi-deterministic, or tendential – that is, whether labor market “rigidities” are hypothesized to be a sufficient condition for bad employment performance or to increase the likelihood of bad employment performance. If the hypothesis is one of sufficiency, the Dutch case contradicts it. This is shown in Figure 1.7. But note that the German case (like country 1 in Figure 1.6), and therefore the crosscountry comparison, is not needed to reach this conclusion. If Schettkat is treating the tradeoff view as a tendential hypothesis, then the crosscountry comparison is helpful. But here the fact that the analysis includes only two countries is problematic. After all, the Netherlands could simply be an exception to the general tendency. Like pooled regression and QCA, then, small-N analysis offers certain advantages but also has important limitations. Its main assets are descriptive detail, care in measurement, ability to consider causal

Germany

Hypothesized Outcome: bad sufficient cause: employment labor market performance “rigidities” ————————————————————— Present Present

Netherlands

Present

Figure 1.7 Summary of analysis in Schettkat (2005)

Absent


variables for which data may not be available for a larger set of countries, process tracing as a means of assessing causal mechanisms, and elimination of hypothesized sufficient or necessary conditions. Its principal drawbacks are limited generalizability and concern about omitted variable bias.

Overview of the chapters The book’s chapters are organized partly by substantive topic and partly by methodological approach. Table 1.2 provides a summary. Chapters 2 and 3 examine the rigidities ➔ poor employment performance hypothesis. These two chapters use the same data set. The employment data are for private sector employment in low-end services – hotels, restaurants, wholesale and retail trade, and community/social/personal services (ISIC revision 2, sectors 6 and 9). These data are available only through the mid-1990s, but they provide the truest test of the rigidities hypothesis (see Iversen and Wren, 1998; Kenworthy, 2004, ch. 5). The analyses focus on six labor market institutions and policies: earnings inequality (a proxy for low-end wage levels), wage increases, payroll and consumption taxes, employment protection regulations, unemployment benefit generosity, and public employment. In chapter 2, Bernhard Kittel offers a clear and careful illustration of some of the potential pitfalls of pooled regression. Perhaps most important, he finds that the choice to pool observations adds little information and introduces significant estimation problems. Adding annual observations does not add relevant variation to several key explanatory variables, as they are largely constant over time. And the dependent variable turns out to be nonstationary. This leads Kittel to prefer a small-N cross-sectional design over a moderate-N pooled one. The lesson is not that simple cross-sectional models are always or even usually preferable, but rather that analysts should use pooled regression where doing so makes theoretical and empirical sense, not simply because data availability makes it possible to do so. Kittel’s substantive conclusion is that generous unemployment benefits may reduce the employment growth of private consumer-service jobs, at least when employment protection regulations are not stringent and when benefit generosity is measured using gross (pretax) rather than net benefits. In chapter 3, Jessica Epstein, Daniel Duerr, Lane Kenworthy, and Charles Ragin use fuzzy-set qualitative comparative analysis to explore the impact of the same institutions and policies on growth of employment in private sector consumer-oriented services. They focus on paths

Table 1.2

Summary of the contributions

Chapter

Author(s)

Dependent variable(s)

Unit(s) of analysis

Countries and years

Method

2

Kittel

Level and change in employment in private-sector consumer-oriented services

Country-years and countries

14 countries, 1979 to 1995

Regression: pooled and cross-sectional

3

Epstein, Duerr, Kenworthy, and Ragin

Change in employment in private-sector consumer-oriented services

Countries


Fuzzy-set QCA

4

Misra and Jude

Level and change in women’s total, full-time, and part-time employment

Countries

France and the Netherlands, 1960s through 1990s

Small-N analysis: within-country process tracing and cross-country ordinal comparison

5

Eliason, Stryker, and Tranby

Level of women’s labor force participation

Country-years


Fuzzy-set QCA with compliers average causal effects (CACE) analysis

6

Hicks and Kenworthy

Level and change in women’s employment

Country-decades

14 countries, 1960s through 1990s

Regression: pooled and cross-sectional

7

Visser and Yerkes

Employment status and employment transitions among women: full-time, long-hour part-time, short-hour part-time, non-employed

Individuals

Germany, the Netherlands, and the United Kingdom, 1992 to 2002

Regression: multinomial logit

8

Ebbinghaus

Early-exit rates and regimes among men and women age 55–59 and 60–64

Early-exit regimes and countries


Small-N analysis: cross-regime ordinal comparison and within-regime process tracing

9

Przeworski

Change in labor force participation

Country-years


Regression (pooled) with selection bias estimators


to slow employment growth or employment loss (“poor employment performance”). One of the aims of the chapter is to carefully illustrate the mechanics of a fuzzy-set QCA analysis, which is far less commonly used than regression in macrocomparative research. The chapter illustrates the potential usefulness of QCA in situations where researchers want to explore hypotheses of sufficiency and/or necessity and where the interest is primarily cross-sectional. Although QCA is particularly adept at examining multiple causal paths to the same outcome and at considering combinations of causal factors, in this particular analysis it turns out that there are only two causal paths consistent with a sufficiency hypothesis and only one of them involves multiple causal factors. The results center on one simple causal configuration and another singular causal factor: (1) low earnings inequality combined with high payroll and consumption taxes; (2) high unemployment benefit generosity. Chapters 4–7 shift the focus to women’s labor force participation and employment, with an emphasis on the impact of family policies. In chapter 4, Joya Misra and Lucian Jude examine the effect of family policy on women’s employment in a small-N analysis of France and the Netherlands. Part of their aim is to carefully trace over-time developments in these two countries and thereby explore in a detailed and nuanced fashion the role of family policy, economic conditions, and cultural support. They also are interested in understanding two differences between these countries: (1) higher full-time female employment in France by the 1960s and 1970s; (2) dramatic growth in (mainly parttime) women’s employment in the Netherlands beginning in the 1980s versus stagnation in France. Based on their analysis, they argue that a combination of supportive family policy, greater economic need for women’s employment, and cultural support explains both the initial higher levels of women’s employment in France as well as the dramatic growth of women’s employment in the Netherlands. However, cultural and policy differences in respect of caregiving for young children remain, helping explain the much higher levels of part-time employment among Dutch women. One of the purposes of this volume is to highlight the advantages and disadvantages of alternative methodological techniques. Equally important, however, is to move beyond these discussions to emphasize payoffs resulting from combining multiple methods. In chapter 5, Scott Eliason, Robin Stryker, and Eric Tranby combine fuzzy-set QCA methods with an analysis of compliers average causal effects (CACE) to explore the impact of left government on family policies and of family policies on female labor force participation from the 1960s through the 1990s. The principal


21

family policies they consider are child care, maternity leave, public employment, and child benefits. They offer a methodological innovation in coupling the QCA analysis with a CACE analysis. The aim of the latter is to assess whether the effect resembles one that would have been observed of compliers had the treatment been randomly assigned, as would be the case in a typical experimental design framework. They conclude that both demand-side and supply-side factors causally influence female labor force participation. On the demand side, they find that an expanded public sector has a substantial impact on female labor force participation. On the supply side, they find that maternity leave and public day care programs also have non-negligible causal effects on female labor force participation, although in some cases modest in comparison to demand-side mechanisms. In chapter 6, we (Hicks and Kenworthy) use Eliason, Stryker, and Tranby’s family policy data to explore the relationship between family policy generosity and female employment via regression analysis. We argue that even if the effect of family policy generosity on women’s employment is conceptualized in a deterministic fashion – as a sufficient condition – there may be reason for concern about omitted variable bias. We examine the possibility that the association between family policy and female employment is spurious – a product of the fact that both are associated with women’s educational attainment. We examine unconstrained pooled models, pooled models with fixed effects for time or country, and a cross-sectional model. The pooled models without unit effects for countries suggest that both family policy and women’s educational attainment have tended to boost women’s employment rates. In pooled models with country unit effects, however, there is little or no indication of a family policy impact. In cross-sectional models, we once again find support for effects of both family policies and female education. Further exploration of the over-time developments within countries confirms that support for the hypothesis that generous family policies tend to increase female employment rates rests largely on the cross-sectional association. This does not mean family policies do not affect women’s employment, but it suggests less confidence than if there were supportive evidence both across countries and within countries over time. In chapter 7, Jelle Visser and Mara Yerkes pursue a very promising analytical strategy in macrocomparative research: the use of individuallevel panel data in a small, but deliberately chosen set of countries. They use individual-level data to examine the effects of institutions, policies, and women’s preferences regarding employment and working hours in


Germany, the United Kingdom, and the Netherlands. These three countries exhibit the highest incidence of women working part-time in the OECD. They also share a “breadwinner legacy”: each traditionally discouraged employment of mothers with young children, though they have in various ways moved away from that position in recent decades. Visser and Yerkes exploit the variation in welfare state and industrial relations institutions and policies across the three countries and the availability of individual panel data to explore how strongly the breadwinner legacies still affect the choice for and nature of part-time work of women. Visser and Yerkes first estimate the effects of motherhood on the probability of adult women to be full-time employed, part-time employed, or entirely outside the labor force (“inactive”). They focus on differences across birth cohorts, controlling for education and household status. Next they analyze transitions from part-time employment into inactivity or full-time employment, focusing on the impact of motherhood. Comparison with the transitions from full-time jobs into long-hour or short-hour part-time jobs or into inactivity can help answer the question of whether part-time employment encourages particular groups of women to remain in the labor force. Finally, they examine the impact of “choice,” bringing into play working-time preferences of women and analyzing whether or not they lead to transitions in the desired direction. In chapter 8, Bernhard Ebbinghaus analyzes comparative patterns of early exit from the labor market. Affluent countries differ sharply in their employment rates among those aged 55 to 64 and in the degree to which those rates have shifted over the past several decades. Ebbinghaus examines the impact of “early-exit regimes” (see also Ebbinghaus, 2006). These regimes are defined by three factors: social policy orientation (protection systems, which are hypothesized to differ in the degree to which they pull older workers out of the labor force), the organization of production (production systems, which are hypothesized to differ in the degree to which they push older workers out of employment), and the organization of labor relations (partnership traditions, which are hypothesized to differentially mediate pull and push factors). Ebbinghaus identifies five regimes. He selects ten countries (this is at the upper end of what we call “small-N” analysis; Ebbinghaus refers to it as “medium-N”) that enable comparison both across and within these regimes. The cross-regime analysis is based on ordinal comparison as described above: regimes are ranked on the degree to which they are expected to promote early exit, and this ranking is correlated with a quantitative measure of early exit (relative exit rate). The within-regime analysis aims to account for the unexplained variation among countries within regimes; it is akin


23

to attempting to account for the residuals in a large-N quantitative analysis. Through his combination of within-case process-oriented and crosscase contextual analysis, Ebbinghaus finds that it is not only the incentives provided by welfare state pathways to early retirement that explain the cross-national variations but also the particular strategies of employers and workplace representatives in coping with particular economic pressures caused by different production strategies. He uses outliers as special cases to investigate the interaction between “pull” and “push” factors. For instance, the Swedish case shows that a generous welfare state does not always produce high early retirement, while Japanese firms use mandatory retirement but also provide re-employment for older workers, explaining their high employment rate in a coordinated market economy. In chapter 9, Adam Przeworski considers the crucial issue of selection bias in macrocomparative analysis. The concern is that what appears to be a causal effect of some institution or policy might rather be an effect of whatever gave rise to that institution or policy. For example, generous family policies are found mainly in the Nordic countries. We observe a positive association between family policy generosity and female employment rates across countries, but the true cause of the latter might be some feature of the Nordic societies or their policy-making processes that led them to adopt generous family policies, rather than the family policies themselves. As Przeworski puts it: “The generic problem in identifying causal effects is how to answer the counterfactual question: what would have occurred had the cause been absent?” In this example, the counterfactual hypothesis is that female employment rates would not be comparatively high in the Nordic countries had those countries not implemented generous family policies. Przeworski notes that “Whether we can successfully solve such problems is . . . largely a matter of luck, namely whether history has been kind enough to generate observations that can be used to inform us about the plausible counterfactuals.” The substantive question Przeworski explores is the impact of political regime – conceptualized dichotomously as democracy or autocracy – on labor force participation. Because the affluent countries are all democracies and because the approach Przeworski uses to estimate selection bias requires a relatively large number of countries, he includes not only rich nations but developing ones as well. His chapter nicely illustrates the use of appropriate techniques for addressing the selection bias worry. In analyses confined to the rich countries, selection bias is, unfortunately, both more likely to be present and less amenable to statistical


estimation. The only recourse for the macrocomparative analyst is careful, and explicit, counterfactual thinking (Fearon, 1991; EspingAndersen and Przeworski, 2001). For the most part the contributions to this book set this issue aside, but it ought to be an increasingly prominent concern in such analyses.

Onward The chapters in this volume attempt to highlight the advantages and drawbacks of some prominent methodological approaches to macrocomparative analysis. The principal aim is to help researchers – ourselves included – to make more informed choices about which approach(es) to use in their research and to make better use of whichever one(s) they choose. We hope the book succeeds in this endeavor.

Notes 1. Thus far, however, relatively few macrocomparative studies have made use of more than one of these methods in analyzing a particular research question. Regression and small-N analysis are combined in Boix (1998), Huber and Stephens (2001), Swank (2002), and Kenworthy (2008). Ebbinghaus and Visser (1999) and Hicks (1999) couple regression with QCA. 2. It is sometimes thought that an analysis of a single country, usually referred to as a “case study,” is not comparative. But most such studies are comparative (Rueschemeyer, 2003; Gerring, 2005). The comparison is not across countries but rather over time and/or across sub-units (regions, localities) within the country. Although small-N analyses tend to be qualitative, they can be quantitative as well; the distinction between small-N and large-N analysis is not the same as that between quantitative and qualitative analysis.

References Ayer, A. J. 1956. The Problem of Knowledge. London: Penguin. Baccaro, Lucio and Diego Rei. 2007. “Institutional Determinants of Unemployment in OECD Countries: Does the Deregulatory View Hold Water?” International Organization 61: 527–569. Barro, Robert J. 2000. “Inequality and Growth in a Panel of Countries.” Journal of Economic Growth 5: 5–32. Blau, Francine D. and Lawrence M. Kahn. 2002. At Home and Abroad: U.S. Labor Market Performance in International Perspective. New York: Russell Sage Foundation. Boix, Carles. 1998. Political Parties, Growth, and Equality. Cambridge: Cambridge University Press. Brady, Henry E. and David Collier, eds. 2004. Rethinking Social Theory. Lanham, MD: Rowman and Littlefield.


25

Collier, David, Henry E. Brady, and Jason Seawright. 2004. “Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology.” Pp. 229–66 in Rethinking Social Inquiry, edited by Henry E. Brady and David Collier. Lanham, MD: Rowman and Littlefield. Daly, Mary. 2000. “A Fine Balance: Women’s Labor Market Participation in International Comparison.” Pp. 467–510 in Welfare and Work in the Open Economy, Volume II: Diverse Responses to Common Challenges, edited by Fritz W. Scharpf and Vivien A. Schmidt. Oxford: Oxford University Press. Dingeldey, Irene. 2002. “European Tax Systems and Their Impact on Family Employment Patterns.” Journal of Social Policy 30: 653–72. Ebbinghaus, Bernhard. 2005. “When Less Is More: Selection Problems in Large-N and Small-N Cross-National Comparisons.” International Sociology 20: 133–52. Ebbinghaus, Bernhard. 2006. Reforming Early Retirement in Europe, Japan and the USA. Oxford: Oxford University Press. Ebbinghaus, Bernhard and Jelle Visser. 1999. “When Institutions Matter: Union Growth and Decline in Western Europe, 1950–1995.” European Sociological Review 15: 135–58. Eckstein, Harry. 1975. “Case Study and Theory in Political Science.” In Strategies of Inquiry: The Handbook of Political Science, vol. 7, edited by F. Greenstein and N. Polsby. Reading, MA: Addison-Wesley. Esping-Andersen, Gøsta. 1999. Social Foundations of Postindustrial Economies. Oxford: Oxford University Press. Esping-Andersen, Gøsta, with Duncan Gallie, Anton Hemerijck, and John Myles. 2002. Why We Need a New Welfare State. Oxford: Oxford University Press. Esping-Andersen, Gøsta and Adam Przeworski. 2001. “Quantitative CrossNational Research Methods.” Pp. 12649–55 in International Encyclopedia of the Social and Behavioral Sciences. Amsterdam: Elsevier. Fearon, James D. 1991. “Counterfactuals and Hypothesis Testing in Political Science.” World Politics 43: 169–95. Ferrarini, Tommy. 2003. Parental Leave Institutions in Eighteen Post-War Welfare States. Swedish Institute for Social Research Dissertation Series, No. 58. Ferrera, Maurizio, Anton Hemerijck, and Martin Rhodes. 2000. “The Future of Social Europe: Recasting Work and Welfare in the New Economy.” Report prepared for the Portuguese Presidency of the European Union. Geddes, Barbara. 1990. “How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics.” Pp. 131–50 in Political Analysis, edited by James A. Stimson. Ann Arbor: University of Michigan Press. George, Alexander L. and Andrew Bennett. 2004. Case Studies and Theory Development in the Social Sciences. Cambridge, MA: MIT Press. Gerring, John. 2004. “What Is a Case Study and What Is It Good For?” American Political Science Review 98: 341–54. Goertz, Gary and Harvey Starr. 2003. “Introduction: Necessary Condition Logics, Research Design, and Theory.” Pp. 1–23 in Necessary Conditions, edited by Gary Goertz and Harvey Starr. Lanham, MD: Rowman and Littlefield. Gornick, Janet C. and Marcia K. Meyers. 2003. Families That Work: Policies for Reconciling Parenthood and Employment. New York: Russell Sage Foundation. Greene, William H. 2003. Econometric Analysis, 5th edition. New York: Prentice Hall.

26 Method and Substance in Macrocomparative Analysis Griffin, Larry J., Pamela Barnhouse Walters, Phillip O’Connell, and Edward Moor. 1986. “Methodological Innovations in the Analysis of Welfare-State Development: Pooling Cross Sections and Time Series.” Pp. 101–38 in Futures for the Welfare State, edited by Norman Furniss. Bloomington: Indiana University Press. Hall, Peter A. 2003. “Aligning Ontology and Methodology in Comparative Research.” Pp. 373–404 in Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. Cambridge: Cambridge University Press. Hicks, Alexander. 1999. Social Democracy and Welfare Capitalism. Ithaca, NY: Cornell University Press. Hicks, Alexander and Lane Kenworthy. 1998. “Cooperation and Political Economic Performance in Affluent Democratic Capitalism.” American Journal of Sociology 103: 1631–72. Hicks, Alexander and Lane Kenworthy. 2003. “Varieties of Welfare Capitalism.” Socio-Economic Review 1: 27–61. Huber, Evelyn and John D. Stephens. 2001. Development and Crisis of the Welfare State. Chicago: University of Chicago Press. Iversen, Torben and Anne Wren. 1998. “Equality, Employment, and Budgetary Restraint: The Trilemma of the Service Economy.” World Politics 50: 507–46. Jahoda, Marie. 1982. Employment and Unemployment: A Social Psychological Analysis. Cambridge: Cambridge University Press. Janoski, Thomas and Alexander Hicks, eds. 1994. The Comparative Political Economy of the Welfare State. Cambridge: Cambridge University Press. Jaumotte, Florence. 2003. “Female Labour Force Participation: Past Trends and Main Determinants in OECD Countries.” Working Paper 376, OECD Economics Department. Paris: OECD. Katz, Aaron, Matthia vom Hau, and James Mahoney. 2005. “Explaining the Great Reversal in Spanish America: Fuzzy-Set Analysis versus Regression Analysis.” Sociological Methods and Research 33: 539–73. Kenworthy, Lane. 2004. Egalitarian Capitalism. New York: Russell Sage Foundation. Kenworthy, Lane. 2006. “Reconsidering the Effect of Public Opinion on Social Policy Generosity in Affluent Countries.” Unpublished. Department of Sociology, University of Arizona. Kenworthy, Lane. 2007. “Toward Improved Use of Regression in Macrocomparative Analysis.” Comparative Social Research 24: 343–50. Kenworthy, Lane. 2008 (forthcoming). Jobs with Equality. Oxford: Oxford University Press. King, Gary, Robert O. Keohane, and Sidney Verba. 1994. Designing Social Inquiry. Princeton, NJ: Princeton University Press. Kittel, Bernhard. 1999. “Sense and Sensitivity in Pooled Analysis of Political Data.” European Journal of Political Research 35: 225–53. Kok, Wim, Carlos Dell’Aringa, Federico Duran Lopez, Anna Eckström, Marta João Rodrigues, Christopher Pissarides, Annette Roux, and Günther Schmid. 2003. Jobs, Jobs, Jobs: Creating More Employment in Europe. Report of the European Commission’s Employment Taskforce. Brussels: European Commission. Korpi, Walter. 2000. “Faces of Inequality: Gender, Class, and Patterns of Inequalities in Different Types of Welfare States.” Social Politics 7: 127–91.


27

Lieberman, Evan S. 2005. “Nested Analysis as a Mixed-Method Strategy for Comparative Research.” American Political Science Review 99: 435–52. Lieberson, Stanley. 1991. “Small N’s and Big Conclusions: An Examination of the Reasoning in Comparative Studies Based on a Small Number of Cases.” Social Forces 70: 307–20. Mahoney, James. 2000. “Strategies of Causal Inference in Small-N Analysis.” Sociological Methods and Research 28: 387–424. Mahoney, James. 2003. “Strategies of Causal Assessment in Comparative Historical Analysis.” Pp. 337–72 in Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. Cambridge: Cambridge University Press. Mahoney, James and Gary Goertz. 2006. “A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research.” Political Analysis 14: 227–49. Mahoney, James and Dietrich Rueschemeyer, eds. 2003. Comparative Historical Analysis in the Social Sciences. Cambridge: Cambridge University Press. Mandel, Hadas and Moshe Semyonov. 2006. “A Welfare State Paradox: State Interventions and Women’s Employment Opportunities in 22 Countries.” American Journal of Sociology. 111: 1910–49. Manicas, Peter T. 2006. A Realist Philosophy of Science: Explanation and Understanding. Cambridge: Cambridge University Press. Meyers, Marcia K., Janet C. Gornick, and Katherin E. Ross. 1999. “Public Childcare, Parental Leave, and Employment.” Pp. 117–46 in Gender and Welfare State Regimes, edited by Diane Sainsbury. Oxford: Oxford University Press. Morgan, Kimberly and Kathrina Zippel. 2003. “Paid to Care: The Origins and Effects of Care Leave Policies in Western Europe.” Social Politics 10: 49–85. OECD (Organization for Economic Cooperation and Development). 1994. The OECD Jobs Study. Paris: OECD. OECD. 2001. “Balancing Work and Family Life: Helping Parents Into Paid Employment.” Pp. 129–66 in OECD Employment Outlook. Paris: OECD. OECD. 2005. Extending Opportunities: How Active Social Policy Can Benefit Us All. Paris: OECD. OECD. 2006. OECD Employment Outlook: Boosting Jobs and Incomes. Paris: OECD. Orloff, Ann. 2002. “Women’s Employment and Welfare Regimes.” Social Policy and Development Programme Paper 12. United Nations Research Institute for Social Development. Passmore, John. 1967. A Hundred Years of Philosophy. London: Macmillan. Pettit, Becky and Jennifer Hook. 2002. “The Structure of Women’s Employment in Comparative Perspective.” Working Paper 330. Luxembourg Income Study. Available at: www.lisproject.org. Phelps, Edmund S. 1997. Rewarding Work. Cambridge, MA: Harvard University Press. Plantenga, Janneke and Johan Hansen. 1999. “Assessing Equal Opportunities in the European Union.” International Labour Review 138: 351–79. Pruss, Alexander R. 2006. The Principle of Sufficient Reason. Cambridge: Cambridge University Press. Ragin, Charles C. 1987. The Comparative Method. Berkeley: University of California Press. Ragin, Charles C. 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press.

28 Method and Substance in Macrocomparative Analysis Ragin, Charles C. 2005. “Core versus Tangential Assumptions in Comparative Research.” Studies in Comparative International Development 40: 33–8. Ragin, Charles C. 2006. “Set Relations in Social Research: Evaluating Their Consistency and Coverage.” Political Analysis 14: 291–310. Ragin, Charles C. and Benoît Rihoux. 2004. “Qualitative Comparative Analysis (QCA): State of the Art and Prospects.” Qualitative Methods Newsletter, Fall: 3–13. Rubery, Jill, Mark Smith, and Colette Fagan. 1999. Women’s Employment in Europe. London: Routledge. Rueschemeyer, Dietrich. 2003. “Can One or a Few Cases Yield Theoretical Gains?” Pp. 305–36 in Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. Cambridge: Cambridge University Press. Ruhm, Christopher J. 1998. “The Economic Consequences of Parental Leave Mandates: Lessons from Europe.” Quarterly Journal of Economics 113: 285–317. Sainsbury, Diane. 1999. “Taxation, Family Responsibilities, and Employment.” Pp. 185–209 in Gender and Welfare State Regimes, edited by Diane Sainsbury. Oxford: Oxford University Press. Scharpf, Fritz W. and Vivien A. Schmidt, eds. 2000. Welfare and Work in the Open Economy. Volume 1: From Vulnerability to Competitiveness. Oxford: Oxford University Press. Schettkat, Ronald. 2005. “Is Labor Market Regulation at the Root of European Unemployment? The Case of Germany and the Netherlands.” Pp. 262–83 in Fighting Unemployment: The Limits of Free Market Orthodoxy, edited by David R. Howell. Oxford: Oxford University Press. Seawright, Jason. 2002. “Testing for Necessary and/or Sufficient Causation: Which Cases Are Relevant?” Political Analysis 10: 178–93. Seawright, Jason. 2005. “Qualitative Comparative Analysis vis-à-vis Regression.” Studies in Comparative International Development 40: 3–26. Shalev, Michael. 2007. “Limits and Alternatives to Multiple Regression in Comparative Research.” Comparative Social Research 24: 261–308. Skocpol, Theda and Margaret Somers. 1980. “The Uses of Comparative History in Macrosocial Inquiry.” Comparative Studies in Society and History 22: 174–97. Stier, Haya, Noah Lewin-Epstein, and Michael Braun. 2001. “Welfare Regimes, Family-Supportive Policies, and Women’s Employment Along the Life-Course.” American Journal of Sociology 106: 1731–60. Swank, Duane. 2002. Global Capital, Political Institutions, and Policy Change in Developed Welfare States. Cambridge: Cambridge University Press. Visser, Jelle. 2002. “The First Part-Time Economy in the World: A Model To Be Followed?” Journal of European Social Policy 12: 23–42. Visser, Jelle and Anton Hemerijck. 1997. “A Dutch Miracle”: Job Growth, Welfare Reform, and Corporatism in the Netherlands. Amsterdam: Amsterdam University Press. Wilson, Sven E. and Daniel M. Butler. 2007. “A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications.” Political Analysis 15: 101–23. Wilson, William Julius. 1996. When Work Disappears. New York: Knopf. Winegarden, C. R. and Paula M. Bracy. 1995. “Demographic Consequences of Maternal-Leave Programs in Industrial Countries: Evidence from Fixed-Effects Models.” Southern Economic Journal 6: 1020–35.

2 Statistical Narratives and the Properties of Macro-Level Variables: Labor Market Institutions and Employment Performance in Macrocomparative Research Bernhard Kittel

2.1 Introduction Panel data have become paramount in macrocomparative research designs in many branches of the social sciences, but in particular in political economy and welfare state research. However, by including the time dimension into cross-sectionally posed research questions, much more is added than just a few observations. Substantive issues like dynamics, variations in effect types, feedback, learning, and path dependency impact on the possibilities for making inferential statements. Technical issues like nonstationarity and serial correlation affect the researcher’s ability to draw conclusions from coefficients in regression models. In this chapter, I attempt to contribute to the quest for more clarity about the potentials and limits of panel data for comparative political economy by analysing a data set containing variables that clearly show the issues at stake in the combination of cross-sectional and longitudinal data. In the first section, I discuss three types of concepts typical of this research area – economic outcomes, policies, and institutions – and focus on their longitudinal properties. In the second section, I reanalyze a well-known study of the statistical associations between employment, the reservation wage, and labor market regulation, thereby including one of each of the three types (Kenworthy, 2003). The third section focuses on the empirical evidence on a simple hypothesis linking institutional, policy, and economic variables. The basic theoretical argument is also based on Kenworthy (2003) and addresses the impact of labor market institutions on employment opportunities. Kenworthy focuses on two 29


dimensions of labor market institutions in particular: pay equality and unemployment compensation. According to a well-established view, relatively high wages at the lower end of the wage scale will discourage employers to create jobs. This is a demand-side argument, linking institutions and policy variables to employment via an assumption about behavior of the typical employer. By contrast, a high reservation wage will negatively affect the willingness of displaced workers to take on employment in the low-wage sector. This is a supply-side argument, because institutions and policies affect the typical potential employee’s behavior. In his analysis, which is based on a pooled time-series crosssection regression model, Kenworthy finds evidence in favor of both propositions, but concludes that the effect is relatively weak. My reanalysis reveals a few inferential problems related to the use of institutional and policy variables in cross-sectionally focused panel analyses and adds a few nuances to Kenworthy’s findings. Finally, in the fourth section I draw some conclusions.

2.2 Time and the properties of macro-level variables In comparative research, the implications of extending the research design with the longitudinal dimension have not yet been fully appreciated (Pierson, 2004). Macrocomparative analysis is concerned with the search for empirical – mostly unobvious – regularities at the level of societal organization. In comparative policy research, the bulk of research identifies nation states as units of analysis and primarily studies research questions about the variation between nation-states. For many questions in this field, this is indeed an appropriate choice, given that both the phenomena to be explained and the phenomena proposed as explanatory factors are emergent at that level of societal organization. The typical explanandum is variation in some aggregate economic variable, which is expected to vary systematically with some explanans. The typical explanans put forward in policy analysis is variation in policies or in institutions. Notwithstanding these modal positions in typical theoretical models, which reflect some implicit assumptions about the nature of these phenomena, this mapping is not crisp. Since there is no clear unidirectional causal structure between these variables, one can find all of them both as explananda and explanans, although there is a clear preference by researchers to consider institutions and policies as independent and economic outcomes as dependent variables (examples of exceptions are Ebbinghaus and Kittel, 2005; Polillo and Guillén, 2005).

Bernhard Kittel

31

Economic outcomes are usually economic aggregates, like economic growth, unemployment, inflation, etc. More recently, researchers have increasingly focused on more direct measures of state intervention, for example social expenditures. Policy differences are often measured indirectly via indicators like the composition of government or the coordination mode of wage bargaining. These operationalizations build on assumptions about the likely behavior of particular collective actors. Sometimes, more direct policy measures are used, for example the percentage spent on active labor market policies, the replacement rate for unemployed workers, or the tax rate for certain population segments. Variation between institutions, in turn, is usually measured by means of an indicator summarizing specific organizational characteristics supposed to be relevant for the outcome. Examples of such variables are indicators of democracy and political regimes, the independence of central banks, or the organizational structure of associations. Although economic outcomes, policies, and institutions can be measured at the same level of aggregation, the three types of variables differ in their ontology in a way that is consequential for the manner they can be dealt with in macrocomparative analysis. Lazarsfeld and Menzel (1961) distinguish between analytical, structural, and global macrovariables. Analytical variables relate to properties of collectives that are derived from properties of members of those collectivities by performing some mathematical operation. Structural variables refer to relations between members of a collectivity, which hence can only be captured at the level of the collectivity. Global variables inform about emergent properties of the collectivity that are not based on information about the properties of individuals. This classification, although useful for many sociological applications, must be adapted for the use in comparative policy research, because relations between members of a collectivity are less relevant in the focused type of research questions, while global variables must be divided into two subclasses. Hence I propose a somewhat different classification. Economic macrovariables are the result of aggregated market behavior of individuals, policy variables relate to collective action, and institutional variables to the framework of action. Here, I want to highlight differences in two dimensions and discuss the consequences of these differences. First, I argue that the aggregate phenomena these variables are meant to measure are constituted in different ways. Secondly, these differences have implications for the time horizon of change in these variables and thus impact on the substantively relevant measurement


intervals. And thirdly, I discuss implications of these observations for the use of econometric techniques in comparative policy analysis. Macroeconomic variables are usually aggregated from individual acts or states. For example, economic growth is derived from individual buying and selling acts by which these individuals create added value. Summing all of these transactions and comparing them to the same sum for the previous accounting period yields a measure of growth. The unemployment rate is defined as the number of unemployed divided by the number of people on the labor market. While the exact definition depends upon various other definitions, which vary across space and time, like the difficult issue of who is to be considered part of the dependent workforce and who is assumed not to be on the job market, the macroeconomic unemployment rate is obtained by comparing the sums of individual states: individual workers are either employed, unemployed, or not available for employment and the aggregate measure gives an indication of the distribution of individuals among these sets. The point to be highlighted is the aggregation mechanism underlying these measures. It is a simple transformation rule – a sum or a ratio of sums. The aggregate measure says something about typical behavior or states of individuals: If the economy grows, individuals have on average been engaged in more economic transactions. The employment rate gives the probability that any randomly selected individual out of a population is employed. Both economic transactions of individuals and their states are the result of individual decisions that are at the core autonomous and independent from each other. While there may be strong structural forces inducing particular individual behavior, which may cause an empirical regularity at the macro level, the point remains that the aggregate measure is the result of individual decisions. Policy variables do not represent typical individual behavior or states. Instead, they relate to decisions of a collective actor and hence are emergent at the level of analysis. In macrocomparative policy analysis this actor is usually the nation-state because the national government is responsible for the decision and implementation of the majority of policies.1 In democracies, the decisions are taken by representatives in the name of the individuals constituting the members of the collective actor. These collective decisions are the result of a deliberation process in which various segments of the population and other interest groups lobby for a formulation of the decision which favors their particular interests. In contrast to economic variables, scores on policy variables are thus not simply the sum of individual phenomena, but result from the tug-ofwar of interests and thus crucially depend in their creation mode on the

Bernhard Kittel

33

prevalent power distribution. For example, the amount of money spent on active labor market policies depends only to a very limited extent on demand factors like individual applications for participation in such programs. More important are supply factors like the amount of public spending earmarked for this aim, eligibility rules, and other regulations. These are not the result of a simple statistical aggregation of individual properties or behavior, but are defined by collective decision. Even more evident is this difference in the case of the tax rate or the replacement rate of unemployment benefits: both are more or less arbitrarily set and redefinable guidelines for the conduct of administrative units. They are not aggregated via some simple transformation rule from individual behavior, but the result of state action and therefore emergent at the level of the nation state. Hence they lack a straightforward microfoundation. Institutional variables are again different. With policy variables they share the emergent characteristic of not being directly traceable to individual action. Instead, while the result of past collective decisions, they are system-level phenomena structuring individual behavior by defining rules and norms. They are meant to be the framework of rules in which both collective policies are convened and individual decisions are taken. Democratic regimes vary in terms of the mechanisms of collective decision making, in the structure of representation, in the distribution of rights and powers between various constitutional actors. Central banks vary in the extent to which they can set their policies independently from governmental preferences. This ontology of macro-level variables has implications for the time horizon of dynamics. Economic variables, which are constituted by the behavior of individuals, obey regularity criteria to a considerable extent. Individuals may need some time to respond to external changes, some will adjust faster and others slower, for some a change will have more impact than for others, but for large populations of individuals there will be some modal response of mutually fairly independent decisions, which in total result in some average behavior with a more or less symmetric, single-peaked distribution of behavior. Hence changes in aggregate behavior at the macro level can be interpreted as shifts in the average individual response to some triggering factor. Given this individuallybased responsiveness, usually the assumption of symmetry in behavior also makes sense (Lieberson, 1985): if prices go up, supply increases while demand decreases, and vice versa. Moreover, adjustments will be smooth because of the individual-level foundation. At increasing prices, individuals will not stop buying all at once, but one after the other will retreat from the market, thereby causing the macro-level indicator to respond


according to a practically continuous function. These processes may not be immediate (compared to the human metric of time), but with response periods of a few months they will be sufficiently fast to reveal adjustment within the usual time metric in macrocomparative research, which is a year by default of availability. Policy variables, as against economic variables, are much less likely to be subject to regularities and have a much longer time horizon of change. The typical time horizon of such changes can better be captured by several years, up to a decade or two, with periods of practically constant scores in between. Political decisions can be changed and even reversed, but they need to follow the formal rules of decision making and will be the object of considerable deliberation. Lock-in, ratchet, threshold, and other effects inhibit regular and symmetric ups and downs. Once a collective decision has been taken, actors will usually be reluctant to immediately change a policy because of public credibility, considerations of public expectations, resistance from groups profiting from a particular policy, and other concerns. Hence changes in such variables can be expected to occur but will be happenstance, non-incremental, and discontinuous. While this can be said for economic variables as well at the individual level, at the aggregate level the large number of individuals averages this out. This statistical foundation of regularity at the macro level is not available for policy variables based on collective decisions taken at the system level. Since there is no micro-level justification for macro-level regularities, the notion of a typical response is inappropriate. In contrast to policy variables, institutions – although not entirely immovable – cannot easily be changed by decision of the collective actor since they are part of the formal or informal constitution of the political or social system. While policies are amenable to government discretion, political institutions usually are not. Procedures to change formal institutions like the nation state’s constitution are protracted and require the consent of many veto players. Moreover, since they define the rules of the game, institutions increase behavioral reliability and consistency of collective actors. As a consequence, incentives for actors to change them are rather limited. Informal institutions cannot be changed even by deliberation because they are based in mores and customs. This, however, marks an important distinction between formal and informal institutions. While the former change in an even more haphazard and discontinuous ways than policies, with – often several – decades of continuity in between shifts, the latter evolve gradually and progressively at a very slow pace. Changes are hardly noticeable and practically irrelevant in the short run, but may cumulate to a veritable upheaval over several decades.

Bernhard Kittel

35

The upshot of these reflections is that the three types of variables differ, first, in the periods in which substantively relevant variation over time can be expected and, second, in the extent to which variables can be expected to fluctuate around some long-term average value. Many economic variables, because they are based on aggregated individual market behavior, do indeed more or less regularly fluctuate as a continuous function around some equilibrium or trend, and these dynamics are usually subject to a periodization of a few months due to response delays. Policy variables, because they result from collective deliberations, are characterized by more extended periods of continuity lasting several years, interrupted by sudden shifts. There is no inherent tendency to revert to some long-run average. Formal institutions, because of being part of the structure of the society or the polity, tend to remain constant for long periods, mostly several decades, but the constant series are sometimes interrupted by sudden shifts reflecting institutional change. Finally, informal institutions tend to evolve over long periods, counted in decades or even centuries. For both formal and informal institutions the concept of a long-term equilibrium value for the scores does not bear any substantive meaning. These characteristics have implications for the possibility to study the various types of variables in a both longitudinal and cross-national comparative research design. In comparative analysis, the use of panel data is generally motivated by the small-N problem, which severely limits the ability of the researcher to control for potentially confounding factors. Adding observations in the time dimension is hence regarded as a way out of the impasse because the number of observations of the cross-sectional setup are multiplied by the number of periods. In consequence, the shortest periodization in which data are available is commonly regarded as the best setup (Freeman, 1990). However, in view of the above considerations, this line of reasoning may be delusive, because adding observations does not necessarily add information (King et al., 1994, p. 48). The use of annual data, which has become something of a mantra in quantitative comparative research, is questionable for many research topics. For example, in finance economics, it makes little sense to measure stock prices at annual intervals given that much of the relevant action occurs on a daily or even hourly basis. Using annual data for such variables will cancel out variation which is important for most research questions in that discipline. Employment, by contrast, may be more reasonably measured at monthly intervals since more fine-grained analyses would contain too much noise and annual averages may remove too


much of the variation. Hence using availability as the main criterion for the periodization of the observations in the data set often does not do justice to the substantively motivated time horizons of change in the variables. On the other hand, annual observations do not add any substantively meaningful information to variables that remain constant or evolve at a very slow pace. This does not imply that annual data should not be used at the outset. My argument is different: Taking into account divergent periodizations in the “real world” means that for any set of variables, the one with the slowest rate of change determines the pace at which observations can be meaningfully related. Hence, if one variable changes at a rate measured in several decades, and another variable varies annually, the only variation in the latter that can be attributed to the former is some summary measure, usually the mean or the variance, of the latter over the decades in question. This can be done explicitly by taking long-term averages of levels or changes or implicitly by letting the regression software package sort out variance components. The problems of including the longitudinal dimension are least for economic variables. Actually, economic theories usually focus on constant conjunctions between changes in variables. They primarily focus on longitudinal associations, and pooling adds cross-sectional heterogeneity. Generally, macroeconomic studies pool time series; hence they combine time series instead of repeating cross-sectional designs. In order to remove any systematic cross-sectional variation, such studies usually express the data in terms of deviations from the unit-specific mean (Maddala, 1999; Baltagi, 2001). Since macroeconomic variables tend to elastically respond to changes in external factors, one can explore the regularity conditions of their dynamics with statistical methods. There may be variation in impact strength, delay, and speed of adjustment over time and across spaces, but these differences can in principle be modeled, as long as they follow some systematic pattern and the time series are sufficiently long. The major concern is the possibility of nonstationarity in the data, but this can often be solved by means of the toolbox developed in time series econometrics over the last two or three decades. Detrending, period-demeaning, first differencing, or estimating a cointegrating system are tools that help to obtain consistent estimates under various conditions. If the nonstationarity problem is solved, the time dimension often adds serial correlation to the error term. All treatments for this property of time series involve some sort of accounting for dynamics. By contrast, both policy and institutional variables have time horizons of change beyond annual intervals. In this respect, and viewed

Bernhard Kittel

37

from a technical perspective, the main difference between the two is that institutional variables change at an even slower pace than policy variables. Hence for typical observation periods in comparative analysis – between about ten and forty years – these variables are either constant or reveal only very limited variation over time, for example one or two changes in scores. Moreover, to the extent that there are changes during the observation period, the discontinuous nature of policy making and institutional change causes possible regularities in the responsiveness with regard to changes in other variables, as well as regularities in effects, to be unlikely to show up in small-N situations. As a result, such variables capture only or predominantly cross-sectional variation in the data and all short-term (e.g., annual) variation in the other variables remains unaccounted for. The problem is not only that the estimates of the cross-sectional variance components are again based on the number of cross-sections (represented by the averages), but also that the short-term variation in the one variable creates too much “noise,” which blurs the association between the variables. For example, assume panel data with a time-constant, cross-sectionally varying institutional variable and an economic variable that fluctuates around some equilibrium value which varies across countries. Mapping this variable on the timeconstant variable will allocate the averages of the former to the latter but treat the deviations from the unit-specific means as errors to be allocated to the standard error. If the time series overlap to a considerable degree, coefficients for the effect of the institutional variable on the economic variable will be unlikely to become statistically significant because of the huge standard errors in comparison to the cross-sectional variation in averages captured by the institutional variable. Even in the case of a perfect match of the cross-sectional variation, the overlap in variation may yield insignificant results. If, in addition, the variable is nonstationary for a few countries, neither the differences in means of the economic variable across countries nor the variance of the estimated coefficient can be interpreted. One could argue that for this reason control variables that deal with the short-term variance components have to be added. This is true, but seldom solves the problem, since in many practical research situations, adding substantive controls neither removes nonstationarity in annual data nor accounts for serial correlation in the error term, both of which invalidate regression results. Hence models need to be transformed, thereby changing the substantive research question from a cross-sectional one to a dynamic one. This implies that cross-sectional variation in levels cannot be assessed anymore.


In the following, I discuss possible narratives about the association between the replacement rate of unemployment and employment in the low-wage sector (private-sector consumer services) that can be extracted from the data set. The data are a typical example of a macrocomparative setup, with a handful of countries (for the variables I use, data on 14 countries are available) and another handful of annual observations (due to missing values, the number of years included varies considerably across countries, but there is a core of 11 years, 1981–91, for which practically all relevant variables are available for all 14 countries). I first explore the main variables (section 2.2) and then proceed with a specification search, thereby following some leads of Leamer (1978) (section 2.3).

2.3 Longitudinal properties of political economic variables Employment in the low-wage sector Employment in the low-wage sector is operationalized as the percentage of the population aged 15 to 64 that is employed in private-sector consumer-oriented services – ISIC6 (wholesale and retail trade, restaurants, and hotels) and ISIC9 (community, social, and personal services).2 Employment in private consumer services is the most appropriate variable to study the effect of labor market institutions on employment, because the expected associations will be least blurred by intervening factors (Iversen and Wren 1998; Scharpf 2000). Panel (a) of Figure 2.1 shows the development of this variable over time. For most countries we observe a gradually and smoothly growing process, although some appear more stationary. This is a typical pattern of time series of macroeconomic data measured in levels: The series for different countries enter the observation period at different levels and the differences between the countries change only marginally each year, though over longer periods these minor differences in average growth add up to more substantial variation. As Table 2.1 shows, the bulk of the variation is between the countries, not within. In the time dimension, panel (a) of Table 2.2 reveals that in a fixed (country) effects specification, the average autoregression coefficient in the countries cannot be distinguished from unity, suggesting nonstationarity of the series. Nonstationarity implies that the mean of the residuals cannot assumed to be zero and that the variance of the residuals tends to infinity instead of remaining constant over time, resulting in invalid parameter estimates. This is indeed confirmed by Maddala and Wu’s Fisher test for nonstationarity (Maddala and Wu 1999), irrespective of which of a set of

1

Replacement rate, 14 countries, 1980–95

0

10

15

20

Replacement rate 2 4 6 8

25

30

Employment in private sector consumer services, 14 countries, 1980–95

5

Employment in private sector consumer services

(b) Reservation wage

(a) Employment

1980

1985

1900

1905

1980

1905

(d) Employment regulation

Employment regulation, 14 countries, 1980–95

.4

0

.5

.6

.7

Employment regulation 2 4 6

8

.8

Pay equality, 14 countries, 1980–95

1980

1900 Year

(c) Pay equality

Pay equality

1985

Year

1985

1900 Year

1905

1980

1985

1900

1905

Year

39

Figure 2.1

Main variables

40 Method and Substance in Macrocomparative Analysis Table 2.1 Cross-sectional and time variance components of main variables Variable

Mean

Std. Dev.

Min

Max

Observations

8.6 9.61 12.39

28.9 25.83 20.29

N = 189 n = 14

epscs

overall between within

16.31

5.44 5.28 1.32

replr


.50

.23 .23 .05

.01 .071 .30

.92 .90 .74

N = 189 n = 14

payeq


.62

.08 .09 .01

.41 .42 .58

.76 .75 .66

N = 189 n = 14

empreg


3.70

2.39 2.45 0

0 0 3.70

7 7 3.70

N = 189 n = 14

Table 2.2 Autoregression

a) EPSCS b) REPLR c) PAYEQ

1 Coefficient

2 Standard Error

3 F-Test

4 r(ui , Xb)

0.991 0.737 0.697

0.025 0.020 0.038

0.058 0.000 0.000

0.772 0.962 0.984

EPSCS: Employment in Private-sector Consumer Services REPLR: Replacement Rate PAYEQ: Pay Equality Column 1: Coefficients are those of the lagged dependent variable in a fixed unit effects specification. Column 2: Associated standard errors. Column 3: F-Test that the fixed unit effects are irrelevant. Column 4: Correlation between lagged dependent variable and unit effects.

potentially plausible assumptions is imposed (Table 2.3). Most notably, neither controlling for a country-specific trend nor demeaning the series (subtracting the cross-country period mean from all observations in order to eliminate a joint trend or drift) solves the problem. Hence, according to the tests, we cannot reject nonstationarity of the series. This, however, is an implausible conclusion in substantive terms because the variable is measured as a percentage, which is bounded by 0 and 100. There are four main substantive possibilities for a series to be nonstationary – ever-growing (many economic aggregates for extended periods of time), drifting randomly (some macroeconomic series), by

Bernhard Kittel

41

Table 2.3 Nonstationarity: Maddala and Wu’s Fisher Test

Nonstationarity Nonstationarity assuming 1 lagged difference Nonstationarity assuming group-specific trend Nonstationarity assuming group-specific drift Nonstationarity, period-demeaned data

EPSCS

REPLR

PAYEQ

5.12 25.37 11.33 29.45 7.55

53.55∗∗∗ 32.92 45.90∗∗ 84.23∗∗∗ 51.12∗∗∗

25.64 25.98 76.10∗∗∗ 71.98∗∗∗ 76.79∗∗∗

Entries are Chi-squared values (28 dF) of joint test H0 : all series are nonstationary against H1 : at least one series is stationary. ∗∗∗ p-value < 0.01, ∗∗ p-value < 0.05. The test does not require a balanced panel. See Maddala & Wu (1999). Calculated with Stata module xtfisher.

construction (e.g., cumulative indices), and because of data limitations (the series is too short relative to the real-world process from which the data are drawn to capture the stationarity of the process). Since the first three do not apply because the series can neither grow nor fall forever by definition, we have to conclude that we are confronted with a situation in which the observation window is too short to adequately observe ups and downs around some long-term average. I will come back to this issue below. A final observation relates to the issue of persistence in betweencountry variation over a longer period of time. Pearson’s correlation coefficient between employment in private-sector consumer services in 1991 (the last year for which data for the whole 14-country sample is contained in the dataset) and its average 1974–79 is r = 0.95, and Spearman’s rank-order correlation is ρ = 0.97. This confirms the visual impression from Figure 2.1a that the major part of the between-country differences was already present before the observation period and did not change in relevant ways during that period. Reservation wage The reservation wage is operationalized as the percentage of former earnings that is replaced by unemployment compensation for a worker at the 33rd percentile in the first year after losing the job. Even more than for employment, most of the variation is between the countries. As panel (b) in Figure 2.1 shows, these series are either fairly constant over time or exhibit one or a few jumps resulting from policy changes. Given the combination of longer constant periods with a few irregular jumps, the


autoregressive coefficient is reasonably far away from unity, and the Fisher test tends to reject the null hypothesis of nonstationarity in favor of the alternative that at least one of the series is stationary. This conclusion is illustrated by the visual inspection of Figure 2.1b, in which some of the series trend into one direction while others tend to be constant over time. Hence the situation is indeterminate. Pay equality Pay equality, which is measured by the ratio of gross annual earnings of a full-time, year-round employed person at the 10th percentile of the earnings distribution to a person at the 50th percentile, again basically varies between countries, with a ratio of within to between variation of about 1:7. The series appear fairly stable, although some have a slightly declining trend. Formal analysis suggests that nonstationarity can be rejected, though – as can be expected from inspection of the graph – considerable autoregression is present. Employment regulation Finally, employment regulation is an index constructed from evaluations of the strictness of legislation in the areas of working time, fixed-term contracts, employment protection, minimum wages, and employee’s representation rights. Since the country scores on these dimensions did not change during the observation period, this is a time-constant variable, and the analysis of its time series properties is moot.

2.4 Statistical narratives on employment in private sector consumer services Pooled analysis As noted in the previous section, the upper limit of employment in private sector consumer services will be much lower than 100 percent since not all workers can be employed in that sector. According to the reservation wage hypothesis, the extent to which this upper limit is reached in a country is affected by the incentives for accepting a job at the wage level offered in this sector. Hence the existence of systematic variation between countries in this assumed upper limit is the core proposition of the reservation wage hypothesis: If workers prefer unemployment to low-wage work because of a high replacement rate, two implications result: (1) fewer workers will be searching for jobs in the low-wage sector, and (2) fewer jobs will be offered because of the higher wages employers

43

30

Employment in private consumer services

10

15

20

25

AU AU AU AU JP AUJP AU AUJP US JP AUJP US AU US AU US JP US AU AUUSAUJP JP AU AU JP US JP JP US JP US US JP UK JP US US US US JP UK UKUK UK UK NO UK DE UK DE UK UK UK UDE KDE U K UK DE DE DE DE DE DE DE DE DE

IT IT IT IT IT ITIT

IT IT IT IT IT

F F IFI I FI IT

FI

NO NO

CA CA CA CA CA CA CA CA NO CA CA NO NO NO NO BE BE NL L N BEBE BE NL BE BE BE BE BEBE BE FR FI FR F R FFI FI R FR FI FI F FR RFR FI FI FI FI

NL NL NL NL NL NL NL NL NL

SE SE SE SE SE SE SE SE DK

FR FR

DKDK DK DK FR DK DK

FI

5


Bernhard Kittel

0

.2

.4 .6 Replacement rate

.8

1

Figure 2.2 Replacement rate and service sector employment

would have to pay in order to exceed the reservation wage. Both mechanisms should result in a negative relationship between the replacement rate and employment in the low-wage sector. Figure 2.2 plots this effect for the whole data set. It reveals that the expectation is indeed confirmed. However, the graph also reveals some disturbing elements. Firstly, the observations are clearly clustered by country, indicating that between-country variation in both variables is indeed much larger than within-country variation. Secondly, there is an important outlier in the lower left corner of the plot, Italy, which combines low replacement rates with low private services employment. Thirdly, the overall pattern of the plot can be interpreted in two ways. On the one hand, including Italy, there seems to be a wedge-shaped curve indicative of heteroskedasticity. On the other hand, if Italy is excluded from consideration the pattern suggests a convex shape indicative of a nonlinear relationship. Actually, since employment in private-sector consumer services is defined as a proportion (population employed in the low-wage sector as a percentage of the total population in working age), one of the basic assumptions of linear regression analysis, unboundedness of the dependent variable, is violated. Instead, we have to expect a curve in which the lower and upper limits are approached at an ever-decreasing rate, while


the slope is steeper at average values. But since the logit function transforms the bounded range into an unbounded one, the linear regression model can still be used. Although the logit model is preferable for substantive reasons, it is an empirical question whether the linear approximation, which comes close to the logit for values sufficiently close to the mean, yields biased estimates for the range of values covered by the explanatory variables. In the present case, the logit transformation does not discernibly differ from the linear specification, presumably because the scores do not get close enough to the extremes for causing a deviation. Hence, although strictly speaking a nonlinear model would be more appropriate, I will stay with the crowd and remain in the linear world. The grouping problem confirms the finding from univariate analysis that autocorrelation is a serious issue. In substantive terms, the plot suggests that there is little information gained from adding observations for the same countries. Because of the country clusters, new observations from a country will be close to the existing ones and thereby simply affirm the stability of the two variables for that country without adding substantively new information. This can be seen by comparing the coefficient of the replacement rate in the pooled model (Table 2.4, model 1), which is a weighted average of the between and within variation, to the one in the fixed-effects model (Table 2.4, model 2), which relates exclusively to the within dimension: It drops from –10.8 to –2.6 and becomes statistically insignificant. Moreover, the within component of the coefficient of determination is only 0.01, and in the dummy variables specification of the fixed effects model (LSDV) this parameter soars to 0.94 due to the contribution of the country dummies. Durbin’s M test for autocorrelation, which consists of regressing the residuals on the lagged residuals and all regressors of the initial model, indicates the potential presence of a unit root. If autocorrelation were due to the clustered pattern of the observations, including fixed effects would remove the problem. This is not the case here, however, since the autocorrelation coefficient drops only to just below 1.0, not being significantly different from unity. If the country series reveal a joint pattern over time, nonstationarity can sometimes be removed by including period effects (Table 2.4, model 3). In the present case, however, this is no solution, given that the coefficient of the lagged residuals remains above 1.0. Moreover, the period effects appear to be nonsignificant, suggesting that there is no reason to include them. The observation of nonstationarity in these specifications suggests to directly shift to first difference models. However, as has been argued above, there is no substantive reason for nonstationarity in private

Table 2.4

Service employment and replacement rate: panel models

Replacement Rate

1 Pool

2 FE(c)

3 FE(t)

4 AR

5 LDV

6 FD

7 FD-LDV

8 FD-LDV-FE(c)

−10.77∗∗∗ (1.52)

−2.63 (1.96)

−10.58∗∗∗ (1.58)

−2.20∗ (1.21)

−0.61∗∗∗ (0.14)

21.77∗∗∗ (0.85) 0.21 1.01∗∗∗ (0.008) –

17.65∗∗∗ (1.00) 0.21 0.98∗∗∗ (0.03) 167.76∗∗∗

0.22 1.01∗∗∗ (0.01) 0.14

17.39∗∗∗ (1.21) 0.66 1.02∗∗∗ (0.01) –

−0.44∗∗∗ (0.15) 1.02 (0.01) 0.13 (0.16) 0.99 0.36∗∗∗ (0.07) –

−0.36∗∗∗ (0.14) 0.43∗∗∗ (0.07) 0.30 (0.08) 0.27 −0.02 (0.17) –

−0.69 (0.72) 0.34∗∗∗ (0.07) 0.47 (0.37) 0.25 0.07 (0.21) 1.17

Lagged EPSCS (D.EPSCS) Constant R2 AR Test (Durbin’s M) F-Test

0.48∗∗∗ (0.08) 0.10 0.39 (0.07) –

Dependent variable: Models 1–5: EPSCS, Models 6–8: first differences of EPSCS (D.EPSCS) Model 4: Panel Corrected Standard Errors F-Test: Joint test that fixed effects are zero.

45


service employment and its indication seems to be more a problem of data shortage. Hence let us first explore approaches to controlling for autocorrelation. Applying the Prais-Winston transformation (Table 2.4, model 4) does not yield a satisfactory result: The estimated autocorrelation coefficient is above unity and has to be forced to a value below 1.0 (0.97 in the present case) and this procedure results in an estimate of residual autocorrelation above unity. An alternative approach is to include a lagged dependent variable (Table 2.4, model 5), the coefficient of which also turns out to be above unity. Thus, none of the conventional approaches to correcting for observation patterns typical of panel data yields a parameterization which removes nonstationarity and residual autocorrelation. Instead, both yield models that cannot be interpreted because of a meaningless estimate of the mean and variance of the dependent variable. In consequence, the next step is to take first differences. Since there are no indications that the replacement rate has a unit root, it is only necessary to transform employment in private sector consumer services. In substantive terms, the possibility to maintain the replacement rate in levels is a fortunate situation because the values this variable takes do not result from atomistic market outcomes, but are set by laws that are based on political decisions. The difference between these two classes of variables in aggregate analysis relates to the expectable time horizon of change. Market outcomes, which are based on a multitude of individual decisions, tend to respond flexibly and fairly quickly to changing conditions, while political decisions have a longer time horizon and thus remain constant at certain levels for longer periods. We can thus analyze the effects of the levels of the replacement rate on growth of employment in private-sector consumer services. Figure 2.3 presents the plot for this association. We observe the country clusters again, as well as a slight negative relationship, indicating that countries with lower replacement rates tend to have higher growth of employment in private sector consumer services. Statistically we still observe a negative effect (Table 2.4, model 6), which remains present after controlling for residual autocorrelation (Table 2.4, model 7). Including fixed effects (Table 2.4, model 8) destroys this association, but since the fixed effects are not jointly significant, we can remove them again and provisionally settle with model 7. Thus, for the time being we conclude that there is a modest, but statistically significant association between the replacement rate and annual growth in employment in private-sector consumer services. Figure 2.4 reiterates this idea from a more narrative perspective. It shows the long-run shifts on the two variables over the period 1982–91

Bernhard Kittel

47

UK

1 0

IT IT IT IT IT IT IT

IT ITIT IT IT

CA

AU UK US UK JPAU AU AU US JP JP JP US JPAU AU DE US US JP US UK JP US DE UK JPAUUK AU JP AU DE UK US UK JP JP DE JP JP US DE DE DE DE DE US UK UKJP US FI US AU FI US FI DE DE US JP AU AU FI UK UK US DE DE

IT

AU AU

⫺1

UK

CA CA

BE

NO NO

CA BE NO BE NO BE NO

BEFR FI CA BE BEFR FR FR FI BE FI CA BE FRFR CA BE BE BE FR FI FI BE FR FR NO NL BE BE FI NO NL NO CA FI CA FI FI CA

NL NL NL FI NL NL FR FR NLFR NLFR

DK

DK DKSE SE DK DK SE DK DKDK SE SESE DK SE SE DK SE

NL

SE

⫺2

Annual change in service employment

2

Employment in private consumer services

CA

0

.2

.4

.6

.8

1

Replacement rate

Figure 2.3 Annual changes in service employment

and tells an even more nuanced story. If the hypothesized negative relationship between the replacement rate and private service employment were a general regularity that is independent of space and time, we would observe only movements from the upper left to the lower right or vice versa: a decline in the replacement rate would induce an increase in employment and an increase in the replacement rate would lead to a decline in employment. This pattern is clearly confirmed for the UK and the US, while France and Belgium tend toward the same direction. However, Norway and Finland have increased the replacement rate without noticeable effect on employment; in Germany, Canada, Denmark, and Sweden employment slightly increased despite the absence of noticeable change in the replacement rate; and in Italy, the Netherlands, Japan, and Australia, an increase in the replacement rate coincided with an increase in employment. This means that only four out of the 14 countries confirm the assumed relationship with respect to changes occurring during the observation period. In terms of the original hypothesis, however, the need to control for nonstationarity in the dependent variable has induced a respecification of the substantive model by focusing on the dynamics in employment in private-sector consumer services. Under the conditions of the present data set, the time dimension does not add substantively relevant


30

AU

25

JP US AU

20

JP US

CA

UK

CA

15

NO

DE

UK

NO NL BE

SE

NL

SE

DE BE

FR

FI

10


Employment in private consumer services 1982–1991

FI

DK

FR

DK

IT IT

0

.2

.4

.6

.8

1

Replacement rate Figure 2.4 Long-time changes, 1982–91 Notes: Small fonts refer to 1982, large fonts to 1991.

information about differences in levels. The time horizon of change of the focused regressor – decades – in relation to the measurement periodization in the current example – years – explains the empirical clustering of the annual observations on the horizontal axis of Figures 2.2 and 2.3. The clustering in the vertical dimension is due to autocorrelation in both instances. The major part of the variation in the dependent variable was already present at the beginning of the observation period and hence the observed dynamics cannot have affected those differences. Cross-sectional analysis Going back to the initial hypothesis, we thus have to modestly stick to a cross-sectional design, as is appropriate for a cross-sectional research question (Jackman, 1985). Models 1 and 2 of Table 2.5 report simple cross-sectional analyses for 1981 and 1991, the first and last periods for which data for all 14 countries are available. While the model for 1981 is a simple cross-section, the regressors in the model for 1991 are defined as averages for 1981–91. Note that the size of coefficients in these snapshots is consistent with a steadily growing coefficient when

Bernhard Kittel

49

Table 2.5 Employment in private sector consumer services, cross-sectional analysis Model 1: Cross-section model, 1981.

REPLR

1 1981

2 1991

3 1991 (excl. IT)

4 1991

5 1991

−4.01 (6.79)

−8.90 (8.09)

−18.99∗∗∗ (4.84)

−6.11 (6.61) −1.40∗∗ (0.51)

16.77∗∗∗

21.17∗∗∗

27.45

25.08

−24.26∗∗∗ (7.32) −2.96∗∗∗ (0.79) 3.79∗∗∗ (1.11) 32.47∗∗∗

(4.32)

(5.24)

(3.14)

(3.49)

(5.01)

0.05 14

0.15 14

0.54 13

0.50 14

0.66 14

EMPREG REPLR × EMPREG Constant R2 N

Note: Robust standard errors in parentheses.

the cross-sectional regression is repeated for every year. This is caused by the increasing variation in the dependent variable (see Figure 2.1a). The negative association is present in both years, but does not attain statistical significance. In order to keep the argument as crisp as possible, the further analysis will be done on the 1991 data, because the coefficient size is largest of the two and hence effects will be the most pronounced. We use averages 1981–91 for the regressors in order to reduce the effect of possible idiosyncratic deviations in 1991 from the overall developments. We noted that Italy was an outlier and, indeed, removing this observation doubles the size of the coefficient and yields a highly significant effect, even for 13 observations (Table 2.5, model 3). This leads back to the issue of substantive reasons for heteroscedasticity. One important potential reason is that another, intervening variable causes variation on one side of a two-dimensional distribution to be larger than on the other side. In the present case, there is an obvious candidate for such a conditional effect: employment regulation. In line with the above reasoning, we should expect that the more regulated employment conditions are, the less will employers be inclined to create jobs in the low-wage sector. Model 4 in Table 2.5 adds this variable and reveals that it has a clear negative effect on employment. At the same time, controlling for employment regulation does not cause the effect of the replacement rate to become significant.


30 25 20

US US US US US

US US US US US US UK US US UK UK UK UK UK UK UK UK UK UK

15

UK

AU AU AU AU AU AU AU

JP JP JP JP

AU

JP JP JP JP JP JP JP JP JP JP JP

CA CA CA CA CA CA CA CA CA

CA NO NO NO

BE BE BE BE BE

DK

DK DK DK DK

10

AU AU AU AU AU AU

BE BE BE BE BE

NO NO NO NO NO NL NL NL NL NL NL NL NL NL FI FI FI FI FI FI FI FI

FI

DE DE

DE DE DE DE DE DE DE DE

FR FR FR FR FR FR FR FR

SE SE SE SE SE SE SE

IT IT IT IT IT IT IT IT IT IT

5


Employment in private consumer services 1991

0

2

4 Employment regulation

6

8

Figure 2.5 Employment regulation and private service employment Note: Large fonts refer to 1991.

Figure 2.5 depicts the bivariate relationship between employment regulation and private service employment. Comparing this graph to the one for the long-term developments in the replacement rate (Figure 2.4), note that the US and the UK, which both most clearly confirm the expected relationship, score zero on the scale of employment regulation, while Italy scores highest. Hence these bits of information do indeed suggest the potential presence of a conditional effect. If employment regulation is high, the effect of the replacement rate on private service employment may be mitigated because strict regulations discourage employers to offer jobs in that sector anyway. Thus while lowering the replacement rate may push workers on the market and reduce the wage rate at which they are willing to take jobs, employers will still be reluctant to demand labor for reasons related to the strictness of regulations and thereby cause employment to remain low. However, if employment regulations are lax, the reservation wage will become the crucial factor structuring the labor market. In consequence, labor supply will be the prime mover and changes in the replacement rate will have the expected effect on employment.

30

40

51

20

AU JP

US CA

UK

NO

10

DE

SE

FR

FI

DK

0

IT

NL

BE

⫺10


Bernhard Kittel

0

.2

.4

.6

.8

1

Replacement rate Figure 2.6 Private service employment: replacement rate effect conditional on employment regulations Notes: Thick line: Effect of Replacement Rate if Employment Regulation set to Maximum, with 95% CI. Thin line: Effect of Replacement Rate if Employment Regulation set to Minimum, with 95% CI. Large country markers: Employment Regulation >4 Small country markers: Employment Regulation < = 4.

In terms of the time horizon of change, employment regulations are even less volatile than replacement rates because they form part of the institutional framework of the labor market, which is even more difficult to change than the proportion of previous wages that is paid in the first year of unemployment. This conditional effect is indeed clearly present (Table 2.5, model 5). All coefficients are highly significant despite the small number of observations and the specification accounts more than 60 percent of the variation in private service employment. Figure 2.6 shows how well the wedge shape in the association between the replacement rate and private service employment is captured. The steeper regression slope represents the effect of the replacement rate on employment if employment regulation scores the minimum, with the associated 95 percent confidence interval (dashed curves). The other, almost horizontal line represents


this effect if employment regulation scores the maximum, again with the 95 percent confidence interval. The seven countries scoring below the median on employment regulation (hence low labor market rigidity) are printed in small font; those scoring above the median (hence high rigidity) are printed in large font. So far, we have not dealt with other factors that can potentially disturb the relationship. Let us introduce a few such variables now and test their impact on the stability of the conditional association found above. For ease of comparison, Table 2.6 reproduces model 5 of Table 2.5 in model 1. First, according to the incomes–jobs tradeoff hypothesis, pay equality is the core factor that makes labor at the low end of the distribution relatively more expensive and hence has a discouraging effect on labor demand. Note, however, that this variable only affects the demand side: if at all, wage equality should have a positive effect on labor supply since fewer jobs will be offered at extremely low-wage levels. The effect (Table 2.6, model 2) is quite impressive in the bivariate model – the estimate for the employment difference between minimum and maximum pay equality, based on the coefficient, is 11.6 percentage points. Model 3 in Table 2.6, however, clears the issue by showing that the pay equality effect drops to almost one-quarter which is statistically insignificant when controlling for the joint effect of the replacement rate and employment regulations. Secondly, public employment may be an important substitute for at least some of the jobs offered in consumer services, in particular those in community, personal, and social services (ISIC 9), and thereby crowd out the private sector. The crucial point of the argument relating to the importance of public employment is that the reason for low levels of employment in private services is not labor market distortions, but system differences in the provision of such services. For example, ISIC 9 contains health services and education. To the extent that such services are considered a public good to be provided by the state, low private employment in cross-country perspective is due to the fact that there are no private employers by design of the system, not necessarily because of labor market distortions. An indicator of public employment in consumer services can be constructed by subtracting private service employment from total service employment (ISIC 6 + ISIC 9). There is indeed a substantial negative correlation between public and private service employment (r = −0.68), suggesting that there may indeed be a substitution effect. Moreover, there is a positive correlation of the same order of magnitude (r = 0.73) between public employment in the service sector and the replacement rate. These associations are much stronger

Table 2.6

Controlling for other factors 1

REPLR EMPREG REPLR × EMPREG

−24.26∗∗∗ (7.32) −2.96∗∗∗ (0.79) 3.79∗∗∗ (1.11)

PAYEQ

2

3

−37.45∗∗∗ (11.78)

−22.55∗∗ (7.95) −2.66∗∗ (0.93) 3.63∗∗∗ (1.11) −10.82 (9.40)

PUBEMP

4

5 −12.77 (8.31) −2.46∗∗∗ (0.67) 3.03∗∗∗ (1.22)

−0.66∗∗∗ (0.18)

−0.51∗ (0.25)

32.47∗∗∗ (5.01)

40.21∗∗∗ (7.86)

37.57∗∗∗ (4.59)

27.28∗∗∗ (3.34)

34.47∗∗∗ (3.32)

6.05∗∗∗ (1.71) 1.31∗∗∗ (3.58)

0.66 14

0.37 14

0.68 14

0.44 14

0.79 14

0.43 14

GDP Growth Constant R2 N

6

7

8

−15.96 (11.01) −2.38∗∗ (0.99) 2.81∗ (1.45)

2.85 (2.89) 20.84 (13.15)

−9.45 (10.15) −2.23∗∗ (0.94) 2.58 (1.59) 1.71 (12.86) −0.46 (0.32) 1.57 (2.56) 27.07∗ (12.51)

0.71 14

0.80 14

Dependent variable: Employment in Private-sector Consumer Services (EPSCS). Robust standard errors.

53


than the one between the replacement rate and private service employment (r = −0.43). Hence we may indeed be confronted with a potentially disturbing effect. In Table 2.6, model 4 gives the effect size of public service employment and model 5 confirms the confounding impact of this variable: While public service employment is marginally significant, the coefficient of the replacement rate is halved and no longer attains statistical significance. Thirdly, low employment in private services may be due simply to a lack of economic dynamism. As models 6 and 7 (Table 2.6) reveal, there is indeed a clear positive cross-country association between GDP growth and private service employment, accounting for 42 percent of the variation, but after controlling for the conditional effect of the replacement rate and employment regulation, this association is halved. However, it weakens the conditional effect considerably. Finally, model 8 adds all controls jointly and thereby causes all effects to become insignificant, although all coefficient signs except for pay equality maintain their (expected) sign and the overall variation in private service employment captured by the model is 80 percent. The discrepancy between the coefficient of determination and the lack of significant individual effects suggests the presence of multicollinearity which, in turn, must be mainly attributed to the small number of cases. Thus far, the analysis has revealed a fairly stable relationship. However, as noted above, we are dealing with levels, the variation of which was already present at the beginning of the observation period. Hence we cannot attribute a causal status to the replacement rate when analyzing levels. But this issue can be remedied by focusing on the long-term development 1981–91 (see Figure 2.4). The change in private service employment from 1981 to 1991 can be attributed to the conditions prevalent during this period because they are not hampered by the persistence in the level differences. In contrast to the short-term variation analysed in model 8 of Table 2.4, the relationship will be less affected by noise. Hence, in the models presented in Table 2.7 the change in employment in private sector consumer services 1981–91 is the dependent variable. All regressors are again defined as the average 1981–91. The setup of the table is basically identical to that of Table 2.6. What is most striking about these models of long-term change is that they appear to show even more clearly the conditional relationship, which accounts for over 80 percent of the variation in the private service employment in the simple specification of model 1. All of the controls introduced in models 2 to 9, apart from the long-term change in public employment in ISIC 6+9, appear to be insignificant if controlled for the conditional

Table 2.7

Explaining variation in long-term change of private service employment (1981–91) 1


2

3

−8.76 (5.91)

−7.89∗∗∗ (1.48) −0.73∗∗∗ (0.16) 0.79∗∗∗ (0.23) −2.38 (2.99)

−7.52∗∗∗ (1.67) −0.66∗∗∗ (0.12) 0.76∗∗∗ (0.27)

PAYEQ PUBEMP

4

5

6

−6.04∗∗ (2.33) −0.60∗∗∗ (0.14) 0.66 (0.39) −0.20∗∗∗ (0.04)

−0.07 (0.05)

D.PUBEMP

7 −5.22∗∗∗ (1.24) −0.41∗∗ (0.14) 0.42∗ (0.22)

−0.60∗∗∗ (0.12)

−0.29∗ (0.14)

6.68∗∗∗ (0.77)

7.42∗ (3.95)

5.56∗∗∗ (1.41)

5.19∗∗∗ (0.83)

6.94∗∗∗ (0.83)

2.71∗∗∗ (0.32)

5.64∗∗∗ (0.70)

1.45∗∗ 0.55 −1.76 (1.25)

0.81 14

0.24 14

0.82 14

0.50 14

0.84 14

0.61 14

0.90 14

0.29 14

GDP Growth Constant R2 N

8

9

10

11

−8.40∗∗∗ (2.53) −0.72∗∗∗ (0.20) 0.86∗∗ (0.35)

−7.20∗∗ (2.28) −0.80∗∗∗ (0.19) 0.84∗ (0.37) 5.22∗ (2.24) −0.13∗ (0.06)

−5.82∗ (2.58) −0.49∗ (0.26) 0.49 (0.37) 1.61 (2.24)

−0.30 (0.76) 7.92∗∗ (2.93)

−0.59 (0.61) 7.13∗ (3.05)

0.82 14

0.89 14

−0.28 (0.16) −0.09 (0.55)

0.90 14

55

10 5

JP US UK AU

IT

DE

BE CA

FR NL

DK SE

0

FI NO

⫺5

Change employment in private sector consumer service


0

.2

.4

.6

.8

1

Replacement rate Figure 2.7 Change in private service employment, 1981–91: replacement rate effect conditional on employment regulations Notes: Thick line: Effect of Replacement Rate if Employment Regulation set to Maximum, with 95% CI. Thin line: Effect of Replacement Rate if Employment Regulation set to Minimum, with 95% CI. Large country markers: Employment Regulation >4 Small country markers: Employment Regulation < = 4.

model of the replacement rate and employment regulation. Moreover, they do not affect this model since the basic specification remains statistically significant in all of these alternative specifications. Note, however, that if one controls for all variables jointly (models 10 and 11), one obtains statistically significant coefficients for all variables, except for economic growth, and the coefficient of determination almost reaches 90 percent, a level seldom attained in cross-sectional models of this kind. Figure 2.7 visualizes the relationship and underlines the better fit of the long-term change model in comparison to the levels model. The cross-sectional models impressively support the hypothesis that labor market policies have an impact on employment in private consumer services. The results suggest that a clear-cut effect of the replacement rate on employment in private consumer services exists as

Bernhard Kittel

57

soon as one controls for the dependence of this effect on the condition that employment regulations do not interfere with the labor market. Not only cross-country differences in levels, but – more importantly – also cross-country variations in the change 1981–91 are well predicted by the conditional model.

A brief return to the pooled model Are we also able to observe this conditional effect in the pooled model? Since conventional comparative political economy would rely more on the findings from that design than from the cross-sectional design discussed above, this is a critical question. Model 8 in Table 2.4 has revealed that first-differencing the dependent variable has resulted in insignificant country effects. This result allows us to drop the fixed effects and to study the impact of employment regulation, which is a time-constant variable, on the employment dynamics. Hence in this section, we analyse pooled models of first differences in employment in private sector consumer services for the period covered by the data, using panel-corrected standard errors which are the panel equivalent of the robust standard errors used in the cross-sectional models. In Table 2.8, model 1 is the baseline specification. There are no indications that the conditional model is valid, given that the interactive term is not significant. However, annual data contain much more noise due to short-term deviations than a cross-sectional design averaging over a longer period. Controlling for the factors that Kenworthy (2003) introduced (model 2) indeed improves the situation considerably, although many of the controls fail to attain statistical significance. An alternative is model 3, which replaces the level of public employment in consumer services by the first difference, which is significant. The relevance and disturbing impact of this factor can be seen from a comparison of model 4 and 6 on the one hand, and model 5 on the other. Including the change in public employment in ISIC 6 and 9 halves the focused coefficients of the conditional effect of the replacement rate and employment regulation and renders the conditional element of the model statistically insignificant. Hence, one would either go for a non-conditional specification including the effect of public employment or sacrifice 10 percentage point of the overall fit by excluding public employment in order to obtain a confirmation of the conditional model. Apart from this ambivalence, the pooled reanalysis controlling for some economic factors broadly confirms the findings from the cross-sectional design.

58 Table 2.8 Pooled model, first differences


1

2

3

4

5

6

−0.65 (0.43) −0.06∗∗ (0.02) 0.08 (0.07)

−0.96∗ (0.50) −0.14∗∗ (0.06) 0.22∗∗ (0.09) 2.13 (1.38) 0.17∗∗∗ (0.03) −0.08 (0.09) 0.02 (0.02) 0.09 (0.07) −0.02 (0.04) −0.09 (0.10) −0.03 (0.04) −0.32 (0.35) −0.02 (0.01)

−0.66∗ (0.36) −0.10∗∗ (0.04) 0.13 (0.09) 1.72 (1.15) 0.19∗∗∗ (0.03) −0.04 (0.84) 0.01 (0.02) 0.07 (0.07) 0.01 (0.03) −0.07 (0.11) −0.01 (0.03) −0.01 (0.00)

−1.20∗∗ (0.59) −0.12∗∗ (0.06) 0.17∗ (0.09) 0.49 (1.13) 0.13∗∗∗ (0.04) 0.13∗ (0.07) 0.03∗ (0.02)

−0.55∗∗ (0.28) −0.06∗∗∗ (0.02) 0.07 (0.04)

−1.05∗∗ (0.46) −0.09∗∗∗ (0.03) 0.15∗∗ (0.07)

0.16∗∗∗ (0.02) 0.19∗∗ (0.08)

0.12∗∗∗ (0.03) 0.16∗∗∗ (0.06) 0.03∗ (0.02)

PAYEQ GDPGROW TRADE RLTIR ALMP UNEMP BENEF LEFTGOV WAGECOR UDENS PUBEMP (ISIC 6 + 9) D.PUBEMP (ISIC 6 + 9) D.EPSCS, lagged

0.39∗∗ (0.18)

EPSCS7479

0.03 (0.12) −0.03 (0.02) 0.54 (1.45)

−0.26∗∗ (0.11) 0.01 (0.11) −0.02 (0.02) −0.01 (1.35)

0.13 (0.21) −0.01 (0.01) 0.13 (0.43)

−0.26∗∗ (0.11) 0.09 (0.10)

0.14 (0.21)

0.05 (0.11)

0.19 (0.16)

Constant

0.52 (0.19)

R2 AR Test (Durbin’s M) N(obs)

0.30 0.05

0.49

0.59

0.44

0.54 0.35∗∗∗

0.43 0.38∗∗

168

146

146

168

164

168

Bernhard Kittel Table 2.9 Country

59

Scores for replacement rate. Replacement Rate (old)

Replacement Rate (new)

AU BE CA DK FI FR DE IT JP NL NO SE UK US

.28 .58 .58 .90 .52 .65 .40 .04 .30 .69 .57 .91 .30 .33

.28 .68 .64 .71 .52 .70 .64 .07 .61 .81 .67 .83 .25 .64

Mean Std. Dev.

.51 .58

.24 .20

Note: Pearson Correlation coefficient: .82.

A digression: data considerations Up to now, the analysis was based on the data used by Kenworthy (2003), which are taken from an unpublished OECD source and refer to gross replacement rates. However, newer data for the net replacement rate are now available from the Scruggs welfare state entitlements data set (Allan and Scruggs, 2004; Scruggs, 2005). Table 2.9 compares the scores for the gross and net replacement rates for the year 1991. The correlation coefficient is sufficiently high (r = 0.82) to believe that the changes do not matter much and the mean and standard deviation changed only to a limited extent. However, a glance at the scores themselves reveals considerable shifts. Most notably, some of the countries originally scoring low, such as the United States and Japan, were recoded by 30 percentage points to above-average values, and Germany shifted by 24 percentage points from below-average to above-average. There are also observable and substantive shifts at the upper end of the distribution: the Netherlands, Belgium, and Norway shifted upwards by about 10 percentage points while the scores for Sweden and Denmark were corrected downwards by 8 and 19 percentage points, respectively. These changes are definitely not immaterial. If the new scores are closer to the truth than the old ones, this would imply that the prototype of the liberal labor

60 Method and Substance in Macrocomparative Analysis Table 2.10 Reanalysis using new data for replacement rate. 1 D10EPSCS

2

3 DEPSCS

4

OLD

NEW

OLD

NEW

−7.52∗∗∗ (1.67) −0.66∗∗∗ (0.12) 0.76∗∗∗ (0.27)

−2.72 (2.81) −0.40∗∗ (0.15) 0.00 (0.45)

Constant

6.68∗∗∗ (0.77)

5.01∗∗∗ (1.01)

−1.05∗∗ (0.46) −0.09∗∗∗ (0.03) 0.15∗∗ (0.07) 0.12∗∗∗ (0.03) 0.15∗∗∗ (0.06) 0.03∗ (0.02) 0.14 (0.21) 0.19 (0.16)

−0.55∗ (0.31) −0.08∗∗∗ (0.02) 0.09∗ (0.05) 0.13∗∗∗ (0.03) 0.16 (0.56) 0.01 (0.02) 0.18 (0.21) 0.19 (0.18)

R2 N

0.81 14

0.54 14

0.43 168

0.41 166

REPLR EMPREG REPLR × EMPREG GDPGROW TRADE RLTIR DEPSCS, lagged

Note: Col. 3, 4 (Panel models): PCSE. D10EPSCS: long-term change in private service employment DEPSCS: annual change in private service employment

market, the United States, scores exactly the same on the core explanatory variable as the currently most-favored scapegoat of so-called rigidity, Germany, as well as other examples of organized market economies. Do these changes have an impact on the empirical findings? Table 2.10 shows that this is indeed the case, for both the estimates of the coefficients of this variable on long-term and short-term changes of employment in private sector consumer services. For ease of comparison, I reprint model 1 of Table 2.7 and a reduced variant of model 4 of Table 2.8 as model 1 and model 3 of Table 2.10. In the long-term analysis (Table 2.10, model 2), nothing remains of the conditional effect of the replacement rate and only employment regulation retains statistical significance. Figure 2.8 confirms this finding. In the short-term analysis (Table 2.10, model 4), the effect size is almost halved, but retains marginal significance at the 10 percent level. Hence the conclusions we can derive from the net replacement rate data are much less convincing

5

10

61

JP US

UK AU

IT

DE CA

BE

FR

DK

0

FI

NO

NL SE

⫺5

Change employment in private sector consumer service

Bernhard Kittel

0

.2

.4 .6 Replacement rate (Scruggs data)

.8

1

Figure 2.8 Change in private service employment, 1981–91: replacement rate effect conditional on employment regulations, new data for replacement rate Notes: Thick line: Effect of Replacement Rate if Employment Regulation set to Maximum, with 95% CI. Thin line: Effect of Replacement Rate if Employment Regulation set to Minimum, with 95% CI. Large country markers: Employment Regulation > 4 Small country markers: Employment Regulation < = 4.

than the ones based on the gross data. It remains an open question whether individuals use their gross or their net earnings as a yardstick for employment decisions which can only be solved by a study of individual-level data.

2.5 Implications and conclusion Twenty years ago, Jackman (1985, pp. 173–5) suggested that it makes little sense to search for regularities in time-series data if there are good reasons to expect the presence of the regularity only in the cross-sectional dimension. More recently, Hall (2003) has questioned the appropriateness of regression analysis for the kind of questions comparative policy analysts tend to ask. Przeworski (2004) has rejected the possibility to draw inferences about institutional effects across countries. And Kittel


(2006) has argued that policy variables lack the necessary microfoundation for finding regularities at the macrolevel. The present analysis, after twenty years of intensive usage of panel models, tends to confirm these warnings. Expanding the research design by adding annual observations does not add substantially relevant variation to the core explanatory variable, the retrenchment rate, which is a policy variable, and does not add any variation to the core intervening variable, employment regulations, which is an institutional variable. Moreover, the dependent variable, employment in private sector consumer services, turns out to be nonstationary by all available information, although this conclusion does not make sense in substantive terms because of its definition in proportions. More specifically, over 90 percent of the variation in employment in 1991 is captured by variation in the 1970s. Hence all we can analyse with panel data is the effect of levels differences in the replacement rate on employment dynamics. This is certainly not the worst of all worlds because such a refocus of the analysis zooms in on the period analysed, instead of relegating the issue to factors present before the observations started. But even in the panel situation, the variance component used for drawing inferences about the effect of the replacement rate on employment growth is almost purely cross-sectional and hence based on the small number of observations. Thus, the model actually estimated is one of average growth over the observation period. The annual variation in the dependent variable is just a nuisance to this relationship, which must be controlled by other variables exhibiting annual variation. As a result, as long as the model capturing the short-term variation is not perfect, the series will contain noise which blurs the cross-sectional pattern. On the other hand, as long as the institutional and policy variables do not capture sufficient cross-sectional variation, the short-term coefficients for the economic model will be biased if no unit fixed effects are included. Including them, however, wipes out that variation completely. Since the coefficient is based on the between variation, nothing is gained in fact for the assessment of the core hypothesis, and much can be lost by pooling the data. The most impressive model, in the end, appears to be a very simple cross-sectional specification which focuses on long-term growth in employment in private-sector consumer services. This finding echoes Hall and Franzese’s (1998, p. 520) greater trust in their cross-sectional results as compared to their time-series–cross-section analysis. In the process of exploring the statistical relationship, several interesting findings emerged. First, the effect of the replacement rate appears

Bernhard Kittel

63

to be dependent on the strictness of employment regulations. Only if employment regulations are lax, a low replacement rate is associated with both above-average levels and growth of employment. Although this result is premised on a single outlier, Italy, there is no case that contradicts the conditional effect. Secondly, the effect of public employment in consumer services partly interferes with the replacement rate effect. This is suggestive of a second mechanism besides labor market distortions impacting on employment: system differences in the provision of public goods. Thirdly, the whole narrative appears to be contingent on the specific scores on the replacement rate. Newer measures indicate that the ones used in previous work differ by up to 100 percent and the detected statistical relationships partly collapse. This is a very discomforting finding, because it suggests that data quality and measurement problems may loom larger than usually acknowledged and may invalidate considerable proportions of established results (De Deken and Kittel, 2007). The least this analysis suggests is to be very cautious with any results obtained from macroquantitative research. Contrary to the initial hopes to rigorously test nomological theories independent of time and space (Przeworski and Teune, 1970; Lijphart, 1971), this chapter underlines the exploratory nature of macroquantitative research (Hoover, 2002) and highlights the possibility to use panel data for recounting statistical narratives.

Appendix: A Brief Overview of the Pooled Time-Series Cross-Section Literature Most econometrics textbooks now include a chapter on panels. A basic introduction can be found in Gujarati (2003) and and more advanced treatment in Greene (2003). Stimson (1985) and Hicks (1994) provide early overviews of the state-of-the art in the late 1980 and early 1990s in the context of political science. Hsiao (2003), Baltagi (2001), and Wooldridge (2002) are more advanced and technical overviews of panel analysis. Mátyás and Sevestre (1996) is a comprehensive handbook of panel data econometrics at an advanced level. Beck (2001) summarizes the discussion on pooled analysis in political science for the “reasonable”-T/small-N data structure typical of macro-level studies. Developments in dynamic panels and panel cointegration are discussed in Banerjee (1999) and Baltagi (2000). Arellano (2003) presents current econometric approaches for dynamic micro panel data, and Wawro (2002) gives a brief introduction into this literature for political scientists. Beck and Katz (2007) trace out some pathways for further development.


Halaby (2004) criticizes the unreflective use of panel methods in many applications and discusses technical issues in typical data sets used in the social sciences. Several conceptual problems are elaborated in Shalev (2007) and Kittel (1999). Kittel and Winner (2005) discuss specification problems and Wilson and Butler (2007) highlight deficiencies in current applications and provide some suggestions for improvement.

Notes 1. To the extent that national governments of EU member states simply implement policy decisions taken in EU councils, the assumption of independence between observation units becomes even more problematic than the concept of policy diffusion suggests. Nevertheless, national governments still make use of their sovereignty to implement their own variant of some joint decision. 2. Data sources for all variables used in this article are available in Kenworthy (2003).

References Allan, James P. and Lyle Scruggs. 2004. “Political Partisanship and Welfare State Reform in Advanced Industrialized Societies.” American Journal of Political Science 48: 496–512. Arellano, Manuel. 2003. Panel Data Econometrics. Oxford: Oxford University Press. Baltagi, Badi H. 2000. Nonstationary Panels, Cointegration in Panels, and Dynamic Panels (Advances in Econometrics Vol. 15). Oxford: Elsevier. Baltagi, Badi H. 2001. Econometric Analysis of Panel Data. 2nd Edition. New York: Wiley. Banerjee, Anindya. 1999. “Panel Data Unit Roots and Cointegration: An Overview.” Oxford Bulletin of Economics and Statistics 61: 607–29. Beck, Nathaniel. 2001. “Time-Series Cross-Section Data: What Have We Learned in the Past Few Years?” Annual Review of Political Science 4: 271–93. Beck, Nathaniel and Jonathan Katz. 2007. “From Statistical Nuisances to Serious Modeling: Changing How We Think About the Analysis of Time-Series CrossSection Data.” Political Analysis 15: 97–100. De Deken, Johan and Bernhard Kittel. 2007. “Social Expenditure under Scrutiny: The Problems of Using Aggregate Spending Data for Assessing Welfare State Dynamics.” Pp. 72–105 in Investigating Welfare State Change: The “Dependent Variable Problem” in Comparative Analysis, edited by Jochen Clasen and Nico Siegel. London: Edward Elgar. Ebbinghaus, Bernhard and Bernhard Kittel. 2005. “European Rigidity Versus American Flexibility? The Institutional Adaptability of Collective Bargaining.” Work and Occupations 32: 163–95. Freeman, John R. 1990. “Systematic Sampling, Temporal Aggregation, and the Study of Political Relationships.” Pp. 61–98 in Political Analysis Vol. 1, 1989, edited by James A. Stimson. Ann Arbor: University of Michigan Press. Greene, William. 2003. Econometric Analysis, 5th edition. Englewood Cliffs: Prentice Hall.

Bernhard Kittel

65

Gujarati, Damodar N. 2003. Basic Econometrics, 4th edition. New York: McGraw Hill. Halaby, Charles N. 2004. “Panel Models in Sociological Research: Theory into Practice.” Annual Review of Sociology 30: 507–44. Hall, Peter A. 2003. “Aligning Ontology and Methodology in Comparative Research.” Pp. 373–404 in Comparative Historical Research in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. Cambridge: Cambridge University Press. Hall, Peter A. and Robert J. Franzese. 1998. “Mixed Signals: Central Bank Independence, Coordinated Wage Bargaining, and European Monetary Union.” International Organization 52: 505–35. Hicks, Alexander M. 1994. “Introduction to Pooling.” Pp. 169–88 in The Comparative Political Economy of the Welfare State, edited by Thomas Janoski and Alexander Hicks. Cambridge: Cambridge University Press. Hoover, Kevin D. 2002. “Econometrics and Reality.” Pp. 152–77 in Fact and Fiction in Economics: Models, Realism, and Social Construction, edited by Uskali Mäki. Cambridge: Cambridge University Press. Iversen, Torben and Anne Wren. 1998. “Equality, Employment, and Budgetary Restraint: The Trilemma of the Service Economy.” World Politics 50: 507–46. Jackman, Robert W. 1985. “Cross-National Statistical Reseach and the Study of Comparative Politics.” American Journal of Political Science 29: 161–82. Kenworthy, Lane. 2003. “Do Affluent Countries Face an Incomes–Jobs Trade-Off?” Comparative Political Studies 36: 1180–209. King, Gary, Robert Keohane, and Sidney Verba. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton: Princeton University Press. Kittel, Bernhard. 1999. “Sense and Sensitivity in Pooled Analysis of Political Data.” European Journal of Political Research 35: 225–53. Kittel, Bernhard. 2006. “A Crazy Methodology? On the Limits of Macroquantitative Social Science Research.” International Sociology 21: 647–77. Kittel, Bernhard and Hannes Winner. 2005. “How Reliable Is Pooled Analysis in Political Economy? The Globalization-Welfare State Nexus Revisited.” European Journal of Political Research 44: 269–93. Lazarsfeld, Paul F. and H. Menzel. 1961. “On the Relationship between Individual and Collective Properties.” Pp. 499–516 in Complex Organizations: A Sociological Reader, edited by Amitai Etzioni. New York: Holt, Rinehart, and Winston. Leamer, Edward E. 1978. Specification Searches: Ad-Hoc Inference with Nonexperimental Data. New York: Wiley. Lieberson, Stanley. 1985. Making It Count: The Improvement of Social Research and Theory. Berkeley: University of California Press. Lijphart, Arend. 1971. “Comparative Politics and the Comparative Method.” American Political Science Review 65: 682–93. Maddala, G.S. 1999. “On the Use of Panel Data Methods with Cross-Country Data.” Annales d’économie et de statistique 55–6: 429–48. Maddala, G. S. and S. Wu. 1999. “A Comparative Study of Unit Roots Tests with Panel Data and a New Simple Test.” Oxford Bulletin of Economics and Statistics 61: 631–52. Mátyás, Lásló and Patrick Sevestre, eds. 1996. The Econometrics of Panel Data: A Handbook of the Theory with Applications. Dordrecht: Kluwer.

66 Method and Substance in Macrocomparative Analysis Pierson, Paul. 2004. Politics in Time: History, Institutions, and Social Analysis. Princeton: Princeton University Press. Polillo, Simone and Mauro F. Guillén. 2005. “Globalization Pressures and the State: The Worldwide Spread of Central Bank Independence.” American Journal of Sociology 110: 1764–802. Przeworski, Adam. 2004. “Institutions Matter?” Government and Opposition 39: 527–40. Przeworski, Adam and Henry Teune. 1970. The Logic of Comparative Social Inquiry. New York: Wiley. Scharpf, Fritz W. 2000. “Economic Changes, Vulnerabilities, and Institutional Capabilities.” Pp. 21–125 in Welfare and Work in the Open Economy, edited by Fritz W. Scharpf and Vivien A. Schmidt. Oxford: Oxford University Press. Scruggs, Lyle. 2005. Comparative Welfare Entitlement Dateset, accessed April 2006. www.sp.uconn.edu/∼scruggs/wp.htm. Shalev, Michael. 2007. “Limits of and Alternatives to Multiple Regression in Macro-Comparative Research.” Pp. 261–308 in Capitalisms Compared (Comparative Social Research, Vol. 24), edited by Lars Mjøset. Amsterdam: Elsevier. Stimson, James A. 1985. “Regression in Time and Space: A Statistical Essay.” American Journal of Political Science 29: 914–47. Wawro, Gregory. 2002. “Estimating Dynamic Panel Data Models in Political Science.” Political Analysis 10: 25–48. Wilson, Sven E. and Daniel M. Butler. 2007. “A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analysis to Alternative Specifications.” Political Analysis 15: 101–23. Wooldridge, Jeffrey M. 2002. Econometric Analyses of Cross Section and Panel Data. Cambridge: MIT Press.

3 Comparative Employment Performance: A Fuzzy-Set Analysis Jessica Epstein, Daniel Duerr, Lane K enworthy, and Charles Ragin

Introduction Much of the debate about comparative employment performance in recent decades has focused on the impact of labor market institutions and policies. A number of studies have found that institutions and policies that restrict or regulate market processes – for example, wage compression, employment protection regulations, high taxes, generous unemployment benefits – have adverse effects on employment outcomes (OECD, 1994, 2006; Nickell, 1997; Scharpf, 1997, 2000; Siebert, 1997; Iversen and Wren, 1998; Blanchard and Wolfers, 2000; Blau and Kahn, 2002; IMF, 2003; Kenworthy, 2004, 2008; Kemmerling, 2005; Nickell, Nunziata, and Ochel, 2005; Bassanini and Duval, 2006). Others question this conclusion (Glyn and Salverda, 2000; Esping-Andersen and Regini, 2000; Martin, 2004; Baccaro and Rei, 2005; Baker et al., 2005; Schettkat, 2005; Stephens and Bradley, 2005; Howell et al., 2006). Almost without exception, quantitative macrocomparative studies on this issue have used regression as the analytical technique. We instead use fuzzy-set qualitative comparative analysis (fuzzy-set QCA). Utilizing fuzzy-set QCA, we explore the determinants of poor employment performance in low-end private-sector services in 14 countries between 1979 and 1995.

Why fuzzy-set QCA? Fuzzy-set QCA offers several advantages. First, it is better-suited than regression for exploring causal configurations – situations in which variables have an impact only in combination with a high or low degree of one or more other factors. In regression analysis, causal configurations are assessed via interaction terms. However, a small N limits the number of interactions terms that can be included in a regression model. 67


In addition, the difficulty of interpreting interaction terms with more than two variables makes modeling complex interactions problematic. Moreover, while assessing interactions in regression requires that variables demonstrate a multiplicative effect, QCA treats any case aspects that appear together systematically – in any quantity – as potentially interdependent. Secondly, fuzzy-set QCA allows us to identify multiple pathways to an outcome. Correlational techniques such as regression treat the presence of an outcome (dependent variable) without a given cause (independent variable) as negative evidence for the strength of that causal explanation. Thus, a factor that has an impact in a subset – but only a subset – of cases tends to become obscured in regression results with deflated coefficients and inflated variance. In contrast, fuzzy-set QCA can reveal causal patterns that differ across subsets of cases. This method thereby allows us to examine relatively large datasets with more complex causal narratives than are generally possible with correlational techniques. Thirdly, whereas regression is useful for examining tendential relationships – the general tendency of a particular factor to influence an outcome of interest – fuzzy-set QCA is helpful in exploring a different kind of relationship: causal sufficiency. Fuzzy-set QCA assesses sufficiency via the logic of set-theoretic relations. Set theory is inherent (though often implicit rather than explicit) in much of social science (Ragin, 2000). Sets are simply conceptual categories like “generous government benefits” or “low income inequality.” Much social science concerns itself with the relative membership of cases in such categories, the theoretical validity of a set designation, or the ways one set might subsume another. The set–subset ordering of social phenomena is key to understanding causal sufficiency. A causal factor is considered sufficient when its presence always (or nearly always) “produces” a particular outcome. Assaulting one’s employer, for instance, is generally a sufficient condition for being fired. But it is not the only way to get fired: one could also stop coming to work, or embezzle, or perform poorly. In set-theoretic terms, the cause (assaulting one’s employer) is a subset of the outcome (being fired): it always produces the outcome, but it is not the only pathway to it. Because sufficient causes are always subsets (or near subsets) of the outcomes they “produce,” discerning subset relations points to potentially sufficient causal pathways. Thus, for instance, if all countries with strict employment protection regulations also have poor employment performance, we might consider strict employment protection a sufficient condition for bad employment performance.

Jessica Epstein, Daniel Duerr, Lane K enworthy, and Charles Ragin

69

Data Because it is concerned with ordering conceptual categories rather than assessing statistical correlations, fuzzy-set QCA requires the use of settheoretic variables. “Fuzzy sets” refer to the pseudo-continuous scale on which cases are coded. Like conventional quantitative indexes, fuzzy sets range from zero to one. But because the interest is in how strongly cases conform to theoretical categories, fuzzy coding schemes are based on connection to qualitative anchors rather than mathematical equivalence. Cases coded zero are considered “fully out” of a set, and those coded one are “fully in” the set. Membership along the continuum between zero and one can be determined qualitatively, on a case-by-case basis, or via scaling using an arithmetic formula. For each of our variables, we use three qualitative anchors of set membership: 0, 0.5, and 1. The 0.5 anchor serves as the “crossover point” from “more in than out” to “more out than in” the set. We then use a mathematical rescaling to distribute the rest of the cases between these points. The process is described in greater detail below.

Outcome The first step is to determine the relevant conceptual set or group. Should we focus on good or bad employment performance? Because much of the scholarly and political debate has centered on understanding why some European countries have struggled with job creation, we opt for bad employment performance as the outcome of interest. We set out to identify causal configurations associated with slow or negative growth in employment rates. The outcome we analyze is “poor” performance in low-end private sector service jobs during the period 1979–95. (The chapter appendix provides descriptions and data sources for all of our variables.) This includes restaurants, hotels, retail and wholesale trade, and community-socialpersonal services. Because productivity in these jobs tends to be low and difficult to increase, they are the most likely to be adversely affected by institutional and policy “rigidities.” In addition, these jobs account for a relatively large share of the cross-country variation in both levels of employment and change in employment in recent decades. Unfortunately, data are available for only 14 countries and only through 1995. Because employment rates change only incrementally, an analysis of employment levels during or at the end of this period will be heavily influenced by employment levels at the beginning. We therefore


Employment change in private-sector consumer services, 1995 minus 1979 (%)

8

6

Ja

US UK Asl

4

Ger Bel It 2

Nth Can Fr

0

Nor Den Swe

⫺2

Fin Country

Figure 3.1 Employment change in low-end private sector services, 1979 to 1995

examine change in employment rates. We measure change in absolute terms: employment rate in 1995 minus employment rate in 1979. A potential concern about measuring employment performance in terms of change over time is that there might be strong ceiling or catchup effects. Countries that began the period with low employment rates in low-end private sector services may have found it easier to increase employment, whereas those that began with high rates might have already been near a ceiling and thus found it more difficult to increase employment. However, no such pattern is in evidence for these countries during this time period. How do we translate absolute employment change into a qualitatively defined fuzzy set? The first step is determination of “bad” employment performance, with both substantive knowledge and raw scores as our guide. Figure 3.1 shows employment changes in all 14 countries. The


1

71

FinSwe Den Nor Fr Can Nth

Fuzzy-set scores

.75

It Bel Ger .5

Asl UK US

.25

0

Ja ⫺2

0

2

4

6

8

Raw values

Figure 3.2 Employment change fuzzy-set scores by employment change raw values

sharpest declines were experienced in Finland and Sweden (−1.7 and −1.0, respectively). The next worst performer was Denmark, but its employment decline (−0.1) was far smaller than those of Sweden and Finland. We therefore draw the membership line around Finnish and Swedish levels, setting their fuzzy scores to “1” to signify full membership in the set of poor employment performers. In a set-theoretic analysis, once the threshold for membership in the group is established, all cases that meet the threshold are coded the same; any variation among cases within the group is treated as irrelevant. Similarly, the sharpest break at the other end of the distribution clearly falls between Japan and the United States. Japan is therefore designated fully out of the set of bad employment performers and is coded zero. The other anchor that is often determined qualitatively in a fuzzy-set coding is the “cross-over point”: 0.5. This separates cases that are “more in the set than out” from those that are “more out than in.” In this instance the obvious break point is between Germany and Australia. A simple rescaling of the values for the countries neither fully in nor fully out – using the formula (raw value – minimum value)/range – captures this break point and distributes these countries between the anchors. Figure 3.2 shows the fuzzy-set scores for employment change plotted against raw values.


Causal conditions There are many hypothesized determinants of cross-country variation in employment performance. We include six labor market institutions and policies that have been central in research and political debate on this issue. We describe each causal condition in a way that corresponds to how it is expected to contribute to bad employment performance. 1. Low earnings inequality. Because productivity tends to be low and difficult to increase in low-end service jobs, high wages may deter employment growth. The P50/P10 earnings ratio among the full-time employed is a fairly good indicator of the level of wages relative to the country median. Lower levels of this ratio, indicating greater pay compression, are expected to contribute to poorer employment change performance. 2. High wage increases. Rapid growth of overall wages – or, more precisely, wages adjusted for inflation and productivity growth, i.e. real unit labor costs – is expected to deter employment growth in all types of jobs. 3. High payroll and consumption taxes. Payroll taxes increase employers’ nonwage labor costs and consumption taxes reduce consumer demand for price-elastic goods and services. High levels of these types of taxes are thereby expected to reduce employment growth. We use payroll and consumption taxes as a share of GDP as our measure. 4. High employment protection regulations. If employers are less able to fire workers during bad times, they may reduce hiring during expansionary periods. Stringent employment protection regulations are thus expected to reduce employment growth. 5. High unemployment benefit generosity. Generous unemployment compensation programs are expected to deter employment growth from the supply side, by reducing the eagerness of benefit recipients to enter or reenter employment. To tap generosity, we utilize the percentage of former earnings replaced by (gross) benefits, averaged over the period 1980–95. 6. High public employment. A number of nations combat unemployment in part via creation of jobs in the public sector. Government jobs may supplant private-sector employment growth, particularly in lowend services. If so, high levels of public-sector employment may contribute to slower growth of jobs in private sector services. The measure we use is public employment as a percentage of the working-age population.

73 High wage increases

Low earnings inequality

1

Swe Bel Nor Den It Fin

.75

Fuzzy-set scores

Fuzzy-set scores

1

Nth Ger Fr Asl Ja

.5 UK

.25

FinDen Ge Jar

.75 UK It Fr Bel

.5

Can As l US

.25 Swe

0

0 NorNth ⫺1.5

US Can

2.1

1.9 1.7 1.5 Raw values (reversed)

1.3

High payroll and consumption taxes Swe Nth Bel Nor Fin Ger

.75 It

.5 UK Den

High employment protection regulations

.25

It Swe Ger Nor Bel Ja Fr Nth Fin

.75

.5

Den

.25

Asl UK Can

Can Ja

0

0

US Asl

10

15

20 25 Raw values

30

US

0

.5

High unemployment benefit generosity 1

1.0 1.5 Raw values

Nor Den Swe

Fuzzy-set scores

Fuzzy-set scores

Nth Fr Can Bel Nor Fin

.5

Ger US UK Ja As l

.25

0

Asl Fin

.75

Fr UK Can

.5 Bel US Ger It Nth

.25 0 Ja

It

0

2.0

High public employment 1

Swe Den

.75

0

1

Fr

Fuzzy-set scores

Fuzzy-set scores

1

⫺1 ⫺.5 Raw values

.2

.4 .6 Raw values

.8

1

5

10

15 20 Raw values

Figure 3.3 Causal condition fuzzy-set scores by raw values Note: Descriptions of the original variables and data sources are in the text.

25


Figure 3.3 shows our scoring for these six causal conditions. Each chart plots the fuzzy-set scores we use for the condition by its raw values. Here our fuzzy-set coding choices were determined largely empirically. For each variable we tried a variety of different codings and used the one that fit best with the data.

Analysis We conducted our fuzzy-set QCA analysis using the software program fs/QCA 2.0 (www.u.arizona.edu/∼cragin/fsQCA/software.shtml). The program simplifies data patterns to identify potentially “sufficient” causal associations. Its ultimate products are a set of logical statements identifying factors or combinations of factors that appear as subsets of an outcome, along with mathematical measures with which to assess their utility. The researcher has discretion at several stages of analysis to refine or alter the logical rules that produce these statements and a great deal of interpretive leeway in utilizing the results. We explain each stage of that process below. As in regression analysis, fs/QCA requires that the researcher specify a model – a set of causal conditions to be included in the analysis. To generate the broadest range of solution sets, we employed multiple models utilizing various combinations of our six causal conditions. After model specification, the next step is examination of a “truth table.” The truth table is an analytic device that displays all logically possible combinations of causal conditions and indicates cases’ distribution across these combinations. Table 3.1 is a truth table produced to assess all six causal conditions: earnings inequality, wage changes, payroll and consumption taxes, employment protection regulations, unemployment benefit generosity, and public employment. The fs/QCA program originally produced a 64-row table, representing all (26 ) logically possible combinations of the causal conditions. To facilitate the presentation, we have removed those with no empirical instances. The truth table is a simplified data map. The “number” column denotes how many cases conform to the listed combination, but the reader should not consider each row to represent only those cases.1 Truth table rows directly correspond to the logical possibility of a particular causal combination. Graphically, imagine each row corresponding to a corner of “vector space” – a multidimensional plot representing all possible combinations of causally relevant case aspects. The 0 s and 1 s in each cell are an instruction about how to consider a case’s location – its membership or nonmembership (1 minus membership) in a particular fuzzy set. Because

Table 3.1 Low earnings inequality

1 1 1 1 1 1 0 0 0 0 0

Truth table from analysis of all six causal conditions. High wage increases

High payroll and consumption taxes


High unemployment benefit generosity

High public employment

Number

1 0 0 1 1 1 0 0 1 0 1

1 1 1 1 0 1 0 0 0 0 0

1 1 1 1 0 1 0 0 0 0 1

1 1 1 1 1 0 1 0 0 0 0

1 1 0 0 1 0 1 1 1 0 0

2 2 1 1 1 2 1 1 1 1 1

Outcome: poor employment change performance

Consist

1 1 0.96 0.96 0.94 0.93 0.86 0.68 0.65 0.61 0.58

75


the lines and cells are theoretical configurations, few cases conform perfectly to the conditions they denote, and any given case has partial membership in multiple rows. For example, a country with a 0.4 score on the employment protection fuzzy set also has a 0.6 membership in its negation, making it a partial member of both the last and nextto-last rows of table 1. The table thus maps case aspects rather than the cases themselves. A primary purpose of the truth table is to guide the researcher in determining standards for the consistency of causal relationships. This is done with consistency scores. These are shown in the “Consist” column in Table 3.1. We are concerned in this analysis with causal sufficiency – the ability of certain configurations of policy and institutional factors to consistently produce bad employment performance. In set theoretic terms, we are interested in the extent to which particular causal factors or configurations are subsets of the outcome. As in regression, increases in the strength of set membership in a cause are expected to result in more complete membership in the outcome as well. If high payroll and consumption taxes are sufficient to produce bad employment performance, we should observe few or no cases with high payroll and consumption taxes (a fuzzy score of 1) and good or moderate employment performance (a fuzzy score of 0.5 or less). The consistency (“Consist”) score for a configuration is a measure of this subset relationship. It is thus a measure of the extent to which membership strength in the outcome set is consistently equal to or greater than membership in the causal configuration. For each configuration (row in the truth table), minimum membership scores (causal combination versus outcome) are added for all cases. This number is divided by the sum of all minimum membership scores in the causal combination. Formally, the calculation is: Consis tency (Xi < Yi ) = (min(Xi ,Yi ))/ (Xi ). When membership in outcome Y is less than membership in causal configuration X, the numerator will be smaller than the denominator and the consistency score will decrease. Consistency scores range from 0 to 1, with 0 indicating no subset relationship and a score of 1 denoting a perfect subset relationship. These consistency scores help the researcher decide which configurations should be considered reasonable subsets of the outcome. Once this decision is made for a particular configuration, the researcher enters a 1 or 0 into the cell in the blank “outcome: poor employment change performance” column in the truth table, which tells the program whether or not to treat that particular causal combination as an instance of bad employment performance. Minimum levels of set-theoretic consistency would be achieved by setting a “Consist” threshold of at least .75 (Ragin,


77

2004), preferably higher. Between this level and full set-theoretic consistency (1), the analyst must choose a minimum threshold. For our analysis, we utilized various thresholds between .85 and .95 for each model. The next step of the analysis concerns the treatment of counterfactual cases (see Ragin and Sonnett, 2005). With six causal conditions, there are 64 (26 ) logically possible combinations of conditions. But only 11 of these combinations are actually represented empirically in our data. These are the 11 listed in Table 3.1. All other logically possible configurations are “remainders” – counterfactual configurations that lack empirical instances. Because remainders constitute neither positive nor negative evidence, the fs/QCA program allows the researcher to treat them as either. In the first case, remainders are treated as potentially positive evidence – cases that could have been – and are utilized by the program as logically simplifying assumptions. (In fs/QCA, this is referred to as the “don’t care” option.) But this option assumes that all non-instantiated configurations are plausible. Where that is not the case, it is best to treat them as negative instances of the outcome. (This is referred to in the program as the “false” option.) Doing so produces less parsimonious results. In our analyses, we examined both options with all models (Ragin and Rihoux, 2004; Ragin and Sonnett, 2005). Once these choices have been made, the program then utilizes set– subset logic to simplify the patterns of association displayed in the truth table. Recall the first row of Table 3.1, where a configuration of all six causal conditions was a perfect subset of the set of bad employment performers. How do we discern which of these six conditions really matter, and which are superfluous? Note that in the following six rows, there are three cases of the outcome that do not include the high wage increases or high public employment causal conditions. We could reasonably conclude that both of these case aspects are superfluous “ingredients” in the causal pathways these rows express. Similarly, there are two cases of the outcome that do not include the high unemployment benefit generosity causal condition, and one that does not include high payroll and consumption taxes. Depending on the choices described above – about consistency thresholds and simplifying assumptions – the fs/QCA program will offer several simplified formulations of these causal pathways by eliminating causal factors that appear superfluous and identifying combinations that consistently appear sufficient to produce the outcome. Table 3.2 shows two such simplified “solution sets.” The first modeled all six causal conditions using a 90 percent consistency threshold and the

78 Method and Substance in Macrocomparative Analysis Table 3.2 Examples of solution sets. Coverage Raw

Unique

Consistency

Solutions (no simplifying assumptions) Low earnings inequality ∗ absence of high wage increases ∗ High payroll and consumption taxes ∗ High employment protection regulations ∗ High unemployment benefit generosity

0.37

0.26

0.90

Low earnings inequality ∗ High wage increases ∗ High payroll and consumption taxes ∗ High employment protection regulations ∗ absence of high public employment

0.31

0.15

0.95

0.18

0.11

0.94

0.84

0.84

0.83

∗

Low earnings inequality High wage increases ∗ absence of high payroll and consumption taxes ∗ absence of high employment protection regulations ∗ High unemployment benefit generosity ∗ High public employment Solution coverage: 0.67 Solution consistency: 0.96 Solutions (simplifying assumptions) Low earnings inequality Solution coverage: 0.84 Solution consistency: 0.83

Note: ∗ = logical “and.” Coverage and consistency are explained in the text.

“false” option favoring empirical complexity. The second solution set differs only in that it was derived using the “don’t care” option, which favors parsimony. These two solutions, the parsimonious and the complex, can be viewed as the two endpoints of a continuum. In between these two endpoints are various intermediate solutions, which are also valid. By definition, an intermediate solution must be a superset of the complex solution (no simplifying assumptions used) and a subset of the parsimonious solution (all possible simplifying assumptions used, regardless of their plausibility).

Jessica Epstein, Daniel Duerr, Lane K enworthy, and Charles Ragin Low consistency

Outcome

79

High consistency, low coverage

Outcome

Cause

Cause

Figure 3.4 Consistency and coverage

The primary way to assess the value of these causal explanations is to examine their consistency scores. Like consistency scores in a truth table, “consistency” in the results produced by fs/QCA refers to a causal combination’s consistency as a subset of the outcome. In the simplest terms, low consistency means that there is no subset relation between a combination of case aspects and the outcome. This relationship is depicted in the first illustration in Figure 3.4. As with the assessment of the consistency of truth table rows, scores closest to “1” represent the strongest connection, while those below .85 should be treated with caution. This assessment should also be guided by case knowledge. The fourth solution in Table 3.2 – the presence of income inequality – is on the threshold of acceptable consistency, with a .83 score. If we had reason to believe that an outlying case – a country with very low inequality but very good employment performance – affected this score without challenging the causal story we want to tell, we could consider this solution set as a strong contender. Note that this is the same logical process we would follow in any comparative case study research. The mathematical markers that QCA provides merely guide our logical comparisons. The second tool for assessing results is the coverage score. Coverage refers to the proportion of the sum of the membership scores in an outcome that a particular configuration explains. Very low coverage scores indicate that even if a causal configuration is consistent with the outcome, it is substantively trivial. This is depicted in the second illustration in Figure 3.4. Coverage and consistency often are inversely related to one another, because very particular or exact explanations (which may be highly consistent) tend to be less generalizable. In Table 3.2, “raw coverage” scores refer to the proportion of the outcome scores covered by an explanation by itself, while “unique coverage” refers to the proportion


of outcome scores covered, net of that solution’s coverage overlap with the other solutions identified. Utilizing coverage scores also entails the use of substantive and theoretical knowledge. Because QCA allows us to find “clusters of commonalities across subsets of cases” (Ragin, 2000, p. 75), it also allows us to consider the possibility that different causal stories work for distinct subsets of cases. These stories can be pieced together into a larger narrative about the differential factors driving a phenomenon across countries or regions. How to balance the complexity of that narrative with the mandate of parsimony is a matter of judgment. In the next section we compare coverage scores with scatterplots to match causal pathways to the countries best explained by them. The next step in processing the fs/QCA program’s results involves the elimination of theoretically implausible terms using counterfactual analysis. Essentially, this process involves using only select subsets of simplifying assumptions to produce solutions that are intermediate between the parsimonious solution (all simplifying assumptions permitted) and the complex solution (no simplifying assumptions permitted). The program analyzes casual conditions in both their present and absent states when it considers all logically possible combinations of causal conditions. After all, the absence of a particular case aspect might be just as important in determining employment trends as its presence. In our analysis, however, all causal conditions are expected to contribute to poor employment performance only when they are present, and we have coded them so that high membership scores should be linked to poor performance. For this reason, we eliminated absent conditions from our complex solutions in order to generate our intermediate solutions, while respecting the rule that intermediate solutions must be subsets of the parsimonious solutions. For instance, the first solution listed in Table 3.2 is: high earnings inequality ∗ absence of high wage increases ∗ high payroll and consumption taxes ∗ high employment protection regulations ∗ high unemployment benefits generosity. Following the procedure just described (and Ragin and Sonnett, 2005), the intermediate solution is: high earnings inequality ∗ high payroll and consumption taxes ∗ high employment protection regulations ∗ high unemployment benefits generosity.

Findings How did we utilize the tools and procedures described in the previous section to reach and interpret results? First, we gathered the


81

multiple solutions generated by (1) multiple models (that is, various combinations of case aspects), (2) multiple consistency thresholds, and (3) different treatment of counterfactual cases (remainders). We then eliminated solutions with consistency scores below 0.85 and coverage scores below 0.30. Next, we crafted intermediate solutions suing the procedures described above. This process yielded five causal pathways. Each of these five pathways was then coded as its own fuzzy set. Each country’s score in each pathway is determined by its weakest membership in the conditions that constitute the configuration. Why? To conclude that a particular causal factor generated an impact requires that factor to be present at least at the level ascribed to the entire combination. So, if there are three factors in the configuration and a country’s scores on them are .50, .85, and .25, the country is coded .25 on the configuration fuzzy set. Each of these configuration fuzzy sets was then run through the fs/QCA program to assess its specific consistency and coverage. The rest of our analysis is based on evidence from scatterplots and judgments about parsimony. The five solutions and their consistency and coverage scores are shown in Table 3.3. The first four solutions point to one fairly simple explanation: a combination of low earnings inequality and high payroll and consumption taxes was a sufficient condition for generating poor low-end private sector service employment performance. This solution accounts for 75 percent of the sum of the membership scores in poor employment performance. It has slightly lower set-theoretic consistency than the first three, but much higher coverage. It also is the logical superset of the first three solutions, subsuming their more complex causal narratives into a more parsimonious explanation. Figure 3.5 shows four scatterplots, each with fuzzy employment change scores on the Y axis and one of the first four solutions from Table 3.3 on the X axis. The higher consistency scores for the first two configurations – #1 and #2 from Table 3.3 – are evidenced by the fact that more countries are located above main diagonal line in these two charts than in the lower two charts. “Perfect” causal sufficiency would be in evidence if every case were located above the main diagonal. That is not true for any of the four configurations, but only two countries lie below the line for configurations 1 and 2, versus three below the line for configuration 3 and four below the line for configuration 4. What cases do these explanations cover? Using rough cutoffs indicated by the dotted 45-degree lines, we determined three broad levels

82 Method and Substance in Macrocomparative Analysis Table 3.3 Five causal pathways.

1.

2.

3.

4. 5.

Consistency

Coverage

Low earnings inequality ∗ High payroll and consumption taxes ∗ High employment protection regulations ∗ High unemployment benefit generosity

0.96

0.58

Low earnings inequality ∗ High wage increases ∗ High payroll and consumption taxes ∗ High employment protection regulations

0.96

0.42

0.95

0.67

Low earnings inequality High payroll and consumption taxes

0.93

0.75


0.87

0.85

∗

Low earnings inequality High payroll and consumption taxes ∗ High employment protection regulations ∗

Note: ∗ = logical “and.” Coverage and consistency are explained in the text.

of coverage: “clearly conforming” cases, “potentially conforming” cases, and “clearly not conforming” cases. Low earnings inequality combined with high payroll and consumption taxes, shown in the lower-right chart, explains the most cases. Japan, the United Kingdom, Italy, Germany, the Netherlands, Norway, and Sweden all are on or very close to the central line – the point of perfect correspondence between cause and outcome. The United States, France, Finland, Belgium, and Australia are close enough to the line that these cases are “potentially” covered by this story. Only Denmark and Canada clearly do not conform. The fifth causal configuration in Table 3.3 consists of a single causal factor: high unemployment benefit generosity. This solution has very high coverage, at 85 percent, but a questionable consistency level of .87. Recall that consistency scores are calculated for a solution’s set-theoretic consistency across all cases. Even a solution with relatively low consistency might explain some cases very well. The scatterplot in Figure 3.6 indicates that high unemployment benefit generosity covers most of the cases in our data set reasonably well. Its consistency is relatively low because so many cases have slightly higher levels of membership in the high unemployment benefit generosity fuzzy set than they do in the set of poor employment performers (they lie to the right of the central line


1

Fin Den

Swe Nor

Fr

.75 Can It

Nth Bel

Ger

.5

.25 Asl US

UK

0

Ja 0

Earnings inequality * wage changes * taxes * employment protection Poor employment change performance

Poor employment change performance

Earnings inequality * taxes * employment protection * unemployment benefits 1

Den Fr

.75 Can Nth

Bel Ger

.25 Asl US

UK

0

Ja 0

.25 .5 .75 1 Causal configuration #2 from table 3

Swe Nor

Fr

.75 Can

Ger

Nth It Bel

.5

.25 Asl US

0

UK

Ja 0

.25

.5

.75

Causal configuration #3 from table 3

Earnings inequality * taxes



Fin

1

It

.5

Earnings inequality * taxes * employment protection

Den

Fin

Swe Nor

.25 .5 .75 1 Causal configuration #1 from table 3

1

83

1

Fin Den

Swe

Nor Fr

.75 Can

Ger

Nth It

Bel

.5

.25 Asl US

0

UK

Ja 0

.25

.5

.75

1

Causal configuration #4 from table 3

Figure 3.5 Poor employment change performance by causal configurations 1–4 from Table 3.3 Note: Lines are 45-degree lines.

in the scatterplot). This discrepancy is potentially the result of coding error, but in any event it is fairly small. All cases except Japan, Finland, and Italy are at least potentially covered. Only Italy is clearly not conforming. Denmark and Canada, which are not covered by any of the solutions shown in Figure 3.5, are among the cases that clearly conform to the high unemployment benefit story. Our analysis thus highlights two causal pathways in accounting for poor employment performance among these 14 countries from 1979 to


Unemployment benefit generosity


1

Fin

Swe Den

Nor Fr

.75 Can It

Ger

Nth

Bel

.5

Asl UK US

.25

0

Ja 0

.25 .5 .75 Causal configuration #5 from table 3

1

Figure 3.6 Poor employment change performance by causal configuration 5 from Table 3.3

1995. The first is low earnings inequality coupled with high payroll and consumption taxes. This configuration appears likely to have been key to poor employment growth in low-end private sector services in Sweden, Norway, Finland, France, Italy, Germany, and the Netherlands. The second is high unemployment benefit generosity. This appears to have been the main contributing factor for Denmark, Canada, and Belgium, but perhaps also for France, Germany, the Netherlands, Sweden, and Norway.

Conclusion In the introductory section of this chapter we highlighted three advantages of using fuzzy-set QCA to analyze comparative employment performance. The first is the technique’s utility in exploring causal configurations. In the end, our results centered on one simple causal configuration and another singular causal factor: (1) low earnings inequality combined with high payroll and consumption taxes; (2) high unemployment benefit generosity. The inequality–taxes configuration could have been captured reasonably well with the type of multiplicative


85

interaction term usually used in regression. Indeed, such a multiplicative term correlates at .98 with the fuzzy-set causal configuration scores shown on the horizontal axis in the lower-right chart in Figure 3.5 above. Yet it is not always the case that the causal story yielded by a fuzzy-set QCA analysis will end up so simple. It is much less likely that an analyst using regression would have identified one of the more complex causal configurations from Table 3.3. Even our modest results, attained with relatively limited data, point to a combination that deserves further attention in regression-oriented work. Indeed, one potential use of fuzzy-set QCA is to identify combinations of case aspects that deserve further scrutiny in quantitative studies. The second advantage of fuzzy-set QCA is that it can identify multiple paths to an outcome. While regression’s focus on average net effects tends to obscure causal relationships that do not operate across all or most of a sample, QCA locates causal pathways that may be distinct to smaller clusters of cases. In our analysis, two pathways emerged as most important. Each helps to account for a number of countries, but not the entire group. Canada and Denmark clearly do not fit the low earnings inequality combined with high payroll and consumption taxes explanation. Similarly, Finland and Italy are not well accounted for by the high unemployment benefit generosity explanation. In other analyses a larger number of pathways may be needed to explain the variety of case outcomes. The third advantage of QCA that we noted at the outset is its focus on sufficient causal conditions. For researchers interested solely in “tendential” relationships, this is a drawback rather than an asset. But an interest in sufficiency is in fact implicit in a nontrivial amount of empirical work in the social sciences. A fourth benefit of fuzzy-set QCA, unexplored here but fruitful for further analysis, is that unlike regression it does not assume causal symmetry at the two “ends” of the dependent variable. That is, the causes of low values of the outcome are not seen as the inverse of the causes of high values. Each is assumed to require a separate theoretical argument and empirical analysis. Though we have not conducted an analysis of the determinants of good employment performance, such an investigation might yield results that are not simply the inverse of the causes of poor employment performance identified here. Of course, like any analytical technique, fuzzy-set QCA has limitations. Some it shares with regression. Perhaps the most important is that QCA too suffers from a small-N problem: with a relatively small number of cases (14 here), only a limited number of causal conditions can


be included in an analysis. This leaves analyses open to concern about “omitted variable bias” (see the Hicks and Kenworthy chapter on family policies in this volume). As with regression, there also is a danger that researchers will mechanically utilize results produced by the fs/QCA program without drawing on theoretical and case knowledge. Though grounded in the case study tradition, this method is no less susceptible to such an approach. QCA offers the greatest insight when paired with careful consideration of theory and cases. QCA also has limitations relative to regression and other types of correlational analysis: it is not designed to examine net effects or tendential relationships. If a researcher’s interest is in identifying the tendential impact of a particular hypothesized cause on an outcome, regression is a more appropriate technique. Perhaps most important, we want to emphasize that the choice of method in macrocomparative research is not an either/or decision. Each technique has strengths and drawbacks. Which is most appropriate will depend on the substantive question and the way in which the researcher wants to approach it. Sometimes this will call for regression, sometimes for QCA, sometimes for other strategies or techniques, and sometimes for the use of multiple methods.

Appendix Poor employment change performance. Raw values: absolute change (1995 value minus 1979 value) in employment in private sector consumeroriented services – wholesale and retail trade, restaurants and hotels, and community/social/personal services (ISIC 6 and 9; ISIC revision3 50–2, 55, 90–3) – as a percentage of the population age 15 to 64. Unfortunately, private sector employment can be distinguished from public sector employment in these industries only through 1995, so the time series for this variable ends in that year. Source: Torben Iversen, Department of Government, Harvard University, calculated from OECD data. For discussion see Iversen and Wren (1998). High earnings inequality. Raw values: P50/P10 ratio for earnings among full-time employed individuals. Averaged over 1979–95. Source: OECD unpublished data set. High wage increases. Raw values: year-to-year percentage change in employee compensation, adjusted for changes in productivity and for inflation (real unit labor costs). Averaged over 1979–95. Source: Authors’ calculations from data in OECD (2004a). High payroll and consumption taxes. Raw values: government revenues from social security contributions, payroll taxes, and taxes on goods

Table 3A.1

Fuzzy-set scores.

Australia Belgium Canada Denmark Finland France Germany Italy Japan Netherlands Norway Sweden United Kingdom United States


Low earnings inequality

High wage increases

High payroll and consumption taxes



.27 .61 .70 .90 1.00 .76 .57 .62 .00 .67 .87 1.00 .26 .21

.49 1.00 .00 .90 .82 .55 .55 .87 .46 .66 .93 1.00 .30 .00

.45 .56 .47 1.00 1.00 .66 .92 .69 .92 .00 .00 .18 .69 .34

.00 .88 .19 .39 .82 1.00 .77 .68 .09 1.00 .86 1.00 .42 .00

.22 .73 .11 .49 .58 .68 .80 1.00 .70 .66 .76 .85 .14 .00

.32 .71 .71 1.00 .67 .81 .45 .00 .33 .85 .68 1.00 .33 .35

High public employment

.76 .43 .57 1.00 .69 .60 .36 .25 .00 .23 1.00 1.00 .58 .38

Note: Variable descriptions and data sources are listed below.

87


and services (consumption) as a share of GDP. Averaged over 1979–95. Source: OECD (2004b, pp. 74 and 77, tables 14 and 20). High employment protection regulations. Raw values: index capturing the strictness of employment protection laws. Range is 0 to 2, with higher scores indicating greater strictness. Averaged over 1979–95. Source: Baker et al. (2004) update of data in Nickell and Nunziata (2002). High unemployment benefit generosity. Raw values: proportion of a worker’s former earnings (pretax) that is replaced by unemployment compensation and related benefits – for a worker with earnings at two-thirds of the national median (for example, the 33rd percentile) in the first year after losing the job. Averaged over 1979–95. Source: OECD unpublished data set; see Martin (1996) for discussion. High public employment. Raw values: public employment as a share of the population age 15 to 64. Averaged over 1979–95. Source: Authors’ calculations from data in OECD (2004a).

Note 1. The number column is useful in larger-N studies, where researchers ought to consider eliminating rows from consideration because too few cases conform strongly to their conditions. With an N of only 14, narrowing the data set would be inappropriate.

References Baccaro, Lucio and Diego Rei. 2005. “Institutional Determinants of Unemployment in OECD Countries: A Time-Series Cross-Section Analysis (1960–1998).” Discussion Paper 160/2005. International Institute for Labour Studies. Geneva. Available at: www.ilo.org. Baker, Dean, Andrew Glyn, David Howell, and John Schmitt. 2004. Data set for “Unemployment and Labor Market Institutions: The Failure of the Empirical Case for Deregulation.” Unpublished. Baker, Dean, Andrew Glyn, David Howell, and John Schmitt. 2005. “Labor Market Institutions and Unemployment: Assessment of the Cross-Country Evidence.” Pp. 72–118 in Fighting Unemployment: The Limits of Free Market Orthodoxy, edited by David R. Howell. Oxford: Oxford University Press. Bassanini, Andrea and Romain Duval. 2006. “Employment Patterns in OECD Countries: Reassessing the Role of Policies and Institutions.” OECD Social, Employment, and Migration Working Paper 35. Organization for Economic Cooperation and Development. Available at: www.oecd.org. Blanchard, Olivier and Justin Wolfers. 2000. “Shocks and Institutions and the Rise of European Unemployment: The Aggregate Evidence.” Economic Journal 110: 1–33.


89

Blau, Francine D. and Lawrence M. Kahn. 2002. At Home and Abroad: U.S. Labor Market Performance in International Perspective. New York: Russell Sage Foundation. Esping-Andersen, Gøsta and Marino Regini, eds. 2000. Why Deregulate Labour Markets? Oxford: Oxford University Press. Glyn, Andrew and Wiemer Salverda. 2000. “Employment Inequalities.” Pp. 35–52 in Labour Market Inequalities, edited by Mary Gregory, Wiemer Salverda, and Stephen Bazen. Oxford: Oxford University Press. Howell, David R., Dean Baker, Andrew Glyn, and John Schmitt. 2006. “Are Protective Labor Market Institutions Really at the Root of Unemployment? A Critical Perspective on the Statistical Evidence.” Unpublished. IMF (International Monetary Fund). 2003. “Unemployment and Labor Market Institutions: Why Reforms Pay Off.” Pp. 129–50 in World Economic Outlook. New York: IMF. Iversen, Torben and Anne Wren. 1998. “Equality, Employment, and Budgetary Restraint: The Trilemma of the Service Economy.” World Politics 50: 507–46. Kemmerling, Achim. 2005. “Tax Mixes, Welfare States, and Employment: Tracking Diverging Vulnerabilities.” Journal of European Public Policy 12: 1–22. Kenworthy, Lane. 2004. Egalitarian Capitalism. New York: Russell Sage Foundation. Kenworthy, Lane. 2008. Jobs with Equality. Oxford: Oxford University Press. Martin, Andrew. 2004. “The EMU Macroeconomic Policy Regime and the European Social Model.” Pp. 20–50 in Euros and Europeans: Monetary Integration and the European Model of Society, edited by Andrew Martin and George Ross. Cambridge: Cambridge University Press. Martin, John P. 1996. “Measures of Replacement Rates for the Purpose of International Comparisons: A Note.” OECD Economic Studies 26: 99–115. Nickell, Stephen. 1997. “Unemployment and Labor Market Rigidities: Europe versus North America.” Journal of Economic Perspectives 11(3): 55–74. Nickell, Stephen and Luca Nunziata. 2002. Data set for “The Beveridge Curve, Unemployment, and Wages in the OECD from the 1960s to the 1990s.” Nickell, Stephen, Luca Nunziata, and Wolfgang Ochel. 2005. “Unemployment in the OECD since the 1960s: What Do We Know?” Economic Journal 115: 1–27. OECD (Organization for Economic Cooperation and Development). 1994. The OECD Jobs Study. Paris: OECD. OECD. 2004a. OECD Statistical Compendium. Edition 01#2004. Paris: OECD. OECD. 2004b. Revenue Statistics, 1965–2003. Paris: OECD. OECD. 2005. “Earnings Database.” Version dated November 5, 2005. Data set. Unpublished. Paris: OECD. OECD. 2006. OECD Employment Outlook. Paris: OECD. Ragin, Charles C. 1987. The Comparative Method. Berkeley: University of California Press. Ragin, Charles C. 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press. Ragin, Charles C. 2008 (forthcoming). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press.

90 Method and Substance in Macrocomparative Analysis Ragin, Charles C. and Benoît Rihoux. 2004. “Qualitative Comparative Analysis (QCA): State of the Art and Prospects.” Qualitative Methods Fall, 3–13. Ragin, Charles C. and John Sonnett. 2005. “Between Complexity and Parsimony: Limited Diversity, Counterfactual Cases, and Comparative Analysis.” Pp. 180– 97 in Vergleichen in der Politikwissenschaft, edited by Sabine Kropp and Michael Minkenberg. Wiesbaden: VS Verlag für Sozialwissenschaften. Scharpf, Fritz W. 1997. “Employment and the Welfare State: A Continental Dilemma.” Working Paper 97/7. Max Planck Institute for the Study of Societies. Available at: www.mpi-fg-koeln.mpg.de. Scharpf, Fritz W. 2000. “Economic Changes, Vulnerabilities, and Institutional Capabilities.” Pp. 21–124 in Welfare and Work in the Open Economy. Volume 1: From Vulnerability to Competitiveness, edited by Fritz W. Scharpf and Vivien A. Schmidt. Oxford: Oxford University Press. Schettkat, Ronald. 2005. “Is Labor Market Regulation at the Root of European Unemployment? The Case of Germany and the Netherlands.” Pp. 262–83 in Fighting Unemployment: The Limits of Free Market Orthodoxy, edited by David R. Howell. Oxford: Oxford University Press. Siebert, Horst. 1997. “Labor Market Rigidities: At the Root of Unemployment in Europe.” Journal of Economic Perspectives 11(3): 37–54. Stephens, John and David Bradley. 2005. “Employment Performance in OECD Countries: A Test of the Deregulation Thesis.” Unpublished. Department of Political Science, University of North Carolina.

4 Do Family Policies Shape Women’s Employment? A Comparative Historical Analysis of France and the Netherlands Joya Misra and Lucian Jude∗

Do family policies influence women’s employment rates? Differences in women’s employment rates, particularly for women of childbearing age, appear to be associated with the complex of work–family supports available to families. In this chapter we explore differences in women’s employment in France and the Netherlands over recent decades, taking a comparative-historical approach to examine the factors that shape women’s employment. We ask whether family policies actually drive women’s employment, or whether they may be better understood as responses to women’s employment patterns. At the same time, we explore alternative explanations for the variation in women’s employment – including the economic conditions that may drive women’s employment and cultural differences regarding gender, care, and work. While quantitative approaches may identify associations across a range of countries, we argue that comparative historical methods are best suited to exploring historically situated relationships among policy, politics, economics, culture, and women’s labor market participation. In this volume, two other chapters examine macrocomparative evidence regarding the contention that family policy drives changes in women’s employment rates. In their examination of women’s labor force participation across 14 countries over four decades, Eliason et al. (2008) provide support for the argument that public child care, maternity leave, and public sector employment play a central role in shaping women’s employment participation.1 Their sophisticated research design, using a fuzzy-set analysis and a formulation of the intention-to-treat design, carefully assesses causal relations and effects. Hicks and Kenworthy (2008) similarly examine whether child care provision, maternity leave, 91


and public sector employment shape women’s employment, using regression analysis for the same countries and time period. Yet, while their bivariate analyses suggest a strong relationship between family policy and women’s employment, their multivariate analyses suggest that this effect may be spurious – that differences in women’s employment may in fact be driven by women’s educational attainment. Clearly, there are still questions about whether and how family policies may influence women’s employment. In this chapter, we examine the historical trajectory of women’s employment in two countries, France and the Netherlands, over recent decades, and attempt to identify how these patterns have been shaped by (or have shaped) family policy. In addition to differences in employment rates, we consider differences in full-time versus part-time employment rates, and in employment patterns among mothers of small children and among single mothers. In the next section, we review the theoretical arguments in support of family policies shaping women’s employment, as well as some counterarguments regarding economic and cultural factors. Next, we discuss our case selection, and provide some simple comparisons between France and the Netherlands regarding employment. Finally, we trace changes in employment in both countries, considering carefully whether the evidence suggests that family policy plays a causal role in shaping women’s employment.

Factors shaping women’s employment Given that women’s employment is highest in Scandinavian countries, which also provide generous family policies, family policies appear to support higher levels of women’s employment. For example, the provision of free or subsidized childcare appears to be a strong predictor of women’s employment (Gustafsson and Stafford, 1992; Buchmann and Charles, 1995; Esping-Andersen, 2000; Gornick and Meyers, 2003; Eliason et al., 2008; Mandel and Semyonov, 2006), although it is a better predictor of full-time rather than part-time employment (Kremer, 2008). Effective school scheduling – with longer school hours and lunch breaks taken at school – has a similarly positive impact on women’s employment (Buchmann and Charles, 1995; Gornick and Meyers, 2003). Short parental leave policies, particularly when they guarantee women’s jobs, help maintain women’s continuity of employment after having children (Pettit and Hook, 2005; Eliason et al., 2008). Yet family policies do not boost women’s employment unambiguously. For example, long periods of care leave may encourage women to return

Joya Misra and Lucian Jude

93

to work, but only after their children enter public pre-school (usually at the age of three), and to work in part-time, less career-oriented jobs (Morgan and Zippel, 2003; Mahon, 2005; Pettit and Hook, 2005).2 Indeed, as Orloff (2002, p. iii) argues, Social policy is also significant, if not so much for increasing women’s employment, then for shaping the patterns of women’s employment, especially the continuity of their participation over the life course, and the conditions under which they work – as well as for helping constitute the stakes in gendered social policies. Particular groupings of policies may shape the patterns of women’s employment in terms of whether women work full time or part time, work after marriage or when their children are small, and so on. This research suggests that we should be examining not only the extent of women’s employment, but the type of women’s employment and variations among groups of women.3 Yet women’s employment continuity is certainly highest in countries with the greatest level of policy support for working mothers (Stier et al., 2001; Gornick and Meyers, 2003; Kenworthy, 2008). While countries with more generous family policies also have higher levels of women’s employment, it may be that higher levels of women’s employment are driving family policy provision. Indeed, Huber and Stephens (2000) show that women’s labor force participation is an important determinant of both public funding and the delivery of welfare state services, suggesting that as women enter the labor force they need relief from traditional caregiving duties, and “make demands on the state for better health, education, and welfare services, regardless of the institutional and political context” (Huber and Stephens, 2000, p. 334). Huber and Stephens (2000) also note that women pressure for the expansion of welfare state services in their roles as social service workers. Therefore, higher levels of women’s employment may produce generous family policy, generous family policy may generate higher levels of women’s employment, or both processes may be occurring simultaneously. An added wrinkle is that family policies do not neatly map onto differences in women’s employment rates. Women’s employment is very high in the United States, which provides very little family policy support, suggesting that other factors clearly must play a role. Rather than assuming that policies shape women’s employment decisions, a second explanation might highlight economic factors. Increased wages and better benefits for women, as well as favorable taxation policies, may encourage women to enter employment (Kenworthy, 2008; Misra


and King, 2005). Indeed, where tax policies reward women’s employment, women are more likely to enter and remain in the labor market (Kenworthy, 2008; Sainsbury, 1999).4 At the same time, where men’s real wages have dropped, women may also be more likely to enter the labor market out of economic need. Economic structure may also influence women’s employment. For example, where manufacturing wanes and the service sector grows, the economy may draw in more women workers. Indeed, women are particularly likely to be drawn in as public sector workers (Huber and Stephens, 2000). Active labor market policies may draw women into employment, and may include education and training programs, or policies meant to create additional jobs through developing parttime employment.Indeed, the availability of part-time employment may serve as a spur to women’s employment, depending on the context (Jenson, 1996).5 At the same time, labor surpluses may discourage women’s employment, and programs may be put into place that reward women for caretaking during periods of slack labor markets. Therefore, economic factors may play a key role in explaining how and where women are drawn into employment, and whether they are primarily viewed as full-time or part-time workers. Finally, a third explanation focuses on differences in women’s preference for employment (Hakim, 2000, 2003, 2004) or in cultural support for women’s employment (Pfau-Effinger, 1998, 2004; Kremer, 2008).6 This literature notes that while “women are treated [by many scholars] as rational individuals who orient their behaviour according to financial incentives” (Pfau-Effinger, 1998, p. 147) such as the costs of childcare and tax penalties for dual earner couples, long-lasting cultural traditions, values, and norms may play a role (Sackmann, 1998; Pfau-Effinger, 1998, 1999, 2004; Kremer, 2005, 2008). Welfare states may promote different “ideals of care” which reflect different – and dynamic – notions of what is appropriate (and gendered) care (Kremer, 2008). As Kremer (2008) argues, structural supports and economic incentives must fit into moral ideals of care in order to effectively promote women’s employment. Therefore, this might suggest that presence of family and labor policies may not shape women’s employment patterns simply, and that larger cultural traditions help explain variations in women’s employment.7 We examine Dutch and French women’s employment patterns to assess whether the historical evidence supports these explanations of women’s employment. Of course, these explanations leave out other important factors – party politics, women’s movements, and other social movements – that play a direct role in shaping policy choices. However,


95

we focus our attention on whether family policies, economic conditions, or gendered ideals of care best explain variations in women’s employment for these two cases.

Method and case selection In order to explore the factors that shape employment patterns in greater detail, we take a comparative historical approach to explore two cases – France and the Netherlands. We rely on secondary data sources to construct contextualized comparisons of women’s employment in these two countries. A focus on two carefully selected cases gains in depth what it lacks in breadth, giving it certain advantages over large-n quantitative studies. First, the emphasis on processes over time highlights similarities and differences across cases on a range of different variables, rather than assuming unit homogeneity. Also, this approach captures the duration, relative timing, and intersection of events, which is crucial for making causal assessments. Indeed, events occur both in time and over time, and historically grounded comparisons can detect these temporal nuances in a way large-n studies cannot. Further, as Mahoney and Rueschemeyer (2003, p. 13) state, Because comparative historical investigators usually know each of their cases well, they can measure variables in light of the broader context of each particular case, thereby achieving a higher level of conceptual and measurement validity than is often possible when a large number of cases are selected. Social phenomena are typically characterized by “causal complexity” (Ragin, 1997), wherein the same variable often has different effects in heterogeneous contexts, and the same outcome is produced by different causal processes. Comparative historical analysis captures these contextual particularities through process tracing, achieving an intensive dialogue between theory and evidence. The result is a more complete picture of the relevant social and political forces at play than is possible with more abstract theorizing. While the direct goal of comparative historical research is not to generate universally applicable knowledge, multiple observations both within and across cases make this approach an effective means to “test” and refine existing theories, formulate new concepts, and develop novel explanations. These contributions are based upon critical historical details that may be overlooked or difficult to measure in large-n studies.


Yet large-n and small-n studies are complementary precisely because of their unique advantages. In constructing logical causal arguments about factors impacting women’s employment, we draw on arguments from large-n studies to legitimate our claims. We do not, however, select supporting data merely to bolster one particular analysis over another. Nor do we presume a deterministic set of forces. We construct a coherent picture of large-scale social processes in specific contexts that make sense given the available evidence. Certainly, there is the risk that our sources do not cover all of the important factors, which is similar to the risk survey researchers face regarding missing variables when analyzing secondary data. However, using a variety of different sources written by scholars in a range of disciplines, and attending to the available quantitative and qualitative data for each country, serve as a “check” on our data. Further testing and refinement of our arguments in other contexts and with other methods will continue the ongoing process of constructing “better” social theories for the advancement of knowledge. We have chosen France and the Netherlands as our two cases because they share some similarities, while they also differ in important ways. According to Esping-Andersen’s now-classic model (1990, 1999), in which he shows that nations tend to cluster in certain groups in terms of policy creation and outcomes, both France and the Netherlands fit amongst the conservative welfare regime (although the Netherlands is sometimes typed as social democratic) (Esping-Andersen, 1990; Hobson, 1994; Knijn, 1998). According to Esping-Andersen, in this regime the state uses policy to uphold status differences and preserve the traditional family. Of course, there are also important differences between the two countries, including very different political cultures. While the Dutch have subscribed to a notion of subsidiarity, which assumes that the central authority (state) should perform only those tasks that cannot be performed more effectively at a more immediate or local level, the French have a longer tradition of strong state intervention (and tolerance for state intervention into the economy). Therefore, the Dutch and French welfare states have been structured differently, and reflect differing notions about the relationship between the states, local communities, and families. Yet, the puzzle we explore is the difference between women’s employment in France and the Netherlands – and its recent convergence. Figure 4.1 describes differences in women’s employment rates in France and the Netherlands. While historically French women had higher levels of employment, the growth in Dutch women’s employment has been unusually steep over the last two decades, and Dutch women’s


97

0.7 France Netherlands 0.6

0.5

0.4

0.3

0.2

0.1

0 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Figure 4.1 Employment rate for women as a proportion of all women, 15–64 Source: ∗ Data from OECDs Corporate Data Environment (CDE) (2006).

employment levels now surpass those of French women. Yet there are clearly differences in employment rates by age groups. For example, employment has been increasing for French women between the ages of 25 and 54 since 1970 and decreasing for women under 25 and over 54. In comparison, Dutch women in all groups have seen an increase in employment rates since 1970, although women over 55 have significantly lower (though increasing) employment rates (Kenworthy, 2008). Given that our interest is in whether family policies may be shaping women’s employment, we focus our attention on women between 25 and 54, women who are particularly likely to be facing care responsibilities that may be mitigated by family policies. Figure 4.2 describes employment for this group for the two countries since about 1970. This graph makes clearer the significant rise in women’s employment for both countries, along with the convergence in rates for the two countries. While the increase in Dutch women’s employment is steeper, given that French women’s employment rates were at much higher levels at the beginning of this period, slightly more than 70 percent of women between 25 and 54 were employed in both nations in 2004. This leaves us with two puzzles: (1) Why were French women’s employment rates


0.8

France Netherlands

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Figure 4.2 Employment rates for women between 25 and 54, 1968–2004 Source: ∗ Data from OECDs Corporate Data Environment (CDE).

initially so much higher than Dutch women’s employment rates? (2) What explains the remarkable increase in Dutch women’s employment? One answer to the second part of this puzzle may be in understanding the different rates of full-time and part-time employment in the two countries. Figure 4.3 describes the difference in women’s full-time and part-time employment rates across the two countries. Since 1983, women’s engagement in part-time employment has increased in both France and the Netherlands. Yet the two countries are still more different than alike. Since about 1983, approximately half of French women have worked full time, while a much smaller proportion (between 12 and 16 percent) has worked part time. In the Netherlands, however, more women work part time than full time, and part-time employment rates have grown more quickly than full-time. Indeed, in 2004, 43 percent of Dutch women worked part time, while 31 percent worked full time. Clearly, simple employment rates do not tell the entire story – Dutch women’s employment has grown in large part through their engagement in parttime employment. Indeed, in comparison to other European countries, part-time work is significantly higher than average in the Netherlands, and lower than average in France (OECD, 2001, p. 96).


99

0.6

0.5

0.4

0.3

France P-T France F-T Neth P-T Neth F-T

0.2

0.1

0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 4.3 Full-time and part-time employment rates for women aged 25–54, 1983–2004 Source: ∗ Data from OECDs Corporate Data Environment (CDE) (2006).

Another way to consider the meaning of these numbers is to consider what proportion of employed women work part time. In 2004, while approximately 24 percent of employed French women worked part time, 60 percent of employed Dutch women worked part time. If we compare the average weekly hours for employed women, French women work on average 34 hours a week, while Dutch women work 25 hours a week (Hantrais and Letablier, 1997, p. 140). Even within part-time workers, we may see differences across the two countries. For example, among women working part time, approximately 25 percent of Dutch women work 25 hours a week or more, as compared to 40 percent of French women (Hantrais and Letablier, 1997, p. 140). Interestingly, parttime women workers in France are also more likely to report that they are working part time involuntarily and would prefer to work full time (see Figure 4.4). This suggests a significant difference between the way women’s work is conceptualized in France and the Netherlands. Employment rates also vary across the two countries for mothers of young children. In 1990, 61 percent of French mothers with children under six were employed, compared to 37 percent of Dutch mothers with children under six. By 2002, the numbers for Dutch mothers had

100 Method and Substance in Macrocomparative Analysis 0.4 0.35 0.3 0.25 0.2 0.15 France 0.1

Netherlands

0.05 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 4.4 Involuntary part-time work as a proportion of part-time work for women, 25–54, 1992–2004 Source: Data from OECDs Corporate Data Environment (CDE) (2006).

changed dramatically, with 69 percent of Dutch women with children under six employed, as compared to 62 percent of French women with children under six. However, high levels of part-time employment in the Netherlands help explain this increase. Indeed, 81 percent of Dutch mothers with children under six are employed part time, in comparison to only 26 percent of French mothers with children under six (OECD, 2005, p. 60, table 4.1). Therefore, Dutch mothers may be somewhat more likely to work, but they are primarily working part time. Similarly, employment rates vary in interesting ways depending on family forms. Families with children under six are a good illustrative case here as well. In 1999, 31 percent of French mothers in two-parent heterosexual families with children under six worked full time while 20 percent worked part time. In comparison, only 4 percent of Dutch mothers in two-parent heterosexual families with children under six worked full time while 48 percent worked part time. Clearly, very different employment patterns exist. Likewise, 35 percent of French lone mothers with children under six worked full time while 14 percent worked part time; in comparison, only 6 percent of Dutch lone mothers with children under six worked full time while 32 percent worked part time (OECD, 2001, p. 135, table 4.2; also see Chambaz, 2001). Over time, Dutch lone mothers with young children have entered the labor force in growing

Joya Misra and Lucian Jude 101

numbers (Bussemaker et al., 1997; Pott-Buter, 1993). On the other hand, French lone mothers with children under six have actually reduced their employment since 1990 (OECD, 2001, p. 134, table 4.1). In the following three sections, we explore these broad differences in greater depth. What explains the stark initial differences in French and Dutch employment rates? What explains the convergence in employment rates for women between 25 and 54 over time? What explains the difference in part-time employment rates across the two countries? Do differences in family policy explain these variations? Do differences in economic factors and conditions? Or do cultural differences make the most persuasive argument?

Family policy Family policy, aimed not only at increasing birth rates, but also at integrating women into the labor market, has a long history in France, dating back to the 1930s (Jenson 1990; Misra 1998).8 These policies supported families generously, although some financial incentives were premised on the idea that women would wish to stay home with very young children. While women’s employment had historically been fairly high, its steady increase since the 1970s – particularly for women of childbearing age – has been in part possible due to the high-quality child care available for working parents. High levels of women’s full-time employment in France are premised on the availability of this child care. While (relative to other countries) fairly generous welfare benefits existed for mothers (including a variety of measures directed at single mothers), family policy since the 1960s has primarily limited benefits to support mothers staying home for care, instead encouraging mothers’ workforce participation and wage reliance.9 A range of additional work–family reconciliation policies put into place during the 1970s included reforms to family allowance and tax laws so that two-income families were no longer penalized (Jenson, 1990; Hantrais, 1994). The benefit levels for the “single salary allowance,” which explicitly supported a male breadwinner family, were reduced, and the allowance was finally eliminated in 1978 (Letablier, 2003). During the same period, Socialist President François Mitterrand expanded the provision of child care for children under the age of three in order to promote gender equality in access to employment and encourage women’s employment (Daguerre and Taylor-Gooby, 2003; Letablier, 2003).10 Maternity leave was also expanded; by the 1980s, maternity leave was six weeks pre-birth and ten weeks after, at 90 percent


income replacement (Jenson, 1990). Official government policy stated that women should have a “free choice” between working and staying at home. By 1982, 37 percent of two-year-olds, 83 percent of three-yearolds, and 97 percent of five-year-olds were in child care in the public care system (Jenson, 1990, p. 118). The majority of the costs for child care were borne by the state; even the crèche system for babies and toddlers was heavily subsidized. Child care was also expanded to provide wraparound care – afterschool care, lunch, and summer programs – prioritizing children with working parents (Jenson, 1990). Yet despite this rhetoric of choice, single mothers have had fewer options than married mothers. While single mothers were eligible for benefits that support their caretaking, policy encouraged them to work, giving them priority in training and childcare. Indeed, single mothers were much more likely to use the crèche system. However, since the 1980s, some family policy reforms have worked against employment, particularly for mothers of small children. For example, rising unemployment led to policies that encouraged women to stay at home, including family supplements and a new allowance for single parents (Jenson, 1990; Hantrais, 1993). The parental care leave (Allocation Parentale d’Education), created in 1985, expanded in 1986, and further expanded and popularized in 1994, provides benefits for previously employed parents with children to care for their children at home.11 This policy may help explain why the rate of increase in women’s employment slowed during the late 1990s, since 99 percent of those who use the leave are women (Kenworthy, 2008; Heinen and Martiskainen de Koenigswarter, 2001).12 Women are more likely to take the leave due to its fairly low replacement rate, and larger cultural expectations. While women do appear to eventually return to the labor market, these longer leaves likely have negative effects on their long-term employment trajectories. Child care remains free and universally available from the age of three. Almost 100 percent of children between the ages of three and six, as well as 75 percent of children between two-and-a-half and three years of age, are enrolled in the ecole maternelle. Yet, in the early and mid 1990s, the Right also developed new benefits and tax relief to support private child care costs for young children, benefiting middle-class parents, while weakening the crèche system necessary for working-class parents (Hantrais and Letablier, 1997; Morgan, 2005; Daguerre and Taylor-Gooby, 2003). While the Socialist Party in 2001 expanded the number of public child care places (Daguerre and TaylorGooby, 2003), it also developed a new benefit to pay for private child


care for women who are returning to work (Letablier, 2003).13 The state now provides less support to the crèche collective, the publicly subsidized centers, and more young children are now in subsidized and regulated family daycare (crèche families) than in crèche collective (Morgan, 2005). This subsidized care may encourage part-time employment, as it covers the cost of only two to three days of child care (Morgan, 2005, p. 253). Clearly, French policy on child care has become more mixed. Yet, as Leslie King (1998, p. 47) argues, “In general, French family policy has managed, compared to other states’ family policies, to help facilitate combining labor force participation and parenting; and women in France tend to have fewer interruptions in their work lives than women in other countries.” While many French mothers exit the labor force briefly when their children are infants, these interruptions remain shorter, on average, than in many other European countries (Laufer, 2003).14 The short 2002 paid paternity leave has been very successful; though the replacement rate does not fully replace the salary for many fathers, 94 percent of fathers take the leave for the maximum period of 11 days in the first four months (18 days for multiple births) (Mazur, 2003; Laufer, 2003). In comparison, Dutch family policy was much less developed during the first several decades of the twentieth century, in part due to the strong principle of subsidiarity, which encouraged care provision within the family, rather than through the state. Yet, in 1965, the General Welfare Act was inaugurated, and the state was delegated a stronger responsibility for ensuring citizens’ well-being, or welzijn. Family policies promoted the principle that all citizens should have equal access to welfare benefits and services, yet policy also reflected the primacy given to maternal care (Korteweg, 2005). For example, welfare policy provided generous social benefits for caregiving by single mothers, who were included in the general cultural ideal of the caring housewife (Pfau-Effinger, 1999). Social rights were tied to the principle of family maintenance, and tax benefits were given to the (usually male) family provider (Sainsbury, 1994). When married women did work for pay, couples were jointly taxed at high rates without the option of separate taxation.15 In addition, child care policy was grossly underdeveloped. During the 1950s, the only support for public childcare came from the Dutch women’s movement (Morgan, 2006). Until 1965, child care was provided primarily by church organizations as a form of poor relief only. Church leaders discouraged mothers’ labor market involvement, which they believed undermined traditional family structures (Bussemaker,


1998; Gustafsson and Stafford, 1995). In the 1960s and 1970s, various types of child care developed, including pre-school playgroups, child day care centers, and out-of-school child care centers (OECD, 2000, 2002). However, these arrangements were oriented toward children’s social development rather than support for working parents and women’s autonomy, and were available on a mostly part-time basis (Bussemaker, 1998; Morgan, 2006). As Morgan (2006, p. 85) notes, even Dutch Social Democratic and Communist parties assumed that mothers “should devote themselves full-time to the care of their children.” In the mid-1970s, a left-wing coalition government sparked a new discourse on formal gender-equality policies: “Left-wing parties and some liberal politicians placed increasingly more emphasis on women’s interests as a part of the childcare debate. Various politicians remarked that equality policies had no chance of success without accessible and affordable childcare” (Bussemaker, 1998, p. 82). That discursive opening, though, was short-lived as more conservative governments returned to power in the 1980s and Christian-democratic ideology again rose to prominence, which branded women’s labor market involvement as problematic. Child care provision thus remained fairly limited throughout the 1980s even though women’s labor market participation increased dramatically.16 At the end of the 1980s, a booming economy, a shortage of labor, an increase in the number of elderly and single households, and a decrease in the number of taxpayers led the government to expand family policy. With the goal being to increase the labor market participation of both single and partnered women, policy provision shifted toward the needs of mothers (and the labor market) (Gustafsson and Stafford, 1995; Knijn and Selten, 2002; OECD, 2000; Plemper, 1996). A 1990 tax reform reduced the basic tax allowance for breadwinners, thus lowering disincentives for second-earners to take up paid work (Visser, 2002, p. 33). That same year, a coalition cabinet made up of Christian Democrats and Social Democrats passed the Stimulation Measure on Childcare to expand childcare facilities (Bussemaker 1998).17 The policy emphasized child care provision as a responsibility that must be shared by the government, parents, and employers.18 Parental contributions are calculated according to income, yet the majority of Dutch families who use formal child care provisions have an average or above-average income (OECD, 2000).19 Child care is subject to collective corporate arrangements. Only 80 percent of employers fall under Collective Agreements; by 2003, 76 percent of collective agreements included childcare provisions (Morgan,


2006, p. 172). However, many employees end up paying full price for child care (Kremer, 2005). Child care policy thus benefits mostly educated, middle- and upper-class men and women in high-level positions (Grünell, 1999). Parents, though, commonly face long waiting lists as they compete for a limited (though growing) number of childcare places.20 Despite serious shortcomings, child care in the Netherlands has improved dramatically over the years. The percentage of children from birth to three years of age in state-subsidized child care was 2 percent in 1988, 8 percent in the mid-1990s, and almost 22 percent around 2001; for children aged three to six years the rate was only 6 percent in the mid-1990s, which is partly explained by the fact that most children start school early (Knijn and Kremer, 1997; Kremer, 2008; Morgan, 2006; OECD, 2000). Yet there remain few options for very young children, with enough child care places for only around 13 percent of children under four years old, which constrains the options available to Dutch mothers (OECD, 2002, p. 97). Since 2005, parents have been reimbursed for a portion of child care costs through the taxation system, while the centers no longer receive state subsidies. These changes were made in order to stimulate supply through demand subsidies, but have led to unusually high childcare costs for Dutch parents. The Netherlands legislated its first leave arrangements in 1990. Since that year, all mothers are entitled to 16 weeks of paid maternity leave, with the government making payments up to a bit over the average wage rate in the economy. In 1991 the Dutch government introduced parental leave, providing leave part time on an unpaid basis. For a six-month period until the child is eight, parents are entitled to work part time (at least 20 hours per week) while caring for their child(ren) (Jaumotte, 2003; Knijn and Kremer, 1997; Morgan and Zippel, 2003; OECD, 2000, 2002; Plantenga and Hansen, 1999).21 Dutch parents in two-adult families can choose to take parental leave simultaneously, yet it is rare for fathers to make use of prolonged child-related leave.22 In addition, the number of mothers using parental leave is significantly lower than the number of mothers using maternity leave. Strict eligibility criteria for parental leave, which maintain that employees must work for an employer for at least 12 months, partly explain this discrepancy (OECD, 2002, p. 130). Paternity leave only became available as a legal right in 2001 with the introduction of the Work and Care Act, and then provided fathers a mere two-day absence from work.23 This combination of leave arrangements seems to have had a significant effect on employment, as the employment rate of Dutch women with a child under age three


increased from 42 percent in 1992 to 70 percent in 2003 (Kremer, 2005, p. 3). The design of leave programs on a part-time basis further explains the popularity of part-time employment in the Netherlands, especially among mothers with children. Dutch family policy also promotes the ideal of “parental sharing,” the equal participation of men and women in both paid and unpaid work. Billboards depicting prominent Dutch men engaged in care tasks are part of a government-sponsored media campaign. Fathers’ roles as carers are emphasized in public discourse, law, and social policy (Knijn and Selten, 2002). Nevertheless, while Dutch men are more involved in caregiving and more likely to take parental leave than in many other countries (Kremer, 2008), women still perform three-quarters of the care work in families with children (Knijn and Selten, 2002). Does family policy explain the similarities and differences we have noted? Clearly, French family policy has historically been more generous, and more oriented toward supporting women’s employment, which may help explain the initial difference in employment rates. Dutch family policy has also expanded over the years, which may help explain the greater convergence. Yet, as Figure 4.2 suggests, increases in Dutch women’s employment actually appear to have begun before the institution of these policies – suggesting that family policy has responded to increased women’s employment, rather than creating it. And there remain fairly important differences in the generosity and availability of family-work reconciliation policies. These differences may help explain the significant divergence, illustrated in Figure 4.3, in fulltime employment rates for the two countries. As Lane (1995, p. 13) notes, work–family measures “explain why a relatively large proportion of French women are able to have continuous working life and why they are not forced to switch to part-time employment after childbirth.” In the absence of high-quality, affordable child care, full-time women’s employment in the Netherlands remains difficult to achieve.

Economic factors and labor market policies Economic factors have substantially shaped French women’s incorporation into the labor market. Before World War II, French women’s labor force participation was high as a repercussion of the high death rate of men during World War I ( Jenson, 1990). Because industrialization was fairly gradual, women were primarily employed in family-based forms of production, including agriculture, small businesses, and crafts (Duane-Richard, 1995). During the postwar period, married women’s


employment dipped, in part because of the decline of the agricultural sector ( Jenson, 1988; Tilly and Scott, 1978; Duane-Richard, 1995). However, the shortage in skilled labor in the postwar period meant that women were not formally excluded from employment. By the 1960s, changes in the economy meant that women were increasingly being drawn into manufacturing, as well as the expanding service sector, and women’s employment increased rapidly during the 1960s and 1970s.24 Many women were employed in low-wage service and industrial sector jobs, although women also entered more protected jobs in the public sector (Jenson, 1988; Beechey, 1989). By 1974, women accounted for three-quarters of semi-skilled and unskilled manufacturing jobs (Jenson, 1988, p. 158). Labor market policies helped reinforce and remake women’s employment during this period. The French constitutions of 1946 (4th Republic) and 1958 (5th Republic) affirmed the principle of women’s right to work, and workplace equality for women.25 Women were given access to civil service jobs, where pay scales were integrated, while the French minimum wage law helped ensure a wage floor for women. Research performed by the Mouvement Démocratique Féminin (established in 1962 by prominent Left feminists) and the Comité du Travail Féminin (established in 1965, as an advisory committee on women’s employment within the Department of Labor) as well as others indicated that wage inequality was due to job segregation, unequal access to education, training, and discrimination (Laufer, 2003; Revillard, 2006). As a result, the legislature passed laws banning wage discrimination in 1972, requiring equal pay for equal work in 1973, and banning discrimination (including pregnancy discrimination) in hiring and firing in 1975. In the 1970s, the government also introduced professional training programs meant to equalize women’s opportunities, provide women greater opportunities for promotion, and facilitate re-entry for women who took time out of the labor market (Jenson, 1990).26 However, as Revillard (2006) notes, these issues were on the political agenda due to the larger context of a labor force shortage. Following the employment crisis of the mid-1970s, men’s labor market participation dropped, but that of women continued to rise, since women served as a more tractable labor force, accepting jobs with lower wages, fewer benefits, and fewer opportunities for advancement (Duane-Richard, 1995, p. 43; Jenson, 1988, 1990, p. 109). By the 1980s, men continued to earn higher wages and be more likely to be promoted, particularly in the private sector and among professional occupations, while women were more likely to be “stuck in dead-end


jobs and paid poorly throughout their working lives” (Jenson, 1990, p. 109). Until the 1970s, there was little part-time employment in France, particularly in comparison to other European nations, and most women worked full time. In the 1970s, due to the employment crisis and shortage of full-time jobs (Duane-Richard, 1995), the state began emphasizing active labor market policies and employment creation, including through part-time work. From 1973 to 1981, there was a 42 percent increase in part-time work for women (Bakker, 1988). While the Socialists initially discouraged part-time work, they eventually put incentives in place to create part-time employment, by allowing employers to reduce their social security contributions for part-time jobs (Malo et al., 2000). As a result, more than two-thirds of new jobs taken by women in the 1980s were part-time ( Jenson, 1990).27 However, the Socialists also put protections in place for part-time workers regarding rights, wages, and benefits similar to those held by full-time workers, and part-time workers work relatively long hours ( Jenson, 1996; Orloff, 2002). Since part-time work is thus more “expensive” for employers, levels of part-time work remain lower in France than in many other countries (Laufer, 1998; Hantrais and Letablier, 1997).28,29 During the 1980s, a number of reforms, led by Yvette Roudy of the Ministry of the Rights of Women (created in 1981 by the Socialists), helped protect and clarify the rights of women workers. These reforms included the 1983 comprehensive anti-discrimination law, the loi Roudy, which filled in the gaps of earlier legislation and was “designed to tackle three problem areas: access to training and promotion, pay, and unemployment” (Hantrais, 1994, p. 95).30 The law equalized opportunities, strengthened equal pay legislation, gave trade unions more power in addressing gender equity issues, and required employers with more than 50 employees to report annually the relative situation of men and women regarding both wages and positions and develop plans regarding recruitment, training, and promotions (under the rubric of “positive action”) (Hantrais, 1993, 1994; Laufer, 2003).31 This law reflects comparable worth policy, by basing pay specifically on “a comparable level of professional knowledge, of equivalent qualifications, experience, and responsibilities” (Laufer 2003, p. 430). Feminists have continued to push for and win expansions in training programs and, since 2001, have helped create a detailed process for negotiations around gender equality within firms, including real sanctions for firms that do not conform to the reporting requirements of the loi Roudy (Mazur, 2003; Laufer, 2003).


In an effort to increase women’s entrance into non-traditional fields, the state also established a contract that subsidized wages and training costs for women in training programs for jobs dominated by men, and worked with the Ministry of Education to create scholarships directed at bringing women into non-traditional careers (Laufer, 1998, 2003; Jenson, 1988). As a result, France has a higher proportion of well-educated women, as well as higher levels of women working in “professional and technical” employment than in comparable countries (Laufer, 1998; Lane, 1995). Yet gender segregation remains a real issue for French women (Blau and Duncan, 2002).32 While wage inequality has generally been decreasing, fairly high levels of labor market inequality remain, both in terms of occupational gender segregation, and in terms of wages – particularly at the top of the wage distribution (Pfefferkorn, 1996; Laufer, 2003).33 Finally, as noted previously, rising unemployment rates have also led to changes in French family policies, such as parental care leave, that encourage women to stay at home. Because the leaves have been conceptualized as a job-creation measure (as workers go on leave), they have been accepted by both the Left and Right, even though the leaves have clearly reduced the number of women with two children in the labor force and reinforced a more traditional gender division of labor in the home (Fagnani, 1995; Silvera, 2000; Laufer, 2003; Heinen and Martiskainen de Koenigswarter, 2001; Morgan and Zippel, 2003). For many years Dutch labor market policies were discriminatory to women, encouraging or coercing women to remain at home so that men could secure needed jobs. For example, in 1924 married women were banned from taking public service jobs, including jobs as teachers (Gustafsson and Stafford, 1995; Pott-Buter, 1993).34 This ban, supported by most unions, was withdrawn in 1957 but persisted in many municipalities even ten years later. Employers could legally fire a woman because of pregnancy, childbirth, or marriage until new legislation was passed in 1973; Swedish women, by contrast, secured such protection in 1939 (Pott-Buter, 1993). Further, as Visser (2002, p. 28) notes, “Under the regime of centrally guided wage policy, in force between 1945 and 1962, women were paid less than men in the same jobs.”35 A statutory minimum wage was instituted in 1969 that applied equal rates to men and women, but until 1971 employers received a dispensation to pay lower wages to women. Until 1993, the minimum wage did not apply to the (mostly female) employees who worked less than one-third of the usual working week (Visser, 2002).


The relative affluence of the Netherlands enabled most people to sustain relatively high living standards on a single income. But during a period of rapid modernization after World War II, women’s opportunities for paid employment began to change. A shortage of workers led to increased demand for labor while the public service sector grew, compelling some companies to try to attract female personnel (Karsten, 1995; Pfau-Effinger, 1998, 1999; Plemper, 1996). The preferred strategy was to attract Spanish, Turkish, and Moroccan men to compensate for the labor shortage (Bussemaker, 1998; Karsten, 1995). However, as early as 1956, some companies began offering childcare for working mothers with young children. By 1973 corporations provided about 10 percent of the nation’s negligible supply of child care. However, when the labor shortage disappeared during the 1970s, firms closed their childcare centers (Bussemaker, 1998). From the oil crisis of 1973 until the mid-1980s, the Netherlands suffered a severe economic crisis, which resulted in high unemployment. The government initially responded with efforts to decrease labor supply, using such strategies as early retirement, repatriating foreign workers, and encouraging women to stay at home.36 However, pressed by economic necessity, the government restructured the welfare state and put into place active labor market policies directed at women. Activity by women’s movements contributed to this shift. In the 1970s, feminists put a call for change in the national policy on women on the political agenda. In 1974 a commission for the promotion of gender equality was established at the highest political level. In the 1980s the government embraced gender equality and the possibility of women to lead an independent existence, as well as a just division of care and housework, as new political aims (Outshoorn, 1995; Pfau-Effinger, 1999). Welfare restructuring during this period led to the dismantling of some elements of the welfare state, a freezing of wages, and the promotion of part-time jobs.37 The government presented part-time work as a way for women to reconcile work and family, and also as an instrument for facilitating more flexibility for employers while decreasing unemployment (Plantenga, 1996). These measures worked to turn the Dutch economy around, and by the end of the 1980s a booming economy and a shortage of labor helped support the expansion of family policies in order to bring women workers into the labor market (Knijn and Selten, 2002). In the tight labor market of the 1990s, the promotion of part-time work helped match labor demand with labor supply by giving employers flexibility, expanding the pool of potential workers, and helping employees meet their goals for work and family balance (Berg et al., 2004).


Two laws regulate part-time work in the Netherlands. The Equal Treatment Act, effective since 1994, mandates that part-time work must be treated the same as full-time work in terms of wages, benefits, and training. This act also prohibits discrimination on the grounds of gender, marital status, race, nationality, religion, belief, political opinion, and sexual orientation. Havinga (2002) reports, however, that this legislation has limited effectiveness in regulating gender-based discrimination due to deficient knowledge of the law, incompatibility with existing routines and traditional ways of doing things, expected costs of compliance, the passive approach by the Equal Treatment Commission, and inadequate support and legal assistance for complainants. Indeed, compared to men, women are concentrated in a relatively small segment of the labor market.38 In addition, as in other countries, both women and men earn lower wages if employed in female-dominated occupations, indicating a gender bias independent of education, skills, and responsibility (De Ruijter et al., 2003). Further, 68 percent of Dutch women in the labor market work part time, compared to 18 percent of men. Of women parttime workers, 32 percent work less than 20 hours per week (Berg et al., 2004; Kremer, 2001; OECD, 2002). The second law that regulates part-time work is the Adjustment of Hours Act, which came into effect in July 2000. This act provides workers with the legal right to periodically request reductions or increases in weekly working time, for whatever reason. This option, though, mainly benefits full-time workers – rarely mothers – who want to reduce their working hours. Employers can refuse workers’ requests if they can demonstrate that fulfilling the request will create a hardship for the company, but most requests are granted as the burden of proof lies with employers (Berg et al., 2004; OECD, 2000, 2002).39 Since 1996, welfare reform has required lone mothers to find parttime work – defined as 12 hours per week – once their youngest child reaches age five.40 Beyond 12 years old, mothers are required, like everyone else, to find full-time work. However, the 1996 law has been rather unsuccessful in getting lone mothers off social assistance. Lone parents on social assistance generally have low educational attainment and poor job prospects, so part-time work is unlikely to make a material difference in their lives. Further, full-time work conflicts with the belief held by many mothers that they alone are best suited to care for their children. Indeed, a lack of access and uncertainty about the quality of public child care, and a lack of after-school care facilities makes work difficult for these mothers.41 Since local authorities wield significant discretionary power, they often grant exemptions to lone mothers based upon lenient


interpretations of paid work rules (Korteweg, 2005; OECD, 2002). A survey conducted by Knijn and van Wel (2001) suggests that 60 percent of lone mothers with children above age five are exempted from “normal” job search requirements. Nevertheless, over the years Dutch lone mothers with young children have entered the labor force in growing numbers (Bussemaker et al., 1997; Pott-Buter, 1993), perhaps due to altered cultural perceptions and increased levels of educational attainment (de Jong Gierveld and Liefbroer, 1995; Jaumotte, 2003). Yet most lone mothers who are employed work only part time, which lowers their chances of achieving economic independence.42 Do economic conditions and labor market policies explain the similarities and differences we have noted? Clearly, French and Dutch policy makers have responded to economic conditions – bringing women into the labor market when labor supply has been low, and encouraging part-time employment as an active labor market strategy. At the same time, family policy has been used as a labor market policy – pushing women out of the labor market and into care when unemployment has been problematic. Yet these strategies do not fully explain the initial differences in Dutch and French women’s employment, or the recent convergence. Indeed, as Figure 4.2 makes clear, throughout this period we see an increase in women’s employment in both countries. These strategies do a better job of explaining the increase in Dutch women’s part-time employment over the last two decades than the fairly stable level of French women’s part-time employment (these trends are evident in Figure 4.3). As explained in OECD (2002, p. 134), the nature of child care provision, leave legislation, and work-time policy fosters part-time work in the Netherlands: The high costs and childcare capacity constraints in the Netherlands mean that formal childcare is used on a part-time basis, if at all, and by their nature informal care arrangements are often used on a part-time basis. In addition Dutch legislation of different sorts facilitates the use of part-time employment solutions. Equal rights to part-time workers and other workers on flexible contracts, the Adjustment of Hours Act and the design of leave programmes on a part-time basis illustrate the role of part-time work in the Dutch policy model. These policy signals contribute to explaining the popularity of part-time employment in the Netherlands, especially among mothers with children. Labor market policies may provide more insight. French women clearly had a higher level of workers’ protection in earlier decades, which might


explain French women’s higher levels of early participation in the labor market. Yet the evidence for this is far from definitive, as these protections may also reflect women’s earlier engagement in employment, Dutch labor market policies, in comparison, offered few protections for (mostly part-time) women workers until the early 1990s, when women’s employment rates had already increased substantially. Thus the timing of reforms in that nation also suggests their responsive nature. Perhaps labor market policies must be read in the context of the larger economic situation. In the French case, reforms in labor market policy appear to be in response to feminist movements and research – yet these policy changes may not have happened if not for a labor force shortage (Revillard, 2006). Similarly, reforms in the Netherlands can be partly attributed to feminist activity, but the promotion of part-time work and increasing emphasis on mothers’ roles as workers were driven mostly by governmental efforts to create jobs, reduce unemployment, and resolve economic strain created by a shrinking tax base.

Cultural factors Cultural traditions, values, and norms may play a role in determining women’s employment. France has a long history of ideological support for women’s labor force participation. For example, Jenson (1990, p. 153) notes that political discourse includes competing representations about gender identities, including the proper roles for women in society, and that even in the early twentieth century “. . .France, such identities included the possibility – and indeed at times the assumption – of the validity and importance of women’s paid work, both for single and married women.”43 This strong cultural support for women’s roles as mothers and workers has to do with the historical threat of dépopulation and dénatalité (the French population dropped precipitously during the nineteenth and early twentieth centuries) as well as the strategic approaches of French feminists. During the early twentieth century, pronatalists encouraged support for women’s roles as mothers, while feminists used pronatalist rhetoric to gain support for women’s employment and for family policies (Cova, 1991; Offen, 1991; Misra, 1998). French feminists used the existing ideological discourse of all citizens in society as both citizens and producers to emphasize women’s duties to the nation as both mothers and workers (Jenson, 1990). Indeed, when pronatalists attempted to restrict women’s roles to motherhood (for example, evicting women from the workforce in order to solve problems of low birth rates and


men’s unemployment), feminists strategically insisted that women’s work was economically necessary in order to support their families (Offen, 1991; Pedersen, 1993).44 Despite these cultural frames of women as both workers and mothers, women continued to be viewed as “different” rather than “equal.” For example, during the 1970s, the ideology of “free choice” for women became more powerful. Laufer (1998, p. 63) argues: During the 1970s, state policies sought to encompass both the ideal of “free choice” for women, that is that they could choose between paid work and unpaid caring work in the family, and the principle that women should not have to choose, but should be able to have both, achieving a full career and simultaneously being a wife and mother, an achievement which involved women being economically active even when their children were small. Yet at the same time there was little change in the gender division of labor in the household, although women working part time did significantly more domestic labor relative to men than women working full time (Duane-Richard, 1995). French cultural ideals emphasized the importance of supporting women’s responsibility to care, without also ensuring – or requiring – men’s caregiving responsibilities. As part-time work expanded during the 1980s, women were much more likely to be viewed as marginal and part-time workers, even when most women preferred full-time work ( Jenson, 1988). As a result, “Ironically, despite the fact that women as a category were becoming more essential to the production process, individual women’s work situation became increasingly precarious as they were offered temporary or parttime work, less protected by collective agreements and state regulations” ( Jenson, 1988, p. 160). Jenson argues that the effect of the government’s acceptance of labor market segmentation in the form of part-time work is that a “profoundly non-egalitarian discourse emerged about the ’difference’ of women workers: their lower training, their supposed fondness for part-time work, their childcare responsibilities” ( Jenson, 1990, p. 121). Therefore, French culture provides support for women’s roles as workers as well as caregivers. At the same time, there appears to be less support for changing men’s roles regarding care, and women continue to be seen as different from men in their work ambitions and interests. Dutch culture provides a different set of expectations about women’s roles. Historically, religious, community, and political norms promoted a common ideal for all Dutch citizens: a traditional family with a male


breadwinner, a wife, and several children (Pott-Buter, 1993; Karsten, 1995; Knijn, 1994). This ideal, coupled with the accepted principle of state subsidiarity emphasizing family as the first line of care, contributed to the devaluation of women’s paid work outside the home. Prevailing gender ideals assigned women responsibility for upholding the moral state of the family. Women were thus valued as wives and mothers, and the idea of women working for wages was seen as neither desirable nor necessary (Pott-Buter, 1993). There was thus little support for women as workers, or for policies that would enable women to balance work and home. In addition, Cold War fears dominated the political climate, and led to the association of public child care either with conditions of poverty where mothers were forced to work for money, or with contemporary totalitarian political systems (Bussemaker, 1998; Gustafsson and Stafford, 1995).45 Traditional women’s organizations also upheld the ideal of separate roles for men and women.46 Therefore, there was substantial consensus that women had a moral obligation to care for their children in the home (Bussemaker, 1998). Bussemaker (1998, p. 71) notes: The commonly held belief that the best way to care for and raise children was in a family environment presided over by a mother was the majority view well into the 1970s. Roman Catholics, Protestants, social democrats, and liberals (i.e., the most important political movements in the Netherlands) all agreed that a stable and tranquil family life was the best guarantee for social prosperity. Given the emphasis on the traditional family, single women and working mothers remained anomalies in Dutch society. Yet Dutch women began to question the traditional housewife arrangement, and a growing number of often highly educated women wanted to sustain paid employment (Bussemaker, 1998). In the 1960s, feminist action groups emerged as part of the feminist movement to challenge women’s seclusion in the home, their dependence upon men, and the impossibility of combining motherhood with work outside the home (Bussemaker, 1998; Outshoorn, 1986, 1995; Plemper, 1996). Responding to feminist demands, in 1974, the Social Democrat-headed government created an external advisory committee called the Emancipatiekommissie (Emancipation Commission [EK]). Yet the commission’s first report, adopted in 1977, simply reinforced women’s being valued as wives and mothers (Outshoorn and Swiebel, 1998).47


During the 1980s, this understanding of women’s roles changed to recognize that women’s equality may also be contingent upon employment opportunities. Feminists supported this emphasis through the Breed Platform (Broad Platform), which focused on the promotion of women’s economic independence. Attitudes and norms about women working for pay underwent large shifts. For example, in 1965 84 percent of the Dutch population expressed reservations about mothers of schoolage children working outside the home; this figure fell to 44 percent in 1970, and 18 percent in 1997 (Social and Cultural Planning Office, 1998, p. 141). Throughout the 1980s women’s labor market participation continued to climb, and by the 1990s, there was a heightened emphasis on labor force participation for all in order to enhance both social cohesion and economic competitiveness, as well as a changing ideology of motherhood (Bussemaker et al., 1997). Yet still today a strong ideology of women as “natural” carers persists, and paid work is considered a choice open mainly to higher-educated women (Grünell, 1999; Karsten, 1995).48 As Morgan (2005, p. 177) notes, “many parents continue to regard child care with suspicion and prefer to maximize parental caring time while minimizing that of outside caregivers.” Employment preferences reflect that culture; of couples with young children, 70 percent prefer the man working full time and the woman working part time (Jaumotte 2003).49 Further, the government continues to espouse an advanced care ethos that values motherhood and caretaking, yet has cut back the welfare state, making it difficult for women outside the labor market to sustain a reasonable standard of living for themselves and their families (Kremer, 2001). Clearly, Dutch culture has emphasized women’s roles as mothers more powerfully than French culture, while there is stronger support in France for women’s roles as workers. In France, however, women’s work roles remain somewhat marginalized, as women are viewed as “different” rather than “equal.” Attitudinal data may help show the different cultural values in these nations. Table 4.1 presents recent data from the World Values Survey regarding ideologies about work and care. Respondents were asked whether they agreed or disagreed with the statements listed in the table.50 These data, presented for 1990 and 1999, suggest that there have been significant changes in values regarding women’s workforce participation, but also that French and Dutch values are significantly different. For example, in both 1990 and 1999 approximately 80 percent of French men and women agree that both husbands and wives should contribute to the household income – as compared to 31 percent of Dutch men and

Joya Misra and Lucian Jude 117 Table 4.1

Values regarding work and family, 1990 and 1999

Percentage agreeing with the following statements . . .

French men

French women Dutch men

1990 1999 1990

Dutch women

1999 1990 1999 1990

1999

Both the husband and wife should contribute to household income

80

79

80

80

26

31

30

40

Having a job is the best way for a woman to be an independent person

77

79

80

85

51

61

55

63

In general, fathers are as well-suited to look after their children as mothers

–

77

–

83

–

69

–

83

A pre-school child is likely to suffer if his or her mother works

66

61

63

51

70

54

54

35

A job is alright but what most women really want is a home and children

70

67

67

65

36

36

42

29

Being a housewife is just as fulfilling as working for pay

62

64

57

60

55

55

55

46

A working mother can establish just as warm and secure a relationship with her children as a mother who does not work

72

72

72

79

65

76

76

86

When jobs are scarce, men should have more right to a job than women

35

21

31

23

22

12

27

13

Note: ∗ Data from World Values Survey (http://www.worldvaluessurvey.org/).

40 percent of Dutch women in 1999 (these numbers are even lower in 1990). Similarly, 85 percent of French women suggest that having a job is the best way for women to be independent, as compared to 63 percent of Dutch women. Yet these two measures may tell us not only about values about women’s work, but also more generally about values about work.

118 Method and Substance in Macrocomparative Analysis 80 French men French women Dutch men Dutch women

70 60

60.5 57.5

69 69.2 61.7

60.3 53.6

53 49.2

50

43.4 40 30 20 10 0

1981

1990

1999

Figure 4.5 Percentage of respondents who believe that work is “very important” in their life . . . Source: ∗ Data from World Values Survey (http://www.worldvaluessurvey.org/).

Figure 4.4 shows the percentage of French and Dutch men and women who view work as “very important” to their lives: while Dutch women have the lowest levels, Dutch men also view work as less important than do either French men or women (who almost equally appear to see work as very important to their lives). Therefore, these values about women’s work may reflect wider values regarding the importance and primacy of work. However, French men appear to be more likely than Dutch men to believe that men are as well suited as women for caring for children. The Dutch government and trade unions have recently sponsored media campaigns to promote fathers’ roles as carers, which may help create greater convergence in men’s values and roles.51 The table does suggest other important changes, particularly for the Dutch, over time. For example, in 1990, 70 percent of Dutch men and 54 percent of Dutch women thought pre-school children would likely suffer if their mothers were employed, compared to 54 percent of Dutch men and 35 percent of Dutch women in 1999. Clearly, there has been a significant change in how the Dutch view work impacting motherhood.


Yet Table 4.1 also suggests that Dutch men and women are less likely to agree than French men and women on statements suggesting that women “really” want a home and children, or being a housewife is more fulfilling than working for pay, or that pre-school children are likely to suffer if their mothers are employed. The Dutch are also slightly more likely to agree that employed mothers can develop as strong a relationship with their children as mothers who primarily provide care, while they are less likely to agree that men should have greater priorities to employment when jobs are scarce. Clearly, the countries have different cultural resonances around these ideas of women’s equality, men’s involvement in caregiving, and the importance of women’s involvement in the labor market. The heavy concentration of employed Dutch women in part-time jobs may explain these somewhat perplexing findings. Since restructuring of the Dutch welfare state during the 1990s, public and political discourse has emphasized labor force participation for all in order to enhance social cohesion and economic competitiveness. This emphasis has contributed to a changing ideology of motherhood wherein (part-time) labor market participation for mothers is more socially acceptable (Bussemaker et al., 1997). Indeed, 70 percent of couples with young children prefer a man working full time and a woman working part time. In France, by contrast, 52.4 percent of couples with young children prefer a man working full time and a woman working full time ( Jaumotte, 2003). Do cultural framings explain the similarities and differences we have noted? Clearly, there has been more cultural support for women’s roles as workers in France, and there remains a stronger sense of the importance of women’s engagement in the labor force. This may help explain both French women’s initially higher levels of employment, and their higher levels of full-time employment (see Figures 4.2 and 4.3). At the same time, ideologies about women’s work have clearly changed dramatically in the Netherlands over the past several decades, providing greater support for women to enter the labor market, albeit primarily through part-time employment (see Figure 4.3). Yet, it remains somewhat difficult to identify whether cultural norms changed in response to patterns of women’s employment, or whether patterns of women’s employment changed in response to cultural norms.

Discussion and conclusion Through our discussion of the Dutch and French cases, we have tried to examine whether women’s employment has been shaped by family


Economic conditions

Family & labor market policies

Women’s employment

Cultural context Figure 4.6 Proposed model explaining women’s employment

policy, or whether economic and cultural factors may play a role. As our findings suggest, we find some evidence in support of all three factors, but also find that there appear to be significant “feedback” loops. Historically, it appears that family and labor market policies have shaped women’s employment, but have also responded to increases in women’s levels of employment. Similarly, cultural norms appear both to respond to and to influence women’s employment. Figure 4.6 illustrates the theoretical argument that emerges from our analysis. We argue that economic conditions shape women’s employment both directly and indirectly through labor market policies and family policies. At the same time, cultural norms shape policies and women’s employment. Changes in policy must fit dominant ideals of care in order to be effective (Kremer, 2008).52 The policies themselves – and also the prevailing economic conditions – influence cultural norms, both directly and also through their impact on women’s labor market participation. Further, women’s employment impacts economic conditions both directly and indirectly through policies and cultural norms. Economic conditions, policies, and cultural norms, then, do not operate in isolation, but intersect and overlap to produce a situation of ‘causal complexity’ (Ragin, 1997). These factors do not have any predetermined effect on women’s employment, but rather operate as important factors, among many others, in unique and dynamic social and political contexts.


To better illustrate our argument, we offer two examples. In France, the system of parental care leave (Allocation Parentale d’Education) was created in 1985 with the express intention of encouraging mothers to leave the labor force, with the added bonus of expanding family size to three or more children. This policy was constructed, in part, as an active labor market policy in response to economic conditions, yet also reflected Christian Democratic pronatalist values. Yet the policy was not initially successful: take-up was extremely low, in part because of the requirement that a parent must have three or more children, and have significant work experience. The policy, in itself, was not enough to encourage women to leave the labor market. The next revision expanded the leave, making it available to anyone employed for two out of the previous ten years, therefore making the policy more available to women who considered themselves to be primarily homemakers (Morgan and Zippel, 2003). Yet, the level of take-up still remained fairly low. It was only when the program shifted eligibility to parents of two or more children that the program became popular. Perhaps the policy did not provide enough of an incentive for families to choose to have a third child; yet once parents of two children could take advantage of the policy, increasing numbers of parents did so. Culturally, the policy also fits into the “free choice” ideology, allowing women the opportunity to stay at home and caretake, but not requiring them to do so. Although the policy is gender neutral, 99 percent of those taking it are women, reflecting clearly gendered understandings of women’s roles as mothers. The Dutch government legislated its first leave arrangements in the early 1990s. As in France, this legislation was motivated by economic conditions, but the impact here was to promote rather than discourage women’s employment. Dutch leave arrangements provide a short period of full-time paid leave, followed by a period of subsidized adjustment to limited hours. This set-up stimulates part-time work, which conforms to the labor market strategy of reducing unemployment while providing more flexibility for employers, and also to persistent gendered ideals of care that deem mothers to be the most appropriate caretakers for their own children. Part-time work enables mothers to reconcile their work-based and family-based identities in a way not required of fathers. Most leave-takers, then, are women, whose labor market participation has increased markedly. Yet while traditional ideals of caretaking persist, a changed ideology of motherhood (and fatherhood) accompanies the increase in women’s employment. The 2001 Work and Care Act provided more expansive provisions for care, reflecting a heightened emphasis in public and political discourse on women’s (and men’s) rights and


responsibilities as workers as well as carers. Ultimately, workers’ ability to balance work and family life within the social and political context influences the extent of their engagement in the labor market, and thus large-scale economic conditions. Another less explicit element to our argument regards the role of politics in shaping women’s employment. While in this chapter we emphasized policies rather than politics, it is critical to recognize that politics, as well as economic and cultural factors, do drive policy decisions. In both countries, shifts between governments have clearly impacted policies that affect women’s labor market involvement. Differences in the complexion of the governing party play a major role in determining how women’s labor force participation has been framed and supported. In France, since the 1960s, the Left has been associated with women’s workplace rights. When in power, it has developed supportive policies, although it has also accepted certain policies (such as the home care allowance or the growth of part-time employment) that work against women’s interests. However, the Center-Right has much more consistently pushed against women’s interests as workers, conceptualizing women primarily in terms of their roles as mothers. In the Netherlands, Social Democrats have been the biggest proponents of women’s rights as workers, although their policies have certainly not been consistently supportive. Christian Democrats, however, have held a central position in Dutch politics since 1945, which partly explains why the Dutch government has demonstrated reluctance to create conditions favorable to women’s labor market participation (de Jong Gierveld and Liefbroer, 1995). Dutch political discourse still emphasizes women’s roles as carers, but cultural and economic developments mean that political powers now recognize the important role women play as workers. Social movements also play a central role in shaping the political agenda around these issues. In both France and the Netherlands, feminist movements of the late 1960s and 1970s helped create the conditions for significant change in labor market policy. For example, major successes for the Dutch women’s movement include the creation of the Emancipatiekommissie (Emancipation Commission) in 1974 and the Directie Coordinatie Emancipatiebeleid (DCE) in 1978; similarly, major successes for the French women’s movements include the creation of the Comité du Travail Féminin (Committee on Women’s Employment) in 1965 and the creation of the Ministere des Droits de la Femme (Ministry of the Rights of Women) in 1981. These movements clearly changed the political landscape and, particularly through their work within the Left, helped create greater employment opportunities for women.53


As our cases should make clear, a variety of structural conditions – including labor market and family policies – provide the framework for women’s employment opportunities. However, cultural frames help inform those structures and infuse them with meaning. Family policies, in particular, reflect cultural notions of men’s and women’s appropriate roles in society. Yet these cultural ideals are not static, and both labor market policies and family policies help to both reinforce and change these ideals. While our argument does not identify the one most crucial determinant of women’s employment, these cases suggest that such a strategy is simply not useful. Clearly family policy does matter in explaining employment. Family policies can provide structural support (or in the case of care leaves, disincentives) for women’s employment, while also helping provide cultural legitimacy for women’s employment. At the same time, however, the economic conditions and the cultural contexts also shape women’s patterns of employment. We began this analysis with two puzzles: (1) Why were French women’s employment rates initially so much higher than Dutch women’s employment rates? And (2) What explains the remarkable increase in Dutch women’s employment in recent decades? Based on our historical analysis, we argue that French employment rates were initially higher due to the economic need for women workers, as well as more effective family policies and a far more supportive cultural context. In respect of the increase in Dutch women’s employment, greater economic need has combined with changing cultural norms, leading to greater family policy support for women’s employment. In the final analysis, it is the entanglement of multiple factors that help explain variations in women’s employment, reflecting the multifaceted complexity of the social world.

Notes ∗

We are grateful to Kathleen Boggs and Karen Mason for excellent research assistance, to the Social and Demographic Research Institute and the Center for Public Policy and Administration at the University of Massachusetts for support, to Lane Kenworthy, Monique Kremer, Ann Orloff, and Anne Revillard for their helpful assistance, and to all of the participants of the “Method and Substance in Macrocomparative Analysis” conference (Amsterdam, April 2006) for their thoughtful comments.

1. Interestingly, child care for children less than three years of age is strongly linked to increased women’s labor force participation, while child care for older children is also linked to increased women’s labor force participation, but only when the Netherlands was excluded from the analysis.

124 Method and Substance in Macrocomparative Analysis 2. “Though often couched in the language of choice and helping parents balance work and family, the underlying goal of care leave policies has been to value and support full-time female caregivers” (Morgan and Zippel, 2003, p. 51). Mahon (2005, p. 4) similarly suggests that these types of care leaves, meant to give women a “choice” between labor market participation and caretaking, “does little for gender equality, and a typically low rate of reimbursement means that it operates primarily as an incentive for working class, not professional women, to withdraw from the labour market.” 3. Indeed, Mandel and Semyonov (2006) argue that family policy and welfare state institutions boost women’s employment, but that women in these countries tend to work in more feminized occupations and are less likely to be represented in more powerful managerial positions. Charles (2005) similarly notes that welfare state policies may shape women’s labor force participation, but have less of an effect on occupational gender segregation. 4. Sainsbury (1999b) argues that a taxation system that is tailored to dualbreadwinner couples may increase women’s labor market participation by as much as 20 percent. Yet tax systems do not neatly map onto differences in women’s employment rates. Women’s employment may rise even when tax rates penalize second earners (Kremer, 2008). 5. Yet, part-time employment still reinforces traditional gender arrangements, though if part-time work was the norm for men as well as women, it need not reinforce traditional gender arrangements (Jenson, 1996; Gornick and Meyers, 2003). 6. Of course, there are important differences between preference theory, as laid out by Hakim, and the cultural arguments of scholars like Pfau-Effinger and Kremer. Hakim (2003) suggests that family policies are put into place to help support childbearing among working women. Yet, counter to many other scholars, she argues that policies should instead focus on supporting homecentered women, who are much more likely to have more than one child. 7. Hakim (2000) makes slightly different arguments, but agrees that women do not consistently respond the same ways to social policies encouraging employment. 8. Indeed, paid maternity leave was adopted in the first decade of the twentieth century (Jenson, 1986, 1990; Heinen and Martiskainen de Koenigeswarter, 2001), significantly earlier than in most of the rest of the world. 9. In the 1960s and 1970s, while Christian Democrats tried to use certain benefits as a wage to encourage women to stay at home, the Left worked to decrease benefit levels and withdraw access to these benefits from women without young children (Jenson, 1990). 10. These changes were driven by a labor shortage, which created a demand from employers for women workers. 11. The 1985 law allowed a parent with three or more children and who had worked for 24 out of the last 30 months to take a short period of paid leave, and had a fairly low take-up. However, the 1986 law expanded the leave to three years, and made it available to anyone employed for two out of the last ten years, making the policy more available to women who thought of themselves primarily as homemakers (Morgan and Zippel, 2003). Even then, levels of take-up remained fairly low. However, after the 1994 reform expanded eligibility to parents of two or more children who had worked for


12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

two out of the previous five years, the program became very popular, even though the benefits remained fairly low (paid at 39 percent of the average wage) (Morgan and Zippel, 2003, p. 55, table 1). “From 175,000 in 1994, the number of beneficiaries has risen to 536,000, of whom 99 percent are women” (Heinen and Martiskainen de Koenigswarter, 2001, p. 173). “The result is that the current policies favor dual earner families to the detriment of lower-income households, who are less able to afford private childcare and use the new benefits” (Daguerre and Taylor-Gooby, 2003, p. 634). Married women, women with more than two children, and immigrants are more likely to interrupt their working career for longer periods. More recent cohorts have become less likely to interrupt their working career for a significant period (Grimm and Bonneuil, 2001; Mattioli, 2003). Further, married women were ineligible for the basic old age pension, extended unemployment benefits, and later general disablement benefits when they were introduced in the mid-1970s (Sainsbury, 1994). An economic recession led to the decentralization of responsibilities and deregulation, along with major budget cuts. Since 1987, municipalities have been responsible for organizing sufficient, quality child care, which has resulted in a considerable variation in the level and quality of services. The government passed this measure in response to a 1990 report by the Scientific Council for Government Policy (WRR) that definitively recognized child care as necessary for women’s labor market participation. At the time the motivation was economic rather than an interest in women’s autonomy per se (Bussemaker, 1998, p. 87). One of the government’s goals through this policy was to extend child day care via the extension of child care places financed by employers. By 1997, the government’s contribution to the total costs of formal child care had fallen to 35 percent, with companies paying 21 percent and parents 44 percent (OECD, 2000). Indeed, half of all child care provision is now linked to companies (Bussemaker, 1998). Under certain conditions, parents may deduct some child care expenses from their income tax, with exemptions being higher for single parents than for two-parent households. All parents with one or more children living at home are also entitled to a child allowance, which ranges from 41 percent of the costs of bringing up a child for low-income families, to 17 percent for highincome families. In addition, the Ministry of Social Affairs has a separate Subsidy Scheme for Childcare that helps to single parents on social security to meet the costs of child care. However, using formal child care may conflict with some parents’ values concerning mothers’ role in caring for young children (OECD, 2000). Child care policy stipulates that day care centers reserve a certain percentage of their places for companies that buy them. However, companies often have limited interest, leaving many places unused even as demand by parents is high (Bussemaker, 1998, p. 88). Some collective agreements include continuation of part of the wages. Public sector employees, for example, receive 75 percent of their wages during parental leave, which explains why nearly two-thirds of workers who use


22.

23.

24.

25. 26.

27. 28.

29.

30.

parental leave are employed in the public sector. Private sector employers rarely pay parental leave (OECD, 2002). Traditional gendered ideals persist that uphold men’s role as breadwinners and women’s role as caretakers. Also, “Men returning to their career after leave are more likely to encounter a prejudice that they do not ‘take work more seriously’ than women in similar circumstances.” Thus, even when leave benefits are fully paid to either parent, the long-run household opportunity costs will be higher if a man takes leave because of the harm it may do to his career-prospects (OECD, 2002, p. 138). Aside from specifically child-related leave benefits, the Work and Care Act of 2001 included other provisions to care for family and household members. Emergency leave provides paid leave of short duration at full wages to cover for unforeseen situations at home, such as the death of a family member. Short-term carers’ leave provides a maximum of 10 days per year to care for sick children or the employee’s partner, paid at minimum wage or 70 percent of full wages, whichever is highest. Career break leave provides 70 percent of the minimum wage for six months, though a longer period is possible with employer agreement (OECD, 2002, p. 135). This sustained increase primarily occurred in the intermediate age groups, particularly women of childbearing age (Grimm and Bonneuil, 2001). Indeed, among two-parent families with children, one-third of women were in the labor force in 1968, compared to almost two-thirds by 1987 (Jenson, 1990, p. 109). France also ratified international legislation during the 1950s, requiring equal pay for equal work. However, women continued to be segregated in particular occupations and jobs, and by and large continued to earn lower pay than men (Jenson, 1988).The laws allowed for significant wiggle room, in that they did not specify the meaning of “equal work,” and allowed employers to present a “legitimate motive” for treating men and women differently (Laufer, 2003). Between 1982 and 1986, 130,000 women lost a full-time job while 450,000 part-time positions were created (Jenson, 1990, p. 113). However, women do the vast majority of part-time work (Laufer, 1998; OECD, 2002), and one-third of all women working part time would prefer to work more hours (Laufer, 2003; Hantrais and Letablier, 1997). Malo et al. (2000, p. 256) note that in 1997, only 5 percent of men worked part time, as compared to 31 percent of women. Yet part-time jobs may be in less rewarding sectors (such as the service sector). Laufer (2003, p. 438) argues, “Part-time work as it is used in France today is not egalitarian. It is deeply unequal – unequal between men and women, but also unequal between different socioeconomic groups of women. Parttime jobs often lead to discrimination against women in pay, training, and career development. Outside of public sector employment, part-time jobs are associated with poor qualifications and with an image of a weak commitment to paid work with fewer career opportunities and, obviously, lower pay than full-time work.” Other reforms include a 1982 reform which clarified the role of spouses of artisans and merchants, ensuring that they received status as workers, and a removal of almost all gender-based restrictions on civil service job categories.

Joya Misra and Lucian Jude 127 31. Yet few firms actually developed and implemented equality plans, in part due to a lack of information, political will, and sanctions, and the weakness of trade unions to enforce the policy (Laufer, 1998, 2003). 32. Almost half of all women are employed in only 20 professions. Women are most evident in administrative and managerial fields, childcare and teaching, and sales (Laufer, 1998). 33. For women managers, few work part time or take extended leave, and, as Laufer (2003, p. 426) notes, “In most firms, the implicit and explicit norms of managerial culture are that part-time work and effective career development are generally incompatible, given the demands of the job.” Laufer and Fouquet (2001) show that while women make up almost one-third of managers and professionals, they compose only 7 percent of top managers. 34. The Depression of the 1930s reinforced the practice of excluding women from the labor market (Gustafsson and Stafford, 1995). 35. Visser (2002, p. 28): “The Netherlands waited till 1971 before ratifying ILO Convention 100 on equal pay of men and women, which became the basis for Article 119 in the Treaty of Rome (1957).” The Equal Pay Act, the first Dutch anti-discrimination act, came into force in 1975 (Havinga, 2002). 36. The nation’s poorly managed disability scheme, characterized by a low threshold for entitlement and generous benefits, became a major tool for industrial restructuring. Employers used the scheme to reduce the supply of labor through early retirement rather than firing redundant workers and creating social friction. Workers, employers, unions, industrial boards, and local governments all misused the scheme, putting tremendous strain on welfare resources. The scheme was originally meant to support no more than 200,000 people, but by 1989 the number of recipients was near one million. Costs exploded as the number of taxpayers shrank in relation to the number of beneficiaries, necessitating welfare state restructuring (Hemerijck and van Kersbergen, 1997). 37. The Agreements of the Wassenaar Deal of 1982 formed the basis of the government’s efforts to restructure the labor market and reduce the costs of the welfare state. Unemployment benefits became more difficult to obtain, disability benefits decreased, elder care was cut back, and child care became more marketized and expensive. Women were hit especially hard, since they were assigned primary responsibility for care for the disabled, children and elders (Kremer, 2001). 38. Popular fields for women include teaching, child care, administrative work, and health care. Havinga (2002) explains that traditional job segregation is reinforced by the preferences within organizations and the application behavior of women and men seeking employment. With few exceptions, women tend not to apply for a “man’s job,” and men tend not to apply for a “woman’s job.” Further, whereas Dutch employers are legally required to submit annual reports on the proportion of ethnic minorities they employ in order to improve their labor market participation, no such legal requirement exists to improve women’s situation in the labor market. 39. Employers with ten or fewer employees are exempt from this law (OECD, 2002, p. 134). 40. Lone parents make up around 60 percent of social assistance recipients, which stems from the social practice – in place since the 1970s – of excluding single


41.

42.

43.

44.

45.

46.

47.

48.

49.

mothers from the work obligation (Knijn and van Wel, 2001, p. 239; OECD, 2002, p. 169). The 1996 reform is an attempt to reduce the financial strain these beneficiaries put on the national budget, and indicates greater cultural acceptance of mothers’ labor force involvement. Further, parents must manage unpredictable school hours, as teacher shortages frequently force schools to close for all or part of a day at short notice (OECD, 2002, p. 22). Misra and Moller (forthcoming) show that Dutch lone mothers face a 22 percent poverty rate despite a 69 percent employment rate (40 percent part time), which reveals the particularly adverse effect policy reforms have had on their lives. Misra (1998, p. 394) further argues that gender identities in France reflect “not only the importance of women’s paid work, but also the validity of women’s unpaid work as mothers.” Although the ideological discourses of many nations assume women will be responsible for caregiving, in most contexts society accords limited value to the role of mother. Women are expected to be mothers, but are not valued for their contributions to society as mothers. When social Catholics succeeded in getting a care subsidy passed to support maternal caregiving in 1938, feminists recast the allowance “as an allowance for families with dependent children living on a single wage [which] could benefit not only families with male earners and dependent wives, but equally divorced, single, or widowed parents – thus providing, ironically, something of a safety net for mothers seeking to survive without men” (Pedersen, 1993, p. 268). As Bussemaker (1998, p. 76) states, “Childcare provisions were not seen as part of new social welfare arrangements, but rather, the absence of such facilities was proof of the achievement of the welfare state.” Activities promoted women’s involvement in “feminine” pursuits including childcare, handicrafts, health and hygiene problems, and volunteer work. Politically neutral by statute, the groups served essentially “to defuse the notion of a battle between the sexes” (Outshoorn and Swiebel, 1998, p. 148). The EK also called for the creation of a special unit for women’s policy within Dutch bureaucracy. In 1978 the government responded by creating the Directie Coordinatie Emancipatiebeleid (Department for the Coordination of Equality Policy [DCE]) within the Ministry of Culture, Recreation, and Welfare, meant to raise consciousness and change attitudes (Outshoorn, 1995). Karsten (1995, p. 192) notes: “The great majority of full-time housewives belong to the lower socioeconomic classes of Dutch society. Until recently, economic necessity had little impact on participation rates.” As Pfau-Effinger (1998) notes, the majority of Dutch women still embrace the cultural ideal that children are most appropriately cared for by their mothers in the household as long as possible. Part-time work thus represents a solution to mothers’ “moral dilemma” between individual self-fulfillment and care for children. Yet the employment orientations of women remain unfulfilled in social practice, as the proportion of women who want to be employed is much higher than that of women who are in fact employed (Pfau-Effinger, 1999, p. 143).

Joya Misra and Lucian Jude 129 50. For purposes of clarity, we combine “strongly agree” and “agree” in these tables, although this means that we lose some interesting variation. 51. Yet whereas 68 percent of Dutch men agree that men and women should share carework equally, only 8 percent of fathers with children under 18 want to reduce their working hours (Knijn and Selten, 2002). Plantenga (1996) reports that whereas half of women part-timers say they work part time to “manage their household,” only 5 percent of men part-timers give caregiving as their reason for working part time. 52. While the expansion of the Allocation Parentale d’Education in France serves as a good example of how successful policy changes must draw on ideals of care, the failure of Dutch welfare reform provides a similar example. While the Dutch 1996 welfare reform dramatically increased labor market requirements for mothers (in less drastic, but similar ways to the American welfare reform), a disproportionate number of Dutch single mothers continue to provide care full time, or are employed only very few hours a week (Bussemaker et al., 1997). In addition, most Dutch men and women believe that men should work full time and women should work part time when children are young (Jaumotte, 2003). 53. Yet, women’s movements in both countries were also somewhat co-opted, as prominent feminists were brought into the state bureaucracy.Linda Hantrais (1993, p. 124) notes that “by taking over control of women’s rights and by introducing equal opportunities legislation, the state has preempted the women’s movement and appropriated its cause.”

References Bakker, Isabella. 1988. “Women’s Employment in Comparative Perspective.” Pp. 17–44 in Feminization of the Labor Force: Paradoxes and Promises, edited by Jane Jenson, Elisabeth Hagan, and Ceallaigh Reddy. New York: Oxford University Press. Beechey, Veronica. 1989. “Women’s Employment in France and Britain: Some Problems in Comparison.” Work, Employment, and Society 3: 369–78. Berg, Peter, Eileen Appelbaum, Tom Bailey, and Arne L. Kalleberg. 2004. “Contesting Time: International Comparisons of Employee Control of Working Time.” Industrial and Labor Relations Review 57: 331–49. Blau, Francine D. and Lawrence M. Kahn. 2002. At Home and Abroad: U.S. Labor Market Performance in International Perspective. New York: Russell Sage Foundation. Buchmann, Marlis and Maria Charles. 1995. “Organizational and Institutional Factors in the Process of Gender Stratification.” International Journal of Sociology 25(2): 66–95. Bussemaker, Jet. 1998. “Rationales of Care in Contemporary Welfare States: The Case of Childcare in the Netherlands.” Social Politics 5: 70–96. Bussemaker, Jet, Annemieke van Drenth, Trudie Knijn, and Janneke Plantenga. 1997. “Lone Mothers in the Netherlands.” Pp. 96–119 in Lone Mothers in European Welfare Regimes: Shifting Policy Logics, edited by Jane Lewis. London: Jessica Kingsley Publishers.

130 Method and Substance in Macrocomparative Analysis Chambaz, Christine. 2001. “Lone Parent Families in Europe: A Variety of Economic and Social Circumstances.” Social Policy and Administration 35: 658–71. Charles, Maria. 2005. “National Skill Regimes, Postindustrialism, and Sex Segregation.” Social Politics 12: 289–316. Cova, Anne. 1991. “French Feminism and Maternity: Theories and Policies, 1890– 1918.” Pp. 119–37 in Maternity and Gender Policies, edited by Gisela Bock and Pat Thane. London: Routledge. Cox, Robert H. 1993. The Development of the Dutch Welfare State: From Workers’ Insurance to Universal Entitlement. Pittsburgh: University of Pittsburgh Press. Daguerre, Anne and Peter Taylor-Gooby. 2003. “Adaptation to Labour Market Change in France and the UK: Convergent or Parallel Tracks?” Social Policy and Administration 37: 625–38. de Jong Gierveld, Jenny and Aart C. Liefbroer. 1995 “The Netherlands.” Pp. 102–25 in The New Role of Women: Family Formation in Modern Societies, edited by H. P. Blossfeld. Boulder, CO: Westview Press. de Ruijter, Judith M. P., Anneke van Doorne-Huiskes, and Joop J. Schippers. 2003. “Size and Causes of the Occupational Gender Wage-Gap in the Netherlands.” European Sociological Review 19: 345–60. Duane-Richard, Anne-Marie. 1995. “Women’s Employment and Different Societal Effects in France, Sweden, and the United Kingdom.” International Journal of Sociology 25: 39–65. Eliason, Scott, Robin, Stryker, and Eric Tranby. 2008. “The Welfare State, Family Policies, and Women’s Labor Market Participation: Combining Fuzzy Set and Statistical Methods to Assess Causal Relations and Estimate Causal Effects.” Chapter 5 in this volume. Esping-Andersen, Gøsta. 1990. The Three Worlds of Welfare Capitalism. Princeton, NJ: Princeton University Press. Esping-Andersen, Gøsta. 1999. Social Foundations of Postindustrial Economies. Oxford: Oxford University Press. Esping-Andersen, Gøsta. 2000. “Regulation and Context: Reconsidering the Correlates of Unemployment.” Pp. 99–112 in Why Deregulate Labor Markets?, edited by Gøsta Esping-Andersen and Marino Regini. New York: Oxford University Press. Fagnani, Jeanne. 1995. “L’Allocation Parentale d’Education: Effets Pevers et Ambiguities d’une Presentation.” Droit Social 3: 287–95. Gornick, Janet C. and Marcia K. Meyers. 2003. Families That Work: Policies for Reconciling Parenthood and Employment. New York: Russell Sage Foundation. Grimm, Michael and Noël Bonneuil. 2001. “Labour Market Participation of French Women over the Life Cycle, 1935–1990.” European Journal of Population 17: 235–60. Grünell, Marianne. 1999. “Employers on Labour and Care in the Future Labour Market Structure.” Netherlands Journal of Social Sciences 35: 140–64. Gustafsson, Siv. 1994. “Childcare and Types of Welfare States.” PP. 45–62 in Gendering Welfare States, edited by Diane Sainsbury. London: Sage. Gustafsson, Siv and Frank Stafford. 1992. “Daycare Subsidies and Labor Supply in Sweden.” Journal of Human Resources 27: 204–30. Gustafsson, Siv and Frank Stafford. 1995. “Links between Early Childhood Programs and Maternal Employment in Three Countries.” The Future of Children 5: 161–74.

Joya Misra and Lucian Jude 131 Hakim, Catherine. 2000. Work–Lifestyle Choices in the 21st Century: Lifestyle Theory. Oxford: Oxford University Press. Hakim, Catherine. 2003. “A New Approach to Explaining Fertility Patterns: Preference Theory.” Population and Development Review 29: 349–74. Hakim, Catherine. 2004. “Lifestyle Preferences versus Patriarchal Values: Causal and Non-Causal Attitudes.” Advances in Life Course Research 8: 69–91. Hantrais, Linda. 1993. “Women, Work, and Welfare in France.” Pp. 116–37 in Women and Social Policies in Europe, edited by Jane Lewis. Brookfield, VT: Elgar. Hantrais, Linda. 1994. “Comparing Family Policy in Britain, France, and Germany.” Journal of Social Policy 23: 135–60. Hantrais, Linda and Marie-Therese Letablier. 1997. “The Gender of Paid and Unpaid Work Time: A European Problem.” Time and Society 6: 131–49. Havinga, Tetty. 2002. “The Effects and Limits of Anti-Discrimination Law in the Netherlands.” International Journal of the Sociology of Law 30: 75–90. Heinen, Jacqueline and Heini Martiskainen de Koenigswarter. 2001. “Framing Citizenship in France and Finland in the 1990s: Restructuring Motherhood, Work, and Care.” Social Politics 8: 170–81. Hemirijck, Anton and Kees van Kersbergen. 1997. “A Miraculous Model? Explaining the New Politics of the Welfare State in the Netherlands.” Acta Politica 32: 258–80. Hicks, Alexander and Lane Kenworthy. 2008. “Family Policies and Women’s Employment: A Regression Analysis.” Chapter 6 in this volume. Hobson, Barbara. 1994. “Solo Mothers, Social Policy Regimes, and the Logics of Gender.” Pp. 170–88 in Gendering Welfare States, edited by Diane Sainsbury. London: Sage. Huber, Evelyne and John Stephens. 2000. “Partisan Governance, Women’s Employment, and the Social Democratic Service State.” American Sociological Review 65: 323–42. Jaumotte, Florence. 2003. “Female Labour Force Participation: Past Trends and Main Determinants in OECD Countries.” Economics Department Working Papers No. 376. OECD. Jenson, Jane. 1986. “Gender and Reproduction: Or, Babies and the State.” Studies in Political Economy 26: 9–46. Jenson, Jane. 1988. “The Limits of ‘and the’ Discourse: French Women as Marginal Workers.” Pp. 155–72 in Feminization of the Labor Force: Paradoxes and Promises, edited by Jane Jenson, Elisabeth Hagan, and Ceallaigh Reddy. New York: Oxford University Press. Jenson, Jane. 1990. “Labor Market and Family Policy in France: An Intersecting Complex for Dealing with Poverty.” Pp. 107–55 in The Feminization of Poverty: Only in America?, edited by Gertrude Schaffner Goldberg and Eleanor Kremen. New York: Greenwood Press. Jenson, Jane. 1996. “Part-Time Employment and Women: A Range of Strategies.” Pp. 92–108 in Rethinking Restructuring: Gender and Change in Canada, edited by Isabella Barker. Toronto: University of Toronto Press. Karsten, Lia. 1995. “Women’s Leisure: Divergence, Reconceptualization and Change—The Case of the Netherlands.” Leisure Studies 14: 186–201. Kenworthy, Lane. 2008. "Women-Friendly Policies." In Jobs with Equality. New York: Oxford University Press.

132 Method and Substance in Macrocomparative Analysis King, Leslie. 1998. “‘France Needs Children’: Pronatalism, Nationalism, and Women’s Equity.” The Sociological Quarterly 39: 33–52. Knijn, Trudie. 1994. “Fish without Bikes: Revision of the Dutch Welfare State and Its Consequences for the (In)dependence of Single Mothers.” Social Politics 1: 83–105. Knijn, Trudie. 1998. “Social Care in the Netherlands.” Pp. 85–110 in Gender, Social Care, and Welfare State Restructuring in Europe, edited by J. Lewis. Aldershot: Ashgate. Knijn, Trudie and Monique Kremer. 1997. “Gender and the Caring Dimension of Welfare States: Toward Inclusive Citizenship.” Social Politics 4: 328–61. Knijn, Trudie and Peter Selten. 2002. “Transformations of Fatherhood: The Netherlands.” Pp. 168–87 in Making Men into Fathers: Men, Masculinities and the Social Politics of Fatherhood, edited by Barbara Hobson. New York: Cambridge University Press. Knijn, Trudie and Frits van Wel. 2001. “Careful or Lenient: Welfare Reform for Lone Mothers in the Netherlands.” Journal of European Social Policy 11: 235–51. Korteweg, Anna. 2005. “Between Punishment and Well-Being: The Role of Ideas in the Work of Welfare in the United States and the Netherlands.” Paper presented at the Research Committee on Poverty, Social Welfare and Social Policy RC19, Northwestern University, Chicago, IL. Kremer, Monique. 2001. “A Dutch Miracle for Women?” Social Politics, 8: 182–5. Kremer, Monique. 2005. “The Cultural Dimension of Welfare States: Ideals of Care and Women’s Work.” Paper presented at the Research Committee on Poverty, Social Welfare and Social Policy RC19, Northwestern University, Chicago, IL. Kremer, Monique. How Welfare States Care: Culture, Gender and Parenting in Europe. Amsterdam: Amsterdam University Press. Lane, Christel. 1995. “Gender and the Labor Market in Europe: Britain, Germany, and France Compared.” International Journal of Sociology 25: 8–38. Laufer, Jacqueline. 1998. “Equal Opportunity between Men and Women: The Case of France.” Feminist Economics 4: 53–69. Laufer, Jacqueline. 2003. “Equal Opportunity Policy in France: Symbolic Support and a Mixed Record.” Review of Policy Research 20: 423–42. Laufer, Jacqueline and A. Fouquet. 2001. “Les Cadres à l’Épreuve de la Féminisation.” Pp. 243–67 in Cadres: La Grande Rupture, edited by Paul Bouffartigue. Paris: Le Découverte. Letablier, Marie-Therese. 2003. “Fertility and Family Policies in France.” Accessed February 10, 2006: www.ier.hit-u.ac.jp/pie/Japanese/discussionpaper/dp2003/ dp160/text.pdf. Mahon, Rianne. 2005. “The OECD and the Reconciliation Agenda: Competing Blueprints.” Paper presented at the Research Committee on Poverty, Social Welfare and Social Policy RC19, Northwestern University, Chicago, IL. Mahoney, James and Deitrich Rueschemeyer, eds. 2003. Comparative Historical Analysis in the Social Sciences. New York: Cambridge University Press. Malo, Migeul A., Luis Toharia, and Jerôme Gautié. 2000. “France: The Deregulation that Never Existed.” Pp. 245–70 in Why Deregulate Labor Markets?, edited by Gosta Esping-Andersen and Marino Regini. New York: Oxford University Press. Mandel, Hadas and Moshe Semyonov. 2006. “A Welfare State Paradox: State Interventions and Women’s Employment Opportunities in 22 Countries.” American Journal of Sociology 111: 1910–49.

Joya Misra and Lucian Jude 133 Mattioli, Marie-Annick. 2003. “Chomage en France et en Grande-Bretagne 1990– 2000: La Situation Specifique des Femmes.” Revue Française de Civilisation Britannique 12: 29–38. Mazur, Amy G. 2003. “Drawing Comparative Lessons from France and Germany.” Review of Policy Research 20: 491–523. Misra, Joya. 1998. “Mothers or Workers? The Value of Women’s Labor: Women and the Emergence of Family Allowance Policy.” Gender and Society 12: 376–99. Misra, Joya and Leslie King. 2005. “Women, Gender, and State Policies.” Pp. 526– 45 in A Handbook of Political Sociology: States, Civil Societies, and Globalization, edited by Thomas Janeoski, Robert Alford, Alexander Hicks, and Mildred Schwartz. Cambridge: Cambridge University Press. Misra, Joya, Stephanie Moller, and Michelle Budig. 2007. “Work–Family Policies and Poverty for Partnered and Single Women in Europe and North America.” Gender and Society, 21: 804–27. Morgan, Kimberly. 2005. “The ‘Production’ of Child Care: How Labor Markets Shape Social Policy and Vice Versa.” Social Politics 12: 243–63. Morgan, Kimberly. 2006. Working Mothers and the Welfare State: Religion and the Politics of Work-Family Policies in Western Europe and the United States. Stanford: Stanford University Press. Morgan, Kimberly and Kathrin Zippel. 2003. “Paid to Care: The Origins and Effects of Care Leave Policies in Western Europe.” Social Politics 10: 49–85. Myles, John. 1991. “Post-Industrialism and the Service Economy.” Pp. 350–66 in The New Era of Global Competition: State Policy and Market Power, edited by Daniel Drache and Meric S. Gertler. Montreal: McGill-Queen’s University Press. OECD. 2000. “Early Childhood Education and Care Policy in the Netherlands.” Background report to the OECD project: Thematic Review of Early Childhood Education and Care Policy. The Hague: Ministry of Health, Welfare and Sport. OECD. 2001. OECD Employment Outlook. Paris: OECD. OECD. 2002. Babies and Bosses: Reconciling Work and Family Life. Volume 1: Australia, Denmark, and the Netherlands. Paris: OECD. Offen, Karen. 1991. “Body Politics: Women, Work, and the Politics of Motherhood in France, 1920–1950.” Pp. 138–59 in Maternity and Gender Policies, edited by Gisela Bock and Pat Thane.New York: Routledge. Orloff, Ann Shola. 2002. “Women’s Employment and Welfare Regimes: Globalization, Export Orientation, and Social Policy in Europe and North America.” United Nations Research Institute for Social Development, Programme Paper Number 12. Outshoorn, Joyce. 1986. “The Feminist Movement and Abortion Politics in the Netherlands.” Pp. 64–84 in The New Women’s Movement: Feminism and Political Power in Europe and the USA, edited by Drude Dahlerup. Beverly Hills, CA: Sage. Outshoorn, Joyce. 1995. “Administrative Accommodation in the Netherlands: The Department for the Coordination of Equality Policy.” PP. 168–85 in Comparative State Feminism, edited by Dorothy McBride Stetson and Amy G. Mazur. Thousand Oaks, CA: Sage. Outshoorn, Joyce and Joke Swiebel. 1998. “Feminism and the State in The Netherlands.” Pp. 143–65 in Women’s Movements and Public Policy in Europe, Latin America, and the Caribbean, edited by Geertje Lycklama à Nijeholt, Virginia Vergas, and Saskia Wieringa. New York: Garland Publishing.

134 Method and Substance in Macrocomparative Analysis Pedersen, Susan. 1993. Family, Dependence, and the Origins of the Welfare State: Britain and France, 1914–1945. Cambridge: Cambridge University Press. Pettit, Becky and Jennifer Hook. 2005. “The Structure of Women’s Employment in Comparative Perspective.” Social Forces 84: 779–801. Pfau-Effinger, Birgit. 1998. “Gender Cultures and the Gender Arrangement: A Theoretical Framework for Cross-National Gender Research.” Innovations: The European Journal of Social Sciences 11: 147–66. Pfau-Effinger, Birgit. 1999. “Change in Family Policies in the Socio-Cultural Context of European Societies.” Comparative Social Research 19: 135–59. Pfau-Effinger, Birgit. 2004. “Socio-Historical Paths of the Male Breadwinner Model – An Explanation of Cross-National Differences.” The British Journal of Sociology 55: 377–99. Pfefferkorn, Roland. 1996. “Métiers ‘Masculins’ et Métiers ‘Féminins’: Des Inégalités Persistantes.” Revue des Sciences Sociales de la France de l’Est 23: 143–52. Plantenga, Janneke. 1996. “For Women Only? The Rise of Part-Time Work in the Netherlands.” Social Politics, Spring: 57–71. Plantenga, Janneke and Johan Hansen. 1999. “Assessing Equal Opportunities in the European Union.” International Labour Review 138: 351–79. Plemper, Esther. 1996. “Women’s Strategies in Dutch Philanthropy.” Voluntas 7: 365–82. Pott-Buter, Hettie A. 1993. Facts and Fairy Tales about Female Labor, Family, and Fertility: A Seven-Country Comparison, 1850–1990. Amsterdam: Amsterdam University Press. Ragin, Charles C. 1997. “Turning the Tables: How Case-Oriented Research Challenges Variable-Oriented Research.” Comparative Social Research 16: 27–42. Revillard, Anne. 2006. “Bringing the Movement within the State: The Comité du Travail Féminin (1965–1981), or The Unknown Origins of French State Feminism.” Paper to be presented at the European Social Science History Association meetings. Sackmann, Rosemarie. 1998. “European Gender Roles: Public Discourses and Regional Practices.” Innovation: European Journal of Social Sciences 11: 167–90. Sainsbury, Diane, ed. 1994. Gendering Welfare States. London: Sage. Sainsbury, Diane, ed. 1999. Gender and Welfare State Regimes. Oxford: Oxford University Press. Silvera, Rachel. 2000. “Gender Impact Assessment and the Employment Strategy in France.” Report for the European Commission’s Expert Group on Gender and Employment. Social and Cultural Planning Office. 1998. Social and Cultural Report 1998. Rijswijk: SCP. Stier, Haya, Noah Lewin-Epstein, and Michael Braun. 2001. “Welfare Regimes, Family Supportive Policies, and Women’s Employment along the Life Course.” American Journal of Sociology 106: 1731–60. Tilly, Louise, and Joan W. Scott. 1978. Women, Work, and Family. New York: Routledge. Visser, Jelle. 2002. “The First Part-Time Economy in the World: A Model to be Followed?” Journal of European Social Policy 12: 23–42. Visser, Jelle and Anton Hemerijck. 1997. “A Dutch Miracle”: Job Growth, Welfare Reform and Corporatism in the Netherlands. Amsterdam: Amsterdam University Press.

5 The Welfare State, Family Policies, and Women’s Labor Force Participation: Combining Fuzzy-Set and Statistical Methods to Assess Causal Relations and Estimate Causal Effects Scott R. Eliason, Robin Stryker, and Eric Tranby

In Why We Need a New Welfare State (2002), four long-time male scholars of the twentieth-century welfare state – Gøsta Esping-Andersen, John Myles, Anton Hemerijck, and Duncan Gallie – argue that the welfare state of the twenty-first century requires “comprehensive redesign.” The twenty-first-century welfare state must be redesigned around not just government–market relations and the life-course patterns of men, but also work–family interactions and the life-course patterns of women. Similarly, as Myles and Quadagno (2002) argue in their recent review of literature on social policy and the welfare state, gender relations, family forms, and women’s employment are central to contemporary welfare state restructuring in a way that they were not during the “golden age” of welfare expansion. Feminist scholars and themes such as “female friendliness,” “social care,” and “work–family conflicts” have contributed much to placing women and children on center stage along with men when it comes to welfare state redesign.1 But likewise, labor market and demographic realities loom large (Huber and Stephens, 2000, 2001; Scharpf and Schmidt, 2000; Myles and Quadagno, 2002; Esping-Andersen et al., 2002). In much of Europe, life-span is increasing as are the costs of supporting retirement, at the same time as fertility rates have plummeted. Especially when the cost of socializing retirement is paid for in large part by payroll taxes on those working, fiscal crisis looms and additional taxable workers are required. Under these conditions, even the core ‘male-breadwinner’ 135


countries of continental Europe – such as Germany – will find it seductive to contemplate increased female labor force participation. Enter the female worker and, with her, new twists on scholarly and policy concern with social care (Lewis, 1998; Daly and Lewis, 2000; Wennemo, 1994; Gauthier, 1996; Randall, 2000; Mahon, 2002; Morgan and Zippel, 2003; Morgan, 2004; Misra, Budig and Moller, 2005). Clearly, facilitating women’s labor force participation on the supply side through the public provision of child care or otherwise will not be sufficient to put the welfare state on a sound fiscal footing. This is especially true in those countries like Germany, in which job growth has been stagnant in part because substantial employer payroll contributions to funding pensions and unemployment may decrease employers’ enthusiasm for job creation, especially when strong unions do not countenance lower wage-lower benefit jobs (Scharpf, 1999; Scharpf and Schmidt, 2000; Streeck, 2001). Nonetheless, encouraging female labor force participation on both the supply and demand sides is now widely viewed as a central pillar of the twenty-first-century welfare state (O’Connor, 2003; Esping-Andersen et al., 2002). Whether the motivation for this has been proactive gender egalitarianism, reactive adaptation to new family structures and labor force participation patterns of both women and men across the life course, public concern with child poverty and development of children’s human capital, or some combination of these, the policy emphasis on women’s labor force participation calls for increased research on the avenues through which this is – or can be – achieved. Key among these are the demand-side mechanism of gendered job creation and the supply-side mechanism of parental leaves and publicly subsidized or provided child care (Rubery et al., 1999; Esping-Andersen et al., 2002; Stryker and Eliason, 2004). This chapter contributes to ongoing debate about design of the “new welfare state” by examining the causal links between these and other welfare state policies and institutions that may facilitate female labor force participation in fourteen advanced capitalist democracies from 1960 to 1999. Countries included in our analyses are Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Sweden, Britain, and the United States. We assess causal relations and estimate causal effects by bringing together two very diverse methodological tools – fuzzy-set analysis and a formulation of the intention-to-treat design. Specifically, we use goodness-of-fit tests developed by Eliason and Stryker (2007) for assessing causal relations and conjunctions in fuzzy-set analyses (Ragin, 2000). We then estimate causal effects suggested by the fuzzy-set analysis using

Scott R. Eliason, Robin Stryker, and Eric Tranby

137

Imbens and Rubin’s (1997) and Hirano, Imbens, Rubin, and Zhou’s (2000) formulation of the intention-to-treat design for non-experimental data. Combining these methods provides a powerful lens with which to study the causal influence that social policies have had, and can be expected to have, on outcomes of interest.

Theoretical and empirical background There is substantial literature on family policies, including child care policies and parental leaves, in the European Union and other advanced industrial democracies.2 Until recently, much of that literature was predominantly historical and descriptive. Scholars who attempt causal analyses often focus on explaining what has led to institutional variation in day care provision, maternity and paternity leaves, family allowances or other family policies, including child and family cash and tax benefits across time and place among advanced industrial democracies (Wennemo, 1994; Gauthier, 1996; Daly, 2000; Randall, 2000; Misra, 2003). Research attempting to examine empirical links between variation in family policies and outcomes – whether quantity and quality of women’s labor force participation, child and family poverty and health, child educational outcomes, or class- and gender-related income inequality still is in its infancy. But it is increasing quickly, given the centrality of these questions to current policy making.3 In this chapter, we are interested in both aggregate-level causes of variation in welfare state institutions and family policies and, in consequence of these, variations for gendered labor markets. Because we are interested in both demand-side and supply-side incentives for women’s labor market participation, we examine causes and consequences of cross-country and over time variation in expansion of public sector employment and also the causes and consequences of variation in day care and parental leave policies. Public sector expansion and female labor force participation It is well established that long-term incumbency of social democratic parties promotes development of generous, redistributive welfare states characterized above all by a public service orientation to welfare state provision. Meanwhile, although long-term Christian democracy likewise promotes welfare state generosity, it does so predominantly through transfer payments that are not especially redistributive (Huber, Ragin and Stephens, 1993; Huber and Stephens, 2001). Although Huber and Stephens (2000) treat public sector expansion as a consequence of rising


female labor force participation when experienced during social democratic governance, we have specified and examined empirically a reverse causal argument (Stryker and Eliason, 2004).4 Our reverse causal argument suggests that cumulative social democratic governance expands the public sector, in turn promoting female labor force participation. Publicly-provided social services allow women to enter the labor force and provide employment for them (Huber and Stephens, 2001). As we elaborate further below, because publiclyprovided care services remove family-oriented time constraints, these services should increase female labor supply (O’Connor, Orloff, and Shaver, 1999, pp. 78–88). At the same time, however, publicly provided care services should increase demand for female – as opposed to male – labor insofar as public sector jobs require tasks considered female-oriented. Welfare state and labor market research has argued consistently that the growth of public sector service occupations expands women’s job opportunities (OECD, 1982; Esping-Andersen, 1990, pp. 206–29; Rosenfeld and Kalleberg, 1990; Myles and Turegin, 1994; Gornick and Jacobs, 1998; Huber and Stephens, 2000; Rubery, Smith, and Fagan, 1999, pp. 44–5; Myles and Quadagno 2002). Likewise, in liberal, market-oriented welfare states, private sector service expansion has increased job opportunities for women, especially in low wage service, including care, jobs that are disproportionately feminized (England, Budig, and Folbre, 2002; Daly, 2000; O’Connor, Orloff, and Shaver, 1999, pp. 97–8; Rubery et al., 1999, p. 22). Underlying these observations is an extensive literature examining how gender-segregated labor markets affect gender inequalities in income, as a function of: (1) the gender of the job task; (2) the (mostly-transcultural) expectations that women should be matched to jobs requiring (mostly) female-oriented tasks and men to jobs requiring (mostly) male-oriented tasks: and 3) the systematic devaluation of skills involved in female-oriented tasks (Rosenfeld and Kalleberg, 1990; Steinberg, 1990; Reskin, 1993; Ridgeway, 1997; Ridgeway and Smith Lovin, 1999; Grusky and Charles, 2001; Bonstead-Bruns and Eliason, 2002; England et al., 2002; Charles and Grusky, 2004; Pettit, 2006).5 In short, incorporating gendered tasks and gendered matching process into welfare state arguments clarifies why public sector expansion – creating jobs requiring female-specific skills – should enhance demand for, as well as supply of, female labor. Consistent with this reasoning, our prior fuzzy-set research on 1977–94 Germany, France, Denmark, Britain, Italy, and Belgium showed that high cumulative left cabinet incumbency was causally necessary for high public sector size. In turn,


139

high public sector size was causally sufficient for high female labor force participation. These results conformed to our theoretical expectations. However, only further data collection and analysis could show whether the results would generalize to a broader, more representative group of advanced capitalist democracies. As well, further data collection and analysis were required to explore whether our prior findings would hold for a longer time period that captured cross-country variation in both early trends toward public sector expansion and later trends toward public sector retrenchment (see, for example, OECD, 1982; Castles, 2001; Clayton and Pontusson, 1998). Social democratic governance, public sector expansion, and family policies Given that a substantial amount of public sector expansion from the 1960s forward was fueled by growth in publicly provided caring services, public sector expansion itself incorporates supply-side as well as demand-side promotion of women’s labor market participation (Stryker and Eliason, 2004). But supply-side incentives can best be examined by directly measuring the family policies that can be expected to shape instrumental incentives and the cognitive and normative context for women’s labor force participation. Here, expectations about causal links from variation in cumulative governance patterns to publicly provided day care, and from day care to female labor market participation are reasonably straightforward. Both theoretically and empirically, however, the situation is murkier with respect to maternity leave (Misra, Budig and Moller, 2005; Pettit, 2006). With respect to day care first, Huber and Stephens (2000, 2001) have pointed out that women already in the labor market are likely to demand government support – including publicly provided or subsidized child care – for managing work–family conflicts. Consistent with social democratic governments’ emphasis on universalistic, public provision of services in general, these governments should be especially likely to respond to such demands. Thus, we would expect cumulative social democratic governance, relative to Christian democratic or center-right secular governance, to be positively related to public provision of child care. From the early 1960s, the Scandinavian countries were leaders in quickly expanding publicly provided day care (Gauthier, 1996; Randall, 2000). Thus, historical descriptions of the advent and trajectories of day care provision across advanced capitalist democracies lend support to the hypothesis of a general positive association between cumulative social democratic governance and public provision of child care.


Even here, however, deeper historical investigation reminds us that motivation for initiating a policy and its subsequent effects or functions can be very different. For example, Gauthier (1996) notes that, contrary to other European countries, Sweden highlighted public day care as an issue early in the twentieth century. But at this time, Swedish day care formed part of a policy package seen as responding to fertility and population concerns rather than those of gender and employment. Randall (2000) points out that, more recently, there has been an international convergence of attention on issues of female labor force participation and related child care concerns. Likewise, there has been substantial recent convergence in public provision of child care for children from 3 to school age – although not for day care for younger children (Randall, 2000). In our earlier fuzzy-set analyses, we found that high cumulative social democratic governance was causally necessary for high public provision of day care for children 0–2 and also for children 3 to school age (Stryker and Eliason, 2004). Again, however, we can not be sure that this finding will hold up for a more representative group of advanced capitalist democracies and over a longer time frame. This is especially so because such diverse countries as Austria, France, Germany, Ireland, and Britain – concerned more about pre-primary child socialization and education than about women’s labor force participation – nonetheless in recent years have accelerated their child care provisions. In short, although we continue to hypothesize a positive causal link between cumulative social democratic governance and public provision of day care, we now mobilize more extensive data to examine whether there are differences in causal patterns involving day care for younger and older children. With respect to maternity leave, our earlier fuzzy-set analyses found no causal relationship between high levels of cumulative social democratic governance and high levels of maternity leave in Denmark, Britain, Germany, France, Belgium, and Italy, 1977–94. Perhaps this is not surprising, given that gender ideologies from the relatively egalitarian “dual earner” and “earner–carer” models to the gender distinctive “separate spheres,” “male-breadwinner” or “primary caregiver/secondary earner” models have motivated the origins and expansion of maternity leaves, as they have other family support policies depending on the country and historical period in question (Gauthier, 1996, 2000; Wennemo, 1994; Misra, 2003; Moss and Deven, 1999; Randall, 2000; Daly and Lewis, 2000; Misra, Budig, and Moller, 2005). In addition, maternity leaves have been motivated by health and fertility-related concerns as well as labor market-related goals. Indeed, Gauthier (1996) argues that


141

maternity leave in Europe arose predominantly from concerns about the health of working mothers and their children, rather than from a desire to promote women’s employment. Partial exceptions to this generalization included Sweden and France, where pre-World War II gender ideologies were more accepting of women’s right to employment (Gauthier, 1996; Jenson, 1990). Diverse historical motivations for adopting and expanding maternity leave policies help us make sense of our prior null findings. But these must be re-examined for a more representative set of advanced capitalist democracies.

Public sector expansion, family policies, and female labor market participation As Gauthier (1996) reminds us, “family” policies encompass an amalgam of legislation and benefits that ordinarily are not part of any coherent framework. They are often as much or more responsive to population, health and education concerns as they are to labor market issues. For example, already in the 1960s, a goal of supporting female employment and working mothers directly motivated day care policies in Scandinavia. These policies also were motivated by post-World War II Scandinavian states’ desires to support early socialization and learning. During a similar time frame, the French government also highlighted public provision of day care, but almost exclusively because of beliefs in early education and socialization, and not because of a preoccupation with working mothers. After 1975, as changes in family structure and life-course patterns of men and women accelerated, Nordic countries continued to espouse gender equality as a major goal for family policies. Meanwhile liberal countries such as the US espoused gender equality without endorsing government responsibility to provide day care. At the same time as Nordic social democracies and Anglo-American liberal governments were professing commitment to gender equality, the German government assumed some responsibility for child care, but continued to hew ideologically to the “male breadwinner” model of work–family division of labor. Whatever the divergent motivations and causes for different policies and policy packages across countries and over time, current debates about welfare state restructuring emphasize the function or impact of these policies for female labor market participation. Among researchers who have tried to assess the causal import of family policies for women’s employment, findings have been mixed. Though results have been more consistent across diverse samples and measures for day care policies than maternity or parental leave, our capacity to interpret the labor market


impact of family policies is diminished when researchers fail to disaggregate composite indices of women-friendly policies that may embody conflicting sets of incentives or norms. For example, in a cross-national study of women’s employment patterns during childrearing in 12 advanced capitalist democracies, including Sweden, Norway, Australia, New Zealand, the United Kingdom, Israel, Italy, Austria, Germany, New Zealand, Canada and the Netherlands, Stier, Lewin-Epstein, and Braun (2001) found that there was cross-country variation in the percentage of women who, in 1994, reported working full-time prior to the birth of children – from 44 percent in the Netherlands to 81 percent in Canada. But there was much greater cross-country variation in the employment patterns of women with pre-school children. In 1994 Sweden and Norway, categories of continuous full-time employment and continuous non-employment were small. Most women worked part time when they had children, or they alternated between non-employment and part-time work or part-time work and full-time work. In 1994 Israel, Italy, and to a lesser extent Austria, there were large percentages of women in both continuous full-time employment and continuous non-employment. Overall, Stier et al.’s results (2001) suggested that observed cross-national variation in women’s employment patterns during childrearing could be accounted for in part by a framework combining the impact of broad welfare state regimes and country-specific patterns of family policies. Most relevant to our research, within all Esping-Andersen regime types – social democratic, liberal and conservative-corporatist – employment continuity was greater in countries that provided support for working mothers. However, Stier et al. (2001) measured support for working mothers as a composite categorical variable – coded high, medium or low based upon Gornick et al.’s (1997) composite index of family policies presumed to support female labor force participation. As adapted by Stier et al. (2001), this composite measure combined multiple indicators of both child care and parental leave. Child care indicators took into account tax relief for child care, the existence of legislation guaranteeing child care for children ages 0–2, and children 3–school age, child care expenditures, the percentages of children ages 0–2 and children 3–school age in publicly funded child care, the percentage of children aged 5 in preprimary school and the percentage of children in public after school programs. Meanwhile, measures of parental leave schemes took into account legislated job protections, weeks of paid maternity leave, the wage replacement rate, policy coverage, and the availability of extended leave and paternity benefits. Thus, although Stier et al.’s (2001) findings


143

do suggest that family policies positively affect female labor market participation, it is impossible to trace labor market effects to the specific, potentially diverse norms and incentives embodied in specific child care or leave policies. With respect to day care, extant economic and sociological theories of the labor market predict that readily available, affordable day care should increase the level of women’s labor market participation. Available, affordable day care is predicted to loosen women’s familyrelated budget and time constraints, thus decreasing their reservation wages and altering their preference formation (Blau and Ferber, 1992; Rønsen and Sundstrom, 2002; Stryker and Eliason, 2004; Pettit, 2006). At the same time, widespread use of publicly provided child care facilities may reshape cognitive expectations and normative evaluations about the acceptability or desirability of child care provided outside the home and by someone other than the mother (Leira, 2002; Stryker and Eliason, 2004). However, expectations about the import of parental leaves of various types may not be so straightforward (Bruning and Plantenga, 1999; Rønsen and Sundstrom, 2002; Stryker and Eliason, 2004; Misra, Budig, and Moller, 2005; Pettit, 2006). Bruning and Plantenga (1999) evaluate how variation in regulations governing parental leave produce country variation in leave take-up rates by gender in the Netherlands, Finland, Norway, Sweden, Denmark, Austria, Germany, and France. Although the authors do not examine how these differences in take-up rates affect labor force participation by women, their study does suggest the importance of separating parental leave variables from public provision and public subsidy of child care. As well, it suggests measuring aspects of leaves such as their target(s), generosity, duration, and flexibility both individually and as a total package. Research that has disaggregated parental leave from day care policies, and looked at the impact of one or the other individually yields mixed results. Based on regression analyses, Esping-Andersen (2002) argued that the paramount barrier to full time employment among women is lack of affordable child care. Somewhat consistent with this conclusion are our own prior fuzzy-set analyses for 1977–94 Denmark, France, Germany, Britain, Belgium, and Italy (Stryker and Eliason, 2004). These showed that both high aggregate levels of publicly available day care for children from 0–2 and for children from 3 to school age were individually causally sufficient, albeit not necessary, for high aggregate labor force participation among women 15–64. Meanwhile, Rønsen and Sundstrom’s (2002) micro level econometric analyses showed that increases in the


local supply of public day care encouraged entry into employment for first- and second-time mothers in Finland (1989), Norway (1988), and Sweden (1992). The impact was stronger in Sweden than in Norway. In Finland, the availability of an allowance for home care seemed to discourage full-time work, although not employment in general. On the other hand, based on data from West Germany, Kreyenfeld and Hank (2000) specified a multinomial logit model in which the probability of an individual mother with one or more children under 12 not working versus working part-time versus working full-time was a function of the availability of day care and diverse other factors. These other factors included the unemployment rate, whether the mother was native or foreign born, whether she was a lone mother, number of children, age of youngest child, mother’s education, partner’s wage and whether or not the child’s grandparents lived in the same town. The authors found that variation in the availability of publicly funded child care did not influence mother’s employment in West Germany. They presumed that their null finding resulted because day care centers in Germany are open during limited hours and because primary school hours are irregular. Thus, they concluded that the structure of German child care fails to facilitate mother’s labor force participation. With respect to parental leave policies, Winegarden and Bracy (1995) provided econometric analysis of the impact of paid maternity leaves on female labor force participation of women, ages 20–34, for four time points between 1959 and 1989 in 17 countries. The analysis included Austria, Canada, Denmark, France, Greece, West Germany, Italy, the Netherlands, New Zealand, Norway, Portugal, Sweden, Spain, the United Kingdom, and the United States. The authors found that, as the duration of paid leave increased, female labor participation rates for those between ages 20 and 34 likewise increased. Conversely, measuring maternity leave provision as a function of both duration and generosity, our own prior fuzzy-set investigation of the relationship between maternity leave schemes and female labor force participation in 1977–94 Denmark, Britain, Italy, France, Germany, and Belgium found no relationship between high aggregate levels of maternity leave provision and high aggregate levels of women’s labor market participation. In their investigation of women-friendly policies, Gornick et al. (1997) and Korpi (2000) had assumed that maternity leave functioned as part of a package of dual-earner support. In contrast, we speculated that for women entering the labor force to meet financial needs, high maternity benefits might substitute for subsequent labor force participation rather than encourage it (Stryker and Eliason, 2004).


145

Alternatively, our failure to find the expected impact of maternity leaves on aggregate female labor force participation could have been due to the limited number of countries, time frames, and measures that we were able to investigate prior to our current study. Consistent with our assumption that maternity leave provisions embody conflicting labor force incentives depending on the type of policy and the demographic profile of woman we are considering, Rønsen and Sundstrom’s (2002) in-depth analysis of Finnish, Norwegian, and Swedish mothers found a highly nuanced pattern of labor market effects. The authors assessed the impact of parental leave and child care policies by comparing the full-time and part-time (re) entry into the labor force of all first- and second-time mothers, regardless of prior work history, in 1988 Norway, 1989 Finland, and 1992 Sweden. Testing statistical models motivated by microeconomic theories of female labor supply, Rønsen and Sundstrom (2000) concluded that generous, flexible parental leave programs, such as the one found in Sweden, contain incentives encouraging more mothers to work prior to the birth of a child. The Swedish program also encouraged more mothers to remain in the labor market throughout the childbearing years. In Norway, a shorter entitlement period created lower pre-birth employment incentives, so that Norwegian mothers were somewhat less likely to work prior to the birth of a child than were their Swedish counterparts. And, although women eligible for maternity leave tended to resume employment more quickly in Norway than in either Sweden or Finland, a larger number of Norwegian mothers remained out-of-the-labor force, because an inflexible entitlement system made it harder for women to reconcile work and family responsibilities. Finland is characterized by generous, extended leaves combined with moderate flexibility and the availability of an allowance to compensate home care. Here, women who had worked pre-birth and thus were eligible for maternity leave tended to remain out of the labor force for a longer time period after the birth of a child than did their Norwegian counterparts. Similarly suggestive of the potentially competing incentives embodied in parental leaves is Pettit’s (2006) multi-level comparative analysis of women’s employment in the United States, continental and eastern Europe, and the Nordic countries. Pettit found that the impact of parental leave depended both on the number of children in the household and on the length of the leave. Parental leaves up to two years were associated with an increased probability of employment for women with young children, but the positive impact diminished as the number of children increased. In addition, leaves of more than


two years were associated with a decreased probability of women’s employment. In the following analyses, we build on our prior fuzzy-set methodology, so that we can continue to distinguish not just between the absence and presence of evidence for some causal relationship among welfare state institutions, family policies and female labor market participation, but also among empirical support for relations of causal necessity, causal sufficiency, and causal necessity and sufficiency combined. Empirical support for causal relations of sufficiency versus those of necessity with respect to various aspects of family policy has quite different implications for policy makers concerned to enhance female labor force participation. In particular, finding causal sufficiency in the absence of necessity tells us that there are alternative routes to achieving the desired policy outcome. Finding causal necessity in the absence of sufficiency, however, tells us that, although a particular welfare state or family policy is essential for achieving a particular labor market outcome, its presence alone will not ensure that outcome. Following the general thrust of our literature review, our current analyses build on our own prior research to continue examining both supply-side and demand-side incentives for increased women’s labor force participation, this time for a larger, more representative group of advanced capitalist democracies over a longer time period. We likewise continue to examine disaggregated policy measures, rather than composite indexes of female-friendly or family-friendly policies. Our analyses focus first on replicating precisely our prior six-country, 1977–94 analyses with a 14-country fuzzy-set analysis, from 1960 to 1999. We then incorporate our fuzzy-set measures into a Rubin-style analysis of compliers average causal effects. This allows us to complement our findings of necessity and sufficiency with respect to causal relations with a more rigorous test of causal effects, and of the relative strength of demand- and supply-side causal effects. We consider this to be a first ‘cut’ on the evidence we can supply to scholarly and policy questions involved in redesigning the welfare state, given the richness of data we are gathering and the combination of methods we employ relative to the cues we take from the totality of prior theory and research.

Data and variables We compiled our data set from the diverse array of sources presented in Appendix 5.1. In total, we currently have gathered information from approximately 50 different data sources, including OECD documents


147

and data sets, country monographs, comparative studies, and data sets collected by other researchers. Whenever possible, we used multiple sources for each measure, so that we could verify consistency among sources, and – when sources were inconsistent – locate the source of the inconsistency and create the most complete and accurate measurement possible for all our variables. In order to maintain data completeness over the full 1960–99 time period, we were constrained to drop Japan, Australia, and New Zealand from our analyses. We also eliminated Switzerland, because family policies are administered at the cantonal, rather than national level. Though we also faced particularly challenging issues in locating data on child care policies for Norway and Ireland, we were able to overcome these sufficiently to maintain these two countries in our data base. Thus, our analyses include data from 1960 to 1999 for Austria, Belgium, Canada, Denmark, Finland, France, (West) Germany, Ireland, Italy, the Netherlands, Norway, Sweden, Britain, and the United States. Although we had hoped to represent the universe of advanced capitalist democracies, we can only do so within the limits of available, comparable data. In addition to eliminating Japan, Australia, New Zealand, and Switzerland, because of the data difficulties mentioned above, we cannot analyze data for countries outside of the OECD or for countries that have joined OECD (or EU) data collection efforts very late relative to our time frame.6 The current analysis extends to these countries and time frame the set of policy measures developed in our earlier research (Stryker and Eliason, 2003, 2004). More precisely, as the foundation for constructing our fuzzy-set membership scores for the fuzzy-set and CACE analyses (discussed below and in Appendix 5.2), we use information on: (1) maternity leaves including wage replacement rates, number of weeks of paid leave, proportion of employed women covered, and public expenditures for paid leaves, (2) weeks of extended leave, (3) public day care including, separately, the proportions of children 0 to 2 years old, and 3 to school-age, in public daycare and public expenditures for daycare, and (4) family/child cash and tax benefits including child care tax relief, family allowances for children, and family support benefits. Except for extended leaves, fuzzy-set membership scores are then obtained from indexes on this empirical information originally developed in Stryker and Eliason (2003). More precisely, membership scores are constructed for high levels of government support for (1) public day care for younger children, (2) public day care for older children, (3) maternity leaves, (4) extended leaves, and (5) family/child cash and tax benefits. We also construct membership scores for high levels of

148 Method and Substance in Macrocomparative Analysis Table 5.1 Decade means and standard deviations for cumulative left cabinet incumbency Decade means

Austria Belgium Canada Denmark Finland France Germany Ireland Italy Netherlands Norway Sweden United Kingdom United States

Decade standard deviations

1960s

1970s

1980

1990s

1960s

1970s

1980s

1990s

8.97 7.01 0.00 13.32 6.39 3.09 0.29 1.99 1.13 5.43 18.32 18.36 6.93

14.95 10.33 0.00 18.91 11.09 3.09 6.22 2.57 2.37 7.10 23.29 27.66 12.93

24.43 12.45 0.00 25.16 16.61 6.44 12.11 3.79 3.45 8.62 30.05 32.61 16.16

30.67 16.11 0.00 27.50 19.71 12.75 12.33 5.20 5.85 11.20 36.78 40.04 16.76

0.97 1.01 0.00 2.50 0.85 0.00 0.52 0.00 0.48 0.34 1.76 3.03 1.96

3.03 0.84 0.00 2.25 1.59 0.00 2.59 0.50 0.15 1.09 2.65 2.18 2.09

2.44 0.31 0.00 0.55 1.49 2.13 0.47 0.43 0.60 0.11 1.37 2.68 0.00

1.61 1.57 0.00 1.99 0.66 1.40 0.06 0.87 0.93 1.26 2.28 1.98 1.07

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Note: The minimum value of 0 includes 1960–99 United States, 1960–99 Canada, and 1960–65 Germany; the maximum value of 43.61 includes 1999 Sweden.

cumulative left governance, high levels of civilian public sector employment, and high levels of female labor force participation. See Stryker and Eliason (2003) for details on the indexes (except for extended leaves, which was constructed solely on the number of weeks of extended leave7 ). See Appendix 5.2 for details on constructing the fuzzy-set membership scores from these indexes. Finally, to give some idea of the general across-country differences and within-country change over time in this empirical information, Tables 5.1–5.8 present the decade means and standard deviations for the indexes and other empirical information used in forming the fuzzyset scores in the following analysis. For each index, higher values refer to higher levels of governmental support. Minimums and maximums, including the corresponding country-times, are noted with each table.

Empirical methods We combine two methodological approaches to examine our hypotheses. First, to assess causal relations, including the possibility of conjunctural


149

Table 5.2 Decade means and standard deviations for percentage civilian government employment Decade means



1960s

1970s

1980

1990s

1960s

1970s

1980s

1990s

7.11 5.80 8.92 7.95 5.46 9.10 5.61 5.93 4.88 5.06 8.20 9.70 10.23

9.52 7.80 12.44 15.38 8.95 10.94 7.57 7.62 6.70 6.06 12.34 18.03 13.23

11.33 9.56 13.59 21.14 12.78 12.19 8.63 8.72 7.83 6.50 17.58 24.31 13.53

13.39 9.65 14.29 21.40 13.85 12.92 8.60 8.36 8.37 6.24 20.48 22.92 10.45

0.43 0.54 1.00 1.52 0.81 0.53 0.51 0.43 0.43 0.12 0.71 1.67 0.73

1.03 0.85 0.64 2.48 1.33 0.45 0.62 0.56 0.70 0.29 1.97 2.87 0.89

0.57 0.16 0.52 0.57 1.01 0.44 0.23 0.18 0.17 0.12 0.95 0.61 0.19

0.70 0.12 0.29 0.21 0.61 0.36 0.18 0.06 0.13 0.07 0.78 1.15 1.76

7.22

8.67

9.13

10.04

0.51

0.65

0.25

0.18

Note: The minimum value of 4.28 includes 1961 Italy; the maximum value of 24.97 includes 1985 Sweden.

relations, we use Eliason and Styker’s (2007) goodness-of-fit extensions of Ragin’s (2000) fuzzy-set methodology. To estimate causal effects arising from the relations suggested in the fuzzy-set analysis, we use Imbens and Rubin’s (1997) and Hirano, Imbens, Rubin, and Zhou’s (2000) formulation of the intention-to-treat design to obtain what are known as compliers average causal effects. We describe both these approaches and how we combine them here.

Assessing causal relations In the short time since Ragin (2000) initially developed fuzzy-set methodology as a tool to assess causal relations, there have been increasing applications of these methods (for example, Mahoney, 2003, Goertz and Mahoney, 2005, and Stryker and Eliason, 2004), as well as a series of methodological advances (see, for example, Smithson 2005; Goertz and Mahoney 2005). As initially developed by Ragin (2000),8 set-theoretic principles in general, and the subset principle specifically, are used to assess causal relations in a fuzzy-set framework. In its simplest form, the

150 Method and Substance in Macrocomparative Analysis Table 5.3 Decade means and standard deviations for maternity leave index (dashed entries indicate missing data for corresponding country/decade) Decade means



1960s

1970s

1980

1990s

1960s

1970s

1980s

1990s

0.52 – 0.00 0.00 0.33 0.48 0.41 0.26 0.39 0.12 0.00 0.59 0.00 0.00

0.50 – 0.00 0.42 0.60 0.53 0.43 0.28 0.45 0.33 0.41 0.81 0.15 0.00

0.51 0.36 0.35 0.63 0.63 0.53 0.42 0.34 0.45 0.32 0.52 0.87 0.15 0.00

0.51 0.44 0.40 0.75 0.69 0.54 0.51 0.34 0.43 0.00 0.80 0.90 – 0.00

0.01 – 0.00 0.00 0.29 0.01 0.01 0.01 0.01 0.16 0.00 0.04 0.00 0.00

0.01 – 0.00 0.22 0.03 0.02 0.02 0.01 0.03 0.01 0.04 0.12 0.19 0.00

0.01 0.01 0.01 0.05 0.06 0.02 0.04 0.03 0.02 0.01 0.05 0.01 0.19 0.00

0.02 0.01 0.02 0.04 0.05 0.01 0.01 0.01 0.01 0.00 0.07 0.07 – 0.00

Note: The minimum value of 0.00 includes many countries (see 0.00 means and standard deviations indicating 0’s for the decade for some country); the maximum value of 0.98 includes 1992 Sweden.

subset principle operates to establish the relation between a hypothesized cause (or conjunction of hypothesized causes) and some outcome. Fuzzy-set membership scores, reflecting the degree to which empirical cases belong to some set, are used to assess the subset principle.9 Empirical evidence for causal necessity is obtained to the extent that the outcome can be established as a subset of the hypothesized cause. Evidence for causal sufficiency, on the other hand, is obtained to the extent that the hypothesized cause can be established as a subset of the outcome. Evidence for causal necessity and sufficiency combined is obtained to the extent that the fuzzy scores on the hypothesized cause (or conjunction of causes) and outcome can be established as equal. See Ragin (2000), Ragin and Pennings (2005), and Eliason and Stryker (2007) for details on the logic of fuzzy-set analysis. (See Appendix 5.2 for details describing the calculation of fuzzy-set scores used in our analysis.) For the fuzzy-set portion of our analysis, we use Eliason and Stryker’s (2007) goodness-of-fit tests to identify potential factors, and conjunctions of factors, expected to produce high levels of female labor force participation. Eliason and Stryker’s (2007) goodness-of-fit tests provide an inferential framework within which to empirically assess subset


151

Table 5.4 Decade means and standard deviations for public day care index, children ages 0–2 (Dashed entries indicate missing data for corresponding country/decade) Decade means



1960s

1970s

1980

1990s

1960s

1970s

1980s

1990s

– – 0.00 0.05 0.00 – – – – 0.00 0.00 – – 0.00

– – 0.00 0.22 0.29 – – – – 0.00 – – – 0.01

0.13 0.14 0.00 0.80 0.46 0.20 0.04 0.03 0.08 0.08 0.27 0.89 0.00 0.01

0.17 0.13 0.00 0.96 0.52 0.40 0.12 0.04 – 0.13 0.53 0.84 0.00 0.01

– – 0.00 0.07 0.00 – – – – 0.00 0.00 – – 0.00

– – 0.00 0.04 0.08 – – – – 0.00 – – – 0.00

0.02 0.00 0.00 0.06 0.06 0.02 0.00 0.00 0.00 0.01 0.00 0.08 0.00 0.00

0.02 0.01 0.00 0.02 0.03 0.01 0.04 0.00 – 0.04 0.16 0.09 0.00 0.00

Note: The minimum value of 0.00 includes many countries; the maximum value of 1.00 includes 1998 Denmark.

relations. That inferential framework is based on the idea that fuzzy-set membership scores are subject to measurement error and that measurement error, in turn, influences the assessment of the subset principle. The goodness-of-fit tests thus indicate whether the data are consistent with some causal hypothesis up to a specified degree of measurement error. While the logic of this approach is derived from fairly standard and widely used ideas in statistics, the empirical relationship assessed by the goodness-of-fit test derives fully from the logic of the subset principle described above. See Appendix 5.3 and Eliason and Stryker (2007) for details. Recall that our general hypothesis, depicted in Figure 5.1, as well as our prior empirical work, suggests a causal chain in which high levels of cumulative left cabinet incumbency give rise to an expanded public sector and increased family support policies. In turn, through the mechanisms described above, public sector expansion and increased family support policies heighten the demand for and facilitate the supply of female labor. This results in higher rates of female labor force participation. Our first objective for the fuzzy-set portion of the analysis, therefore, is to test an alternative conjunctural hypothesis, in

152 Method and Substance in Macrocomparative Analysis Table 5.5 Decade means and standard deviations for Public Daycare Index, Children ages 3–school age (dashed entries indicate missing data for corresponding country/decade) Decade means



1960s

1970s

1980s

1990s

1960s

1970s

1980s

1990s

– – 0.00 0.09 0.00 – – – – 0.31 0.00 – – 0.01

– – 0.00 0.34 0.40 – – – – 0.42 – – – 0.03

0.52 0.31 0.00 1.07 0.64 0.45 0.36 0.14 0.31 0.44 0.49 1.15 0.00 0.03

0.78 0.26 0.00 1.28 0.81 0.80 0.59 0.21 – 0.45 0.75 1.09 0.00 0.03

– – 0.00 0.12 0.00 – – – – 0.03 0.00 – – 0.01

– – 0.00 0.05 0.11 – – – – 0.04 – – – 0.00

0.06 0.01 0.00 0.08 0.08 0.04 0.02 0.01 0.01 0.02 0.00 0.10 0.00 0.00

0.10 0.03 0.00 0.03 0.02 0.00 0.01 0.05 – 0.02 0.01 0.12 0.00 0.00

Note: The minimum value of 0.00 includes many countries; the maximum value of 1.34 includes 1998 Denmark.

which these factors act in a combinatoric contextual manner, rather than in the chain-like manner suggested by our hypothesis. To do this, we use Eliason and Stryker’s (2007) partitioning of the goodness-of-fit statistic which tests whether a conjunction provides a better fit to the data than does any component of the conjunction, including lowerorder conjunctions and each of the individual factors making up the conjunction. As we show below, the partitioning of the goodness-of-fit statistic suggests that, for our data, we can reject the conjunctural hypothesis. Therefore, our second objective for the fuzzy-set portion of our analysis is to test how well the data conform to each portion of the hypothesized causal chain. To do this, we use Eliason and Stryker’s (2007) goodness-offit statistics to test causal hypotheses involving cumulative left cabinet incumbency and, in turn, civilian public employment, public day care for children ages 0–2, public day care for children ages 3 to school age, maternity leave, extended leave, and family/child cash and tax benefits. We next use these goodness-of-fit tests to test causal hypotheses involving female labor force participation as an outcome of each of the welfare state policy measures and of civilian public employment.10


153

Table 5.6 Decade means and standard deviations for weeks of extended leave Decade means



1960s

1970s

1980s

1990s

0.00 0.00 0.00 10.00 9.96 88.00 0.00 0.00 26.00 0.00 0.00 26.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14.00 10.00 10.00 10.00 16.60 24.36 26.30 88.00 119.20 140.00 0.00 29.60 129.60 0.00 9.60 12.00 26.00 26.00 26.00 0.00 0.00 0.00 52.00 36.40 0.00 26.00 26.00 26.00 16.00 40.00 40.00 0.00 0.00 0.00

1960s 1970s 1980s 1990s 0.00 0.00 0.00 0.00 8.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.11 0.00 0.00 0.00 0.00 4.09 0.00 0.00 26.85 0.00 0.00 26.04 30.36 0.00 5.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 25.12 0.00 0.00 0.00 0.00 20.66 0.00 0.00 0.00 0.00 0.00

Note: The minimum value of 0.00 includes many countries; the maximum of 144 includes 1992–1999 Germany.

Estimating causal effects While we use fuzzy-set methods to assess potential causal factors giving rise to high female labor force participation, we use Imbens and Rubin’s (1997) and Hirano, Imbens, Rubin, and Zhou’s (2000) intentionto-treat (ITT) design to estimate the causal effects on female labor force participation rates deriving from each of these factors.11 In the general ITT design, counterfactual causal treatment effects are obtained in part through some mechanism encouraging subjects to obtain some treatment. For example, Imbens and Rubin (1997) examine the causal effect of vitamin supplements (the treatments) on children’s survival rates in various Indonesian communities in the 1990s. In that case, the encouragement (or intention) to treat was given by the assignment of children to receive the supplements, while the treatment itself was given by the actual receipt of the supplements. In the Hirano et al. (2000) study, the effectiveness of a flu vaccine is assessed, where there is imperfect compliance with the encouragement to be vaccinated. We apply this logic to estimate the causal effect that specific policies may have on female labor force participation. Consistent with results

154 Table 5.7 Decade means and standard deviations for Cash/Tax Family/Child Benefits Index Decade means



1960s

1970s

1980s

1990s

1960s

1970s

1980s

1990s

0.32 0.38 0.25 0.08 0.18 0.59 0.09 0.16 0.35 0.27 0.07 0.14 0.08 0.21

0.33 0.40 0.30 0.12 0.11 0.48 0.17 0.14 0.21 0.28 0.20 0.15 0.12 0.25

0.37 0.44 0.25 0.12 0.19 0.49 0.23 0.16 0.12 0.27 0.33 0.14 0.21 0.22

0.46 0.49 0.26 0.17 0.38 0.42 0.34 0.29 0.07 0.17 0.38 0.16 0.15 0.21

0.03 0.03 0.06 0.01 0.04 0.03 0.03 0.01 0.03 0.05 0.01 0.01 0.02 0.01

0.06 0.04 0.04 0.01 0.02 0.01 0.10 0.04 0.05 0.01 0.06 0.01 0.04 0.01

0.03 0.05 0.01 0.03 0.08 0.03 0.06 0.02 0.03 0.05 0.02 0.01 0.02 0.02

0.06 0.01 0.02 0.00 0.05 0.06 0.06 0.01 0.01 0.02 0.01 0.02 0.01 0.01

Note: The minimum value of 0.04 includes 1960 Denmark; the maximum value of 0.63 includes 1963 France.

Table 5.8 Decade means and standard deviations for female labor force participation rates Decade means



1960s

1970s

1980s

1990s

1960s

1970s

1980s

1990s

0.48 0.38 0.37 0.52 0.63 0.47 0.49 0.35 0.36 0.26 0.37 0.55 0.48 0.45

0.48 0.43 0.51 0.63 0.65 0.51 0.49 0.34 0.35 0.30 0.53 0.66 0.54 0.53

0.51 0.49 0.68 0.75 0.73 0.55 0.53 0.37 0.42 0.43 0.68 0.78 0.60 0.64

0.60 0.56 0.65 0.78 0.71 0.59 0.60 0.47 0.44 0.58 0.73 0.75 0.67 0.69

0.01 0.01 0.04 0.06 0.02 0.00 0.01 0.00 0.03 0.00 0.01 0.02 0.01 0.02

0.01 0.02 0.06 0.03 0.03 0.02 0.01 0.00 0.02 0.02 0.07 0.05 0.03 0.04

0.02 0.01 0.05 0.02 0.01 0.00 0.01 0.01 0.02 0.05 0.04 0.02 0.03 0.03

0.03 0.02 0.08 0.02 0.01 0.01 0.02 0.06 0.02 0.04 0.02 0.03 0.01 0.02

Note: The minimum value of 0.26 includes 1960–65, 69 Netherlands; the maximum value of 0.81 includes 1989 Sweden.


Long-term left incumbency Variable: Cumulative left Cabinet Incumbency

Public sector expansion Variables: Civilian Government Employment, Maternity leave, Public day care for ages 0–2, Public day care for ages 3-School age, and family/child cash and tax benefits

155

Female labor force participation Variable: Female LF Participation Rate

Figure 5.1 Hypothesized causal chain, including variables used in the empirical analysis

from the fuzzy-set portion of our analysis (described below), we consider high levels of cumulative left cabinet incumbency as the mechanism through which specific policies are implemented and maintained. That is, high levels of cumulative left cabinet incumbency act as the encouragement portion in the ITT design, while the specific policy itself (for example, public daycare for young children) acts as the treatment. To be more precise, our ITT analysis requires only the following assumptions. 1. Mechanisms exist, though need not be observed, such that high levels of cumulative left cabinet incumbency acts as an encouragement for governments to enact left-specific policies. 2. There are tendencies for governments to comply with these mechanisms and tendencies for governments to not comply with these mechanisms. 3. The tendency to not comply with these mechanisms is considered a mixture of a. the tendency to always enact a specific policy regardless of the level of cumulative left cabinet incumbency and b. the tendency to never enact a specific policy regardless of the level of cumulative left cabinet incumbency. 4. The only non-negligible way that governments can have an impact on aggregate female labor force participation at time t is through policies or related mechanisms operating at times prior to t. 5. Policies at time t and female labor force participation at time t + 1 cannot affect cumulated left cabinet incumbency up to time t.


All of these assumptions are consistent with our empirical findings from the fuzzy-set portion of our analysis (described below). Importantly, no additional assumptions – save for the parametric form of the loglikelihood – are necessary to generate intention-to-treat (ITT) effects (Hirano et al., 2000) or compliers average causal effects (CACE) (Imbens and Rubin, 1997).12 Below we adopt Imbens and Rubin’s (1997) approach and terminology because it is more widely adopted in the literature. In general, the CACE identifies the expected counterfactual causal effect on some outcome were a complier to change treatment statuses. This is the effect that would have been observed in the population of compliers had the treatment been randomly assigned, as would be the case in a typical experimental design framework. In our case, the CACE identifies, for the population of compliers as described in assumptions 2 and 3 above, the counterfactual causal effect on female labor force participation rates due to specific policies as enacted through left governments. Thus, the estimated CACE gives the expected change in female labor force participation rates due to specific policies implemented through left governments. Given that, to our knowledge, compliers average causal effects have never been estimated in the context of fuzzy-set measures, we detail our CACE estimation in Appendix 5.4. To estimate the CACE, we use a bootstrapped estimator on 1000 replications derived from the likelihood given in Appendix 5.4. Using bootstrapped estimators alleviates the need to impose any distributional assumptions (such as normality) on the CACE itself. We thus present the median and inter-90% percentile range from the bootstrapped empirical distribution function.13

Results Fuzzy-set analysis: testing conjunctural relations In this section we present results on tests for all possible conjunctions as given by the full set of hypothesized causal factors. We are especially interested in conjunctions involving cumulative left cabinet. If any of these conjunctions provide a better fit to the data when compared to each individual factor, then the causal chain hypothesis would necessarily be inconsistent with the data. Instead, the data would then be consistent with a more complex conjunctural set of relations. Here we use Eliason and Stryker’s (2007) partitioning of the F statistic to test whether each higher-order conjunction provides a better fit to the data than the set of lower-order conjunctions or the single factors nested within each higherorder configuration.14


157

Table 5.9 Select five-way and four-way partition tests of goodness-of-fit F statistics for the outcome “High Female Labor Force Participation – Subsequent Year.” Hypothesized causal factors, and corresponding letter representations, include A = High Level Cumulative Left Cabinet Incumbency C = High Level Public Sector Employment D = High Level Public Daycare, Ages 0–2 E = High Level Public Daycare, Ages 3 to School Age F = High Level Maternity Leave G = High Level Family/Child Cash and Tax Benefits. Conjunctions Causal hypotheses and partitions

SD

DF

MSD

F

P

Select Six-Way Conjunction ACDEFG Necessity 746.5978 248 3.0105 11.7258 0.0000 Sufficiency 0.7181 15 0.0479 0.1865 0.9997 Necessity & Sufficiency 747.3159 263 2.8415 11.0677 0.0000 ACDEF v ACDEFG – – – 0.7569 0.9879 ACDEG v ACDEFG – – – 0.9759 0.5784 ACDFG v ACDEFG – – – 1.0000 0.5000 ACEFG v ACDEFG – – – 0.9341 0.7095 ADEFG v ACDEFG – – – 1.0000 0.5000 CDEFG v ACDEFG – – – 0.9887 0.5368 Select Five-Way Conjunction ACDEF Necessity 561.8962 248 2.2657 Sufficiency 3.762 15 0.2508 Necessity & Sufficiency 565.6582 263 2.1508 ACDE v ACDEF – – – ACDF v ACDEF – – – ACEF v ACDEF – – – ADEF v ACDEF – – – CDEF v ACDEF – – –

8.8250 0.9769 8.3773 0.9627 0.9986 0.9113 1.0000 0.9527

0.0000 0.4800 0.0000 0.6209 0.5046 0.7741 0.5000 0.6526

Note: See Appendix 5.5 for complete set.

Table 5.9 gives goodness-of-fit statistics and partitions for select fiveway and four-way conjunctions derived from the full set of factors discussed above, with respect to their causal relation with the set high female labor force participation in the subsequent year. The full set of partitions and tests can be found in Appendix 5.5. Note that the partition tests include only those cases (country-times) for which we have valid empirical information on all factors. This reduces the total number of cases to 263 for these tests. Since no conjunction fits the causal necessity and sufficiency hypothesis, we begin with the set of six-way conjunctions that fit the causal


sufficiency hypothesis, and then test whether lower order conjunctions fit the data as well as the higher order conjunction. If all tests indicate that lower-order conjunctions fit just as well as the parent higher-order conjunction (tests with p-values greater than 0.05), we then continue to the next set of conjunctions. This approach will lead to the lowest-order, most general, sufficient conjunctural or single-factor condition that fits the data. See Eliason and Stryker (2007) for details. The first panel of Table 5.9 gives select six-way conjunctions, and Appendix 5.5 provides test statistics for the remaining six-way conjunctions. All six-way conjunctions provide a strong fit between the data and the causal sufficiency hypothesis (all p-values are greater than 0.05). In other words, these data are consistent with all possible sixway conjunctions of the causal factors considered in our analysis – high level of cumulative left cabinet incumbency; high public sector employment; high levels of daycare, maternity, and extended leave policies; and high levels of family/child cash and tax benefits – combining for a causally sufficient relation with high female labor force participation. The question then becomes, do any or all of the five-way conjunctions nested within these six-way conjunctions provide equally good fits to the hypothesis of causal sufficiency? If so, there is redundant empirical information in the six-way conjunction used to explain female labor force participation. Partitioning the F statistic from this set of six-way conjunctions into each nested five-way conjunction reveals that none of the six-way conjunctions provides a better fit to these data than each of the nested five-way conjunctions. This is given by a p-value greater than 0.05 for each of the partition test statistics, and indicates that the six-way conjunction contains redundant empirical information in explaining female labor force participation. Similarly, the partitions on each of the possible five-way conjunctions suggest that each of the nested four-way conjunctions fit these data just as well as the parent five-way conjunction. Continuing on in this manner, the information in Table 5.9 and in Appendix 5.5 reveals that no higher-order conjunction provides a better fit to these data for any causal hypothesis than the fit achieved by each factor alone. In sum, these data do not support the idea that conjunctions provide a better understanding of female labor force participation beyond that gained by examining each potential causal factor separately. In the next section we analyze each factor separately, to examine further the idea of a causal chain in which, once left political parties accumulate power, they better implement and subsequently maintain family policies. These,


159

along with left governments’ expansion of the public sector, create the condition for high female labor force participation. Fuzzy-set analysis: assessing the impact of left governance levels on policy Having rejected the set of conjunctural hypotheses, in this section we turn to replicating our prior analyses on the larger, more representative 14 country data set and for the substantially longer 1960–99 time frame.15 We also consider extended leaves as an additional policy outcome. Overall, there is substantial similarity, but there also are some important differences between the findings of our current analysis and those of our earlier six-country, 1977–94 study. Given that the combination of an expanded set of countries and the longer time frame substantially shifts the maximum and minimum values on most of the variables used to construct membership scores, such differences in results should come as no surprise. (See Appendix 5.2 for details on the construction of membership scores.) For each causal hypothesis, Table 5.10 gives the goodness-of-fit statistics for the relationship between female labor force participation and all the factors examined in our past work, as well as for extended leave (Stryker and Eliason, 2004).16 Specifically, Table 5.10 gives the sums of squared distances (SSD) from that expected under each hypothesis, the corresponding degrees of freedom (DF) and mean squared distance (MSD). Table 5.10 also provides the goodness-of-fit F statistic (F) and corresponding descriptive level of significance (P). The SSC, DF, MSD, F, P, and therefore the goodness-of-fit tests are all unaffected by the omission of any third factor. Because there are no parameters estimated from these data to calculate any of the relevant quantities that enter the goodnessof-fit tests, any concern about biased estimation or results due to, e.g., spuriousness, is misplaced (see also Appendix 5.3, Eliason and Stryker, 2007 for details). Those who are used to employing regression methods, in which we must always concern ourselves with the possibility of model misspecification, may find this refreshing. As shown in Table 5.10, a high level of cumulative left governance continues to exhibit a strong causal relationship with high levels of public sector expansion for data representing 1960–99 Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Sweden, Britain, and the United States. However, whereas our earlier analyses for 1977–94 Denmark, Britain, France, Germany, Italy, and Belgium supported only a relationship of causal necessity, for the larger, more representative group of advanced capitalist democracies

Condition

Outcome

Causal hypothesis

SSD

DF

MSD

F

P

High Level of Cumulative Left Cabinet Incumbency

High Level of Public Sector Employment

Causal Necessity Causal Sufficiency Causal Nec & Suf

171.62 37.29 208.91

291 193 490

0.5898 0.1932 0.4264

2.30 0.75 1.66

0.0000 0.9891 0.0000

High Level of Public Daycare Ages 0–2


24.11 95.57 119.68

71 121 262

0.3395 0.7898 0.4568

1.32 3.08 1.78

0.0607 0.0000 0.0000

High Level of Public Daycare Ages 3–School Age


41.47 46.07 87.54

137 71 278

0.3027 0.6489 0.3149

1.18 2.53 1.23

0.1270 0.0000 0.0446

High Level of Maternity Leave


267.84 62.97 330.81

320 87 450

0.8370 0.7237 0.7351

3.26 2.82 2.86

0.0000 0.0000 0.0000

High Level of Extended Leave


184.80 435.39 620.19

90 321 490

2.0533 1.3564 1.2657

8.00 5.28 4.93

0.0000 0.0000 0.0000

High Level of Family/Child Cash & Tax Benefits


390.12 172.50 562.62

298 185 484

1.3091 0.9324 1.1624

5.10 3.63 4.53

0.0000 0.0000 0.0000

160

Table 5.10 Goodness-of-fit for relationships between hypothesized causal condition “High Level of Cumulative Left Cabinet Incumbency” and select outcomes. Bold-faced and italicized entries indicate a good fit between the hypothesized causal relation and the data at the .05 and .01 levels respectively


161

and longer time frame, we find instead that a high level of cumulative left cabinet incumbency is causally sufficient for a high level of public sector employment. Results of the goodness-of-fit F tests indicate that these data fit the causal sufficiency hypothesis extremely well (F = 0.75 and P = 0.9891). Goodness-of-fit tests given in Table 5.10 also show that the data are consistent with a causally necessary relationship between high levels of cumulative left governance and high levels of public day care for children 0–2 (F = 1.32, P = 0.0607) and children 3 to school-age (F = 1.18, P = 0.1270). Perhaps most interesting in Table 5.10 is that, using a slightly more liberal Type I error rate of 0.01 for the goodness-of-fit test, these data are consistent with a causally necessary and sufficient relationship between high levels of cumulative left governance and high levels of public day care for children 3 to school-age (F = 1.23, P = 0.0446). Finally, as we found in our prior analyses, the current analysis fails to support any causal relation between high levels of cumulative left governance and either high levels of maternity leave or high levels of family/child cash and tax benefits. In addition to these null results, there is no support for any causal relation between high levels of cumulative left governance and high levels of extended leave.

Fuzzy-set analysis: causal relations with high female labor force participation Table 5.11 examines causal hypotheses linking public sector expansion and family policies to female labor force participation measured in the subsequent year. Goodness-of-fit tests in Table 5.11 reveal that a high level of public sector employment is causally sufficient for high female labor force participation (F = 0.78, P = 0.8996), as are a high level of public day care for children ages 0–2 (F = 0.82, P = 0.4828) and a high level of maternity leave (F = 0.97, P = 0.6041). Perhaps surprisingly, results in Table 5.12 also show no support for any causal hypothesis linking high levels of public day care for older children with high levels of female labor force participation. However, inspection of the fuzzy-set graph for this relation (not shown) suggests that the entire lack of fit in these data for this relation is attributable to the Netherlands. This is confirmed in that, once the Netherlands data are removed from consideration, the goodness-of-fit F test reveals a strong fit to the causal sufficiency hypothesis for the remaining countries (F = 0.23, P = 0.9193; not shown in Table 5.11). Finally, there is no support in these data for a causal relation between high levels of extended benefits and female labor force

Outcome

Condition

High Female Labor Force Participation – Subsequent Year

High Level of Public Sector Employment

High Level of Public Daycare Ages 0–2

High Level of Public Daycare Ages 3–School Age

High Level of Maternity Leave

High Level of Extended Leave

High Level of Family/Child Cash & Tax Benefits

Causal hypothesis

SSD

DF

MSD

F

P


301.37 14.05 315.42

460 70 530

0.6551 0.2007 0.5951

2.55 0.78 2.32

0.0000 0.8996 0.0000


892.88 0.63 893.51

276 3 280

3.2351 0.2109 3.1911

12.60 0.82 12.43

0.0000 0.4828 0.0000


735.22 29.53 764.75

267 30 297

2.7536 0.9843 2.5749

10.73 3.83 10.03

0.0000 0.0000 0.0000


642.98 44.37 687.35

304 179 483

2.1151 0.2479 1.4231

8.24 0.97 5.54

0.0000 0.6041 0.0000


1879.53 76.88 1956.41

471 58 530

3.9905 1.3255 3.6913

15.54 5.16 14.38

0.0000 0.0000 0.0000


582.98 139.33 722.31

339 181 520

1.7197 0.7698 1.3891

6.70 3.00 5.41

0.0000 0.0000 0.0000

162

Table 5.11 Goodness-of-fit for relationships between outcome “High Female Labor Force Participation – Subsequent Year” and select hypothesized causal conditions. Bold-faced and italicized entries indicate a good fit between the hypothesized causal relation and the data at the .05 and .01 levels respectively


163

Table 5.12 Bootstrapped EDF estimates of compliers average causal effects on female labor force participation rates, with strong left political tradition as the instrument in the intention-to-treat analysis∗ Bootstrapped EDF estimates 5th percentile Demand-Side Factor Public Sector Employment Supply-Side Policies Public Daycare – Ages 0–2 Public Daycare – Ages 3 to School Age Maternity Leave Extended Leave Family/Child Cash & Tax Benefits

Median

95th percentile

0.35

0.38

0.40

0.08 0.07 0.16 −0.07 −0.25

0.11 0.09 0.18 0.00 −0.23

0.12 0.11 0.20 0.03 −0.21

∗ Bootstrapped estimates are based on 1000 replications. Country-times with high likelihoods of strong left political traditions include Norway after 1992 and Sweden after 1987. Likelihoods of strong left political traditions are based on cumulative left cabinet incumbency fuzzy-set scores. See text for details.

participation, nor for high levels of family/child cash and tax benefits and female labor force participation. Summarizing the results in Tables 5.10–5.11, the fuzzy-set goodnessof-fit F tests and partitions indicate that these data are consistent with the following: 1. High levels of cumulative left governance are (a) causally sufficient for high levels of public sector employment and (b) causally necessary for high levels of public daycare for younger and older children. Somewhat weaker, though detectable, evidence exists suggesting a causally necessary and sufficient relationship between high levels of cumulative left governance and high levels of public daycare for older children. 2. High levels of public sector expansion, public daycare for younger children, and maternity leave are, separately, causally sufficient for high levels of female labor force participation. Excluding the Netherlands, a high level of public day care for older children is also causally sufficient for high levels of female labor force participation. 3. No hypothesis involving two-way or higher-order conjunctions provides a better fit to these data than the series of hypotheses involving single factors.


CACE estimates of supply and demand factors on female labor force participation For each policy and for public sector expansion, Table 5.12 gives the median, the lower 5th percentile, and the upper 95th percentile from the bootstrapped empirical distribution estimate of the CACE. These percentiles are similar to confidence intervals. However, no distributional assumptions are made because the percentiles derive from the observed boostrapped empirical distribution function (EDF), which was obtained on 1000 replicates. For useful overviews and details on various bootstrapped estimators, see Efron and Tibshirani (1994) and DiCiccio and Efron (1996). Importantly, the CACE, as derived by Hirano et al. (2000) and Imbens and Rubin (1997), is unaffected by traditional notions of spuriousness.17 High levels of public sector employment, the single demand-side factor, have a substantial effect on female labor force participation rates. The median causal effect for that factor is an increase of 38 percentage points, with lower and upper bounds on that effect of 35 and 40 percentage points respectively.18 Thus, the expansion of public sector employment as generated through cumulative left governance is responsible for a substantial increase in female labor force participation. It is, in fact, the single most important factor in our analysis in accounting for high levels of female labor force participation. Of the supply-side policies, maternity leave has the largest causal effect on female labor force participation rates, with a median effect of 18 percentage points, and lower and upper bounds of 16 and 20 percentage points respectively. The CACE estimate for public day care for younger children is in the range of 8 to 12 percentage points with a median effect of 11 points. For public day care for older children, the range of the effect is from 7 to 11 percentage points, with a median effect of 9 points. For extended leave, the median of 0 and lower and upper bounds of −7 and +3 suggest no effect. For family/child cash and tax benefits, the median of −23 and bounds of −25 and −21 suggest a substantial negative causal effect.19 To understand better the causal effects highlighted by the CACE, recall that the CACE identifies the counterfactual causal effect on female labor force participation rates due to specific factors as operating through cumulative left governance. Thus, the estimated CACE gives the expected change in female labor force participation rates due to factors and policies that are an outgrowth of left government legacies.20 Importantly, these CACE estimates do not give the effects due to some


165

Table 5.13 Bootstrapped EDF estimates of compliers average causal effects on female labor force participation rates, with all other non-left political traditions as the instrument in the intention-to-treat analysis∗ Bootstrapped EDF estimates 5th percentile Demand-Side Factor Public Sector Employment Supply-Side Policies Public Daycare – Ages 0–2 Public Daycare – Ages 3 to School Age Maternity Leave Extended Leave Family/Child Cash & Tax Benefits

Median

95th percentile

0.45

0.46

0.48

0.14 0.46 0.36 −0.18 −0.04

0.14 0.48 0.39 −0.16 −0.02

0.15 0.51 0.41 −0.14 −0.01

∗ Bootstrapped estimates are based on 1,000 replications. Country-times with high likelihoods of other non-left political traditions include France prior to 1982, Germany prior to 1973, Ireland prior to 1993, Italy prior to 1989, and Canada and the US over the entire time period 1960–99. Likelihoods of other non-left political traditions are based on the complement of the cumulative left cabinet incumbency fuzzy-set membership scores. See text for details.

specific left government’s policy implementation and/or maintenance. Rather, these CACE estimates give the effects due to each factor or policy as implemented by any government where strong prior left government legacies are observed. But what about other political legacies? To answer that question we estimate CACE where, now, low levels of cumulative left governance acts as the instrument in the intention-to-treat analysis. Given in Table 5.13, this set of CACE estimates tell us the expected change in female labor force participation rates due to factors and policies implemented by governments in country-times with no strong left government legacy. Such countries in our data include current-day Canada and the United States. Table 5.13 shows once again that expanded public sector employment has a substantial impact on female labor force participation, with a median CACE of 46 percentage points and bounds of 45 and 48. Thus, it appears that an expanded public sector would have a significant effect on female labor force participation regardless of the political tradition. This, in turn, gives strong credence to the demandside component of our theoretical model, and suggests that the narrow creation of public sector jobs with traditionally female-tasked skills


drives a large portion of any observed increase in female labor force participation. Also showing very strong positive effects in Table 5.13 are public day care for older children and maternity leave programs, with median effects of 48 and 39 percentage point increases respectively. These effects are substantially higher for these factors than those found in Table 5.12 with the strong left government legacy instrument. This suggests that, were these programs implemented in countries with non-left political legacies (such as Canada and the United States), substantially higher levels of female labor force participation than are currently observed might be achieved. Although considerably smaller in magnitude, results in Table 5.13 also show that public day care programs for younger children are likely to net an increase in female labor force participation, with a median CACE of 14 percentage point increase. By way of contrast, both extended leaves and family/child cash and tax benefit programs have negative impacts on female labor force participation rates, with median CACEs of −16 and −2 respectively and with both upper bounds negative. This suggests that both extended leaves and family/child cash and tax benefit programs would likely decrease female labor force participation were they implemented in countries with non-left political legacies.

Discussion and conclusions Both the fuzzy-set and CACE analyses and results have important scholarly and policy implications. For these analyses, we have compiled and used data for 14 countries, including Austria, Belgium, Britain, Canada, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Sweden, and the United States. We likewise have compiled data for a substantially longer time frame – 1960–99 – than has been mobilized in prior aggregate level cross-national research on the relationship between diverse family policies and female participation in paid employment. Consistent with our own theoretical understanding and predictions, we have shown that both supply-side and demandside mechanisms must be considered by scholars and policy makers promoting female labor force participation as part of a redesign for the twenty-first-century welfare state. We have further shown that, in terms of fit to the empirical information, it is unnecessary to consider these mechanisms as operating in some complex conjunctural fashion. Instead, the data are more consistent with understanding these mechanisms as operating in an individual and serial fashion. This is consistent


167

with conceptualizing the process as the causal chain described above and in Figure 5.1. Consistent with the general thrust of prior research, the fuzzy-set portion of our analysis revealed that high levels of cumulative left governance are causally sufficient, albeit not necessary, to create an expanded public sector. In turn, an expanded public sector is sufficient, albeit not necessary, for high female labor force participation. Our CACE analysis further showed a causal effect of 35 to 40 percentage points on female labor force participation rates, due to an expanded public sector as resulting from high levels of cumulative left governance. Consistent with our fuzzy-set findings suggesting a relationship of sufficiency but not necessity between cumulative left governance and public sector expansion, we were led to ask whether a large public sector achieved in the absence of high cumulative left governance would nonetheless increase female labor force participation. This would allow us to estimate the causal effect of public sector expansion in countries like France, which have relatively large public sectors but also high cumulative experience with secular right governments. As scholars have noted, France’s long-standing “strong state” tradition and its across the political spectrum commitment or willingness to tolerate government intervention in the economy has produced relatively high levels of civilian government employment (see Stryker and Eliason, 2004). When we added to fuzzy-set results by conducting the CACE analysis using non-left political traditions as the intention-to treat component, we found an effect of 45–48 percentage points (Table 5.13). This shows that an expanded public sector would indeed have a significant effect on female labor force participation regardless of the political tradition. Thus, although cumulative left governance does provide a route to public sector expansion, and public sector expansion does provide a route to rather substantial gains in female labor force participation rates, this is not the only route to this end. Similarly, whereas the demand-side mechanism of public sector expansion promotes increased female labor force participation, such an increase can also be achieved by alternative, supply-side routes. Our fuzzy-set analysis showed that high levels of public day care for younger children and high levels of maternity leave were individually sufficient for high female labor force participation. Removing the Netherlands from the analysis further revealed that high levels of public day care for older children are sufficient for high female labor force participation. As further elaborated in the Misra and Topp chapter in this volume, public opinion in the Netherlands continues to place a particularly high value


on mothers rearing children in the ho,e relative to support levels for this value in other European countries. It is possible that the strength of this value in the Netherlands encourages mothers there to remain outside the labor force even when the public day care that elsewhere facilitates labor force participation of such mothers is provided. Our CACE analyses further shows that the causal effects of maternity leave and public day care programs on female labor force participation are non-negligible, although in some cases they are modest in comparison to demand-side mechanisms. For countries with strong left political legacies (Table 5.12), we see effects in the neighborhood of 10 percentage points deriving from both types of public day care and an upper bound of 20 percentage points due to maternity leave. For countries with other political legacies (Table 5.13), we see substantially higher effects due to public day care for older children (in the range of 46 to 51 percentage points) and due to maternity leave (in the range of 36 to 41 percentage points). Finally, our analysis shows that there is no evidence in these data to suggest that either extended leaves or family/child cash and tax benefits operate to increase female labor force participation. On the contrary, the CACE analysis reveals that both of these programs would do harm in this regard. For countries with strong left political legacies, extended leaves have no effect, while introducing family/child cash and tax benefits produces negative consequences for female labor force participation. For countries with other political legacies, family/child cash and tax benefits have negligible negative effects, whereas extended leave policies have more substantial negative effects on female labor force participation. Our findings for extended leaves complement and further nuance findings from regression analyses suggesting that leaves will have a positive impact up to some threshold point of duration, but that very lengthy leaves are likely to have a negative impact (Pettit, 2006). Because none of the factors in our fuzzy-set analysis exhibited a causally necessary relationship with high levels of female labor force participation, we cannot infer that the absence of publicly provided day care or maternity leave policies invariably is a barrier to women’s entry into paid employment (cf. Esping-Andersen et al., 2002). On the contrary, countries with liberal welfare state regimes, such as the United States, have managed to achieve relatively high rates of female labor force participation, albeit much of it through the low-wage service-sector route. Thus, on the one hand, welfare state policy makers would be correct to infer from our analysis that achieving high levels of female labor force


169

participation does not require them to invest heavily in publicly provided or subsidized day care. But this is not to suggest that all is well. Government commitment to the provision of affordable day care on a large scale may well be causally related to women’s capacity to work full-time and/or in higher-quality jobs with better wages, benefits, and working conditions. Thus, government commitment to providing available, affordable child care may well help diminish gender inequalities in earnings and other employment conditions. In future research, we plan to pursue these key questions for scholarship and policy making, by investigating how sectoral employment patterns and diverse types and aspects of family policies are related to variations in the type and quality of women’s employment across countries and over time. From a methodological standpoint, we have shown how fuzzy-set methods to assess causal relations can be fruitfully combined with statistical methods to assess causal effects for non-experimental data. We have shown how goodness-of-fit tests developed by Eliason and Stryker (2007) may be employed in fuzzy-set analysis to assess hypotheses about causal relations, including hypotheses that include higher-order conjunctions. Likewise, we have shown how an intention-to-treat form of analysis as developed by Imbens and Rubin’s (1997) and Hirano, Imbens, Rubin, and Zhou (2000), along with the bootstrapped estimation of the compliers average causal effect, can be leveraged to obtain a distribution on specific causal effects. Using all these tools together allows us to better answer the scholarly and policy questions with which we began. We close by noting that, for our data set of 14 advanced capitalist democracies from 1960 to 1999, our fuzzy-set analysis showed that high levels of cumulative social democratic governance were necessary, albeit not in themselves sufficient, for high levels of maternity leave and for high levels of public day care both for younger and for older children. This is an important finding, because it suggests that, historically speaking, without high levels of cumulative social democratic governance, key female-friendly and family-friendly policy measures are not likely to be achieved. Citizens in advanced industrial democracies could usefully consider this finding when they decide whether and how to participate in electoral politics, including deciding whether – and for which party – they will vote. Our future research will try to establish what other factors, in combination with cumulative social democratic governance, might produce a causal relationship of necessity and sufficiency to provision of high levels of publicly provided day care and other family and female-friendly policies.


Appendix 5.1: Data Sources Baker, Maureen. 1995. Canadian Family Policies: Cross-National Comparisons. Toronto: University of Toronto Press. Bastelaer, Alois van, Georges Lemaitre, and Pascal Marianna. 1997. “The Definition of Part-Time Work for the Purpose of International Comparisons.” Labour Market and Social Policy Occasional Papers. Paris: OECD 22. Bruning, C. and J. Plantega. 1999. “Parental Leave and Equal Opportunities: Experiences in Eight European Countries.” Journal of European Social Policy 9(3). Caramani, Daniele. forthcoming. The Formulation of National Elections and Party Structure in Europe: A Comparative and Historical Study. Census Bureau. 2000. Statistical Abstract of the United States. Washington: US Government Printing Office. Cochran, Moncrieff. 1993. International Handbook of Child Care Policies and Programs. London: Greenwood Press. Council of Europe. 1982–98. Comparative Tables of Social Security Schemes: In Council of Europe Member States and Other Countries: General Scheme. 1st–9th Editions Strasbourg, Germany: Council of Europe Press. Commission of the European Communities. 1970–1984. Comparative Tables of Social Security Schemes: In Member States: General Scheme. 4th–13th Editions Strasbourg, Germany: Council of Europe Press. Commission of the European Communities. 1990. Childcare in the European European Communities. Women of Europe Supplements No. 31. Brussels: Commission of the European Communities. Esping-Andersen, Gøsta, et al. 2002. Why We Need a New Welfare State. Oxford: Oxford University Press. EURYDICE and CEDEFOP. 1990. Structures of Education and Initial Training Systems in the Member States of the European Community. Brussels: EURYDICE and CEDEFOP. European Commission Network on Childcare and Other Measures to Reconcile Employment and Family Responsibilities. 1995. A Review of Services for Young Children in the European Union. Brussels: European Commission. European Commission. 1998. Care in Europe: Joint Report of the “Gender and Employment” and the “Gender and Law” Groups of Experts. European Commission, Eurydice, and Eurostat. 2000. Key Data on Education in Europe: Primary Education 1999/2000. European Journal of Political Research. 1995–2000. “Issues in National Politics.” Various Authors.


171

Gauthier, Anne H. 1996. The State and the Family: A Comparative Analysis of Family Policies in Industrialized Countries. Oxford: Clarendon Press. Gornick, Janet C., Marcia K. Meyers, and Katherine E. Ross. 1996. “Supporting the Employment of Mothers: Policy Variation Across Fourteen Welfare States.” Journal of European Social Policy 7(1): 45–70. Hofferth, Sandra J. et al. 1991. National Child Care Survey, 1990. Washington: Urban Institute Press. Huber, Evelyne, Charles Ragin, and John D. Stephens. 1997. Comparative Welfare States Data Set. Available at: www.lisproject.org/publications/ welfaredata/welfareaccess.htm. International Labour Office. 1985. Maternity Benefits in the Eighties: An ILO Global Survey (1964–84). Geneva: International Labour Office. International Labour Office. 1994. “Maternity and Work.” Conditions of Work Digest Vol. 13. Geneva: International Labour Office. International Social Security Association. 1999. Social Security Programs Throughout The World. Available at: www.issa.int/ssw. Kamerman, Sheila. 1980. “Maternity and Parental Benefits and Leaves: An International Review.” Impact on Policy Series Monograph. New York: Columbia University. Kamerman, Sheila. 2000. “Early Childhood Education and Care: An Overview of Developments in the OECD Countries.” International Journal of Educational Research 33: 7–29. Kamerman, Sheila and Alfred J. Kahn. 1975. Child Care Programs in Nine Countries: A Report Prepared for the OECD Working Party on the Role of Women in the Economy. Washington, DC: US Department of Health, Education, and Welfare. Kamerman, Sheila and Alfred J. Kahn. 1978. Family Policy: Government and Families in Fourteen Countries. New York: Columbia University Press. Kamerman, Sheila and Alfred J. Kahn. 1981. Child Care, Family Benefits and Working Parents: A Study in Comparative Policy. New York: Columbia University Press. Kamerman, Sheila and Alfred J. Kahn. 1983. Income Transfers for Families with Children: An Eight-Country Study. Philadelphia: Temple University Press. Kamerman, Sheila and Alfred J. Kahn. 1991a. Child Care, Parental Leave, and the Under 3s: Policy Innovation in Europe. New York: Auburn House. Kamerman, Sheila and Alfred J. Kahn. 1991b. “Government Expenditures for Children and Their Families in Advanced Industrialized Countries, 1960–1985.” Innocenti Occasional Papers: Economic Policy Series No. 20.


Kamerman, Sheila, Alfred J. Kahn, and Paul Kingston. 1983. Maternity Policies and Working Women. New York: Columbia University Press. Kreyenfeld, Michaels and Karsten Hank. 2000. “Does the Availability of Childcare Influence the Employment of Mothers? Findings from Western Germany.” Population Research and Policy Review 19 (4) 317–37. MZES/Eurodata. 2002. European Family Policy Database. Available for order online at: www.mzes.uni-mannheim.de/fs_daten_e.html. O’Connor, Julia S., Ann Shola Orloff, and Sheila Shaver. 1999. States, Markets, Families: Gender, Liberalism, and Social Policy in Australia, Canada, Great Britain, and the United States. Cambridge: Cambridge University Press. OECD. 1974. Educational Statistics Yearbook, Vol. 1: International Tables. Paris: OECD. OECD. 1975. Educational Statistics Yearbook, Vol. 2: Country Tables. Paris: OECD. OECD. 1981. Educational Statistics in OECD Countries. Paris: OECD. OECD. 1982. Employment in the Public Sector. Paris: OECD. OECD. 1984a. Tax Expenditures: A Review of the Issues and Country Practices. Paris: OECD. OECD. 1984b. “Social Expenditure: Erosion or Evolution.” OECD Observer 126: 3–6. OECD. 1989, 1990, 1993a. Education in OECD Countries: A Compendium of Statistical Information 86–87, 87–88, 88–89 and 89–90. Paris: OECD. OECD. 1993b. Private Pay for Public Work: Performance-related Pay for Public Sector Managers. Paris: OECD. OECD. 1994a. Women and Structural Change: New Perspectives. Paris: OECD. OECD. 1994b. Educational Statistics in OECD Countries. Social Policy Studies, No. 12. Paris: OECD. OECD. 1999. Labour Force Statistics: 1978–1999. Paris: OECD. Available at: www.sourceoecd.org. OECD. 2001. Social Expenditure Database. Paris: OECD. Available at: www.sourceoecd.org. OECD. 2002. Economic Outlook Database. Paris: OECD. Available at: www.sourceoecd.org. Olmsted, Patricia P and David Weikart. 1989. How Nations Serve Young Children: Profiles of Child Care and Education in 14 Countries. Michigan: High/Scope Press. Randall, Vicky. 2000. “Childcare Policy in the European States: Limits to Convergence.” Journal of European Public Policy 7: 346–68.


173

Rønsen, Marit and Marianne Sundstrom. 2002. “Family Policy and afterBirth Employment Among New Mothers: A Comparison of Finland, Norway and Sweden.” European Journal of Population 18: 121–52. UN Department of Economic and Social Affairs. 1995–1998. Demographic Yearbook Nos 47–50. New York: United Nations. US Department of Education. 1999. “Preprimary Education Enrollment.” Statistics in Brief: National Center for Education Statistics. Washington, DC: US Department of Education. US Department of Education. 1995. Project Head Start Statistical Fact Sheet. Varley, Rita. 1986. “The Government Household Transfer Data Base.” OECD Department of Economics and Statistics Working Papers No. 36. Paris: OECD Wennemo, Irene. 1994. Sharing the Costs of Children: Studies on the Development of Family Support in the OECD Countries. Swedish Institute for Social Research No. 25. West, Jerry. 1995. “Child Care and Early Education Program Participation of Infants, Toddlers, and Preschoolers.” Statistics in Brief: National Center for Education Statistics US Department of Education.

Appendix 5.2: Coding Fuzzy-Set Membership Scores A fuzzy-set membership score reflects the degree to which a case belongs to a set as defined on some hypothesized cause or on some outcome. Because the subset principle is critically sensitive to choice of measurement, great care is required in coding membership scores. Researchers will necessarily be confronted with two primary considerations. The first is the logic used to establish minimum and maximum membership scores. The second is the logic underlying the distribution of empirical cases between that minimum and maximum. For each, there are a number of informed choices. For the current analysis, we use what we have elsewhere termed a Min/Max Uniform Distribution coding (Stryker and Eliason, 2004).21 This measurement logic depends on a continuous distribution of information on the attribute of interest.22 Membership scores are then constructed based on that information, uniformly distributed across a range defined on the observed minimums and maximums in the data itself. That is, the fuzzy-set membership score si is defined as

si =

xi − min {X} max {X} − min {X}


where X gives the measurement on the attribute of interest and xi is the observation for the ith case. For this type of coding, a case with the maximum score on the attribute of interest will have a membership score of 1, indicating certainty (up to measurement error) that that case belongs to the corresponding set. A case with the minimum score on the attribute of interest will have a membership score of 0, indicating certainty (up to measurement error) that that case does not belong to the corresponding set. All other degrees of belonging to the set are measured on a uniform distribution relative to the range given by [min {X}, max {X}] on the original information. For example, the case in our data that has the maximum score on “Cumulative Left Cabinet Incumbency” (1999 Sweden) is measured as belonging to the set “High Cumulative Left Cabinet Incumbency” with membership score 1. The cases in our data that have the minimum score on the measure “Cumulative Left Cabinet Incumbency” (United States and Canada, 1960–1999) are measured as belonging to the set “High Cumulative Left Cabinet Incumbency” with membership score 0. All other cases have membership scores relative to that maximum and minimum. Distributions other than the uniform, or none for that matter, could be used to distribute cases across the range given by the min and max. Using no reference distribution exposes coding, and thus the analysis, too much to known and unknown vagaries of the individual researcher. This, in turn, leaves the validity of the analysis in question. Using a more complex reference distribution can produce empirical results that are artifacts of the complexity of the distribution chosen. Using the uniform distribution, which gives equal weight to cases across the range of the empirical information, avoids both these potential problems. Additionally, researchers may wish to consider minimums and maximums outside the range of the observed data when constructing membership scores. This is not advisable, however, as moving off the data range will necessarily bias the subset assessment up to known multiplicative and additive factors, and will thus adversely affect internal validity. To understand this more precisely, let min∗ {X} = min {X} + a and max∗ {X} = max {X} + b be, respectively, the minimum and maximum obtained outside the data range (say, for example, based on theoretical minimums and maximums not present in the data), where a and b are arbitrary constants and where min {X} and max {X} are, respectively, the minimum and maximum for the data. Further, let R∗ = max∗ {X} − min∗ {X} and R = max {X} − min {X} define the respective ranges. From this, the fuzzy-set membership score s∗i based on


175

min∗ {X} and max∗ {X} can be shown to be a simple function of si , xi − min∗ {X} R a = s s∗i = − ∗ i max∗ {X} − min∗ {X} R∗ R Thus, relative to si , the subset assessment using s∗i will be artifactually influenced multiplicatively as a function of the ratio of the two ranges and additively as a function of the ratio of a to R∗ . In general, fuzzy-set analysis based on the Min/Max Uniform Distribution coding provides results that are certain to have internal validity. That is, results hold for the data we use. External validity – moving off the support of the data to draw inferences to, in our case, other countries and time points – depends on the degree to which our data (countries at specific time points) represent other data not used in our analysis (other countries at specific time points) with respect to the variables used in our analysis. Additionally, assessment of the subset relationship based on our goodness-of-fit tests for any two factors – say for example high levels of cumulative left incumbency and high levels of support for public day care – is unaffected by consideration of a third factor – say, for example, aggregate education levels. This is because: (1) any one subset assessment is completely and solely defined on a nonparametric function of the observed membership scores si , (2) the observed membership scores defined above are solely a function of xi and independent of any information on some third factor, and (3) for the Eliason–Stryker goodness-of-fit test statistics there are no parameters estimated from the data that may, in turn, be influenced by the omission of some third factor. Thus, nowhere can a third factor contaminate the subset assessment. Moreover, there can be no concern over biased estimation of parameters due to omitted variables simply because there are no parameters estimated in the Eliason–Stryker goodness-of-fit statistics. From a practical standpoint, therefore, results from our fuzzy-set analysis may be extended to those country-periods not used in the analysis, insofar as these country-periods exhibit the same (or highly similar) distributions on the observed factors used in the analysis. Second, moving off the support of the data to infer results to country-periods not used in our analysis does not depend on unobserved factors. Other countries and time periods we do not study will differ on a wide range of factors. For inferences to be drawn reasonably to country-periods not in our analysis, such country-periods must resemble the country-periods in our analysis only on the factors and outcomes used in our analysis. We underscore these features of fuzzy-set analytic methods because their scholarly and policy implications are so striking. On the one hand,


in contrast to inferences made from many econometric models, fuzzyset analyses are not prone to specification bias from failure to include all appropriate covariates. That we do not (yet) analyze the relationship between, say, global or international factors and country-level family policies does not invalidate the results we do obtain for relationships among the factors we do analyze. It means only that we can not (yet) speak about additional paths to over time or cross-country variation in specific family policy outcomes that might involve such international factors. On the other hand, caution is required when scholars and policy makers consider generalizing fuzzy-set analytic findings off the data set that produced the findings. When we replicate a fuzzy-set analysis on an expanded data set that includes more countries and time points, we can expect to see different results to the extent that the additional data changes the empirical distribution on the factors and outcomes we have used in prior analyses and in the replication on the more extensive data set, especially with respect to the minimum and maximum anchoring fuzzy-set membership scores of 1 and 0 respectively.

Appendix 5.3: Goodness-of-Fit Tests for Fuzzy-Set Data To assess fuzzy-set relations based on the subset principle articulated in Ragin (2000), we use the goodness-of-fit strategy developed by Eliason and Stryker (2007). Here we provide a brief description of this approach, and refer the reader to Eliason and Stryker (2007) for details. This strategy is based on comparing, in the context of fuzzy-set relations and the subset principle (Ragin, 2000), the observed distance of cases from that expected under some causal hypothesis (necessity, sufficiency, and necessity and sufficiency) with the distance that would be expected given the truth of the causal hypothesis while accounting for measurement error. To articulate this more precisely, define xi and yi as the fuzzy-set membership scores for an hypothesized causal condition and outcome, respectively, for case i. To ensure distributional properties hold for the goodness-of-fit tests, Eliason and Stryker (2007) work with standardized normal scores, zx(i) = −1 {xi } and zy(i) = −1 {yi }, where −1 {·} is the inverse cumulative distribution function of the standard unit t t normal distribution. Let zy(i) = zy(i) + εi and zx(i) = zx(i) + ηi be equations linking the observed membership scores to the standardized membership t t scores measured without error, zy(i) and zx(i) , and errors in measurement, εi and ηi .23 Finally, define observed and expected distances


D(nec & suf) =

N

(zy(i) − zx(i) )2 and E{D(nec & suf) } =

i=1

N

177

(εi − ηi )2 . Eliason and

i=1

Stryker (2007) show that these distances are, respectively, the observed distance of the data from, and the expected distance under the truth of, the causal necessity and sufficiency hypothesis. With this, they then show that the ratio ∗ F(nec & suf) =

D(nec & suf) /N E{D(nec & suf) |causal necessity & sufficiency}/N

will be distributed as an F random variable on (N, N) degrees of freedom if the causal necessity and sufficiency hypothesis is indeed true given the data. Similar goodness-of-fit F ratios are derived for causal necessity and causal sufficiency hypotheses separately. In addition, Eliason and Stryker (2007) show that partitioning of the F ratio for conjunctions provides inference tests for conjunctions as compared to components making up the conjunction. A computer program to conduct the above tests and partitions can be found at www.soc.umn.edu/∼eliason/. Importantly, these goodness-of-fit F tests and partitions are appropriate regardless of whether the data constitute some sample (random or not) from a population or the entire population. Additionally, these goodness-of-fit F tests and partitions do not require any assumptions about the functional relationship – beyond that expected under the subset principle – between fuzzy-set membership scores on the outcome and fuzzy-set membership scores on the hypothesized cause (or conjunction of causes). No type of statistical model (linear or otherwise) is assumed to fit the fuzzy-set data, nor is any type of statistical model assumed to fit the empirical information on which the fuzzy-set scores are based. Thus, concerns over model specification, distributional assumptions (on errors or outcomes), and undesirable statistical properties (bias, inefficiency, etc.) in estimators (least squares, maximum likelihood, etc.) of model parameters are not applicable to these goodness-of-fit tests. That is, none of the baggage that comes along with estimating a statistical model is applicable to these goodness-of-fit tests.

Appendix 5.4: Estimating Compliers Average Causal Effects using Fuzzy-Set Membership Scores on Intention and Treatment Variables In this appendix we detail our compliers-average-causal-effect (CACE) analysis using fuzzy-set membership scores. To formalize ideas, let πc be


the probability of compliance with the cumulative left cabinet incumbency mechanism in enacting or maintaining a specific policy. Let πa and πn be the probabilities of noncompliance with these mechanisms as given in assumptions 3a and 3b (described above) respectively. That is, πa gives the probability of always enacting a specific policy, and πn gives the probability of never enacting a specific policy, regardless of the level of cumulative left cabinet incumbency. Next, define Y1it as the female labor force participation rate for country i at time t under high levels of policy P’s implementation in country-time (i, t). Similarly, define Y2it as the female labor force participation rate for country i at time t under non-high levels of policy P’s implementation in country-time (i, t). Finally, define sxit as the fuzzy score measuring the degree to which country-time (i, t) belongs to the set “high level of policy P’s implementation” and szit as the fuzzy score measuring the degree to which country-time (i, t) belongs to the set “high level of cumulative left cabinet incumbency.” From this, the likelihood function described in Imbens and Rubin (1997) and Hirano et al. (2000) adapted for use with fuzzy measures sxit and szit , is given by CACE =

I T

x z

(πc fc1 {Y1it } + πa fa1 {Y1it })sit sit

i=1 t=1

×

I T

x

z

(πc fc2 {Y2i } + πn fn2 {Y2i })(1−sit )(1−sit )

i=1 t=1

×

I T

x

z

(πa fa2 {Y1i })sit (1−sit )

i=1 t=1

×

I T

x

z

(πn fn1 {Y2i })(1−sit )sit

(5A.1)

i=1 t=1

Here, fjk {·}, with j = c, a, n and k = 1, 2, refers to the density functions for female labor force participation rates for compliers, always-takers, and never-takers for outcomes Y1it and Y2it . From this likelihood function, the CACE can be defined as CACE = E{Y1it − Y2it |Compliance} = yfc1 {Y1 }dy − yfc2 {Y2 }dy (5A.2) (See Imbens and Rubin’s (1997) or Hirano et al. (2000) for details.) As described above, this gives the expected change in female labor force


179

participation rates due to specific policies implemented through left governments. Importantly, for observations on Yit , within-country i serial correlation is likely across time points t and within-time t spatial correlation is likely across some countries i. However, for the CACE to be free of bias due to these sources, we need only assume, for the population of compliers, that the correlation structures are the same for the errors on the paired random variables (Y1it , Y2it ) prior to t. Given that the process generating pre-treatment serial and spatial correlation in the errors of random variables Y1it and Y2it derives from the same process generating serial and spatial correlations for those in Yit , this assumption appears reasonable for these data. Moreover, no assumptions are necessary for noncompliers. To estimate the CACE, we use a bootstrapped estimator derived from the above likelihood on 1000 replications. Using bootstrapped estimators alleviates the need to impose any distributional assumptions, such as normality, on the CACE itself. We thus present the median and inter-90 percent percentile range from the bootstrapped empirical distribution function.

Appendix 5.5: Goodness-of-fit F Tests and Partitions on conjunctions, for outcome “High Female Labor Force Participation – Subsequent Year” Hypothesized causal factors, and corresponding letter representations, include A = High Level Prior Year Cumulative Left Cabinet Incumbency C = High Level Public Sector Employment D = High Level Public Daycare, Ages 0–2 E = High Level Public Daycare, Ages 3 to School Age F = High Level Maternity Leave G = High Level Family/Child Cash and Tax Benefits ---------------------------------------------------------------------HYPOTHESES CONJUNCTIONS & TESTS SSD DF MSD F P ---------------------------------------------------------------------AC NECESSARY 431.7389 243 1.7767 6.9203 0.0000 SUFFICIENT 5.5584 19 0.2925 1.1395 0.3115 NEC AND SUFF 437.2973 263 1.6627 6.4763 0.0000 A v AC ----- --------0.9279 0.7277 C v AC ----- --------0.4083 1.0000


AD

NECESSARY SUFFICIENT NEC AND SUFF A v AD D v AD

537.0686 3.7620 540.8306 ---------

248 1 263 ---------

2.1656 3.7620 2.0564 ---------

8.4350 14.6530 8.0097 0.7503 0.9375

0.0000 0.0002 0.0000 0.9899 0.6995

AE

NECESSARY SUFFICIENT NEC AND SUFF A v AE E v AE

457.2793 7.4913 464.7706 ---------

238 23 263 ---------

1.9213 0.3257 1.7672 ---------

7.4836 1.2686 6.8832 0.8731 0.8977

0.0000 0.1885 0.0000 0.8641 0.8089

AF

NECESSARY SUFFICIENT NEC AND SUFF A v AF F v AF

465.9570 6.2466 472.2036 ---------

237 18 263 ---------

1.9661 0.3470 1.7955 ---------

7.6578 1.3517 6.9933 0.8593 0.6768

0.0000 0.1561 0.0000 0.8902 0.9992

AG

NECESSARY SUFFICIENT NEC AND SUFF A v AG G v AG

605.5539 4.6652 610.2191 ---------

230 31 263 ---------

2.6328 0.1505 2.3202 ---------

10.2549 0.5862 9.0373 0.6650 0.6683

0.0000 0.9624 0.0000 0.9995 0.9994

CD

NECESSARY SUFFICIENT NEC AND SUFF C v CD D v CD

503.0120 5.5812 508.5932 ---------

242 3 263 ---------

2.0786 1.8604 1.9338 ---------

8.0960 7.2463 7.5322 0.3511 0.9969

0.0000 0.0001 0.0000 1.0000 0.5101

CE

NECESSARY SUFFICIENT NEC AND SUFF C v CE E v CE

436.7079 7.2128 443.9207 ---------

245 16 263 ---------

1.7825 0.4508 1.6879 ---------

6.9428 1.7559 6.5744 0.4022 0.9399

0.0000 0.0373 0.0000 1.0000 0.6923

CF

NECESSARY SUFFICIENT NEC AND SUFF C v CF F v CF

354.3491 6.4369 360.7860 ---------

240 14 263 ---------

1.4765 0.4598 1.3718 ---------

5.7508 1.7908 5.3432 0.4949 0.8858

0.0000 0.0400 0.0000 1.0000 0.8369

CG

NECESSARY SUFFICIENT NEC AND SUFF C v CG G v CG

422.3340 4.1499 426.4839 ---------

237 25 263 ---------

1.7820 0.1660 1.6216 ---------

6.9409 0.6466 6.3162 0.4186 0.9562

0.0000 0.9036 0.0000 1.0000 0.6417

DE

NECESSARY SUFFICIENT NEC AND SUFF D v DE E v DE

497.7304 11.0621 508.7924 ---------

246 3 263 ---------

2.0233 3.6874 1.9346 ---------

7.8807 14.3623 7.5352 0.9965 0.8200

0.0000 0.0000 0.0000 0.5113 0.9459


181

DF

NECESSARY SUFFICIENT NEC AND SUFF D v DF F v DF

531.1132 6.6225 537.7357 ---------

245 3 263 ---------

2.1678 2.2075 2.0446 ---------

8.4436 8.5982 7.9638 0.9429 0.5943

0.0000 0.0000 0.0000 0.6832 1.0000

DG

NECESSARY SUFFICIENT NEC AND SUFF D v DG G v DG

717.0342 0.7181 717.7523 ---------

248 1 263 ---------

2.8913 0.7181 2.7291 ---------

11.2615 2.7971 10.6298 0.7064 0.5682

0.0000 0.0956 0.0000 0.9975 1.0000

EF

NECESSARY SUFFICIENT NEC AND SUFF E v EF F v EF

443.1947 17.8073 461.0020 ---------

233 22 263 ---------

1.9021 0.8094 1.7529 ---------

7.4088 3.1527 6.8274 0.9050 0.6933

0.0000 0.0000 0.0000 0.7905 0.9985

EG

NECESSARY SUFFICIENT NEC AND SUFF E v EG G v EG

626.1874 16.5087 642.6960 ---------

232 29 263 ---------

2.6991 0.5693 2.4437 ---------

10.5129 2.2173 9.5183 0.6492 0.6345

0.0000 0.0006 0.0000 0.9998 0.9999

FG

NECESSARY SUFFICIENT NEC AND SUFF F v FG G v FG

543.1012 14.8448 557.9460 ---------

225 30 263 ---------

2.4138 0.4948 2.1215 ---------

9.4017 1.9274 8.2631 0.5728 0.7309

0.0000 0.0036 0.0000 1.0000 0.9944

ACD

NECESSARY SUFFICIENT NEC AND SUFF AC v ACD AD v ACD CD v ACD

539.9968 3.7620 543.7588 -------------

248 15 263 -------------

2.1774 0.2508 2.0675 -------------

8.4810 0.9769 8.0530 0.8042 0.9946 0.9353

0.0000 0.4800 0.0000 0.9611 0.5174 0.7059

ACE

NECESSARY SUFFICIENT NEC AND SUFF AC v ACE AE v ACE CE v ACE

486.2627 5.4128 491.6755 -------------

246 17 263 -------------

1.9767 0.3184 1.8695 -------------

7.6992 1.2402 7.2817 0.8894 0.9453 0.9029

0.0000 0.2330 0.0000 0.8287 0.6758 0.7960

ACF

NECESSARY SUFFICIENT NEC AND SUFF AC v ACF AF v ACF CF v ACF

485.6026 4.4649 490.0675 -------------

245 18 263 -------------

1.9821 0.2481 1.8634 -------------

7.7201 0.9662 7.2579 0.8923 0.9635 0.7362

0.0000 0.4994 0.0000 0.8219 0.6182 0.9934

ACG

NECESSARY SUFFICIENT

636.6543 2.3690

246 17

2.5880 0.1394

10.0804 0.5428

0.0000 0.9295

182 Method and Substance in Macrocomparative Analysis NEC AND SUFF AC v ACG AG v ACG CG v ACG

639.0233 -------------

263 -------------

2.4297 -------------

9.4639 0.6843 0.9549 0.6674

0.0000 0.9989 0.6457 0.9995

ADE

NECESSARY SUFFICIENT NEC AND SUFF AD v ADE AE v ADE DE v ADE

537.8769 3.7620 541.6389 -------------

248 15 263 -------------

2.1689 0.2508 2.0595 -------------

8.4477 0.9769 8.0216 0.9985 0.8581 0.9394

0.0000 0.4800 0.0000 0.5048 0.8924 0.6938

ADF

NECESSARY SUFFICIENT NEC AND SUFF AD v ADF AF v ADF DF v ADF

561.0879 3.7620 564.8499 -------------

248 15 263 -------------

2.2625 0.2508 2.1477 -------------

8.8123 0.9769 8.3654 0.9575 0.8360 0.9520

0.0000 0.4800 0.0000 0.6376 0.9265 0.6549

ADG

NECESSARY SUFFICIENT NEC AND SUFF AD v ADG AG v ADG DG v ADG

725.8544 0.7181 726.5725 -------------

248 15 263 -------------

2.9268 0.0479 2.7626 -------------

11.4000 0.1865 10.7605 0.7444 0.8399 0.9879

0.0000 0.9997 0.0000 0.9915 0.9212 0.5394

AEF

NECESSARY SUFFICIENT NEC AND SUFF AE v AEF AF v AEF EF v AEF

495.0917 240 6.2060 23 501.2977 263 ----- --------- --------- -----

2.0629 0.2698 1.9061 -------------

8.0349 1.0510 7.4242 0.9271 0.9420 0.9196

0.0000 0.4019 0.0000 0.7300 0.6859 0.7513

AEG

NECESSARY SUFFICIENT NEC AND SUFF AE v AEG AG v AEG EG v AEG

651.7791 4.4475 656.2266 -------------

238 25 263 -------------

2.7386 0.1779 2.4952 -------------

10.6667 0.6929 9.7187 0.7082 0.9299 0.9794

0.0000 0.8629 0.0000 0.9973 0.7220 0.5670

AFG

NECESSARY SUFFICIENT NEC AND SUFF AF v AFG AG v AFG FG v AFG

659.3546 3.1846 662.5393 -------------

238 25 263 -------------

2.7704 0.1274 2.5192 -------------

10.7907 0.4962 9.8121 0.7127 0.9210 0.8421

0.0000 0.9806 0.0000 0.9969 0.7474 0.9179

CDE

NECESSARY SUFFICIENT NEC AND SUFF CD v CDE CE v CDE DE v CDE

504.8016 5.5620 510.3636 -------------

247 16 263 -------------

2.0437 0.3476 1.9405 -------------

7.9603 1.3540 7.5584 0.9965 0.8698 0.9969

0.0000 0.1649 0.0000 0.5112 0.8706 0.5100


183

CDF

NECESSARY SUFFICIENT NEC AND SUFF CD v CDF CF v CDF DF v CDF

531.9591 5.5812 537.5403 -------------

246 17 263 -------------

2.1624 0.3283 2.0439 -------------

8.4227 1.2788 7.9609 0.9461 0.6712 1.0004

0.0000 0.2057 0.0000 0.6731 0.9994 0.4988

CDG

NECESSARY SUFFICIENT NEC AND SUFF CD v CDG CG v CDG DG v CDG

720.0992 0.7181 720.8173 -------------

248 15 263 -------------

2.9036 0.0479 2.7408 -------------

11.3096 0.1865 10.6752 0.7056 0.5917 0.9957

0.0000 0.9997 0.0000 0.9976 1.0000 0.5138

CEF

NECESSARY SUFFICIENT NEC AND SUFF CE v CEF CF v CEF EF v CEF

469.1479 6.2646 475.4124 -------------

245 18 263 -------------

1.9149 0.3480 1.8077 -------------

7.4585 1.3556 7.0408 0.9338 0.7589 0.9697

0.0000 0.1539 0.0000 0.7106 0.9872 0.5985

CEG

NECESSARY SUFFICIENT NEC AND SUFF CE v CEG CG v CEG EG v CEG

657.9967 2.3690 660.3656 -------------

246 17 263 -------------

2.6748 0.1394 2.5109 -------------

10.4183 0.5428 9.7800 0.6722 0.6458 0.9732

0.0000 0.9295 0.0000 0.9993 0.9998 0.5870

CFG

NECESSARY SUFFICIENT NEC AND SUFF CF v CFG CG v CFG FG v CFG

574.8020 1.4207 576.2227 -------------

246 17 263 -------------

2.3366 0.0836 2.1910 -------------

9.1010 0.3255 8.5338 0.6261 0.7401 0.9683

0.0000 0.9953 0.0000 0.9999 0.9925 0.6030

DEF

NECESSARY SUFFICIENT NEC AND SUFF DE v DEF DF v DEF EF v DEF

532.5317 6.5671 539.0988 -------------

247 16 263 -------------

2.1560 0.4104 2.0498 -------------

8.3976 1.5987 7.9840 0.9438 0.9975 0.8551

0.0000 0.0689 0.0000 0.6803 0.5082 0.8974

DEG

NECESSARY SUFFICIENT NEC AND SUFF DE v DEG DG v DEG EG v DEG

717.0342 0.7181 717.7523 -------------

248 15 263 -------------

2.8913 0.0479 2.7291 -------------

11.2615 0.1865 10.6298 0.7089 1.0000 0.8954

0.0000 0.9997 0.0000 0.9973 0.5000 0.8145

DFG

NECESSARY SUFFICIENT NEC AND SUFF DF v DFG

737.7776 0.7181 738.4957 -----

248 15 263 -----

2.9749 0.0479 2.8080 -----

11.5873 0.1865 10.9371 0.7282

0.0000 0.9997 0.0000 0.9948

184 Method and Substance in Macrocomparative Analysis DG v DFG FG v DFG

---------

---------

---------

0.9719 0.7555

0.5913 0.9883

EFG

NECESSARY SUFFICIENT NEC AND SUFF EF v EFG EG v EFG FG v EFG

656.3900 11.9584 668.3483 -------------

234 29 263 -------------

2.8051 0.4124 2.5412 -------------

10.9258 1.6061 9.8982 0.6898 0.9616 0.8348

0.0000 0.0292 0.0000 0.9987 0.6244 0.9281

ACDE

NECESSARY SUFFICIENT NEC AND SUFF ACD v ACDE ACE v ACDE ADE v ACDE CDE v ACDE

540.8051 3.7620 544.5671 -----------------

248 15 263 -----------------

2.1807 0.2508 2.0706 -----------------

8.4937 0.9769 8.0650 0.9985 0.9029 0.9946 0.9372

0.0000 0.4800 0.0000 0.5048 0.7960 0.5174 0.7004

ACDF

NECESSARY SUFFICIENT NEC AND SUFF ACD v ACDF ACF v ACDF ADF v ACDF CDF v ACDF

561.0879 3.7620 564.8499 -----------------

248 15 263 -----------------

2.2625 0.2508 2.1477 -----------------

8.8123 0.9769 8.3654 0.9627 0.8676 1.0000 0.9517

0.0000 0.4800 0.0000 0.6211 0.8749 0.5000 0.6559

ACDG

NECESSARY SUFFICIENT NEC AND SUFF ACD v ACDG ACG v ACDG ADG v ACDG CDG v ACDG

728.5613 0.7181 729.2795 -----------------

248 15 263 -----------------

2.9377 0.0479 2.7729 -----------------

11.4425 0.1865 10.8006 0.7456 0.8762 0.9963 0.9884

0.0000 0.9997 0.0000 0.9912 0.8577 0.5120 0.5377

ACEF

NECESSARY SUFFICIENT NEC AND SUFF ACE v ACEF ACF v ACEF AEF v ACEF CEF v ACEF

511.0231 4.4646 515.4877 -----------------

246 17 263 -----------------

2.0773 0.2626 1.9600 -----------------

8.0912 1.0229 7.6343 0.9538 0.9507 0.9725 0.9223

0.0000 0.4333 0.0000 0.6492 0.6590 0.5894 0.7439

ACEG

NECESSARY SUFFICIENT NEC AND SUFF ACE v ACEG ACG v ACEG AEG v ACEG CEG v ACEG

677.1361 2.3690 679.5050 -----------------

246 17 263 -----------------

2.7526 0.1394 2.5837 -----------------

10.7213 0.5428 10.0634 0.7236 0.9404 0.9657 0.9718

0.0000 0.9295 0.0000 0.9955 0.6906 0.6112 0.5915

ACFG

NECESSARY SUFFICIENT NEC AND SUFF ACF v ACFG

677.9242 1.4207 679.3450 -----

246 17 263 -----

2.7558 0.0836 2.5831 -----

10.7338 0.3255 10.0610 0.7214

0.0000 0.9953 0.0000 0.9959


185

ACG v ACFG AFG v ACFG CFG v ACFG

-------------

-------------

-------------

0.9406 0.9753 0.8482

0.6899 0.5804 0.9087

ADEF

NECESSARY SUFFICIENT NEC AND SUFF ADE v ADEF ADF v ADEF AEF v ADEF DEF v ADEF

561.8962 3.7620 565.6582 -----------------

248 15 263 -----------------

2.2657 0.2508 2.1508 -----------------

8.8250 0.9769 8.3773 0.9575 0.9986 0.8862 0.9530

0.0000 0.4800 0.0000 0.6374 0.5046 0.8360 0.6516

ADEG

NECESSARY SUFFICIENT NEC AND SUFF ADE v ADEG ADG v ADEG AEG v ADEG DEG v ADEG

725.8544 0.7181 726.5725 -----------------

248 15 263 -----------------

2.9268 0.0479 2.7626 -----------------

11.4000 0.1865 10.7605 0.7455 1.0000 0.9032 0.9879

0.0000 0.9997 0.0000 0.9912 0.5000 0.7952 0.5394

ADFG

NECESSARY SUFFICIENT NEC AND SUFF ADF v ADFG ADG v ADFG AFG v ADFG DFG v ADFG

746.5978 0.7181 747.3159 -----------------

248 15 263 -----------------

3.0105 0.0479 2.8415 -----------------

11.7258 0.1865 11.0677 0.7558 0.9722 0.8866 0.9882

0.0000 0.9997 0.0000 0.9882 0.5902 0.8353 0.5383

AEFG

NECESSARY SUFFICIENT NEC AND SUFF AEF v AEFG AEG v AEFG AFG v AEFG EFG v AEFG

681.3751 3.1621 684.5372 -----------------

240 23 263 -----------------

2.8391 0.1375 2.6028 -----------------

11.0582 0.5355 10.1379 0.7323 0.9586 0.9679 0.9764

0.0000 0.9618 0.0000 0.9941 0.6339 0.6043 0.5769

CDEF

NECESSARY SUFFICIENT NEC AND SUFF CDE v CDEF CDF v CDEF CEF v CDEF DEF v CDEF

533.3574 5.5620 538.9194 -----------------

247 16 263 -----------------

2.1593 0.3476 2.0491 -----------------

8.4106 1.3540 7.9813 0.9470 0.9974 0.8822 1.0003

0.0000 0.1649 0.0000 0.6704 0.5083 0.8450 0.4989

CDEG

NECESSARY SUFFICIENT NEC AND SUFF CDE v CDEG CDG v CDEG CEG v CDEG DEG v CDEG

720.0992 0.7181 720.8173 -----------------

248 15 263 -----------------

2.9036 0.0479 2.7408 -----------------

11.3096 0.1865 10.6752 0.7080 1.0000 0.9161 0.9957

0.0000 0.9997 0.0000 0.9974 0.5000 0.7610 0.5138

CDFG

NECESSARY SUFFICIENT

738.1357 0.7181

248 15

2.9764 0.0479

11.5929 0.1865

0.0000 0.9997

186 Method and Substance in Macrocomparative Analysis NEC AND CDF v CDG v CFG v DFG v

SUFF CDFG CDFG CDFG CDFG

738.8538 -----------------

263 -----------------

2.8093 -----------------

10.9424 0.7275 0.9756 0.7799 0.9995

0.0000 0.9949 0.5793 0.9779 0.5016

CEFG

NECESSARY SUFFICIENT NEC AND SUFF CEF v CEFG CEG v CEFG CFG v CEFG EFG v CEFG

677.5220 1.4207 678.9427 -----------------

246 17 263 -----------------

2.7542 0.0836 2.5815 -----------------

10.7274 0.3255 10.0551 0.7002 0.9726 0.8487 0.9844

0.0000 0.9953 0.0000 0.9980 0.5889 0.9079 0.5507

DEFG

NECESSARY SUFFICIENT NEC AND SUFF DEF v DEFG DEG v DEFG DFG v DEFG EFG v DEFG

737.7776 0.7181 738.4957 -----------------

248 15 263 -----------------

2.9749 0.0479 2.8080 -----------------

11.5873 0.1865 10.9371 0.7300 0.9719 1.0000 0.9050

0.0000 0.9997 0.0000 0.9945 0.5913 0.5000 0.7906

ACDEF

NECESSARY SUFFICIENT NEC AND SUFF ACDE v ACDEF ACDF v ACDEF ACEF v ACDEF ADEF v ACDEF CDEF v ACDEF

561.8962 3.7620 565.6582 ---------------------

248 15 263 ---------------------

2.2657 0.2508 2.1508 ---------------------

8.8250 0.9769 8.3773 0.9627 0.9986 0.9113 1.0000 0.9527

0.0000 0.4800 0.0000 0.6209 0.5046 0.7741 0.5000 0.6526

ACDEG

NECESSARY SUFFICIENT NEC AND SUFF ACDE v ACDEG ACDG v ACDEG ACEG v ACDEG ADEG v ACDEG CDEG v ACDEG

728.5613 0.7181 729.2795 ---------------------

248 15 263 ---------------------

2.9377 0.0479 2.7729 ---------------------

11.4425 0.1865 10.8006 0.7467 1.0000 0.9317 0.9963 0.9884

0.0000 0.9997 0.0000 0.9909 0.5000 0.7165 0.5120 0.5377

ACDFG

NECESSARY SUFFICIENT NEC AND SUFF ACDF v ACDFG ACDG v ACDFG ACFG v ACDFG ADFG v ACDFG CDFG v ACDFG

746.5978 0.7181 747.3159 ---------------------

248 15 263 ---------------------

3.0105 0.0479 2.8415 ---------------------

11.7258 0.1865 11.0677 0.7558 0.9759 0.9090 1.0000 0.9887

0.0000 0.9997 0.0000 0.9882 0.5784 0.7800 0.5000 0.5368

ACEFG

NECESSARY SUFFICIENT NEC AND SUFF ACEF v ACEFG ACEG v ACEFG

696.6613 1.4207 698.0821 ---------

246 17 263 ---------

2.8320 0.0836 2.6543 ---------

11.0305 0.3255 10.3385 0.7384 0.9734

0.0000 0.9953 0.0000 0.9929 0.5865


187

ACFG v ACEFG AEFG v ACEFG CEFG v ACEFG

-------------

-------------

-------------

0.9732 0.9806 0.9726

0.5872 0.5631 0.5891

ADEFG

NECESSARY SUFFICIENT NEC AND SUFF ADEF v ADEFG ADEG v ADEFG ADFG v ADEFG AEFG v ADEFG DEFG v ADEFG

746.5978 0.7181 747.3159 ---------------------

248 15 263 ---------------------

3.0105 0.0479 2.8415 ---------------------

11.7258 0.1865 11.0677 0.7569 0.9722 1.0000 0.9160 0.9882

0.0000 0.9997 0.0000 0.9879 0.5902 0.5000 0.7614 0.5383

CDEFG

NECESSARY SUFFICIENT NEC AND SUFF CDEF v CDEFG CDEG v CDEFG CDFG v CDEFG CEFG v CDEFG DEFG v CDEFG

738.1357 0.7181 738.8538 ---------------------

248 15 263 ---------------------

2.9764 0.0479 2.8093 ---------------------

11.5929 0.1865 10.9424 0.7294 0.9756 1.0000 0.9189 0.9995

0.0000 0.9997 0.0000 0.9946 0.5793 0.5000 0.7533 0.5016

ACDEFG

NECESSARY SUFFICIENT NEC AND SUFF ACDEF v ACDEFG ACDEG v ACDEFG ACDFG v ACDEFG ACEFG v ACDEFG ADEFG v ACDEFG CDEFG v ACDEFG

746.5978 0.7181 747.3159 -------------------------

248 15 263 -------------------------

3.0105 0.0479 2.8415 -------------------------

11.7258 0.1865 11.0677 0.7569 0.9759 1.0000 0.9341 1.0000 0.9887

0.0000 0.9997 0.0000 0.9879 0.5784 0.5000 0.7095 0.5000 0.5368

----------------------------------------------------------------------

Notes 1. See, for example, Siim 1988; Hernes 1987; Lewis 1992, 1998; Orloff 1993; Gauthier 1996; Gornick, Meyers and Ross 1997; Daly 1997; O’Connor, Orloff and Shaver 1999; Sainsbury 1999; Rubery, Smith and Fagan 1999; Crompton 1999; Daly and Lewis 2000; Montanari 2000; Korpi 2000; Stier, Lewin-Epstein and Braun 2001; Williams 2001; Brush 2002; Michel and Mahon 2002, Leira 2002; Morgan and Zippel 2003; Gornick and Meyers 2003; Morgan 2004; Orloff 2004; Misra, Budig and Moller 2005. 2. See, for example, OECD 1990, 2001; European Commission Network on Childcare and Other Measures to Reconcile 1995; Kammerman and Kahn 1991a, 1991b; Wennemo 1994; Ruhm 1995; Ruhm and Teague 1995; Gauthier 1996; Gornick, Meyers and Ross 1997; Leira 1992, 1998, 2002; Lewis 1998; Bruning and Plantenga 1999; Moss and Deven 1999; Rubery et al. 1999, pp. 157–164; Roch 1999; Daly 2000; Daly and Lewis 2000; Montanari 2000; Randall 2000; Kammerman 2000; Meyer 2000; Meyers and Gornick 2000; Vlemicks and Smeeding 2001; Michel and Mahon 2002; Mahon 2002;


3.

4.

5.

6.

Sundstrom and Duvander 2002; Waldfogel 2002; Ferrarini 2003; Henderseon and White 2003; Morgan and Zippel 2003; Morgan 2004; Misra, Budig and Moller 2005. See especially, Kammerman and Kahn 1991a; Winegarden and Bracy 1995; Ruhm and Teague 1995; Gornick, Meyers and Ross 1997; Korpi 2000; Brewster and Rindfuss 2000; Stier, Lewin-Epstein and Braun 2001; Esping-Andersen et al. 2002; Waldfogel 2002; Ferrarini 2003; Mandel and Semyonov 2003; Morgan and Zippel 2003; Stryker and Eliason 2004; Misra, Budig and Moller 2005; Pettit 2006. These scholars have used a variety of analytic techniques, each of which has strengths and weaknesses in assessing potential causal links. The totality of prior work teaches us much about empirical associations and potential causal links, but almost nothing about causal effects of policies per se. See Stryker and Eliason 2004, for a more complete elaboration of how and why our arguments are similar in some respects, yet different in others, from those of Huber and Stephens (2000, 2001). We discuss empirical support for both points of view (see also Myles and Quadagno 2002). Matching theory takes on different empirical specifications and leads to somewhat different understandings of the relationship between gender segregated labor markets and the gender pay gap depending on how the gendering of tasks is conceived and measured. Bonstead-Bruns and Eliason (2002) further elaborate on Alice Eagly’s agentic versus communal characterization of male-typed versus female-typed tasks to investigate how gender of the person and the matching of both men and women to gender-typed jobs affects the gender pay gap in the United States. Theoretically framing their cross national analyses of gender segregated labor markets, Charles and Grusky (2004) suggest two key processes underlying that segregation. The first is a “vertical” dimension privileging men over women, such that especially within non-manual occupations, men are matched to more prestigious, higher paying jobs. The second is a “horizontal” dimension of gender essentialism encouraging the matching of women to non-manual jobs. Where male privilege increases both segregation and gender inequality, gender essentialism increases segregation while undermining gender inequality. Charles and Grusky’s (2004) approach provides leverage to investigate and explain the apparent paradox of greater gender segregation associated with lesser genderpay gaps in Scandinavia (see also Rosenfeld and Kalleberg 1990). However, the equation of non-manual with female typed jobs is extremely crude. It does not capture gender stereotyped expectations that caring (versus non-caring) work is female work, that community oriented (versus self-actualizing) work is female work, that working with people (as opposed to working with things) is female work, or that coordinating (versus managing) is female work. All these distinctions have been important to the sex-typing of jobs (Steinberg 1990; Bonstead-Bruns and Eliason 2002; England et al. 2002). In short, “gender essentialism” and the matching of women to female-typed jobs may be entering Charles and Grusky’s (2004) vertical dimension as well as their horizontal dimension underlying gender segregated labor markets. We were especially disappointed about having to drop Japan, Australia and New Zealand from our analyses, because these countries provide at least some insight into, on the one hand, a non-western political-cultural context and,


7.

8.

9.

10.

11. 12.

13. 14.

15. 16.

189

on the other hand, the two “wage-earner” welfare state regimes identified by Huber and Stephens (2001). However, after months of data collection efforts, we were convinced that this was the most reasonable decision. Available empirical information on public expenditures for paid maternity leave, wage replacement rates, one-time lump sum payouts, and coverage statistics tend to be related to maternity and/or parental leaves per se, and typically not including extended leaves. Thus, we use only weeks of extended leave to construct our fuzzy-set membership scores on extended leaves. Ragin’s (1987, 2000) logic is similar to and draws on the constant-conjunction causal logic established by Mill (1967 [1843] in the indirect method of difference. There is ongoing debate about the nature of fuzzy scores and fuzzy theory, and their relation to probabilities and probability theory. For informative background and discussion, see Puri and Ralescu (1985), Kandel, Martins, and Pacheco (1995), Laviolette, Seaman, Barrett, and Woodall (1995), and Zadeh (1995). We plan more nuanced analyses of the impact of family policies on both the nature and quantity of women’s labor force participation across countries and over time. However, our current analyses do not distinguish between employment in the public sector and the private sector, nor between parttime and full-time employment, nor between employment in higher wage versus lower wage jobs. See Przeworski’s contribution to this volume for a similar methodological approach to establishing causal effects on employment outcomes. Note that assumptions 3a and 3b encapsulate in our case what is more generally called the monotonicity assumption. Also note that assumption 5 is related to what is often called the ignorable assignment-to-treatment assumption. Here, however, we are establishing the exogeneity of cumulated left cabinet incumbency up to time t compared to policies at time t and female labor force participation rates at time t + 1. So, this assumption is better thought of as an exogenous assignment-to-treatment assumption, which is weaker than the ignorable assignment-to-treatment assumption. See Imbens and Rubin (1997) or Hirano et al. (2000) for details. See Efron and Tibshirani (1994) and DiCiccio and Efron (1996) for useful description and discussion of bootstrapped estimators. 14 For all tests we use a measurement error factor such that the maximum measurement error of .05 is found at fuzzy-set membership scores of 0.5, with measurement error diminishing smoothly and symmetrically toward 0 at the boundary membership scores of 0 and 1. See Eliason and Stryker (2007) for relevant details. See Stryker and Eliason (2004) for details of the prior analysis. Eliason and Stryker (2007) show that the goodness-of-fit F statistics on the fuzzy scores are unaffected by correlated data (either temporal or spatial) on the original measures used to construct fuzzy scores or on the fuzzy scores themselves. However, the degrees of freedom for the test may be affected to the extent that the correlated structure is also present in the fuzzy scores. Thus, the degrees of freedom may require downward adjustments proportional to the order of the correlated structure in those scores. Given the number of cases in our analysis, unless the correlated structure in the fuzzy scores


17.

18.

19.

20.

21. 22. 23.

is beyond an order of three (which is very unlikely), the effect on the assessment of fit will be negligible. This, combined with the fact that we achieved very good fit levels in our analyses (with p values often greater than 0.05, suggests that the results from our fuzzy-set analysis are not likely affected by the longitudinal and spatially connected nature of these aggregate data. To better understand this, see Hirano et al. (2000) and Imbens and Rubin (1997). Also see Heckman et al. (1999) for a valuable more general discussion of related causal estimators and estimands. Given this large CACE effect on the additive percentage point change in female labor force participation rates, we also estimated multiplicative CACE’s giving the percentage change in those rates. The multiplicative CACE results reveal similarly large effects. Thus, we continue to present the additive percentage point change effects throughout. This finding may appear, at first glance, inconsistent with results from the fuzzy-set analysis. Recall, however, that the fuzzy-set analysis examined whether high levels of family/child cash and tax benefits were necessary and/or sufficient for high levels of female labor force participation. Finding, as we did, that high levels of family/child cash and tax benefits were neither necessary nor sufficient for high levels of female labor force participation is entirely consistent with the CACE analysis findings presented here. It does, however, highlight that the fuzzy-set analysis was focused on those factors that may have an impact on high, and not low, levels of female labor force participation. In the language of the classic intention-to-treat design, cumulative left governance acts as the intention-to-treat instrument for the results discussed in table 12, while specific policies act as the treatments. Similarly, other cumulative governance patterns act as the intention-to-treat instrument for the results discussed in Table 5.13, with specific policies again acting as treatments. In both cases it is the specific policy in question, and not cumulative governance, that acts as the treatment, and thus carries causal status, in influencing female labor force participation. See also Verkuilen (2005) for a fairly comprehensive and useful discussion of measurement strategies for fuzzy-set methods. With minor modifications, this logic and coding extends to an ordinal distribution on the original information. It is important to note that, while these are equations, they do not have any parameters to be estimated from the data. Thus, issues of bias, inconsistency, inefficiency, and the like, are not applicable here.

References Blau, Francine D. and Mariane A. Ferber. 1992. The Economics of Women, Men and Work. Englewood Cliffs, NJ: Prentice Hall. Bonstead-Bruns, Melissa and Scott Eliason. 2002. “Reconsidering the Role of Gender in Labor Market Attainments and Evaluating the Wage Gap.” Unpublished manuscript. Brewster, K. L. and R. R. Rindfuss. 2000. “Fertility and Women’s Employment in Industrialized Nations.” Annual Review of Sociology 26: 271–96.


191

Brush, Lisa D. 2002. “Changing the Subject: Gender and Welfare Regime Studies.” Social Politics 9: 161–86. Bruning, Gwennaële and Plantenga. 1999. “Parental Leave and Equal Opportunities Experiences in Eight European Countries.” Journal of European Social Policy 9: 195–209. Castles, Francis G. 2001. “On the Political Economy of Recent Public Sector Development.” Journal of European Social Policy 11: 195–211. Charles, Maria and David B. Grusky. 2004. Occupational Ghettos: The Worldwide Segregation of Women and Men. Stanford: Stanford University Press. Clayton, Richard and Jonas Pontusson. 1998. “Welfare State Retrenchment Revisited: Entitlement Cuts, Public Sector Restructuring and Inegalitarian Trends in Advanced Capitalist Societies.” World Politics 51: 567–98. Crompton, Rosemary, editor. 1999. Restructuring Gender Relations and Employment: The Decline of the Male Breadwinner. Oxford: Oxford University Press. Daly, Mary. 1997. The Gender Division of Welfare: The Impact of the British and German Welfare States. Cambridge: Cambridge University Press. Daly, Mary. 2000. “A Fine Balance: Women’s Labor Market Participation in International Comparison.” In Welfare and Work in the Open Economy: Diverse Responses to Common Challenges, volume 2, edited by F. Scharpf and V. Schmidt. Oxford: Oxford University Press. Daly, Mary and Jane Lewis. 2000. “The Concept of Social Care and the Analysis of Contemporary Welfare States.” British Journal of Sociology 51: 281–98. Diciccio, Thomas J. and Bradley Efron. 1996. “Bootstrap Confidence Intervals.” Statistical Science 11: 189–212. Efron, Bradley and Robert J Tibshirani. 1994. An Introduction to the Bootstrap. Boca Raton: CRC Press. Eliason, Scott R. and Robin Stryker. 2007. “Goodness-of-Fit Tests and Descriptive Measures in Fuzzy-Set Analysis.” Unpublished manuscript. Available at www.soc.umn.edu/∼eliason. England, Paula, Michelle Budig and Nancy Folbre. 2002. “Wages of Virtue: The Relative Pay of Care Work.” Social Problems 49: 455–73. Esping-Andersen, Gøsta. 1990. The Three Worlds of Welfare Capitalism. Princeton: Princeton University Press. Esping-Andersen, Gøsta, John Myles, Anton Hemerijck and Duncan Gallie. 2002. Why We Need a New Welfare State. Oxford: Oxford University Press. Ferrarini, Tommy. 2003. “Paid Parental Leave and Poverty: Analyzing the Policy Outcome Link Among Families with Infants in Western Countries.” Paper presented at RC 19 Annual Meetings, Toronto, August 21–4. Gauthier, Anne. 1996. The State and the Family: A Comparative Analysis of Family Policies in Industrialized Countries. Oxford: Clarendon Press. Gauthier, Anne. 2000. “Public Policies Affecting Fertility and Families in Europe: A Survey of the 15 Member States.” Paper prepared for the European Observatory on Family Matters: Annual Seminar, 2000: “Low Fertility, Families and Public Policies,”’ Sevilla, Spain, September 15–16. Goertz, Gary and James Mahoney. 2005. “Two-Level Theories and Fuzzy-Set Analysis.” Sociological Methods and Research 33: 497–538. Gornick, Janet, Marcia Meyers and Katharin Ross. 1997. “Supporting the Employment of Mothers: Policy Variation Across Fourteen Welfare States.” Journal of European Social Policy 7: 45–70.

192 Method and Substance in Macrocomparative Analysis Gornick, Janet and Jerry Jacobs. 1998. “Gender, the Welfare State and Public Employment: A Comparative Study of Seven Industrialized Countries.” American Sociological Review 63: 688–710. Gornick, Janet and Marcia K. Meyers. 2003. Families That Work: Policies for Reconciling Parenthood and Employment. New York: Russell Sage. Grusky, David B. and Maria Charles. 2001. “Is There a Worldwide Sex Segregation Regime?” Pp. 689–703 in Social Stratification in Comparative Perspective: Class, Race and Gender, edited by D. Grusky. Second edition. Boulder, CO: Westport. Heckman, James J., Robert J. LaLonde, and Jeffrey A. Smith. 1999. “The Economics and Econometrics of Active Labor Market Programs.” In Handbook of Labor Economics, volume 3, edited by Orley Ashenfelter and David Card. Amsterdam: Elsevier. Henderson, Ailsa and Linda A. White. 2003. “Shrinking Welfare States? Comparing Maternity Leave Benefits and Child Care Programs in European Union and North American Welfare States, 1985–2000.” Paper presented at RC 119 Meetings, Toronto, August 21–4. Hernes, Helga. 1987. The Welfare State and Women Power. Oslo: Univeritetsforlaget. Hirano, Keisuke, Guido W. Imbens, Donald B. Rubin, and Xiao-Hua Zhou. 2000 “Assessing The Effect Of An Influenza Vaccine In An Encouragement Design.” Biostatistics 1: 69–88. Huber, Evelyne and John Stephens. 2000. “Partisan Governance, Women’s Employment and the Social Democratic Service State.” American Sociological Review 45: 323–42. Huber, Evelyne and John Stephens. 2001. Development and Crisis of the Welfare State: Parties and Policies in Global Markets. Chicago: University of Chicago Press. Huber, Evelyne, Charles Ragin, and John Stephens. 1993. “Social Democracy, Christian Democracy, Constitutional Structure and the Welfare State.” American Journal of Sociology 99: 711–49. Imbens, Guido W. and Donald B. Rubin. 1997. “Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance.” The Annals of Statistics 25: 305–27. Jenson, Jane. 1990. “Representations of Gender Politics to ‘Protect’ Women: Workers and Infants in France and the United States Before 1914.” Pp. 152–77 in Women, the State and Welfare, edited by Linda Gordon: Madison: University of Wisconsin Press. Kammerman, Sheila. 2000. “Early Childhood Education and Care: An Overview of Developments in the OECD Countries.” International Journal of Educational Research 33: 7–29. Kammerman, Sheila and Alfred J. Kahn. 1991a. Child Care, Parental Leave and the Under 3s: Policy Innovation in Europe. New York: Auburn House. Kammerman, Sheila and Alfred J. Kahn. 1991b. “Government Expenditures for Children and Their Families in Advanced Industrialized Countries, 1960–1985.” Innocenti Occasional Papers: Economic. Policy Series No. 20. Kandel, Abraham, Alejandro Martins, and Roberto Pacheco. 1995. “On the Very Real Distinction between Fuzzy and Statistical Methods.” Technometrics 37: 276–81. Korpi, Walter. 2000. “Faces of Inequality: Gender, Class and patterns of Inequalities in Different Types of Welfare States.” Social Politics Summer: 127–91.


193

Kreyenfeld, Michael and Karsten Hank. 2000. “Does the Availability of Childcare Influence the Employment of Mothers? Findings from Western Germany.” Population Research and Policy Review 19: 317–37. Laviolette, Michael, John W. Seaman, Jr., J. Douglas Barrett, and William H. Woodall. 1995. “A Probabilistic and Statistical View of Fuzzy Methods.” Technometrics 37: 249–61. Leira, Arnlaug. 1992. Welfare States and Working Mothers: The Scandinavian Experience. New York: Cambridge University Press. Leira, Arnlaug. 1998. “Caring as a Social Right: Cash for Child Care and Daddy Leave.” Social Politics 5: 362–79. Leira, Arnlaug. 2002. Working Parents and the Welfare State: Family Change and Policy Reform in Scandinavia. Cambridge: Cambridge University Press. Lewis, Jane. 1992. “Gender and the Development of Welfare State Regimes.” Journal of European Social Policy 2: 159–73. Lewis, Jane, ed. 1998. Gender, Social Care and Welfare State Restructuring in Europe. Aldershot: Ashgate. Mahon, Rianne. 2002. “Child Care: Toward What Kind of ‘Social Europe’?” Social Politics 9: 343–79. Mahoney, James. 2003. “Long-Run Development and the Legacy of Colonialism in Spanish America.” American Journal of Sociology 109: 50–106. Mandel, Hadas. Moshe Semyonov. 2003. “The Prevalence of Welfare State Policies and Gender Socio-Economic Inequality: A Comparative Analysis.” Paper presented at Meeting of the ISA Research Committee on Stratification and Mobility (March, 2003), Tokyo. Meyer, Madonna Harrington, ed. 2000. Care Work, Gender, Labor and the Welfare State. New York: Routledge. Meyers, Marie K. and Janet C. Gornick. 2000. “Early Childhood Education and Care (ECEC: Cross National Variation in Services Organization and Financing).” Paper presented at the Consultative Meeting on International Developments in Early Childhood Education and Care. New York, May 11–12. Michel, Sonya and Rianne Mahon, eds. 2002. Child Care Policy at the Crossroads: Gender and Welfare State Restructuring. New York: Routledge. Mill, John Stuart. 1967 [1843]. A System of Logic: Ratiocinative and Inductive. Toronto: University of Toronto Press. Misra, Joya. 2003. “Women as Agents in Welfare State Development: A CrossNational Analysis of Family Allowance Adoption.” Socio-Economic Review 1: 185–214. Misra, Joya, Michelle Budig, and Stephanie Moller. 2005. “Employment, Wages and Poverty: Family Policies and Gender Equity.” Paper presented at Annual Meetings of Research Committee 19, Sociological Association, Washington, DC, September 8–10. Montanari, Ingalill. 2000. “From Family Wage to Marriage Subsidy and Child Benefits: Controversy and Consensus in the Development of Family Support.” Journal of European Social Policy 10: 307–33. Morgan, Kimberly J. and Kathrin Zippel. 2003. “Paid to Care: The Origins and Effects of Care Leave Policies in Western Europe.” Social Politics 10: 49–85. Morgan, Kimberly J. 2004. “Caring in Western Europe: Trends and Implications.” Paper presented at annual meetings of Research Committee 19, Social Welfare and Social Policy, Paris, September.

194 Method and Substance in Macrocomparative Analysis Moss, Peter and Fred. F Deven, eds. 1999. Parental Leave: Progress or Pitfall? The Hague/Brussels: NIDI:CBGS Publications. Myles, John and Jill Quadagno. 2002. “Political Theories of the Welfare State.” Social Service Review March: 34–66. Myles, John anmd Adrian Turegin. 1994. “Comparative Studies in Class Structure.” Annual Review of Sociology 20: 103–24. O’Connor, Julia. 2003. “Measuring Progress in the European Social Model: Policy Coordination, Social Indicators and the Social Policy Agency in the European Union.” Paper presented at RC-19 Annual Meetings, Toronto. August. O’Connor, Julia, Ann Shola Orloff and Sheila Shaver. 1999. States, Markets, Families: Gender, Liberalism, and Social Policy in Australia, Canada, Great Britain, and the United States. Cambridge: Cambridge University Press. OECD. 1982. Employment in the Public Sector. Paris, OECD. OECD. 1990. “Child Care in OECD Countries.” Employment Outlook 1990. OECD. 2001. “Starting Strong: Early Childhood Education and Care.” Paris, OECD. Orloff, Ann Shola. 1993. “Gender and the Social Rights of Citizenship: The Comparative Analysis of Gender Relations and Welfare States.” American Sociological Review 58: 303–28. Orloff, Ann Shola. 2004. “Farewell to Maternalism (and Hello to Employment for All?): The State and Employed Women.” Paper presented at annual meetings of Research Committee 19, Social Welfare and Social Policy, Paris, September. Pettit, Elizabeth M. 2006. Presentation at Department of Sociology, University of Minnesota, February. Puri, Madan L. and Dan A. Ralescu. 1985. “The Concept of Normality for Fuzzy Random Variables.” The Annals of Probability, 13: 1373–9. Ragin, Charles. 1987. The Comparative Method. Berkeley: University of California Press. Ragin, Charles. 2000. Fuzzy-Set Social Science. Chicago and London: University of Chicago Press. Ragin, Charles and Paul Pennings. 2005. “Fuzzy Sets and Social Research.” Sociological Methods and Research 33: 423–30. Randall, Vicky. 2000. “Childcare Policy in the European States: Limits to Convergence.” Journal of European Public Policy 7: 346–68. Reskin, Barbara. 1993. “Sex Segregation in the Workplace.” Annual Review of Sociology 19: 241–70. Ridgeway, Cecilia. 1997. “Interaction and the Conservation of Gender Inequality: Considering Employment.” American Sociological Review 62: 218–35. Ridgeway, Cecilia and Lynne Smith-Lovin. 1999. “The Gender System and Interaction.” Annual Review of Sociology 25: 191–216. Roch, Maria Teresa Soler, ed. 1999. Family Taxation in Europe. London: Kluwer Law International (Academic Committee on European Tax Law). Rønsen, Marit and Marianne Sundstro m. 2002. “Family Policy and After-Birth Employment Among New Mothers: A Comparison of Finland, Norway and Sweden.” European Journal of Population 18: 121–52. Rosenfeld, Rachel A. and Arne L. Kalleberg. 1990. “A Cross-National Comparison of the Gender Gap in Income.” American Journal of Sociology 96: 69–106. Rubery, Jill, Mark Smith, and Colette Fagan. 1999. Women’s Employment in Europe: Trends and Prospects. London and New York: Routledge.


195

Ruhm, Christoper. 1995. “Parental Leave Policies in Europe and North America.” Working Paper 5065. Cambridge, MA: National Bureau of Economic Research. Ruhm, C. J. and J. L. Teague. 1995. “Parental Leave Policies in Europe and North America.” Working Paper 5065. National Bureau of Economic Research. Sainsbury, Diane. 1999. Gender and Welfare State Regimes, Oxford: Oxford University Press. Scharpf, Fritz. 1999. Governing in Europe: Effective and Democratic? Oxford: Oxford University Press. Scharpf, Fritz and Vivien A. Schmidt, eds. 2000. Welfare and Work in the Open Economy. Oxford: Oxford University Press. Siim, Berte. 1988. “Towards a Feminist Rethinking of the Welfare State.” In The Political Interests of Gender, edited by K. B. Jones and A. G. Jonasdottir, London: Sage. Smithson, Michael. 2005. “Fuzzy Set Inclusion: Linking Fuzzy-Set Methods with Mainstream Techniques.” Sociological Methods and Research 33: 431–61. Steinberg, Ronnie J. 1990. “The Social Construction of Skill: Gender, Power and Comparable Worth.” Work and Occupations 17: 449–82. Stier, Haya, Noah Lewis-Epstein, and Michael Braun. 2001. “Welfare Regimes, Family-Supportive Policies and Women’s Employment Along the Life-Course.” American Journal of Sociology 106: 1731–60. Streeck, Wolfgang. 2001. “High Equality, Low Activity: The Contribution of the Social Welfare System to the Stability of the German Collective Bargaining Regime.” Industrial and Labor Relations Review 54: 698–706. Stryker, Robin and Scott Eliason. 2003. “The Welfare State, Gendered Labor Markets and Aggregate Political Orientations in France, Belgium, Germany, Italy, Denmark and Britain, 1977–1994.” European University Institute. Robert Schuman Centre Advanced Studies Working Paper [RSC2003/20]. Stryker, Robin and Scott Eliason. 2004. “The Welfare State, Gendered Labor Markets and Political Orientations in France, Belgium, Germany, Italy, Denmark and Britain, 1977–1994.” Paper circulated at Annual Meetings of RC-19, Toronto, August 21–4. Sundstrom, Marie and Ann-Zofie Duvander. 2002. “Gender Division of Childcare and the Sharing of Parental Leave Among New Parents in Sweden.” European Sociological Review 18: 433–47. Verkuilen, Jay. 2005. “Assessing Membership in Fuzzy Set Analysis.” Sociological Methods and Research 33: 462–96. Vleminks, K. and Timothy Smeeding, eds. 2001. Child Wellbeing, Child Poverty, and Child Policy in Modern Nations: What Do We Know? Bristol: Policy Press. Waldfogel, Jane 2002. “Child Care, Women’s Employment, and Child Outcomes.” Journal of Population Economics 15: 527–48. Wennemo, Irene. 1994. Sharing the Cost of Children. Doctoral Dissertation Series #25. Swedish Institute for Social Research. Stockholm: Stockholm University. Williams, Joan. 2001. Unbending Gender: Why Family and Work Conflict and What to Do About It. Oxford: Oxford University Press. Winegarden, C. R. and Paula M. Bracy. 1995. “Demographic Consequences of Maternal-Leave Programs in Industrial Countries: Evidence from Fixed-Effects Models.” Southern Economic Journal 6: 1020–35. Zadeh, Lotfi. 1995. “Probability Theory and Fuzzy Logic are Complimentary Rather than Competitive.” Technometrics 37: 271–6.

6 Family Policies and Women’s Employment: A Regression Analysis Alexander Hicks and Lane Kenworthy

There is good reason to suspect that “family-friendly” or “womenfriendly” policies such the government provision or subsidization of child care, paid maternity leave, and extensive public employment will increase levels of female employment. There is supportive evidence from studies of individual behavior (Gustafsson and Stafford, 1992; Leibowitz, Klerman, and Waite, 1992; Barrow, 1996; Ondrich, Spiess, and Yang, 1996; Ilmakunnas, 1997; Joesch, 1997; Fagnani, 1998; Kimmel, 1998; Powell, 1998; Anderson and Levine, 1999; Ondrich et al., 1999; Michalopoulos and Robins, 2000; Smith, Downs, and O’Connell, 2001; Chevalier and Viitanen, 2002; Del Boca, 2002; Pylkkänen and Smith, 2003; Rønsen and Sundstrom, 2002; Gottschall and Bird, 2003; Hofferth and Curtin, 2003). But at the macro (country) level, the association has been largely assumed rather than demonstrated. This assumption is based principally on cross-country differences between affluent nations. Most notable is the fact that the Nordic countries have been at the forefront in introducing and expanding these types of policies and are also the countries with the highest rates of female employment. But this apparent cross-sectional association has been more often the subject of casual observation than of careful analysis. And seldom have researchers examined the relationship between these policies and over-time changes within countries. In their chapter in this volume, Scott Eliason, Robin Stryker, and Erik Tranby (2008) attempt to do just this. They create new measures of the extensiveness of these types of family policy, and they use qualitative comparative analysis (QCA) to examine the impact of such policies on the extent of women’s labor force participation in 14 OECD countries since 1960. In this chapter we use regression analysis to explore this issue. We examine the same set of countries during the same time periods, and 196

Alexander Hicks and Lane Kenworthy

197

we use Eliason, Stryker, and Tranby’s family policy measures. The countries are Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Sweden, the United Kingdom, and the United States. The time periods are decades: the 1960s, 1970s, 1980s, and 1990s. Our findings are in some respects complimentary to and in other respects inconsistent with those of Eliason, Stryker, and Tranby. Eliason, Stryker, and Tranby use women’s labor force participation rates as their dependent variable. For consistency with the other chapters in this volume, we instead examine women’s employment rates. This is unlikely to affect the findings, as the two correlate very strongly across countries and over time.

Patterns of female employment For those seeking to understand differences among affluent countries in employment performance, getting a handle on gender-specific employment patterns is important. Figure 6.1 shows employment rates (employed as a share of the working-age population) for men and for women as of the period 2000–05. We organize the countries into three groups familiar to macrocomparative researchers: Nordic, continental, and Anglo. These groups have been found to differentiate sets of cases that vary in socioeconomic characteristics and processes of policy determination but are relatively homogeneous internally (Esping-Andersen, 1990, 1999; Goodin et al., 1999; Huber and Stephens, 2001). However, we use the grouping simply for heuristic purposes; no causal importance of group membership is implied. Much of the cross-country variation in Figure 6.1 is in female employment. The coefficient of variation (standard deviation divided by the mean) for women’s employment is .14, compared to .07 for men’s employment. Figure 6.2 shows trends in women’s employment over time in the 14 countries. Several things are worth noting. First, there is considerable cross-country variation in levels of women’s employment (already apparent in Figure 6.1). The Nordic countries have tended to have the highest female employment rates, followed by the Anglo countries, with the continental countries lagging behind. Secondly, women’s employment has increased in all of the countries. Thirdly, the countries vary markedly in degree and timing of this over-time increase. The Nordic countries tended to experience growth in the 1960s, 1970s, and 1980s, but then stagnation or decline in the 1990s. The Anglo countries experienced steady growth throughout the four decades. Three of the continental


Nor Swe Den Fin

Men’s employment rate Women’s employment rate

Nth Aus Ger Fr Bel It US Can UK Ire 0

20

40

60

80

100

Share of male or female population age 15–64 (%)

Figure 6.1 Men’s and women’s employment, 2000–2005 Note: For data definitions and sources, see the chapter appendix.

countries – France, Germany, and Italy – experienced no significant change in women’s employment rates in the 1960s, 1970s, and 1980s, but then some increase in the 1990s. Another continental country, the Netherlands, experienced by far the most dramatic change, with an increase in the female employment rate of nearly 30 percentage points since the mid-1980s. What role have family policies played in generating this cross-country and over-time variation in female employment? We follow Eliason, Stryker, and Tranby in focusing on the impact of three types of policy: public child care (separated into two age groups: 0 to 2 and 3 to 5), maternity leave, and public employment. They also examine the impact of child allowances/benefits, but we do not because there is no reason to expect this type of policy to increase women’s employment (if anything, the reverse is true, as a child allowance provides income that is not conditional on prior or current employment).

Family policies as causally sufficient? Eliason, Stryker, and Tranby’s conclusion with respect to the impact of family policies is that “High levels of public sector expansion, public daycare for younger children, and maternity leave are, separately, causally sufficient for high levels of female labor force participation” (p. 163). These inferences are based on the patterns shown in Figure 6.3.

60

40

20

Den Nor

Fin Swe

0

Anglo Countries

80

60

40

Bel Ger Nth

Aus Fr It

20

0 1960

1970

1980

1990

2000

80

60

40

20

Can UK

Ire US

0 1960

Year

Figure 6.2

Women’s employment rate (%)

Continental Countries Women’s employment rate (%)


Nordic Countries 80

1970

1980 Year

1990

2000

1960

1970

1980

1990

2000

Year

Women’s employment rates, 1960–2000

Note: Missing data: 1960–67 for Austria; 1960–75 for Belgium; 1960–65 for Canada; 1961–64, 1996, 1968 for Denmark; 1960–62 for Finland; 1960–65 for Ireland; 1972–76 for Italy; 1960–74 for the Netherlands; 1960–71 for Norway; 1960–62 for Sweden; For data definitions and sources, see the chapter appendix.

199

200 Method and Substance in Macrocomparative Analysis Public child care, age 0–2

Public child care, age 3–5

80

80 Swe90s Den90s Den80s

US90s

60

50

40

Fin90s

UK90s Can90s US80s Den70s Aus90s Can80s Ger90s UK80s Nth90s Ger80s US70s Aus80s Bel90s Fr80s Can70s US60s Ire90s Bel80s

Fr90s

Nth80s Nor60s It80s Ire80s

Swe80s



Swe80s Nor90s Fin80s

70

30

Nor90s Fin80s

70 US90s

60

50

40

UK90s Can90s US80s Can80s UK80s

Fin90s Den70s Aus90s Ger90s Nth90s

Ger80s US70s Bel90s Aus80s Fr80s Can70s US60s Ire90s Bel80s Nth80s Nor60s Ire80s It80s

.2

.4

.6

.8

1.0

0

Public child care, age 0–2 (index)

.2

.4

Maternity leave

1.0

1.2

1.4

Public employment

Swe80s Nor90s Swe90s Den90s Fin80s Den80s US90s

Swe70s Fin90s Can90s Den70s US80s Aus90s UK80s Can80s Ger90s Nth90s UK70s Fr90s Ger80s Ger60s Aus80s Ger70s Bel90s Fr80s Aus70s Fr70s Ire90s Bel80s

US70s UK60s Can70s US60s Nor60s

30 0

Nth80sIt90s Ire80sIt80s Ire70sIt60s

.2

.4



.8

80

70

40

.6


80

50

Fr90s

30 0

60

Swe90s Den90s Den80s

Swe80s

70 US90s

60

.6

.8

1.0

Swe70s UK90s Fin90s Can90s Den70s US80s Aus90s Can80s Ger90s UK80s Nth90s UK70s

50 Ger60s Ger80s Fr90s Aus80s US70s Ger70s Bel90s Fr80s UK60s Aus70s Fr70s Can70s US60s Ire90s Bel80s

40

30

Maternity leave (index)

Nor90s Swe90s Den90s Den80s

Fin80s

Nth80s It90s Nor60s It80s Ire80s It60s Ire70s

5

10

15

20

25

Public employment (%)

Figure 6.3 Women’s employment by family policies, 1960s–1990s

decade averages,

Note: For data definitions and sources, see the chapter appendix.

The figure includes four scatterplots. Each chart has women’s employment rates on the vertical axis and one of the four family policy measures on the horizontal axis. The data points are decade averages for each of the twelve countries (so each country appears up to four times, depending on missing data). The patterns in the charts in Figure 6.3 are consistent with an inference of “causal sufficiency.” Causal sufficiency means that “if X, then Y.” Here this can be read as “if family policy is generous, then the female employment rate will be high.” In Figure 6.3, in all observed instances


201

Women’s education

Women’s preference for employment 75


Women’s employment rate (%), 2000

80 Nor Swe

70 US Fin

65

60 Ger

55

Swe80s

70

40

65

Share of women age 25–59 strongly agreeing that both husband and wife should contribute to household income (%), 1995–97

Nor90s Swe90s

US90s Swe70s Fin90s Can90s UK90s Den70s US80s Asl90s Aus90s Ger90sCan80s UK80s Nth90s UK70s Asl80s Fr90s Ger80s US70s Ger60s Aus80s Ger70s Bel90s UK60s Aus70s Fr80s Asl70s Fr70s Can70s US60s Bel80sIre90s

60

50

40

30 15

Den90s Fin80s Den80s


4

6

8

10

12

Average years of schooling completed

Figure 6.4 Women’s employment by women’s preference for employment and women’s education Note: For data definitions and sources, see the chapter appendix.

of generous family policy (to the right on the charts’ horizontal axes), women’s employment is indeed high. But are these patterns enough to warrant a reading of causal sufficiency? No. As is almost always the case in analyses of non-experimental data, there is reason to worry about spuriousness. The “sufficiency” patterns in Figure 6.3 hinge on the position of the Nordic countries, and there are factors other than generous family policies that might be the true cause(s) of those countries’ high female employment rates. One is women’s preferences for employment (Hakim, 2000; Bielenski, Bosch, and Wagner, 2002). Perhaps more women in the Nordic countries prefer employment over staying home than is the case in other countries. The first chart in Figure 6.4 shows a positive association between the share of women aged 25 to 59 strongly agreeing that both husband and wife should contribute to household income and the female employment rate. Unfortunately, we are unable to include women’s preferences in our analyses because the number of countries for which data are available is too small. Moreover, reliable longitudinal data on women’s preferences are altogether absent. This is particularly problematic because women’s preferences might be endogenous; if family policies (or some other factors) boost the share of women in employment, this may become the norm and generate a preference in favor of it. A second potential source of spuriousness is women’s educational attainment. Within countries there is a positive association across


individuals between educational attainment and likelihood of employment (OECD, 2004, p. 147). Across countries, too, the average level of educational attainment among women and the female employment rate are positively correlated. This can be seen in the second chart in Figure 6.4, which uses the average number of years of schooling completed among women age 25 and over as a measure of women’s educational attainment. The data points in this chart are decade averages. Eliason, Stryker, and Tranby’s sufficiency argument is that when family policy is generous, female employment will be high, regardless of other institutions, policies, and conditions in the country. As noted in the introductory chapter to this volume, cases that score high on the hypothesized causal factor are the key in assessing a hypothesis of causal sufficiency; cases scoring low on the causal factor are analytically irrelevant. For a hypothesis of causal sufficiency, then, spuriousness is a concern if there is a causal factor that is plausibly related to the outcome on theoretical grounds and that is similar to the hypothesized causally sufficient factor(s) for the cases that score high on the hypothesized factor(s). Women’s educational attainment fits the bill. There is good reason to think that high levels of female educational attainment increase women’s employment, by changing women’s preferences and by increasing their earnings capacity. And the Nordic countries, which are characterized by generous family policies, have high levels of female educational attainment. This can be seen in the charts in Figure 6.5, which plot women’s education by the family policy measures for the Nordic countries. To feel confident that generous family policy is a sufficient condition for high women’s employment, we need a case with generous family policy but low women’s educational attainment. Unfortunately, no such cases exist.

A regression approach If we shift from a deterministic hypothesis of causal sufficiency to a tendential hypothesis, we can get more leverage on the question of whether the apparent impact of generous family policies on women’s employment is spurious. A tendential hypothesis would be that generous family policies tend to increase women’s employment, rather than that they will always yield high women’s employment. For a tendential hypothesis, the fact that family policy generosity correlates strongly with women’s educational attainment among the Nordic countries is not an obstacle as long as they do not correlate too strongly among the full set of countries. As it turns out, they do not. Women’s education correlates at .17 with public child care for age 0–2, at .04 with public child care for age

12 Nor90s Swe90s

10 Fin90s Den60s

8


Den70s Nor80s Fin80s Fin70s

6

Nor60s Fin60s

4 0

.2

.4

.6

.8

Women’s average years of schooling completed



12 Nor90s Swe90s

10 Fin90s Den60s

8

Fin70s

6

Nor60s Fin60s

4

1.0

0

Swe90s Fin90s Swe80s Den90s Den70s Den80s Fin80s Nor80s Swe70s Swe60s Nor70s Fin70s

6

Nor60s

Fin60s

4 0

.2

.4

.6

Maternity leave (index)

.8

1.0



Nor90s

8

.2

.4

.6

.8

1.0

1.2

1.4


12

Den60s


Den70s Fin80s Nor80s


10

203

12 Nor90s Swe90s

10 Fin90s Den60s

8

Den90sSwe80s

Den80s Den70s Fin80s Nor80s Swe70s

Swe60s Fin70s Nor70s

6

Nor60s Fin60s

4 5

10

15

20

25

Public employment (%)

Figure 6.5 Women’s educational attainment by family policies, Nordic countries, 1960s–1990s Note: For data definitions and sources, see the chapter appendix.

3–5, at −.06 with maternity leave, and at .47 with public employment. These correlations are weak or moderate in strength because the Anglo countries tend to have moderate-to-high levels of female educational attainment but low levels of family policy generosity, while the reverse is true for a number of the continental countries. A standard approach for assessing a tendential hypothesis about the impact of a variable net of one or more other variables is multivariate regression. Because we are interested in the independent effects of the three types of family policies, our first inclination would be to include all four of the family policy measures – two of public child care and one each of maternity leave and public employment – together in a regression with

204 Method and Substance in Macrocomparative Analysis Table 6.1

Correlations between family policy measures

Public child care, age 3–5 Maternity leave Public employment



Maternity leave

0.89 0.79 0.84

0.80 0.70

0.70

Note: N = 37. For data definitions and sources, see the chapter appendix.

Table 6.2 Principal components analysis of the four family policy measures Factor loadings

Public child care, age 0–2 Public child care, age 3–5 Maternity leave Public employment

Factor 1

Uniqueness

0.96 0.93 0.90 0.88

0.07 0.14 0.20 0.22

Note: N = 35. One factor retained. Eigenvalue: 3.37. Proportion of variance in items explained: 0.84. For data definitions and sources, see the chapter appendix.

women’s educational attainment. However, the family policy measures are too closely correlated with one another to permit this. Table 6.1 shows a correlation matrix for the four measures. For the 37 country-decade observations, the correlations among the measures range between .70 and .89. The inclusion of variables this highly intercorrelated in the same regression would create a severe multicollinearity problem. One option, therefore, is to estimate separate regressions, each including one of the family policy measures along with women’s educational attainment. Another option is to combine the family policy variables into a single measure. A “principal components” analysis of the four family policy measures yields a component with four high loadings (the smallest is .88) which explains 85 percent of the variance in the variables. A “principal factor” analysis yields a first factor that correlates at .99 with this first principal component “factor” (Table 6.2). We use this first factor as a composite measure of family policy. We estimate a series of pooled cross-section time-series regressions of female employment rates on each of the family policy variables – first on the family policy measures alone and then controlling for women’s


205

educational attainment. To avoid multicollinearity, we enter the family policy variables in separate regressions. A pooled regression approach is useful because we are interested both in the cross-country variation at particular points in time and in the over-time variation within countries. We estimate these regressions using ordinary least squares, with a “robust cluster” option that adjusts the standard errors for the nonindependence of observations within countries (clusters) and is appropriate for “unbalanced” panels in which the number of observations differs across countries (Bradley et al., 2003; Moller et al., 2003). The regression results are presented in Table 6.3. For each regression we list the unstandardized coefficients, t-statistics (in parentheses), and adjusted R2 . The odd-numbered columns show the results of bivariate regressions. The even-numbered columns show results controlling for women’s educational attainment. We describe the former first and then turn to the latter.

Bivariate results In the bivariate regressions in the table’s first set of rows, each of the family policy variables as well as the composite (factor score) measure has the expected positive sign and a substantively strong coefficient. For example, the coefficient for the public child care age 0–2 variable suggests that a country scoring 1 on the index had, on average, a female employment rate about 25 percentage points higher than a country scoring 0 on the index (column 1). The estimated magnitude of the difference between the low and high end is very similar for each of the other three family policy measures. To focus on the cross-country variation, we can add a set of period (decade) dummy variables to these regressions (we use the 1980s as the reference group). The results are shown in the second set of rows in Table 6.3. There is no noteworthy reduction in the magnitude of the family policy coefficients. A pooled model with time (period) dummies essentially duplicates the findings of a simple cross-sectional model; it expunges over-time withincountry variation and estimates an average set of results based on purely cross-country variation. What this type of pooled model does, in effect, is to stack cross-sections together. Where there is little change in crosssectional variation across time, the chief advantage is to increase degrees of freedom. That is the case here: between 61 and 68 percent of the variance in the independent variables, and 70 percent of the variance in female employment, is between countries. In the third set of rows

206

Table 6.3

Regression results Public child care, Public child care, age 0–2 age 3–5 Maternity leave

All countries; no period or country dummies Family policy variable

1

2

3

4

5

6

7

26.9 (5.6)

24.5 (8.1) 4.2 (9.5) 0.73

16.6 (3.2)

17.6 (5.2) 4.9 (11.5) 0.69

22.1 (2.3)

23.7 (5.2) 4.8 (10.6) 0.70

1.9 (8.0)

17.3 (6.7) 3.9 (7.1) 0.71

14.1 (2.0)

23.2 (3.9) 4.8 (7.8) 0.66

17.5 (1.5)

26.0 (4.8) 5.2 (8.4) 0.69

1.8 (6.6)

23.9 (4.5) 4.9 (4.7) 0.81

15.8 (1.9)

18.5 (4.1) 5.9 (5.3) 0.78

22.8 (1.8)

28.8 (3.9) 6.0 (5.2) 0.77

0.5 (3.5)

Women’s education Adjusted R2 Time period dummies a Family policy variable

0.43 24.2 (4.2)

Women’s education Adjusted R2 Cross-section of 1980s and 1990s averages Family policy variable

0.47 26.4 (3.0)

Women’s education Adjusted R2

Family policy Public employment factor scores

0.38

0.28

0.29

0.24

0.21

0.22

0.20

0.62

0.63

0.47

8

9

10

1.5 (9.4) 2.9 (7.6) 0.76

8.1 (5.1)

1.5 (8.5) 2.7 (5.5) 0.75

7.5 (3.5)

1.6 (5.7) 3.6 (3.8) 0.84

5.3 (4.6)

0.48

0.47

0.65

7.5 (12.1) 4.3 (9.0) 0.79 7.6 (8.4) 4.3 (9.0) 0.77 7.6 (5.4) 5.1 (5.5) 0.82

(Continued)

Table 6.3

(Continued) Public child care, age 0–2 1

Country dummies (full set) a Family policy variable

30.4 (2.6)

Women’s education Adjusted R2 Denmark and Sweden excluded b Family policy variable

0.55 28.4 (2.3)

Women’s education Adjusted R2 N

0.17 31

2

Public child care, age 3–5 3

8.3 (1.0) 6.9 (5.3) 0.83

22.4 (2.7)

29.8 (4.4) 4.4 (9.7) 0.63 31

9.1 (1.0)

0.56

0.02 31

4

Maternity leave 5

5.1 (0.8) 6.1 (5.2) 0.83

33.5 (4.0)

15.8 (2.9) 4.9 (11.9) 0.59 31

9.7 (0.8)

0.73

0.02 40

6

Public employment 7

7.1 (1.0) 5.2 (5.5) 0.87

2.3 (5.4)

17.5 (3.1) 4.6 (12.1) 0.63 40

2.2 (5.0)

0.78

0.42 41

8

Family policy factor scores 9

0.5 (1.1) 5.0 (4.5) 0.88

10.3 (3.5)

1.5 (3.7) 3.1 (5.5) 0.65 41

7.6 (2.1)

0.64

0.20 27

10

2.5 (0.9) 5.8 (4.2) 0.83 8.3 (5.0) 4.5 (7.9) 0.72 27

Notes: Unstandardized coefficients and absolute t-statistics (in parentheses) from ordinary least squares (OLS) regressions with “robust cluster” option (country is the clustered variable). Unit of analysis is the country-decade. Dependent variable is women’s employment rate. For data definitions and sources, see the chapter appendix. a Results for dummy variables are not shown to save space. b No time period or country dummies included. Excluding Denmark reduces the number of observations by three. Excluding Sweden reduces the number of observations by two for the public child care regressions and by three for the maternity leave and public employment regressions.

207


in Table 6.3, we show the results from a model that simply estimates the 1980s and 1990s means of variables’ values for the 1980s and 1990s panels alone, as these are virtually free of missing data for any variable. (Actually, 1990s data are missing for child care in Italy and for maternity leave in the United Kingdom and for the composite family policy measure for these two countries; we substitute 1980s values for these variables.) The results for these strictly cross-sectional estimates of family policy effects are always similar to those in most of the other models. To focus on the over-time variation within countries, we can add a set of country dummies. This seems particularly likely to reduce the estimates of the impact of family policies because there are more countries than time periods (14 versus four), there is more variation in family policies across countries than over time within countries, and countryspecific features such as culture may influence both family policy and women’s employment. The results with country dummies are in the fourth set of rows in Table 6.3. Perhaps surprisingly, the coefficients do not decrease, at least in models without a control for female educational attainment (odd-numbered columns). The scatterplots in Figure 6.3 above suggest a key role for Denmark and Sweden. These two countries have tended to have by far the most generous family policies and also the highest rates of women’s employment. To what degree does the association between family policy and women’s employment hinge on these two nations? Regression results with Denmark and Sweden omitted are shown in the fifth set of rows in Table 6.3. Here we do observe a noteworthy decline in the magnitude of the estimated effect for two of the family policy measures – public day care for children age 3–5 and maternity leave. The coefficients for these two measures decrease by more than half, the t-statistics are only around 1.0, and the adjusted R2 s fall to nearly zero.

Multivariate results controlling for women’s education What happens when we control for women’s educational attainment (average years of schooling completed among women aged 25 and over)? The findings are in the even-numbered columns in Table 6.3. Consistent with the bivariate pattern shown in the second chart in Figure 6.4 above, substantively strong and statistically significant effects are evident for female educational attainment in all of the models. In many of the models the estimates for the family policy measures remain similar to those in the bivariate regressions. However, in the models with country fixed effects (country dummies) the coefficients


209

and t-statistics for the family policy variables drop dramatically. The coefficient for female education, by contrast, does not decline. What should we make of this? The common interpretation is that the associations between the family policy measures and women’s employment are probably spurious; they are artifacts of unmeasured nationspecific differences, rather than truly causal. That might be the correct inference. But we should be careful about settling on this conclusion too quickly. The presumed spuriousness may be mistaken, either because (a) there are no such unmeasured variables with the stipulated traits and inclusion of units simply truncates variance in X or (b) the unmeasured correlates of the units affecting Y and attenuating the estimate for X are consequences of X and thus mere technical causes of bias in a structural coefficient but not causes of any spuriousness. It is also possible that the units correlate with unmeasured interaction terms which would, could they be measured, reveal notable effects for a subset of cases. But that does not mean such a variable has no causal impact. Suppose the economic or social or political environment in countries changes in ways conducive to women’s employment. Perhaps, for instance, women’s (and maybe men’s) attitudes toward female employment become more encouraging. But suppose the degree to which this yields an actual increase in female employment depends on the degree to which a country has supportive family policies. Ideally, the researcher would model this via an interaction between women’s attitudes and family policies. But suppose we lack good data on women’s attitudes; perhaps crossnationally comparable survey data are not available, or those data do not effectively capture attitudes. Without such an interaction, it would be impossible to capture the effect of unchanging family policies in a pooled regression with country fixed effects. Instead, an appropriate model might be a cross-sectional design with women’s employment measured in change scores and family policies measured in (average) levels (see, for example, Kenworthy, 2004, 2008). Yet this is not what accounts for the family policy variables’ results in the “country dummies” regressions in Table 6.3 here. Figures 6.6 through 6.9 show the over-time data for the four family policy measures: public child care for age 0–2, public child care for age 3–5, maternity leave, and public employment. Figure 6.10 shows over-time trends in the factor scores derived from these four policy measures and used in the regressions in Table 6.3. While some of the trend lines indicate little or no change over time – most notably, child care support in the Anglo countries – in most countries family policy generosity has increased. Thus, lack of over-time variation does not appear to be the problem for the family policy variables.

210

Continental Countries

1

.8

.8

.6

.6

.4

Anglo Countries

1

Bel Ger Nth

Aus Fr It

.4 .2

.2 Den Nor

0 1960

Figure 6.6

1970

1980 Year

1990

Fin Swe 2000

Can UK

Ire US

.8

Index

1

Index

Index

Nordic Countries

.6 .4 .2

0

0 1960

1970

1980 Year

1990

2000

1960

1970

1980 Year

1990

2000

Public child care, age 0–2, 1960s–1990s

Note: Data are decade averages. Missing data: 1960s and 1970s for Austria, Belgium, France, Germany, Ireland, Sweden, and the United Kingdom; 1960s, 1970s, and 1990s for Italy; 1970s for Norway. For data definitions and sources, see the chapter appendix.

Nordic Countries


1.4

1.4

1.4 Bel Ger Nth

Aus Fr It

1.2

1.2

1

1

1

.8

.8

.8

.6 .4 .2

Den Nor

0 1960

1970

1980

Fin Swe 1990

.6

.4

.2

.2 0 1960

Year

Figure 6.7

Ire US

.6

.4

0 2000

Can UK

1.2

Index

Index

Index

Anglo Countries

1970

1980 Year

1990

2000

1960

1970

1980

1990

2000

Year

Public child care, age 3–5, 1960s–1990s

Note: Data are decade averages. Missing data: 1960s and 1970s for Austria, Belgium, France, Germany, Ireland, Sweden, and the United Kingdom; 1960s, 1970s, and 1990s for Italy; 1970s for Norway. For data definitions and sources, see the chapter appendix.

211

212

Nordic Countries


1 .8

.6

.6

.4 .2

Den Nor

1 Aus Fr It

Bel Ger Nth

.4 .2

Fin Swe

0 1970

1980

1990

2000

Year

Figure 6.8

Ire US

.6 .4 .2

0 1960

Can UK

.8

Index

.8

Index

Index

1

Anglo Countries

0 1960

1970

1980

1990

2000

1960

Year

Maternity leave, 1960s–1990s

Note: Data are decade averages. Missing data: none. For data definitions and sources, see the chapter appendix.

1970

1980 Year

1990

2000

Nordic Countries


25

20 15 10 5

Den Nor

Fin Swe

0

25 Aus Fr It

20

Bel Ger Nth

% of working-age population



25

Anglo Countries

15 10 5

Figure 6.9

1970

1980 Year

1990

2000

Ire US

1980 Year

1990

20 15 10 5 0

0 1960

Can UK

1960

1970

1980 Year

1990

2000

1960

1970

2000

Public employment, 1960s–1990s

Note: Data are decade averages. Missing data: 1960s and 1970s for Belgium. For data definitions and sources, see the chapter appendix.

213

214

Nordic Countries


2.5

2.5 2

1.5

1.5

1 .5 0 Den Nor

⫺.5

Fin Swe

⫺1

Bel Ger Nth

Aus Fr It

2 Factor scores

2 Factor scores

Factor scores

2.5

Anglo Countries

1 .5

1970

1980

1990

2000

Ire US

1 .5 0

⫺.5

⫺.5 ⫺1 1960

Year

Figure 6.10

1.5

0

⫺1 1960

Can UK

1970

1980 Year

1990

2000

1960

1970

1980

1990

2000

Year

Family policy factor scores, 1960s–1990s

Note: Data are decade averages. Missing data: 1960s and 1970s for Austria, Belgium, France, Germany, Ireland, and Sweden; 1960s, 1970s, and 1990s for Italy and the United Kingdom; 1970s for Norway. For data definitions and sources, see the chapter appendix.


215

Moreover, the coefficients for the family policy variables shrink not when country fixed effects are added to the regressions, but rather when the country fixed effects and women’s educational attainment are added. This suggests that over time within countries, female employment is more closely associated with women’s educational attainment than with family policy generosity. Careful inspection of the scatterplots and time plots in Figures 6.2–6.10 suggests that this is due to developments in a variety of countries. The generosity/extensiveness of family policies increased in the Nordic countries, particularly during the 1960s, 1970s, and 1980s. This corresponds to the steady increase in female employment rates in these countries during those three decades. But other countries are more problematic. Women’s employment rates in the Anglo countries rose steadily during the four decades, and the extent of this rise was on par with that in the Nordic countries. Trends in public child care are of no help in explaining this development. Trends in maternity leave and public employment are consistent with the rise in women’s employment, but they seemingly were not large enough to be of much explanatory relevance – certainly not in the United States, at least. A second problematic pattern is the rise in the level of women’s employment in a number of the continental countries in the 1990s after effectively no change in the 1960s, 1970s, and 1980s. For the family policies for which early data are available, the data suggest increasing generosity in these countries throughout the period, not just in the 1990s. Perhaps the policies produced a slow release of cultural pressures on women and employers that inhibit female labor force participation. The Netherlands is also a problematic case in terms of over-time trends. From the mid-1980s the female employment rate in the Netherlands rose dramatically. None of the country’s family policies were significantly altered prior to or during this period. Indeed, studies of the Dutch case seldom assert a substantial role for government policy in precipitating the rise in female employment (for example, Visser, 2002; Misra and Jude, this volume). Finally, a fourth problematic trend is the stagnation (and, in some cases, the decline) in female employment rates in the Nordic countries in the 1990s. Part of this owes to the economic crises experienced by Finland and Sweden during the first half of the decade, and part of it may be a product of a “ceiling” effect (female employment may have neared its maximum achievable level by the early 1990s). Still, the pattern is not what we would expect given the continued increase in family policy generosity in most of these countries during that decade.

216 Method and Substance in Macrocomparative Analysis Family policies

Women’s education 80 Swe80s

70

Fin80s

Nor90s Swe90s Den90s Den80s

US90s Fin90s Can90s Den70s US80s Aus90s Can80s Ger90s UK80s Nth90s

60

50

40

Fr90s Aus80s US70s Ger80s Bel90s Fr80s Can70s US60s Ire90s Bel80s Nor60s Nth80s It80s Ire80s

30



80

Swe80s

70

⫺.5

.5

1.5

Composite family policy (factor scores)

2.5

Nor90s Swe90s

US90s Swe70s Fin90s Can90s UK90s Den70s US80s Aus90s Ger90sCan80s UK80s Nth90s UK70s

60

50

Fr90s Ger80s US70s Ger60s Aus80s Ger70s Bel90s UK60s Aus70s Fr80s Fr70s Can70s US60s Bel80sIre90s

40

30 ⫺1.5

Den90s Fin80s Den80s


4

6

8

10

12

Women's educational attainment (years)

Figure 6.11 Women’s employment by family policies and by women’s education: over-time within-country patterns Note: For data definitions and sources, see the chapter appendix.

Over-time trends in female educational attainment cannot fully account for these developments, but the association between women’s schooling and female employment is much stronger than is the case for family policies. A useful way to see this is via the scatterplots in Figure 6.11. The first shows women’s employment by the composite family policy variable; the second shows women’s employment by women’s education. Both plots feature pooled data, and in each we show withincountry regression lines. One issue is that the data are more complete for women’s educational attainment than for the family policy variable, but the key thing to note is that several of the regression lines in the family policy plot are vertical or nearly so. This indicates a rise in female employment despite little or no increase in the generosity of family policy. By contrast, virtually all of the lines in the women’s education plot have the expected positive slope. What of the possibility that it is, in fact, levels of family policy generosity that have influenced over-time trends in women’s employment? We can test this with an OLS regression of women’s employment change (measured as 1990s average minus 1970s average; 1960s data are not available for many countries) on the average level of family policy generosity over the four decades. Table 6.4 shows the results of such a regression for each of the family policy measures, both with and without a control for change in female educational attainment. These findings offer little support for the hypothesis. Virtually all of the family policy


217

Table 6.4 Regression results: change in women’s employment on level of family policy generosity

Public child care, age 0–2 Public child care, age 3–5 Maternity leave Public employment Family policy factor scores

No control for change in women’s educational attainment

Control for change in women’s educational attainment

Controls for initial level of women’s employment and for change in women’s educational attainment

−10.0 −8.4 −23.0 0.04 −3.4

−17.9 −11.7 −31.4 −0.42 −5.8

−3.3 −4.7 −23.2 1.12 −2.3

Note: Unstandardized coefficients. N = 14. For data definitions and sources, see the chapter appendix.

coefficients are negative, even without controlling for women’s schooling. The one exception is the coefficient for public employment in the third column. In the regressions reported in that column, the initial level of women’s employment is controlled for. This takes into account the possibility of a ceiling effect, whereby countries beginning (in 1979) with a high level of female employment find it more difficult to realize further increases. Here the coefficient is positive, as expected. It also is statistically significant (at the .10 level), and the standardized coefficient is larger than that for the change in women’s education variable. This suggests some indication of a positive effect of family policy on over-time trends in female employment rates. On the other hand, public employment is less directly a family policy than are child care and maternity leave, in the sense that the level of public employment is aimed less at promoting women’s employment. Arguments for generous family-friendly policies tend to focus on child care and parental leave.

Conclusion We have suggested reasons to question Eliason, Stryker, and Tranby’s conclusion that generous family policy is a sufficient condition for high levels of women’s employment. That conclusion hinges on the generous family policies and high female employment rates in the Nordic countries, and it could be that high levels of women’s education are the true cause of high female employment in those countries. To examine the net effect of family policies, we turn to a tendential hypothesis and


use regression analysis. When we control for female educational attainment and country fixed effects, much of the association between family policy generosity and women’s employment disappears. Our best guess is that generous family policies have helped to boost women’s employment, but the macrocomparative evidence is less than overwhelming in support of this conclusion.

Appendix: Variable Definitions and Data Sources The data used in this chapter are available at www.u.arizona.edu/ ∼lkenwor. Employment: men’s. Employed men as a share of the male population age 15 to 64. Source: Author’s calculations from data in OECD (2006). Employment: women’s. Employed women as a share of the female population age 15 to 64. Source: Author’s calculations from data in OECD (2006). Family policy factor scores. See the text for discussion. Source: Author’s calculations. Maternity leave. Index of the generosity of maternity leave. Source: Stryker et al. (2008, table 3). Public child care, age 0–2. Index of the generosity of government provision and subsidization of child care for children age zero to two. Source: Stryker et al. (2008, table 4). Public child care, age 3–5. Index of the generosity of government provision and subsidization of child care for children age zero to two. Source: Stryker et al. (2008, table 5). Public employment. Persons employed in the public sector as a share of the population age 15 to 64. Source: Stryker et al. (2008, table 2). Women’s education. Average years of schooling completed among women age 25 and over. Source: Barro and Lee (n.d.). Women’s preference for employment. Share of women age 25 to 59 strongly agreeing that both husband and wife should contribute to household income. Source: Author’s calculations from data in World Values Survey (1995–97).

References Anderson, Patricia M. and Andrew Levine. 1999. “Child Care and Mother’s Employment Decisions.” Working Paper 7058. Cambridge, MA: National Bureau of Economic Research. Barro, Robert J. and Jong-Wha Lee. n.d. “International Data on Educational Attainment.” Available at: www.cid.harvard.edu/ciddata/ciddata.html.


219

Barrow, Lisa. 1996. “An Analysis of Women’s Labor Force Participation Following First Birth.” Working Paper 363. Industrial Relations Section, Princeton University. Bielenski, Harald, Gerhard Bosch, and Alexandra Wagner. 2002. Working Time Preferences in Sixteen European Countries. Dublin: European Foundation for the Improvement of Living and Working Conditions. Bradley, David, Evelyne Huber, Stephanie Moller, François Nielsen, and John Stephens. 2003. “Distribution and Redistribution in Postindustrial Democracies.” World Politics 55: 193–228. Chevalier, A. and T. Viitanen. 2002. “The Causality Between Female Labour Force Participation and the Availability of Childcare.” Applied Economics Letters 9: 915–18. Del Boca, Daniela. 2002. “Low Fertility and Labour Force Participation of Italian Women: Evidence and Interpretations.” Labour Market and Social Policy Occasional Paper 61. Organization for Economic Cooperation and Development. Available at: www.oecd.org. Esping-Andersen, Gøsta. 1990. The Three Worlds of Welfare Capitalism. Princeton, NJ: Princeton University Press. Esping-Andersen, Gøsta. 1999. Social Foundations of Postindustrial Economies. Oxford: Oxford University Press. Fagnani, J. 1998. “Recent Changes in Family Policy in France: Political Tradeoffs and Economic Constraints.” Pp. 58–65 in Women, Work, and Family in Europe, edited by Eileen P. Drew, Ruth Emerek, and Evelyn Mahon. London: Routledge. Goodin, Robert E., Bruce Headey, Ruud Muffels, and Henk-Jan Dirven. 1999. The Real Worlds of Welfare Capitalism. Cambridge: Cambridge University Press. Gottschall, Karin and Katherine Bird. 2003. “Family Leave Policies and Labor Market Segregation in Germany: Reinvention or Reform of the Male Breadwinner Model?” Review of Policy Research 20: 115–34. Gustafsson, Siv and Frank Stafford. 1992. “Child Care Subsidies and Labor Supply in Sweden.” Journal of Human Resources 27: 204–30. Hakim, Catherine. 2000. Work–Lifestyle Choices in the 21st Century: Preference Theory. Oxford: Oxford University Press. Hofferth, Sandra L. and Sally C. Curtin. 2003. “The Impact of Parental Leave on Maternal Return to Work after Childbirth in the United States.” OECD Social, Employment, and Migration Working Paper 7. Organization for Economic Cooperation and Development. Available at: www.oecd.org/dataoecd/ 26/45/2955849.pdf. Huber, Evelyne and John D. Stephens. 2001. Development and Crisis of the Welfare State. Chicago: University of Chicago Press. Ilmakunnas, S. 1997. “Public Policy and Child Care Choice.” Pp. 178–93 in Economics of Family and Family Policy, edited by I. Persson and C. Jonung. London: Routledge. Joesch, Jutta M. 1997. “Paid Leave and the Timing of Women’s Employment Before and After Childbirth.” Journal of Marriage and the Family 59: 1008–21. Kenworthy, Lane. 2004. Egalitarian Capitalism. New York: Russell Sage Foundation. Kenworthy, Lane. 2008. Jobs with Equality. Oxford: Oxford University Press.

220 Method and Substance in Macrocomparative Analysis Kimmel, Jean. 1998. “Child Care Costs as a Barrier to Employment for Single and Married Mothers.” Review of Economics and Statistics 2: 287–99. Leibowitz, A., J. A. Klerman, and L. J. Waite. 1992. “Employment of New Mothers and Child Care Choice.” Journal of Human Resources 27: 112–33. Michalopoulos, Charles and Philip K. Robins. 2000. “Employment and Child Care Choices in Canada and the United States.” Canadian Journal of Economics 33: 435–70. Moller, Stephanie, David Bradley, Evelyne Huber, François Nielsen, and John D. Stephens. 2003. “Determinants of Relative Poverty in Advanced Capitalist Democracies.” American Sociological Review 68: 22–51. OECD (Organization for Economic Cooperation and Development). 2004. Education at a Glance. Paris: OECD. OECD. 2006. OECD Labour Force Statistics Database. Available at: www. oecd.org/scripts/cde/members/lfsdataauthenticate.asp. Ondrich, Jan, C. Katherina Spiess, and Qing Yang. 1996. “Barefoot in a German Kitchen: Federal Parental Leave and Benefit Policy and the Return to Work After Childbirth in Germany.” Journal of Population Economics 9: 247–66. Ondrich, Jan, C. Katherina Spiess, Qing Yang, and G. G. Wagner. 1999. “Full Time or Part Time? German Parental Leave Policy and the Return to Work After Childbirth in Germany.” Research in Labor Economics 18: 41–74. Powell, Lisa M. 1998. “Part-Time versus Full-Time Work and Child Care Costs: Evidence for Married Mothers.” Applied Economics 30: 503–11. Pylkkänen, Elina and Nina Smith. 2003. “Career Interruptions Due to Parental Leave: A Comparative Study of Denmark and Sweden.” Social, Employment, and Migration Working Paper 1. Organization for Economic Cooperation and Development. Available at: www.oecd.org. Ragin, Charles 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press. Rønsen, Marit and Marianne Sundstrom. 2002. “Family Policy and After-Birth Employment Among New Mothers: A Comparison of Finland, Norway, and Sweden.” European Journal of Population 18: 121–52. Smith, Kristen, Barbara Downs, and Martin O’Connell. 2001. “Maternity Leave and Employment Patterns: 1961–1995.” Household Economic Studies Report P70–9. Washington, DC: US Bureau of the Census. Stryker, Robin, Scott Eliason, and Eric Tranby. 2008. “The Welfare State, Family Policies, and Women’s Labor Force Participation: Combining Fuzzy-Set and Statistical Methods to Assess Causal Relations and Estimate Causal Effects.” Chapter 5 in this volume. Swank, Duane. 2002. Global Capital, Political Institutions, and Policy Change in Developed Welfare States. Cambridge: Cambridge University Press. Visser, Jelle. 2002. “The First Part-Time Economy in the World: A Model To Be Followed?” Journal of European Social Policy 12: 23–42. World Values Survey. 1995–97. Available at: www.icpsr.org.

7 Part-Time Work and the Legacy of Breadwinner Welfare States: A Panel Study of Women’s Employment Patterns in Germany, the United Kingdom, and the Netherlands, 1992–2002 Jelle Visser and Mara Yerkes

7.1 Introduction If there is one non-controversial stylized fact about the development of employment in the western world, it is the feminization of labor markets. On average, calculated across twenty OECD countries, the female employment rate rose from 49.2 to 59.0 percent between 1983 and 2003, whereas the male employment rate decreased from 77.7 to 73.6 percent. In these twenty years the male–female employment gap halved from 28.5 to 14.5 percent. The gender employment gap widens in all countries when we take the presence of children into account (Table 7.1, left panel). Women are still predominantly responsible for looking after children: the presence of children in the household reduces the level of female employment whereas it increases the level of male employment. On average, calculated for the 17 countries shown in Table 7.1, the employment rate of women aged 25 to 54 years decreases from 72.0 percent for women without children to 62.7 percent for women with two or more children, and the gender gap increases from 12.5 percent to 30.5 percent. This effect is found in all countries, though it is smallest in the welfare states of Northern Europe (Denmark, Sweden, Finland, and Norway). The largest contrast is with the three Mediterranean countries (Greece, Italy, and Spain), which have the lowest female employment rates to start with, experience the largest fall in employment due to the presence of 221

Table 7.1

Employment rates by presence of children, 2000

No children Employment rate

Gender gap

Persons aged 25 to 54 years

222

Women aged 25 to 54 years Two or more children

No children

Two or more children

Employment rate

Incidence part-time

Incidence part-time

Female

Male

Female

Male

Gender gap

Denmark Finland Norway Sweden

78.5 79.2 82.9 81.9

−7.7 −9.1 −5.9 −0.4

77.2 73.5 78.0 81.8

−12.9 −19.7 .. −9.4

18.5 7.5 24.7 14.6

.. .. 5.0 5.2

16.2 13.6 41.1 22.2

3.7 3.7 5.0 4.3

Austria Belgium France Germany Netherlands

76.0 65.6 73.5 77.3 75.3

−10.5 −17.4 −9.6 −7.2 −15.6

65.7 69.3 58.8 56.3 63.3

−29.0 −24.7 −32.9 −35.6 −30.8

17.4 29.2 20.0 24.0 38.3

2.1 6.5 5.2 4.2 6.2

43.7 46.1 31.8 60.2 82.7

1.9 5.9 4.4 3.4 5.5

Greece Italy Spain Portugal

53.1 52.8 54.6 72.6

−31.1 −26.2 −26.0 −13.4

50.3 42.4 43.3 70.3

−45.4 −49.9 −48.6 −24.8

8.4 20.0 13.7 11.5

2.8 5.5 2.6 2.7

11.2 34.4 18.6 11.3

2.7 5.1 1.9 2.0

Ireland United Kingdom Canada United States

65.8 79.9

−14.1 −5.4

40.8 62.3

−43.2 −28.2

16.6 23.7

4.3 4.1

46.4 62.8

4.0 3.7

76.5 78.6

−6.0 −7.2

68.2 64.7

−23.6 −29.0

17.0 10.1

5.2 3.5

30.7 23.6

4.3 2.7

Average

72.0

−12.5

62.7

−30.5

18.5

4.3

35.1

3.8

Source: based on figures from OECD Secretariat, derived from labour force surveys (national data); . . = no data.

Jelle Visser and Mara Yerkes

223

children and have the largest gender gaps for women with and without children. However, the presence of children also lowers the level of women’s employment and widens the gender employment gap in the welfare states of mainland Western Europe, the UK and the US. Women tend to work fewer paid hours than men, however, so focusing on the gender gap in employment–population rates understates the differences in employment levels between men and women. Generally, the incidence of part-time work is largest among students (especially in countries where student grants do not match the costs of higher education), among mothers of young children, and among men and women close to retirement. Focusing on persons aged 25 to 54 years, we see in Table 7.1 (right panel) that the incidence of part-time work among employed women increases with the presence of children, from an average of 18.5 percent for women without children to 35.1 percent for women with two or more children. The presence of children tends to increase the share of part-time jobs among women in employment in all countries except Denmark and Portugal. In contrast, the presence of children decreases the incidence of part-time work among men, from 5.2 percent to 4.3 percent on average, with remarkably little cross-national variation.1 The effect of children on the incidence of part-time employment among women is small or absent in Finland, Greece, Spain, and Portugal, countries in which part-time work has been relatively undeveloped and the choice for most women is between a full-time job or no job, resulting in traditionally high female employment rates in Finland and Portugal, and low rates in Greece and Spain (Italy belongs to the same group, but more women have taken to part-time employment in recent years).2 The presence of children also has only a modest or negligible effect in two high employment countries in which the level of part-time jobs among women used to be common but has decreased since the 1980s: Sweden and Denmark. The pattern in the two Scandinavian welfare states suggests that part-time employment may be a lifestyle choice unconnected to motherhood, whereas women with children who want to continue working full-time can do so thanks to the public provision of child care and employment relationships, which include extensive leave rights for parents.3 The largest impact of children on the take-up of parttime jobs among women aged 25 to 54 years is found in the Netherlands, the United Kingdom and Germany – the three countries where the incidence of part-time working women with two or more children exceeded 60 percent in 2000 (Table 7.1, right panel).


Part-time employment is a complex phenomenon. It may mean anything from working one hour to working 34 hours a week.4 Part-time jobs may be offered the same protection as full-time jobs or they may fall under a separate regime; they may be involuntary, because full-time jobs are not available, or voluntary, as it best suits the personal situation and preferences. Employers may use part-time work to increase workforce flexibility and part-time employment may come with a penalty to wages, training and career opportunities, but it can also be used by women or parents as a means to increase family income while organizing the job around other priorities, such as caring for young children. Scholars working within the framework of a “transitional labor market” speculate about the potential of the part-time model as a new form of “full” employment over the life cycle, based on working weeks of 30 hours on average (Rogowski and Wilthagen, 2002; Schmid and Gazier, 2002). In the Netherlands, the government endorsed a so-called “combination model” of work and care during the 1990s, in which the choice of part-time employment is expected to bring about a less gendered division of paid work and household tasks (Plantenga, 2002; Visser et al., 2004). Bollé (2001), writing for the ILO, concludes his overview that, if chosen freely and protected by high standards, part-time employment may be an attractive option and offer a means of striking a balance between time to earn a living and time for other activities. Since its inception in 1998, the European Employment Strategy has tried to promote higher employment levels as a foremost policy goal for EU member states. Increasing the popularity of part-time work is seen as an instrument for easing the transition into the labor market and reaching higher rates of female employment, especially in Southern and continental Europe (Taskforce, 2003). In recent years, ten member states, mostly in Southern and Eastern Europe, received specific recommendations from the European Council to modernize labor law in order to raise the attractiveness of part-time work. Citing the title of a well-known collection concerning women working part-time in Europe and the US, part-time employment can be either an equalizing force by bringing more women into the labor market or preventing them from withdrawing, or a force marginalizing female employment to unrewarding, dead-end jobs (Blossfeld and Hakim, 1997). Which of the two tendencies is dominant is a key issue for research and policy. In the collection cited above, Catherine Hakim raises the question of whether part-time work is a qualitatively different type of workforce involvement from full-time employment, one which gives priority to some other non-market activities around which the part-time


225

job must be fitted (Hakim, 1997). This is consistent with the notion that most part-time employment is highly gendered and concentrated among mothers with young children. This may be sufficient cause for marginalization, though this need not be so. Much will depend on institutions and policies shaping female labor supply and employer demand. Even if organized around other, possibly dominant non-market activities such as child care, part-time workers may gain pay levels, working conditions and protection on a par with full-time workers. Marginalization of women and part-time workers can have its main causes in factors not relating to the characteristics of supply, but to labor demand and employer behavior. We will argue that with regard to part-time work two sets of institutions are especially important: (1) welfare state provisions and interventions, in particular relating to child care and leave rights; and (2) employment relations, in particular labor law, union behavior and collective bargaining, since many issues related to working time and employee rights are settled by law and collective agreement or union pressure.5

7.2 Choice of countries, research questions and data For our empirical study we have chosen the three countries with the highest incidence of part-time employment among women: Germany, the Netherlands, and the United Kingdom. As can be seen from Figure 7.1, part-time employment remained at a high level (UK) and increased to a high (Germany) or very high level (Netherlands), quite distinct from the patterns shown elsewhere. In Denmark, for example, women’s part-time employment peaked in 1978 and has halved since this time. This decrease reflects various changes in taxation and unemployment insurance, making part-time work less attractive, but the availability of full-time public child care for young children of pre-schooling age is probably decisive (Rasmussen et al., 2005). There was also a decline in the incidence of part-time work in Sweden and the US, whereas it has remained at low levels in France and Southern Europe. The UK, (West) Germany6 and the Netherlands are welfare states in which the “male breadwinner/female carer” model has strong roots and paid employment of mothers of young children was long discouraged (Lewis, 1992; Knijn, 1994). Childcare provisions were lacking or unaffordable and opening times of schools and kindergartens limited, making part-time employment the dominant choice of mothers with young children (Killmann and Klein, 1997; Pfau-Effinger, 1998; Blossfeld and Rohwer, 1997; Visser, 2002). Furthermore, spouse-based splitting

Women in part-time employment as % of total female employment

226 Method and Substance in Macrocomparative Analysis 70,0

60,0

50,0

40,0

30,0

20,0

10,0

0,0 1983

1985

1987

1989

1991

1993

1995

1997

1999 2001

DK

FR

GE

IT

NL

SW

UK

US

2003

2005

Figure 7.1 Incidence of part-time employment among women Source: OECD data.

joint tax systems supported high levels of sole earner family income (Dingeldey, 2001). Under these conditions, part-time employment became an inroad into the labor market for many women in each of these countries. Beyond this common legacy, there are major institutional and policy differences in how the welfare state and industrial relations operate. The UK is usually classified as a liberal welfare state and uncoordinated market economy (Hall and Soskice, 2001; Esping-Andersen, 1990). Work–family life choices are deemed a private matter not to be interfered with by the state, and the role of unions and collective bargaining has been much diminished in the past two decades (Milward et al., 2000; Kersley et al., 2006). Germany is commonly classified as a coordinated market economy and conservative welfare state with the status rights of working families shielded against market forces. Unions and collective bargaining, although in decline, have remained important in shaping employment, wage and benefit patterns throughout the 1990s (Hassel,


227

2006). The Netherlands is a mixed case, with an increasingly liberalized and flexible economy and labor market, and a revived tradition of union and employer cooperation and involvement in government policy making (Visser and Hemerijck, 1997). How these differences play out in shaping female employment – and especially part-time employment – is the key question of this chapter. In the next section, we specify the institutional conditions in relation to our hypotheses concerning patterns of female employment and part-time work. Our strategy is to test these hypotheses in three steps, using individual panel data covering the period 1992–2002. First we estimate the effects of motherhood on the probability of adult women to be employed full-time or part-time, or remain outside the labor force. In particular, we are interested in the differences across birth cohorts, controlling for education and household status. Decline of the breadwinner state legacy should show in a weakening of the association between motherhood and particular employment patterns such as inactivity and small part-time jobs. Our next step is to analyze the transitions from part-time employment into inactivity or full-time employment, again focusing on the impact of motherhood upon relative risk ratios. The continued strength of a breadwinner legacy is expected to show in a higher risk to move from full-time to small part-time jobs and from part-time employment to inactivity. Following, a comparison with the transitions from full-time jobs into long or small part-time jobs or into inactivity can help answer the question whether part-time employment does in fact help particular groups of women to remain in the labor force. In the final part, we address the question of choice, bringing into play working time preferences of women and whether or not they lead to transitions in the desired direction. We use three separate panel data sets covering the years 1992– 2002: the British Household Panel Survey (BHPS), the German SocioEconomic Panel (GSOEP) and the OSA Labor Supply Panel (LSP) for the Netherlands.7 These surveys allow us to follow the same individuals and households over several consecutive years. Rather than using the European Community Household Panel (ECHP, 1995–2001), we have chosen to use national data in order to cover a longer period (1992–2002) and include questions relating to the working time preferences of women. The main comparability issue is that the British and German panels interview the same respondents on a yearly basis, while the Dutch panel interviews respondents every two years.8 Samples are restricted to persons aged between 15 and 64 (working-age population) at the time of the interview. Full-time students are excluded from the analyses, as they are


likely to increase the amount of inactivity found in the sample or create a bias towards small hours part-time jobs. The use of panel surveys has some well-known advantages and disadvantages. Panel data give researchers the opportunity to follow behavioral choices through time for the same individuals, increasing our ability to identify causal factors and allowing for a robust statistical analysis (Halpin, 2004). The main drawback is that a panel based on a representative sample of the population in its initial year may have lost its representativeness due to changes in the underlying population and panel attrition. Attrition is a particular problem if the respondents who drop out of the panel share certain characteristics systematically related to the subject of study (for instance, mothers of large families or women retiring from the labor market). The three panels use several methods to control for such biases, among others by renewing the panel on an incremental basis and the use of weights for descriptive statistics. We believe that the advantages outweigh the disadvantages and that using longitudinal panel data gives us a better hold on identifying the changing relationship between marital status, motherhood and employment than cross-sectional analysis or time-series studies based on annual surveys (see Bielenski et al., 2002; van der Lippe and van Dijk, 2001). In this study, we adopt a multinomial logit approach to measuring women’s employment. Multinomial logit models use maximum likelihood estimation for models with a polytomous dependent variable. These models assume a choice of outcomes. In our analysis, the dependent variable is represented by a four-point dummy variable indicating the respondent’s allocation to one of four possible labor market states: a full-time job of 35 hours per week or more, a substantial part-time job (20–34 hours), a small part-time job (1–19 hours), or a position outside the labor force (inactive status in the labor market).9 Although various classifications of part-time working hours are feasible (Gustafson et al., 2001; Lemaitre et al., 1997), most standard full-time workweeks in Europe vary between 36 and 41 hours a week. We therefore place the distinction between full-time and part-time employment at 35 hours a week. The distinction between “small” and “substantial” part-time jobs is commonly drawn at jobs involving half a working week or day, reflecting the threshold for employment rights and involvement in the workplace in many firms and countries and hence serving as indicator of the marginality of the part-time worker (Ellingsaeter, 1992; Rubery et al., 1999). The propensity of one individual to be in a particular labor market state versus a reference category can be expressed by using relative risk


229

ratios. The relative risk ratio is calculated by taking the exponential of the log risk ratio. In this study, if we are interested in the characteristics of the “risk” that women will find themselves in any state (m) other than full-time employment (=1), i.e. to be out of the labor force or work in a small or substantial part-time job, the equation log

Pr(Yi = m) Pr(Yi = l)

=

R

βmr χir = Zim , (m = 2, . . . , M)

r=ft

expresses the logarithm of the risk-ratio of the probability of that outcome versus its alternative of full-time employment, where β is the coefficient associated with the rth characteristic (r = 1, . . ., R) for the mth alternative pattern of labor market participation (m = 2,. . ., M), where X is the value of the rth characteristic for individual i. Since we are interested in analyzing whether part-time work is used as a stepping stone to get a full-time job or as a pathway to exit the labor market, we will also apply this model to a selected sample of part-time working women whose “risk” to move to full-time employment or exit from the labor force is estimated relative to those remaining in a part-time job. We are especially interested in the differences between small and substantial part-time positions. Finally, we will attempt to analyse women’s choices by taking into account not only actual transitions but also preferences of working more or less hours and how they affect women’s actual working hours. For that part of the analysis, we have used a fixed effects model with pooled data for women who stay in employment.

7.3 Different pathways moving away from the breadwinner state In this section we delineate our expectations regarding the development of women’s part-time work based on what we know about economic, political and institutional differences between the three countries. These expectations will help us orient our empirical analysis and interpret the results in a meaningful way. Each of the three countries is moving away from the breadwinner model and married women and mothers of young children have turned increasingly to paid employment. Parttime employment has played a big role in this development, but the nature of part-time work is quite different across the three countries. The Netherlands, coming from a lower level, experienced the fastest growth in female employment of all OECD countries during the 1990s, and


68

13

66

12 11

64

10 9

60

8

58

7

UErate

EPratio

62

6

56

5

54

4

52

3

50

2 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Germany EPratio United Kingdom EPratio Netherlands UErate

Netherlands EPratio Germany UErate United Kingdom UErate

Figure 7.2 Employment population ratios and unemployment rates, women, aged 15–64 years Source: Employment in Europe, 2005 (based on European Labour Force Survey).

the biggest contribution came from women working part-time. Something similar – accelerating growth in female employment associated with more women accepting part-time jobs – happened in Britain during the 1980s. But there the growth of part-time employment halted in the 1990s, in contrast to the Netherlands, where part-time employment continued its ascent. In Germany, the rise of part-time employment is a more recent phenomenon, mostly concentrated in (former) West Germany and situated in a context of overall stagnation of employment growth and slowly growing female employment rates. Limiting ourselves to the period 1992–2002, we observe that conditions – as indicated by female unemployment – seemed more benign in the UK and the Netherlands compared to Germany (Figure 7.2). We expect that in Germany a larger share of women working part-time do so involuntarily; and that the gap between preferred and actual working hours is larger. The main factor behind discrediting the male breadwinner model in these countries is probably the stagnation in wage growth, the rise in (male and female) unemployment and increased job insecurity since the 1980s (Western and Healy, 1999). This happened earlier in Britain than


231

in Germany, with the Netherlands in between. Moreover, the British “liberal” welfare state did less to compensate the stagnation in earnings and rise in unemployment of male breadwinners than the two “conservative-corporatist” welfare states, though the Netherlands started to revise its tax system and social security programs towards greater “individualization” in various steps throughout the 1980s and 1990s.10 In addition to the “push” of married women into employment, there was the “pull” of rising relative female wages, related to increased participation in education and women’s investment in human capital, as well as female-biased technological change with the rise of services (Hartog and Theeuwes, 1985). The later and more limited change to a service economy in West and East Germany, combined with stricter legislation on business operating hours compared to the UK and the Netherlands, is likely to have dampened employer demand for part-time work and limited opportunities for women wanting to work part-time. Women’s decisions to seek part-time jobs, or to switch from a full-time to a part-time job, are often understood as an adjustment of women seeking to balance their roles as mothers and workers.11 Part-time work is then seen as a coping strategy for women at a life stage when family obligations are most comprehensive, i.e. when mothering young children, especially during the pre-schooling years. Consequently, postnatalist state and employer policies and services are very important for the employment patterns of women (Beechy and Perkins, 1987; Pettit and Hook, 2005). The provision of good quality public child care is probably the single most important political measure enabling mothers to continue working and to choose full-time rather than part-time employment. In this respect our three countries are very different from Denmark or Sweden, and also from France and Belgium, countries where there is an existing tradition of day schools and kindergartens for children of pre-schooling age (Daly, 2000). Until the 1990s, the UK, Germany and the Netherlands had negligible coverage rates of child care for children less than four years of age, compared to 48 percent in Denmark (CEC data, cited in Elligsæter, 1992). In the Netherlands and Germany, most day care centers only offer child care places on a part-time basis, requiring one or both parents to work part-time or flexible hours (Plantenga, 2007). With regard to children aged three to compulsory schooling age, differences have narrowed. According to Brannen and Moss (1990), policies directed at supporting the double role of parents and mothers have been met by a mixture of indifference and hostility by postwar UK governments. In keeping with the philosophy of the liberal welfare state and the sanctity of markets,


the provision and financing of child care in the UK has remained a matter for parents and employers. In the Netherlands and Germany, child care was seen as a shared responsibility between families, firms, social partners and the government. Characteristically, in the Dutch “tripartite” approach, child care expenses have been a matter of subsidies, tax deductions and exhortations to make employers pick up part of the bill. This has met with limited success: research in 2005 showed that one-third of all employees receives no childcare support from employers (Plantenga, 2007).12 In short, the limited availability of childcare provision, although easing over time, has remained a constraint on women wanting to work full-time in all three countries. The high cost of child care probably has the strongest impact on the choices of women with low education and, we may assume, low earnings, and this effect should be largest in the UK, other conditions held equal. The lack of sufficient and affordable care facilities and services has led the governments in the three countries to enact legislation to facilitate the change from full-time to part-time employment during parenthood. The Netherlands has been a forerunner in this regard. As early as 1982, the Wassenaar agreement between unions and employers recommended part-time work as a work-sharing solution to rising unemployment and in 1993, the peak federations of unions and employers recommended that local negotiators include a “part-time clause” in collective agreements in order to make sure that employers “recognize a qualified right for full-time employees to work reduced hours, unless this cannot reasonably be granted on grounds of conflicting business interests.”13 At the same time, the conditions and benefits of parttime workers were brought on par with full-time workers, clauses which denied (small) part-time workers the benefits granted to full-time workers were removed from law and collective agreements, and in 1996, all remaining discrimination on the basis of working hours was made unlawful (Yerkes, 2006). During the 1990s, the percentage of firms with a so-called “part-time clause,” allowing a reduction in the working hours of full-time workers, increased from 23 to 70 percent. Yet, the unions criticized that too few firms cooperated and in 2000, Parliament voted in a new law granting a statutory right to work part-time for all employees (Visser et al., 2004). The same law also introduced a qualified right for part-time working employees to demand more hours. It was not until 1996 that, faced by rising unemployment and stagnating employment, the German government, inspired by the Dutch job “miracle,” began to promote part-time employment (Killman and Klein, 1997). In its bid to reduce unemployment, the Center-Left, which


233

gained office in 1998, began to promote mini jobs, allowing employees to earn up to 400 euros monthly, exempt from payroll taxes and social security. These jobs are mostly found in the service sector, part-time and are typically performed by women. This development is likely to have reinforced a negative image, associating part-time work with substandard jobs in terms of social security and employment protection in the eyes of workers, unions and employers. Recently, and in response to European legislation that “forbids” discrimination on the grounds of working hours (Directive 97/81/EC),14 there have been attempts to re-regulate “mini-jobs” and part-time work. In 2001, the new law on part-time and fixed-term work came into effect – this gives employees the right to request a reduction in working time in companies with more than 15 employees and places restrictions on the use of fixed-term contracts (Yerkes, 2006). German collective bargainers seem to have been less prominent in their attempt to raise the profile of part-time work. Until recently, many collective agreements contained clauses against small part-time work. Eligibility thresholds, usually at around 15–18 hours per week, tend to exclude small part-time workers from employment protection or social insurance. Only in the case of part-time work of substantial length are conditions similar to those of full-time workers. The development of part-time work in the United Kingdom differs from the pattern observed both in Germany and the Netherlands mainly because it is shaped in a much less regulated labor market. The collective statutes that existed had been weakened significantly during the 1980s and relevant EU legislation did not apply until the Labour Party returned to power in 1997, after 18 years of opposition. The expansion of parttime employment that did take place was market-driven and before 1998 hardly constrained by legal norms. In the UK, part-time work is more marginalized and organized around small jobs than in the other two countries. Like part-time workers in mini-jobs in Germany, employees with low incomes are not covered by occupational social insurance, but thresholds are much higher than in Germany and were raised several times.15 Working hours are much more polarized in the UK than on the European continent and part-time work often consists of short hours for women and very long hours for their partners or husbands.16 British trade unions often remain quite supportive of overtime and unsocial working hours because their members need the extra pay (Barnard et al., 2003; Dickens and Hall, 2005). British governments, including “New Labour,” maintain a policy of allowing firms to choose their own working time arrangements, even where it contradicts avowed policy objectives such


as creating a better work–family balance and eradicating an unproductive “long hours” culture. As is argued by Berg and colleagues (2003), in countries with little labor market regulation, as in the UK, employers have more power to shape when and how long people work, and levels of employee autonomy are rather low. In the case of the UK, this is reflected in higher levels of conflict and reported levels of stress over work-family issues compared to Germany and, especially, the Netherlands according to ISSP data from 2002. Fagan (2004) concurs that – apart from the length of working hours – it is the control workers have over their individual working hours that is crucial. We expect, therefore, that the differences between actual and preferred working hours will be largest in the UK and smallest in the Netherlands. The second pillar of a post-natalist state and employer policy is based on guaranteeing that non-work issues such as parenthood and caring responsibilities can be pursued within the employment relationship. Key to this is the granting of leave of absence rights relating to parenthood, sickness or caring duties. European legislation introduced in 1995 establishes minimum leave of absence rights related to maternity and parenthood, but entitlement to these rights, compensation for the loss of earnings, and the right to return to the same job or career, are issues dealt with by national states. The UK leaves most of these issues to the free play of negotiations between employers and employees (or unions, as the case may be), though under Labour this tendency was partially reversed. Each of the three countries now guarantees a 16–18 weeks base parental leave at a 100 percent replacement rate (90 percent in the case of the UK) and optional leave rights for up to 24 weeks in the case of the UK and the Netherlands, and up to 136 weeks in Germany (until recent legal changes, effective in 2007). Long-term leave arrangements, up to three years after childbirth, tend to encourage withdrawal from the labor market (Buddelmeyer et al., 2004; EC, 2004). In Germany we expect therefore that the presence of young children will be associated with withdrawal while in Britain and the Netherlands there will be a tendency to work part-time, with a pronounced tendency to small part-time jobs in the UK. In the 1990s, as part of its strategy to “normalize” part-time jobs (Visser, 2002) and propagate a combination-model of equal sharing of work and care (Plantenga, 2002), the Netherlands tried to develop a choice model, based on ample opportunities for both employees and employers to choose individualized (part-time) working hours while minimizing the inequalities deriving from such choices. Preceded by ten years of experiment by means of collective bargaining between


235

unions and employers, covering nearly 80 percent of all employees, this development culminated in broad parliamentary support for legislation guaranteeing employees the right to request either an increase or decrease in their working hours. The Working Hours Adjustment Act, passed in 2000, is quite unique in so far it gives employees an almost unilateral right to change contractual working hours. Its framers believed that this would strengthen the resolve of men to demand shorter working hours and of women to demand longer working hours (in line with consistent survey findings of the 1990s) and, hence, bring about a more equal gender division of work and family tasks. In 2001, Germany enacted a weaker version of this idea, giving employees a conditional right to demand part-time working hours. Under Labour’s “Fairness at Work” program, since 2003, the UK has granted parents with children under the age of six to ask for changes in working hours and request to be allowed to work at home. German and British legislation came too late to affect the choices of women before 2002, the final year of our study. In the Netherlands we do expect the effect of collective agreements with parttime clauses to show in an alignment of actual and preferred working hours among employed women in the second half of the 1990s.

7.4 The “breadwinner” determinants of part-time employment In this section we analyze panel data for the period 1992–2002 in order to identify the key determinants affecting women’s decisions to accept a part-time job. The probability of being “inactive” in the labor market, working full-time, a “small” or “substantial part-time job” is estimated conditional on a vector of explanatory variables. Since we want to verify the continuing strength of the “breadwinner” model regarding women’s choice of working pattern, “marital status” and “motherhood” are the two main variables of interest. The definition of marital status is based upon the respondents’ own definition and unmarried women living in “couples” or civil partnership are classified as married women, since they are in recent years treated almost identically in terms of income tax, family allowances, child care benefits and leave rights in the three countries studied here. Single women are the reference category. Motherhood is measured using two variables: the presence and age of children in the household. We distinguish between children of pre-schooling age (0–5 years) and older children (6–15 years); the reference category is a household without children.17 The control variables are level of education and birth cohort. For educational level we use the internationally

236 Method and Substance in Macrocomparative Analysis Table 7.2 Distribution of women aged 15–64 years by labor market status in 1992–2002, by birth cohort Birth cohorts

Germany

1932–44 1945–54 1955–64 1965–74 1975–86

62.5 26.8 28.0 36.3 41.0

5.1 9.7 12.0 8.0 3.3

11.4 21.1 20.9 10.5 4.7

21.0 42.3 39.3 45.2 51.1

100.0 100.0 100.0 100.0 100.0

All

38.1

8.3

14.7

39.0

100.0

75.0 43.3 31.8 20.5 5.1

9.0 20.2 23.1 14.2 6.8

11.2 25.0 31.2 30.4 35.4

4.9 11.5 14.0 34.9 52.7

100.0 100.0 100.0 100.0 100.0

All

40.1

16.9

25.5

17.5

100.0

1932–44 1945–54 1955–64 1965–74 1975–86

55.4 25.8 25.5 25.2 5.7

12.1 16.0 17.0 12.6 9.0

15.3 22.9 21.8 14.2 13.0

17.2 35.4 35.8 48.1 59.9

100.0 100.0 100.0 100.0 100.0

All

30.2

14.1

18.2

37.6

100.0

Netherlands 1932–44 1945–54 1955–64 1965–74 1975–86

UK

No job Short partLong PartFull-time time job time job job (35 hours (1–19 hours) (20–34 hours) or more)

Source: Germany: GSOEP; UK: BHPS; Netherlands: OSA LSP 1992–2002.

comparable CASMIN scale (Müller, 2005; Kerckhoff et al., 2002; Bernardi et al., 2004) to determine three levels of education: high (university), intermediate (secondary) or low (less than secondary), corresponding to CASMIN levels 1, 2 and 3. We use higher education as the reference category. Finally, we distinguish five birth cohorts: 1932–44; 1945– 54; 1955–64; 1965–74 and 1975–86. Our reference category is the first cohort – that is, women who entered the labor market when the breadwinner model reached its postwar apex, before the expansion of mass education and before the general diffusion of oral contraceptives in the late 1960s. Table 7.2 shows that the largest differences in patterns of labor market participation are those between the oldest and the other four cohorts. This also reflects an age effect, since labor market participation in nearly all European countries decreases sharply after reaching 55,18 and the


237

women in this cohort were in their fifties and sixties between 1992 and 2002.19 Remarkably, of the German women who do work in this age cohort, most are employed in full-time jobs, a pattern barely evident in the Netherlands among women of this cohort. UK women in this cohort are just as likely to work part-time or full-time. A full-time job is the dominant choice only for women in the youngest cohort in the Netherlands and the two youngest cohorts in the UK and Germany. These are women who entered the labor market in the 1980s or later, and it is likely that the working hours choice of the youngest women among them has not yet been compromised by the choice of motherhood.20 We observe that for these younger women the choice to work full-time dominates the part-time option, although in the Netherlands this tendency is less pronounced and long part-time jobs appear to be an alternative to full-time jobs even for these younger women. Finally, we note that part-time jobs are less common in Germany among women of all ages and that, in sharp contrast to the other two countries, the “no job” option is particularly widespread among German women in the two youngest birth cohorts.21 The data suggest that for them the dominant choice is the one between a full-time job and no job, whereas in the Netherlands and in the UK, part-time jobs, often short-hours part-time jobs, have offered a third option. Considering the period of observation, that is, 1992–2002, it seems plausible to see this difference as reflecting the poor labor market conditions, especially for women, in Germany, as well as the stronger “welfare-to-work” pressures building up in these years in both the UK and the Netherlands.22 Moving from description to analysis, we present the results of our multinomial logit model in Table 7.3. In all three countries there is a clear tendency of both motherhood and marital status to lower the probability that women work full-time and to increase the probability of women remaining or becoming inactive. The effect of the presence of young children is particularly pronounced and as a rule, motherhood tends to have a stronger impact than marital status.23 Marital status effects are strongest in the Netherlands, especially in respect of the expressed preference for short-hours part-time jobs. They are least pronounced in the UK. The effects of education are also unequivocal: a lower level of education decreases the probability of full-time work and increases the risk of inactivity. This tendency has become stronger in most countries during the period under investigation and is most pronounced in the UK. In the Netherlands and Germany intermediate-level education is no (longer) a significant predictor of the choice between full-time and long part-time working hours.

Table 7.3A

Determinants of labor market status of women – inactive (REF: FULL-TIME) (multinomial logits for probabilities)

2002 Main

1998

UK 1992

2002 Main

Interaction RRR & (SE) Marital status (ref: single) Married/cohabitating

2.520∗∗∗ (.464)

1.676∗ (.416)

2.219∗∗ (.574)

Germany 1998

1992

2002 Main

Interaction RRR & (SE) 1.156∼ (.101)

.940 (.114)

.957 (.135)

.306∗

.166∗∗∗

.633∗ (.113)

.210∗∗∗ (.045)

∼p < .10, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001. Sources: British Household Panel Study, German Socio-Economic Panel, OSA Labour Supply Panel 1992–2002.

1992

Interaction RRR & (SE) 2.703∗∗∗ (.284)

One or more children (ref: no children) .688 16.316∗∗∗ 1.226∼ 2.170∗ 20.733∗∗∗ 0–5 years old 9.250∗∗∗ 1.125 (4.994) (.876) (.511) (1.983) (.220) (.442) (2.864) 1.190 3.270∗∗∗ 1.328 1.248 3.160∗∗∗ 6–15 years old 4.144∗∗∗ 1.032 (1.654) (.566) (.641) (.359) (.084) (.236) (.304) Educational level (ref: high) .972 2.067∗∗∗ .780∗ .714∗ 1.540∗∗∗ Medium 3.053∗∗∗ 1.275 (.610) (.359) (.313) (.183) (.097) (.110) (.167) .959 7.520∗∗∗ .723∗ .565∗∗∗ 3.069∗∗∗ Low 9.126∗∗∗ 1.437 (2.009) (.448) (.319) (.758) (.096) (.089) (.345) Cohort (ref: born 1932–1944) 1.527 2.704∗ .123∗∗∗ 1.109 2.117∗∗∗ .081∗∗∗ born 1945–1954 .109∗∗∗ (.045) (.735) (1.279) (.019) (.196) (.425) (.011) .041∗∗∗ 1.586∗ 3.259∗∗∗ .037∗∗∗ born 1955–1964 .040∗∗∗ 2.668∗ 3.215∗ (.167) (2.332) (1.532) (.007) (.321) (.740) (.005) 1.137 .772 .032∗∗∗ 1.488 2.957∗∗∗ .037∗∗∗ born 1965–1974 .037∗∗∗ (.154) (.571) (.369) (.006) (.302) (.679) (.006) .602 8.181∗∗∗ .024∗∗∗ 1.441 .535 .081∗∗∗ born 1975–1986 0.007∗∗∗ (.003) (.471) (7.320) (.004) (.350) (.565) (.012) Year effects (ref: 2002)

1998

.671∗∗∗ (.073)

.573∗∗∗ (.072)

1.700∗ (.350) .820 (.108)

.581∗∗ (.114) .683∗∗ (.099)

.700∗ (.122) .758 (.131)

.694 (.143) .859 (.177)

2.052∗∗∗ 3.843∗∗∗ (.295) (.665) 2.323∗∗∗ 5.805∗∗∗ (.384) (1.087) 2.697∗∗∗ 11.536∗∗∗ (.444) (2.293) 3.172∗∗∗ 35.560∗∗∗ (.613) (14.043) .690∗ (.140)

.221∗∗∗ (0.052)

238

Netherlands

Table 7.3B Determinants of labor market status of women – short part-time. 1–19 hrs (REF: FULL-TIME) (multinomial logits for probabilities) Netherlands 2002 Main

UK 1998

1992

2002 Main

Interaction RRR & (SE) Marital status (ref: single) Married/cohabitating One or more children (ref: no children) 0–5 years old 6-15 years old Educational level (ref: high) Medium Low Cohort (ref: born 1932–1944) born 1945–1954 born 1955–1964 born 1965–1974 born 1975–1986

1998

1992

2002 Main

Interaction RRR & (SE)

1998

1992


10.197∗∗∗ (2.720)

.493∗ (.167)

.684 (.257)

1.623∗∗∗ (.172)

.986 (.152)

1.567∗ (.290)

3.287∗∗∗ (.400)

.688∗ (.128)

.675 (.153)

10.342∗∗∗ (5.580) 6.107∗∗∗ −2337

.875 (.682) .844 (.457)

.422 (.326) .809 (.437)

10.687∗∗∗ (1.413) 4.137∗∗∗ (.490)

.931 (.184) 1.448∗ (.261)

1.121 (.264) 1.278 (.248)

14.847∗∗∗ (2.550) 5.914∗∗∗ (.712)

1.313 (.364) .937 (.182)

.554∗ (163) .549∗∗ (127)

2.290∗∗∗ (.535) 5.106∗∗∗ (1.153)

.735 (.206) .861 (.276)

.256∗∗∗ (.084) .361∗∗ (.122)

1.432∗∗∗ (.145) 2.892∗∗∗ (.343)

.984 (.143) 1.038 (.168)

.989 (.170) .807 (.147)

1.143 (.154) 1.712∗∗∗ (.247)

.717 (.173) .929 (.227)

.508∗ (.150) 1.016 (.299)

.431 (204) .366∗ (.171) .252∗∗ (.118) .047∗∗∗ (.027)

1.810 (1.007) 1.894 (1.056) .766 (.431) 2.631 (1.926) 1.198 (.718)

1.916 (1.036) .887 (.480) .415 (.232) 4.940∗∗∗ (4.770) 1.461 (.894)

.126∗∗∗ (.058) .164∗∗∗ (.033) .127∗∗∗ (.025) .024∗∗∗ (.018)

1.450∼ (.326) 1.544∼ (.377) 1.365 (.336) 1.487 (.475) .590∗ (.143)

1.408 (.336) 1.496 (.372) .818 (.230) 4.191∗ (2.803) .360∗∗∗ (.099)

.534∗∗ (.115) .329∗∗∗ (.072) .248∗∗∗ (.057) .180∗∗∗ (.049)

1.303 (.366) 1.338 (.400) 1.305 (.413) 1.289 (.573) .690 (.245)

1.113 (.332) 1.1072 (.342) .982 (.375) 3.165 (3.435) .582 (.234)

∼p < .10, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001. Source: British Household Panel Study, German Socio-Economic Panel, OSA Labour Supply Panel 1992–2002.

239

Year effects (ref: 2002)

Germany

Table 7.3C Determinants of labor market status of women – long part-time, 20–34 hrs (REF: FULL-TIME) (multinomial logits for probabilities)

2002 Main

1998

UK 1992

2002 Main

Interaction RRR & (SE) Marital status (ref: single) Married/cohabitating One or more children (ref: no children) 0–5 years old 6–15 years old Educational level (ref: high) Medium Low Cohort (ref: born 1932–1944) born 1945–1954 born 1955–1964 born 1965–1974 born 1975–1986

1998

1992

2002 Main


1998

1992


2.586∗∗∗

.972 (.218)

1.264 (.301)

1.540∗∗∗ (.137)

.888 (.117)

1.083 (.169)

1.822∗∗∗ (.169)

.845 (.113)

.998 (.166)

5.576∗∗ (2.979) 2.882∗∗ (1.093)

1.196 (.909) 1.088 (.576)

.855 (.636) .942 (.507)

4.669∗∗∗ (.558) 2.810∗∗∗ (.280)

.977 (.183) 1.404∗ (.221)

.977 (.216) 1.488∗ (.258)

4.812∗∗∗ (.759) 2.724∗∗∗ (.268)

.906 (.233) 1.1017 (.148)

1.107 (.273) 1.117 (.194)

1.192 (.191) 1.962∗∗ (.380)

1.006 (.235) .865 (.239)

.845 (.237) .798 (.238)

1.393∗∗∗ (.118) 2.017∗∗∗ (.216)

.907 (.114) .911 (.135)

.697∗ (.107) .721∗ (.119)

1.202 (.135) 1.467∗∗ (.176)

.594∗∗ (.105) .661∗∗ (.112)

.560∗∗ (.121) .716 (.158)

.586 (.259) .606 (.264) .438∼ (.191) .243∗∗ (.109)

1.392 (.715) 1.233 (.634) .840 (.433) 1.550 (.863) .653 (.328)

1.286 (.706) .582 (.294) .449 (.227) 4.097 (4.210) .490 (.248)

.589∗∗ (.108) .402∗∗∗ (.075) .258∗∗∗ (.048) .166∗∗∗ (.034)

.805 (.165) .705 (.155) .707 (.157) .906 (.247) 1.116 (.237)

.963 (.212) .718 (.166) .648∼ (.170) 1.534 (.915) .699 (.163)

.640∗∗ (109) .461∗∗∗ (.080) .323∗∗∗ (.059) .139∗∗∗ (.030)

1.177 (.226) .916 (.194) .804 (.180) .984 (.323) 1.194 (.287)

.874 (.196) .752 (.181) .730 (.201) 1.594 (1.712) .618 (.177)

Year effects (ref: 2002) Pseudo R2 N

Germany

240

Netherlands

.163 5853

.147 13424


.156 14777


241

Is the breadwinner condition retreating? Controlling for the presence of children, marital status and education, the cohort effects clearly suggest that in each successive cohort a position outside the labor force has become less common for women and also that the relative risk of part-time compared to full-time work is lower in the youngest age cohorts. However, during the 1990s, cohort effects concerning the choice between (long) part-time and full-time working hours are absent or weaker in the Netherlands compared to the other two countries, reflecting the rapid diffusion of part-time work. Interacting our main variables with the results of earlier years, we may conclude that motherhood, especially when it involves young children, has become less important as a predictor of inactivity in the UK, but stronger in Germany. In the UK, but not in the other countries, caring for older children is also less associated with the choice of part-time hours than in the recent past. In Germany, especially, the opposite development is found. Finally, only in the Netherlands has marital status, or living as a couple, become a less strong predictor of inactivity during the 1990s, again with signs of an opposite trend in Germany. However, in the Netherlands this development seems to have involved a displacement of inactivity by small part-time jobs, possibly as a result of the new Benefit Claimant Act of 1995, which required women claiming benefits and mothers of young children to seek paid work on at least a part-time basis (Visser and Hemerijck, 1997).

7.5 Part-time work as a coping strategy and bridgehead out of inactivity Our next interest is in part-time employment as a way for women to forestall exit from the labor market and in part-time jobs as a stepping-stone towards full-time jobs. For this purpose, we proceed with a multinomial logit estimation based on selected samples of women in a particular labor market state and analyze the relative risk of their transition into other states within a two-year time span. For instance, when analyzing the risk of inactivity of women in full-time jobs, our dependent variable will be a four-point variable that takes the value 0 if the individual remains in a full-time job, 1 if she becomes inactive, 2 if she shifts into a small part-time job, 3 if she adjusts her hours into a long part-time job. Taking respondents who remain employed on a full-time basis as the omitted category, three sets of coefficients can be estimated. As in our earlier tables, we present them as relative risk ratios to make it easier to interpret the results.


In Table 7.4A, we compare the impact of motherhood and marital status upon the relative risks of a labor market exit for three different populations: women with full-time jobs and women working in large and small part-time jobs. As before, we keep education as a control variable and we control for birth cohort. However, to avoid problems with very small Ns in transition analysis, we take the 1945–54 cohort, aged between 38 (youngest) and 57 (oldest) between 1992 and 2002 and thus with a significant presence in the labor market, as our reference group. We are especially interested in the differences between that cohort, of women who have their marital and motherhood choices behind them, and the two younger birth cohorts. The main finding is that in each country motherhood strongly increases the risk of inactivity for full-time working women, no matter the age of the child. The risk of a labor market exit associated with motherhood is smaller for women working part-time. The risk of a change into inactivity still exists, especially in the case of young children, for women working in small and long part-time jobs, but has disappeared in the Netherlands. In other words, for Dutch women who work in part-time jobs, motherhood does not seem a significant determinant of discontinuing their labor market careers. In Germany and the UK, holding a small part-time job lowers the risk associated with the presence of children, of exiting the labor market, but only when children are older and of school age. Of further interest is that in each country, but most clearly in the Netherlands, being married, or living in couples, strongly increases the risk of a labor market exit for full-time working women, but not for women working part-time. In fact, being married or living together, while increasing the probability that full-time working women exit from the labor market, lowers the risk that women with small part-time jobs stop working. For women working in long-hours part-time jobs we find no effect either way. Lower levels of educational attainment strongly increase the risk of leaving the labor market and nearly the same pattern is found among women working in full-time or substantial part-time jobs. In Germany, an intermediate-level education appears to guarantee more continuity, whereas in the Netherlands and in the UK this is only the case among women working in small part-time jobs. Possibly, these are “every women’s jobs” in services, which require only a low level of qualification and no specific vocational training. Hence, educational attainment appears to matter little for these jobs. Finally, against our expectations, younger cohorts of women in Germany and the UK are more likely than their mothers to exit the labor market, no matter what their prior status

Table 7.4A

Determinants of labor market transitions into inactivity (multinomial logits for probabilities) Netherlands

From:

full-time N = 1769

UK small PT 584

long PT 1027

RRR & (SE) Marital status (ref: single) Married/cohabitating One or more children (ref: no children) 0–5 years old 6–15 years old Educational level (ref: high) Medium Low Cohort (ref: born 1945–1964) born 1965–1974 born 1975–1986

full-time 11909

Germany small PT 2955

long PT 351

RRR & (SE)

full-time 13126

small PT 1838

long PT 3098

RRR & (SE)

4.963∗∗∗ (1.376)

.299∗ (.170)

.889 (.349)

1.533∗∗∗ (.177)

.636∗ (.119)

.873 (.160)

2.005∗∗∗ (.174)

.506∗∗ (.114)

.752 (.130)

13.490∗∗∗ (6.280) 11.586∗∗∗ (4.361)

1.183 (.507) .862 (.343)

1.389 (.547) .990 (.426)

25.480∗∗∗ (2.655) 4.352∗∗∗ (.616)

2.057∗∗∗ (.405) .679 (.151)

3.623∗∗∗ (.638) .828 (.166)

50.715∗∗∗ (5.739) 2.641∗∗∗ (.237)

2.645∗∗∗ (.650) .491∗∗ (.114)

12.733∗∗∗ (2.979) .900 (.173)

2.368∗∗ (.628) 8.484∗∗∗ (2.408)

1.188 (.819) 2.926 (1.896)

4.976∗ (3.112) 15.169∗∗∗ (9.308)

1.975∗∗∗ (.197) 4.874∗∗∗ (.629)

1.010 (.161) 1.754∗∗ (.316)

1.640∗∗ (.269) 3.059∗∗∗ (.577)

1.247 (.167) 2.977∗∗∗ (.434)

1.337 (.380) 1.172 (.357)

1.277 (.309) 1.572 (.432)

.554∗∗ (.108) .241∗∗∗ (.241)

1.713 (.557) 2.646 (2.528)

1.497 (.415) .455 (.359)

1.232 (.139) 1.663∗∗∗ (.237)

1.354∗ (.211) .2.099∗∗ (.573)

1.683∗∗ (.271) 3.059∗∗∗ (.755)

1.625∗∗∗ (.160) 5.957∗∗∗ (.724)

1.963∗∗∗ (.354) 9.420∗∗∗ (3.012)

2.145∗∗∗ (.390) 37.614∗∗∗ (11.187) 243



of employment in terms of working hours. It is impossible to say whether this reflects choice (in combination with marriage and motherhood), or a lack of opportunity and fewer job security rights compared with the older generation. In the Netherlands, instead, we see – at least for the youngest birth cohort – a move in the opposite direction. They tend to have a much lower risk of a transition into inactivity compared to women born two decades before them, especially when they held full-time jobs or part-time jobs of substantial length. We think that indeed part of the explanation is historical and is related to the low levels of participation of the Dutch women in our reference category, not only much lower than later cohorts, but also much lower than British and German women of that generation (see De Graaf and Vermeulen, 1997). The preceding analysis naturally leads to the next question: to what extent is part-time employment a bridgehead out of inactivity? Again, we compare three types of movement: into full-time jobs, short and long part-time jobs (Table 7.4B). The first thing to note is that marital status and having children always diminishes the probability of moving out of inactivity into a full-time job. Further analysis, not shown in Table 7.4B, reveals that this is also true for women working part-time. This barrier to full-time jobs for married women with children is rather similar in all three countries and the fact that we still find signs of it in the 1990s is testimony of the strength of the breadwinner legacy. Our analysis also shows that it is very important to make a distinction between short- and long-hours part-time jobs. The latter behave more like full-time jobs, while the barriers between employment and inactivity, as well as the effects of marital status, children and education are rather slight in the case of small part-time jobs. The chances of changing from inactivity into small part-time jobs are even increasing for married or cohabitating women in the UK, compared to those who are single, and the same is true for women with older children, compared to childless women, in both Germany and the UK. Lower levels of education, however, tend to depress women’s chances of changing from inactivity into any job, no matter its size. The inhibiting effects of low education are strongest in the UK and weakest in the Netherlands. Finally, the youngest birth cohort has better chances of moving out of inactivity into full-time or substantial part-time jobs, except perhaps in Germany, a finding that probably reflects the depressed state of the German labor market during this period. Only in the youngest generation and only in Germany, is the probability of making a transition from inactivity to small part-time jobs significantly lower than the probability of remaining inactive. This may indicate the undesirability of these jobs,

Table 7.4B

Determinants of labor market transitions to job from inactive status (multinomial logits for probabilities)

To

Marital status (ref: single) Married/cohabitating One or more children (ref: no children) 0–5 years old 6–15 years old Educational level (ref: high) medium low Cohort (ref: born 1945–1964) born 1965–1974 born 1975–1986

Netherlands

UK

Germany

N = 1064

5804

7928

small PT long PT RRR & (SE)

Full-time


Full-time


Full-time

.640 (.148)

.277∗∗∗ (.086)

.099∗∗∗ (.030)

1.372∗∗ (.226)

.916 (.111)

.570∗∗∗ (.069)

1.025 (.133)

.689∗∗ (.101)

.328∗∗∗ (.038)

.783 (.240)

.153∗∗ (.090)

.076∗∗ (.081)

.943 (.133)

.449∗∗∗ (.062)

.086∗∗∗ (.012)

1.142 (.161)

.905 (.141)

.249∗∗∗ (.031)

1.369 (.328)

.494∗ (.179)

.332∗∗ (.174)

1.480∗∗ (.227)

.702∗ (.114)

.316∗∗∗ (.044)

1.421∗ (.202)

1.129 (.183)

.564∗∗∗ (.063)

.897 (.370) .979 (.386)

.634 (.251) .393∗ (.163)

.314∗∗ (.123) .106∗∗∗ (.047)

.760∗ (.091) .422∗∗∗ (.055)

.681∗∗ (.092) .280∗∗∗ (.044)

.537∗∗∗ (.072) .154∗∗∗ (.026)

.956 (.165) .591∗∗ (.108)

.686∗ (.120) .381∗∗∗ (.066)

.593∗∗∗ (.088) .382∗∗∗ (.060)

.951 (.218) 1.823 (1.665)

1.668∗ (.422) 11.753∗∗∗ (.7336)

3.882∗∗∗ (1.176) 37.020∗∗∗ (25.141)

1.270∗ (.133) 1.098 (.196)

1.203 (.154) 1.787∗∗ (.331)

1.650∗∗∗ (.223) 3.329∗∗∗ (.590)

1.141 (.127) .475∗∗∗ (.080)

.998 (.127) (.381)∗∗∗ (.076)

1.421∗∗ (.167) 1.313 (.181) 245

∼p < .10, ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001. Sources: British Household Panel Study, German Socio-Economic Panel, OSA Labour Supply Panel 1992–2002.


though it is more plausible to hypothesize that, compared to British and Dutch women, German women faced fewer pressures to exit inactivity.

7.6 The preferences of employed women To what extent do women realize their preferences with regard to paid employment? In the previous sections we saw that the choices of women are still strongly related to motherhood and marital status. We may indeed be struck by the similarity between the three countries and the continued strength of the breadwinner legacy. Comparison of these rather similar countries also allowed us to make some fine distinctions – showing how part-time work in the Netherlands, far from offering women an assured path to careers and economic independence, nonetheless may help combine different demands on women’s time and reduce the risk of inactivity. It would seem plausible to see this as a result of a choice model based on increased opportunities for both employees and employers to choose individualized (part-time) working hours, with which the Netherlands experimented during the 1990s before it became diffused to the other two countries. Since 2000, as part of the European Employment Strategy (EES) and as an effect of the Part-Time Directive of the European Union, discrimination on the basis of working hours has been outlawed and the right of parents to switch from full-time to part-time hours has been established as an optional right in all three countries. Before the statutory changes of 2000, such choice rights did exist as part of collective agreements in the Netherlands and perhaps on an individual basis in all three countries. It was, of course, always possible to change working hours by changing employer, but making this a choice within continuing employment relationships lowered the costs of such choices. Finally, in order to improve the status of part-time employment and enhance the quality of work, the EES also promotes the conditional right of women and parents to increase working hours, or switch back from part-time to full-time work, business conditions permitting. The Dutch law of 2000 contains a conditional provision to this effect (Visser et al., 2004). The other two countries have moved in the same direction, but on a more limited base and they did not have the long period of experimentation and learning of the Netherlands. In the previous section we saw that transitions from part-time to fulltime employment – and vice versa – are strongly associated with and conditioned by motherhood, marital status and educational attainment. Our next step is to try and relate these transitions to working preferences


247

Table 7.5 Preferred and actual working hours of women with children under six, living in a couple, 1998 Full-time job

Part-time job

No job

Preferred Actual Gap Preferred Actual Gap Preferred Actual Gap Finland Sweden

80 67

49 51

31 15

9 22

6 13

2 9

11 11

44 36

−33 −25

Austria Belgium France Germany Netherlands

36 55 52 32 6

19 46 39 16 5

17 9 14 16 1

40 29 22 43 70

28 19 14 23 55

12 10 8 20 15

24 16 26 25 24

53 35 47 61 40

−28 −18 −21 −36 −16

Italy Spain Portugal Greece

50 60 84 66

35 26 75 42

16 34 10 23

28 12 8 11

12 6 5 8

16 6 3 3

22 29 8 24

53 68 21 50

−31 −39 −13 −26

UK Ireland

21 31

25 31

−4 0

42 42

32 19

10 24

37 27

43 50

−6 −24

Source: Calculated from Employment Options for the Future survey of the European Foundation for the Improvement of Living and Working Conditions.

research conducted by the European Foundation for the Improvement of Living and Working Conditions. It compares the preferred and actual working hours of women who have one or more child(ren) under the age of six and live in a couple (married or cohabitating), i.e. the subcategory that is the focus of our investigation throughout this chapter. The survey reveals, firstly, that on average some 20–25 percent of these women in Europe prefer not to work or seek paid employment, with a variation that runs from 11 percent in Scandinavia to 29 percent in Spain and 39 percent in the UK. One might interpret this as an indication of the “burden” of motherhood, in Spain worsened by the lack of public child care and in the UK by the tendency of men to work long hours and the absence of state and employer support. Note that these figures refer to 1998, before New Labour’s moderate attempts at reconciling the demands of work and family, discussed in section 7.2. Secondly, we note that there is a huge gap between actual and preferred working hours among women with young children and living with a partner: only a third to one-half of the women who do not have a job actually prefer not to have one. They are jobless either because of

248 Method and Substance in Macrocomparative Analysis Table 7.6 Preferences for more or less working hours and chances to realize preferences, 1992–2002 Preferences (at t-2)

Less hours Same hours More hours

Change in desired direction (at t)

Netherlands %

UK

Germany

Netherlands odds ratios∗

UK

Germany

20.2 66.4 13.4

30.8 61.1 8.2

44.7 30.2 25.1

2.2 – 4.2

1.6 – 2.8

1.2 – 2.5

∗ compared to those preferring the same hours. Source: British Household Panel Study, German Socio-Economic Panel, OSA Labour Supply Panel 1992–2002.

failing labor demand or because available jobs do not match their time preferences (that is, the number of hours of work and the specific time of work) and other (unobserved) qualities of female labor supply. In 1998, most European countries suffered from high unemployment and female unemployment rates were generally much higher than male unemployment rates, the UK being an exception. Unsurprisingly, the “no job” syndrome among these mothers is strongest in Germany, next to Spain, whereas it is lowest in Britain and also quite low in the Netherlands, probably thanks to strong female job growth in these years (Visser and Hemerijck, 1997). Thirdly, there is a considerable shortage of jobs, but also a large mismatch in the working hours that these women want to work and the working hours they actually work when they do have a paid job. The cross-national variation in the preferences for part-time or full-time hours is striking, with the Netherlands leading the pack in favor of parttime hours, at some distance followed by the UK and Germany, Ireland and Austria – all with “breadwinner” traditions (Daly, 2000). The fact that, according to this survey, only 6 percent of Dutch mothers with young children and living with a partner wanted to work full-time and only 5 percent actually work full-time, is quite remarkable. In Britain, second to the Netherlands in this regard, a quarter of these mothers worked full-time, though fewer said that they preferred to work full-time. In the other countries, including Germany, many more mothers would have liked to work full-time than was actually the case. This would suggest that for some of them, part-time work is involuntary.24 Part-time jobs, too, are in short supply, and again, especially in Germany, according to


249

the Federal Agency of Employment (IAB), there were many more women who would have preferred to work part-time in the 1990s had these jobs been available (IAB, 1999). In order to sort out whether preferences for more or less hours lead to adjustments in the desired direction, we now return to the three panel data sets used in this study. Women were asked to consider their current weekly working hours and whether they would prefer a workweek of a different length. Unfortunately, the BHPS questions allow only a categorical answer of more, fewer or the same hours, rather than indicating the actual number of hours per week that respondents want to work. This limits our possibilities and we have therefore used the simple categorization in the British study for the other two countries as well. To begin with, Dutch and British women tend to be much more content with their existing working hours than German women. Only 33.6 percent of Dutch and 39.0 percent of British women want to change their working hours, whereas 69.0 percent of German women express a wish to do so. The direction of change is towards fewer hours in all three countries, though especially in Germany there is also a large group of women, one-quarter of the total, which would prefer to work longer hours. Unsurprisingly, in each country the preference for fewer hours is strongest among women working full-time and the preference for more hours strongest among women working in short-hours part-time jobs. However, it is remarkable that three-quarters of the Dutch and British, but only one-quarter of the German women working in jobs of less than 20 hours per week do not want a change in hours. It is also interesting that only 27.2 percent of full-time working women in Germany express satisfaction with existing working hours, compared to nearly 68.8 percent in the Netherlands and 52.1 percent in the UK, and that as many as 25.7 percent of these German women want to work longer, a proportion that drops to 0.6 percent in the Netherlands and 1.6 percent in the UK. This might be an indication of economic hardship (perhaps related to conditions in Eastern Germany) or reflect dissatisfaction with working hours reduction for full-time workers set by collective agreement as part of the campaign for the 35 hours workweek of German trade unions. Reflecting the opposite conditions – long working hours and few legal or contractual limits – British women, especially when working full-time, but also those working in large part-time jobs, tend to want fewer hours. Our panel data allow us to check whether or not women have realized a change in working hours in the desired direction two years later. We can express this in terms of either conditional probabilities or odds ratios, comparing the changes in the working hours of women who expressed


a desire to make that change with women who did not want a change. This leads to two conclusions. First, it appears that women who want to work more hours are more effective in realizing their preferences than women who want to work less. The odds ratios are nearly twice as high. It needs to be added that women who want to work more hours are much less numerous, especially in the UK and the Netherlands, than women who want fewer hours. Moreover, women who want to change to fewer hours because of motherhood more often leave employment. If we control for motherhood, marital status, education and age, and apply a pooled regression over all waves of the panel, with fixed effects, we find that a preference for more hours leads to a small, but significant increase in working hours in all three countries, but that the preference for less hours has no significant effect, or, in the case of Germany, even an opposite effect. This is largely explained by the fact that a large number of mothers wanting to work fewer hours (temporarily) leave employment. Secondly, both women who want to work more and women who want to work less hours stand a better chance of realizing their wishes in the Netherlands than in Germany, with the UK somewhere in between. This may simply reflect economic opportunity – there was much more slack in the labor market for women in Germany than in the other two countries – but it would seem plausible to argue that institutional differences explain the differences between the Netherlands and the UK. We finish this chapter with a brief analysis of the relative risk ratios of women preferring more or less hours for two groups of women, those working full-time and those in small part-time jobs, relative to women in part-time jobs of considerable length (Table 7.7). Our assumption is that the latter group (our omitted category) has best realized its preference and that our questions of interest – how are preferences influenced by motherhood and marital status, controlling for education – concern the other two groups.25 It appears that, especially in Germany, marriage or living as a couple reduces the probability to want more working hours. We also note that in the Netherlands women with young children and in Britain women with the lowest level of educational attainment prefer to work more hours, all other things held constant. With regard to the wish to work fewer hours, this is most clearly expressed by women working full-time, compared to those in part-time jobs of substantial length, and only in the case of Germany is this very clearly related to motherhood. This suggests that in the Netherlands and the UK women with young children have already, to a much greater extent, realized their preference for part-time work, though our analysis suggests that many, especially

Table 7.7

Determinants of women’s preferences for more or less working hours (multinomial logits for probabilities) More hours

Current working hours (ref: long PT) Small part-time Full-time Marital status (ref: single) Married/cohabitating Children in household (ref: no children) 0–5 years old 6–15 years old Educational level (ref: high) Medium Low

Fewer hours

Germany

UK

Netherlands

Germany

UK

Netherlands

2.094∗∗∗ (.239) .809∗ (.069)

1.670∗∗ (.262) .727∗ (.109)

1.797∗∗ (.346) .964 (.174)

1.220 (.176) 2.954∗∗∗ (.269)

.544∗∗∗ (.062) 2.012∗∗∗ (.156)

.470∗∗ (.105) 1.365∗ (.184)

.646∗∗∗

.641∗

.779

1.033

.958

.796

(.068) .814 (.152) .793∗ (.088)

(.117) .832 (.214) .865 (.183)

(.236) 1.875∗ (.700) 1.675 (.549)

(.107) .437∗∗∗ (.098) .764∗ (.088)

(.087) 1.226 (.141) .863 (.090)

(.169) .911 (.301) .641 (.222)

.856 (.110) .840 (.117)

.783 (.142) 1.515∗ (.304)

.830 (.234) 1.457 (.420)

.736∗ (.093) .634 (.088)

.701∗∗∗ (.059) .720∗∗ (.080)

.550∗∗ (.105) .472∗∗ (.105)


251


those with the lowest levels of education, regret to hold steadily onto only a small part-time job. Marital status does not seem to matter, but a lower level of education, which, as we saw before, is strongly associated with working in small part-time jobs, does decrease the preference for shorter hours, especially in the Netherlands and the UK. Combined with the finding that in both countries women in short-hours part-time jobs, compared to those in longer part-time jobs, clearly want no further reduction of their working hours, this is an indicator of dissatisfaction, perhaps economic hardship, lack of independence or full realization of one’s possibilities.

7.7 Conclusions and further work We began our analysis by considering women’s labor market participation patterns in a context of individual constraints. Given the variation in institutional contexts and developments in employment prior to 1992 described in section 7.1, we expected cross-country variation in this small-N analysis to be evident. Data for the Netherlands, Germany, and the UK demonstrate various points of convergence as well as divergence. Our main variables of interest are motherhood and marital status, as indicators of the breadwinner legacy in these countries. They show in each country a clear negative effect on women’s labor market participation patterns, in terms of both the chances of holding a job and the length of that job. Motherhood also offers an example of divergence, where German mothers are most strongly affected by having a young child under the age of three in the household and the number of children matters more in the other countries (recent legal changes may be expected to move Germany to more convergence). Marital status effects, irrespective of the presence of children in the household, are more in evidence in the Netherlands and Germany than in the UK. At the same time, a lack of (continued and tertiary) education leads to a decreased probability of employment – and of full-time employment in particular – in all three countries, and this effect grew stronger across time, most strikingly in the UK. Finally, cohort effects are present in each country, showing a decreased probability of inactivity for younger cohorts, especially in the Netherlands. Finally, the difference between short- and long-hours part-time jobs is most clearly visible when analyzing the transition of women out of a prior state of inactivity. While long part-time jobs seem to present the same barriers to women with children than full-time jobs, short-hours part-time jobs seem to present fewer obstacles. This does not,


253

however, imply that women do not try to avoid these mini-jobs if they can (Germany) or will be satisfied with these jobs as they are (UK, the Netherlands). The effect of the conditions of motherhood and marital status changes little after taking into account individual desires in the form of preferences. After controlling for individual preferences, the conditions of motherhood, marital status and education are the strongest determining factors of women’s employment patterns. Holding the other conditions constant, we find a clear trend that a majority of mothers want to reduce their weekly working hours across time. Our analysis of the transitions and preferences, the issue of the two final sections of this chapter, suggests that Dutch women are more able or likely to realize their preferences than women in the other two countries, with German women in a less enviable position than those in the UK. But Dutch and British women who have ended up in short-hours part-time jobs do want more, even or rather especially when they have little education and (we may speculate with confidence) low wages. In conclusion, women are heterogeneous according to conditions of motherhood, marital status, and education. Their preferences tend to be tied, if not determined, by these differences, but that does not mean that they do not matter. The comparison between these three rather similar countries tells us that women’s preferences stand a better chance of being realized when labor market conditions are not too constraining and labor market institutions are facilitating – a situation that, during the 1990s, was closer to being realized in the Netherlands than in Germany, with the UK in between. Still, many women find themselves in conditions that are far from what they would desire ideally.

Notes 1. The high incidence of part-time employment among men reported in some countries (the Netherlands, Australia, Denmark, to mention the three with the highest incidence in 2006 according to the OECD) is mostly due to parttime working high school and university students. We must therefore be cautious to interpret these figures as indicating a shift in the traditional gender division of household tasks and paid employment in families with young children. 2. The extent to which low employment rates in Greece, Spain and (Southern) Italy reflect the existence of a large informal labor market and family-based activities is an issue we cannot deal with here.

254 Method and Substance in Macrocomparative Analysis 3. Norway, the third Scandinavian welfare state, suggests a different pattern, closer to that of mainland Western Europe (Ellingsæter, 1992). 4. In this study we use 35 hours per week as the cut-off point between parttime work (1–34 hours per week) and full-time work (35 and more). This choice is arbitrary but common, since 35 or 36 hours is the lowest number of weekly hours set for full-time workers by collective agreement or law in Europe (EIROnline, Working Hours in Europe, 2003). The OECD proposes 30 hours as the cut-off point on the ground that shift workers sometimes work 32–4 hours a week and might erroneously be counted as part-timers (Lemaitre et al., 1997), but this nearly exclusively affects male employment and is therefore not relevant for this study, which focuses on female employment patterns. 5. While the importance of welfare state interventions for female employment is generally acknowledged, the role of industrial relations is less commonly recognized. For early studies which did stress the importance of both sets of institutions: see Beechy and Perkins, 1987; Ellingsæter, 1992). 6. In the former German Democratic Republic, both men and women were expected, and needed, to be in paid employment, but reunification after 1989 meant that West German institutions were applied in the East, and working patterns of women have converged since, albeit with some significant differences (Rosenfeld, Trappe and Gornick, 2004). 7. The data have been made available to us by the Institute for Social and Economic Research in Colchester (BHPS); the German Institute for Economic Research (DIW) in Berlin (GSOEP); and the Organisation for Strategic Labour Market Research at the University of Tilburg (OSA-LSP). 8. This has forced us to calculate transition rates on a two-year basis for Germany and also the UK. 9. Since we are interested in analyzing choice, we do not consider “unemployment” as one of the possible labor market “states” women “choose” to be in. 10. The 1990 tax reform reduced the basic tax allowance for breadwinners and people with care responsibilities, and integrated social security contributions, thus lowering disincentives for second earners to take up more hours. The 2001 tax reform removed the remaining shared taxation components for wage earners (though not for all benefits). 11. The Danish case reminds us that part-time work may also be a lifestyle choice, unconnected to motherhood. 12. Meanwhile, the government has introduced legislation making employers’ contributions obligatory. In Germany, too, recent legislation (2006) has moved the system closer to the Scandinavian model. 13. Foundation of Labour (Stichting van de Arbeid), Overwegingen en aanbevelingen ter beoordeling van deeltijdarbeid en differentiatie van arbeidsduurpatronen, The Hague, 1993. 14. This Directive implemented a Framework Agreement between the European unions and employers and had to be transposed in national law by 2000 (Sciarra et al., 2005). 15. The first attempt to introduce European-wide legislation in the European Union providing income protection for part-time workers goes back to 1982, proposing a rather low but common threshold, but defending the notion


16.

17.

18.

19. 20. 21. 22. 23.

24.

25.

255

that part-time workers should in principle not be excluded from occupational social insurance schemes. Acceptance of the 1982 Voluntary Part-time Directive was, at the time, subject to unanimity among EU Member States. The Directive was in effect vetoed by the British government (Rubery and Tarling, 1988; Hakim, 1989). According to the European Labour Force Survey of 2001 (Eurostat, Luxembourg), about one-third of all men in the UK and close to 10 percent of all women worked 48 hours (the EU maximum) or more; in Germany the percentages drop to 15 and 5 percent, in the Netherlands to below 10 and 5 percent. We have also experimented with a further distinction between the very young (0–2 years) and young (3–5 years old), and with the number of children, distinguishing between 1, 2 and 3 or more child(ren) in the household. The results do not differ from those shown here and are available from the authors upon request. Our three countries are remarkably similar in this respect. According to the European Labour Force Survey, in 2002 the employment rate of women aged 25–54 was 71.6 percent in Germany and 73.6 percent in both the UK and the Netherlands, dropping to just around 30 percent in the 55–64 age group in all three countries. Note, however, that only women under the “working age” of 65 are included in our sample. In 1995, the average age at which women have their first child had increased to 29 years in all three countries. We recall that students have been excluded from our sample. The Harz IV reforms in Germany might change all that, but these reforms only started to be felt in 2004 and beyond. Further analysis shows that labor market choices of German mothers are, as expected, particularly affected by having a young child under the age of three, greatly increasing the risk of inactivity, whereas in the other two countries the number of children in the household matters most (available from authors). The European Labor Force Survey of 2005 does include a question about the “reasons why you work part-time” with pre-coded response categories that are not really mutually exclusive: “care,” “education,” “disability,” “could not find full-time job,” “did not want full-time job,” and “other.” Non-response on this item is very high (around 50 percent). German women working parttime respond three times as often “could not find full-time job” than British women and about four times as often as Dutch women. Oddly, less than 3 percent of Dutch women working part-time mention “care” as a reason for working part-time, against a quarter of German and a fifth of British women. Half of Dutch (compared with 6.4 percent of German and 7.2 percent of British) women working part-time mark the answer “did not want full-time job” and this is perhaps how they see it. In this case, controlling for birth cohorts does not reveal any extra information, except that in younger cohorts both the wish to work more and the wish to work less is more pronounced. The explanation might be that older women have had more time to match their working time preferences to jobs of different length, with many of them disappearing from the labor market. This analysis is available from the authors.


References Beechy, V. and T. Perkins. 1987. A Matter of Hours: Women, Part-time Work, and the Labour Market. Cambridge and Oxford: Polity Press and Basil Blackwell. Berg, P., E. Appelbaum, T. Bailey, and A.L. Kalleberg. 2003. “Contesting Time: International Comparisons of Employee Control of Working Time.” Industrial and Labor Relations Review 56: 331–49. Bernardi, F., M. Gangl, and H.G. van de Werfhorst. 2004. “The From School-toWork Dynamics: Timing of Work and Quality of Work in Italy, the Netherlands, and the United States, 1980–1998.” Working Paper 2004/201. Madrid: Instituto Juan March de Estudios e Investigaciones. Bielenski, H., G. Bosch, and A. Wagner. 2002. “Working Time Preferences in Sixteen European Countries.” Working Paper. Dublin: European Foundation for the Improvement of Living and Working Conditions. Blossfeld, H.-P. and C. Hakim. 1997. “Introduction: A Comparative Perspective on Part-Time Work.” Pp. 1–21 in Between Equalization and Marginalization: Women Working Part-Time in Europe and the United States of America, edited by H. P. Blossfeld and C. Hakim. Oxford: Oxford University Press. Blossfeld, H.-P. and G. Rohwer. 1997. “Part-Time Work in West-Germany.” Pp. 164–90 in Between Equalization and Marginalization: Women Working Part-Time in Europe and the United States of America, edited by H. P. Blossfeld and C. Hakim. Oxford: Oxford University Press. Bollé, P. 2001. “Part-time Work: Solution or Trap?” Pp. 215–38 in Women, Gender, and Work: What Is Equality and How Do We Get There?, edited by M.F. Loutfi. Geneva: International Labour Office. Brannen, J. and P. Moss. 1990. Managing Mothers: Dual Earner Households after Maternity Leave. London: Unwin Hyman. Buddelmeyer, H., G. Mourre, and M. Ward. 2004. “The Determinants of Parttime Work in EU Countries: Empirical Investigations with Macro-Panel Data.” Economic Papers, no. 213. Brussels: European Commission, Directorate-General for Economic and Financial Affairs. Calmfors, L., A. Booth, M. Burda, D. Checci, R. Naylor, and J. Visser. 2001. “The Future of Collective Bargaining in Europe.” Pp. 1–156 in The Role of Unions in the Twenty-First Century, edited by T. Boeri, A. Brugiavini, and L. Calmfors. Oxford: Oxford University Press. Daly, M. 2000. “A Fine Balance: Women’s Labour Market Participation in International Comparison.” Pp. 467–511 in Welfare and Work in the Open Economy. Volume II: Diverse Responses to Common Challenges, edited by F.W. Scharpf and V.A. Smith. Oxford: Oxford University Press. Dickens, L. and M. Hall. 2005. “The Impact of Employment Legislation: Reviewing the research.” London: Department of Trade and Industry, Employment Relations Research Series, no. 45: 7–72. Dingeldey, I. 2001. “European Tax Systems and their Impact on Family Employment Patterns.” Journal of Social Policy 30: 653–72. European Commission (EC). 2004. Industrial Relations in Europe in 2004. Directorate General of Social Affairs and Employment. Luxembourg: Office of Official Publications of the European Community.


257

Ellingsaeter, A. 1992. Part-time Work in European Welfare States: Denmark, Germany, Norway and the United Kingdom Compared. Oslo: Institute for Social Research. Esping-Andersen, G. 1990. The Three Worlds of Welfare Capitalism. Cambridge: Polity Press. Fagan, C. 2004. “Gender and Working Time in Industrialized Countries.” In Working Time and Workers’ Preferences in Industrialized Countries: Finding the Balance, edited by J. Messenger. London: Routledge. Fagan, C. 2000. Employment Options of the Future: Actual and Preferred Working Hours. National Working Paper for the United Kingdom. Dublin: European Foundation for the Improvement of Living and Working Conditions. Gustafson, S., E. Kenjoh, and C. Wetzels. 2001. “Does Part-Time and Intermittent Work during Early Motherhood Lead to Regular Work Later? A Comparison of Labor Market Behavior of Mothers with Young Children in Germany, Britain, the Netherlands, and Sweden.” Vierteljahrshefte zur Wirtschaftsforschung 70(1): 15–23. Hakim, C. 1997. “A Sociological Perspective on Part-Time Work.” Pp. 22–70 in Between Equalization and Marginalization: Women Working Part-Time in Europe and the United States of America, edited by H.-P. Blossfeld and C. Hakim. Oxford: Oxford University Press. Hall, P. and D. Soskice, eds. 2001. Varieties of Capitalism. New York: Oxford University Press. Halpin, B. 2004.Introduction to the British Household Panel Study. Course Notes. University of Essex, Colchester, England. Hartog, J. and J. Theeuwes. 1985. “The Emergence of the Working Wife in Holland.” Journal of Labor Economics 3: 235–56. Hassel, A. 2006. “What Does Business Want? Labour Market Reforms in CMEs and Its Problems.” Pp. 223–52 in Beyond Varieties of Capitalism, edited by B. Hancké, M. Rhodes, and M. Thatcher. Oxford: Oxford University Press. IAB (Institut für Arbeitsmarkt- und Berufsforschung). 1999. “Teilzeitarbeit.” Mitteilungen aus der Arbeitsmarkt- und Berufsforschung 1: 5–30. Kerckhoff, A.C., E.D. Ezell, and J.S. Brown. 2002. “Toward an Improved Measure of Educational Attainment in Social Stratification Research.” Social Science Research 31: 99–123. Kersley, B., C. Alpin, J. Forth, A. Bryson, H. Bewley, G. Dix, and S. Oxenbridge. 2006. Inside the Workplace: Findings from the 2004 Workplace Employment Relations Survey. London: Routledge. Killmann, C. and M. Klein. 1997. “Part-Time Work in Germany: Gender-Specific Structures of Working Hours.” Pp. 81–94 in Part-time Work in Europe: Gender, Jobs, and Opportunities, edited by M. Klein. Frankfurt/Main: Campus Verlag. Klein, M., ed. 1997. Part-Time Work in Europe: Gender, Jobs, and Opportunities. Frankfurt/Main: Campus Verlag. Knijn, T. 1994. “Social Dilemmas in Images of Motherhood in the Netherlands.” European Journal of Women’s Studies 1: 183–206. Lemaitre, G., P. Marianna, and A. Van Bastelaer. 1997. “International Comparisons of Part-Time Work.” OECD Economic Studies 29: 139–52. Lewis, J. 1992. “Gender and the Development of Welfare Regimes.” Journal of European Social Policy 2: 159–73.

258 Method and Substance in Macrocomparative Analysis Lippe, T. van der and L. van Dijk, eds. 2001. Women’s Employment in Comparative Perspective. New York: Aldine de Gruyter. Milward, N., A. Bryson, and J. Forth. 2000. All Change at Work: British Employment Relations 1980–1998, as Portrayed by the Workplace Industrial Relations Survey Series. London: Routledge. Müller, W. 2005. Casmin Education Classification. www.nuff.ox.ac.uk/Users/Yaish/ NPSM/Casmin%20Education.htm. Accessed 10 February 2005, Oxford, Internet. O’Reilly, J. and C. Fagan, eds. 1998. Part-Time Prospects: An International Comparison of Part-Time Work in Europe, North America, and the Pacific Rim. London: Routledge. Orloff, A. S. 1993. “Gender and the Social Rights of Citizenship: The Comparative Analysis of Gender Relations and Welfare States.” American Sociological Review 58: 303–28. Pfau-Effinger, B. 1998. “Culture or Structure as Explanations for Differences in Part-Time Work in Germany, Finland, and the Netherlands?” Pp. 177–98 in Part-Time Prospects: An International Comparison of Part-Time Work in Europe, North America, and the Pacific Rim, edited by J. O’Reilly and C. Fagan. London: Routledge. Petit, B. and J. Hook. 2005. “The Structure of Women’s Employment in Comparative Perspective.” Social Forces 84: 779–800. Plantenga, J. 2002. “Combining Work and Care in the Polder Model: An Assessment of the Dutch Part-Time Strategy.” Critical Social Policy 22: 53–71. Plantenga, J. 2007. “Arbeid en zorg in een participatiemaatschappij.” Beleid en Maatschappij 34: 47–56. Rasmussen, E., J. Lind, and J. Visser. 2005. “Divergence in Part-Time Work in New Zealand, the Netherlands, and Denmark.” British Journal of Industrial Relations 42: 637–58. Rogowski, R. and A.C. Wilthagen. 2002. “Legal Regulation of Transitional Labour Markets.” Pp. 233–73 in The Dynamics of Full Employment: Social Integration through Transitional Labour Markets, edited by B. Gazier and G. Schmid. Cheltenham: Edward Elgar. Rubery, J., M. Smith, C. Fagan, and D. Grimshaw. 1998. Women and European Employment. London: Routledge. Schmid, G. and B. Gazier, eds. 2002. The Dynamics of Full Employment: Social Integration through Transitional Labour Markets. Cheltenham: Edward Elgar. Sciarra, S., P. Davies, and M. Freedland, eds. 2005. Employment Policy and the Regulation of Part-time Work in the European Union: A Comparative Analysis. Cambridge: Cambridge University Press. Taskforce on Employment. 2003. Jobs, Jobs, Jobs – Creating More Employment in Europe. Report of the Employment Taskforce, chaired by Wim Kok. Brussels. Tijdens, K. 2000. Employment Options of the Future: Actual and Preferred Working Hours. National Working Paper for the Netherlands. Dublin: European Foundation for the Improvement of Living and Working Conditions. Visser, J. 2002. “The First Part-time Economy in the World: A Model To Be Followed?” European Journal of Social Policy 12: 23–42. Visser J. and A.C. Hemerijck. 1997. A Dutch Miracle: Job Growth, Welfare Reform, and Corporatism in the Netherlands. Amsterdam: Amsterdam University Press.


259

Visser, J., A.C. Wilthagen, R. Beltzer, and E. Koot-van der Putte. 2004. “The Netherlands: From Atypicality to Typicality.” Pp. 190–222 in Employment Policy and the Regulation of Part-Time Work in the European Union: A Comparative Analysis, edited by S. Sciarra, P. Davies, and M. Freedland. Cambridge: Cambridge University Press. Yerkes, M., and J. Visser. 2006. “Women’s Preferences or Delineated Policies: The Development of Part-Time Work in the Netherlands, Germany, and the UK.” Pp. 235–61 in Decent Working Time: New Trends, New Issues, edited by J.Y. Boulin, M. Lallement, J. Messenger, and F. Michon. Geneva: International Labour Office. Yerkes, M. 2006. What Women Want: Individual Preferences, Heterogeneous Patterns? Women’s Labour Market Participation Patterns in Comparative Perspective. PhD thesis. Amsterdam School for Social Science Research/AIAS. University of Amsterdam.

8 Comparative Regime Analysis: Early Exit from Work in Europe, Japan, and the USA Bernhard Ebbinghaus

Introduction: early exit from work One of the fundamental policy problems of contemporary welfare states, particularly in Continental Europe, has been the extensive use of early retirement as a labor-shedding and passive labor market policy since the mid-1970s. Early exit from work – that is, before the age of 65 – has not only played a major role in lowering the overall employment rate for the working-age population, thereby reducing the social contribution and income tax base for welfare state financing; it has also pushed up the levels of social expenditure through the increased take-up of various social transfer programs. The labor-shedding problem has been criticized by many scholars as a fundamental part of the “Continental dilemma” (Scharpf, 2001) and the “welfare state without work” syndrome (Esping-Andersen, 1996). Using a comparative regime perspective, I will analyze the specific institutional configurations of these political economies that can explain the general rise in early exit from work, but also its cross-national variations. In recent years, there have been some policy reversals, such as the European Union’s commitment to increase the activity rate of older workers, yet in order to evaluate these reforms we need to understand the initial causes of and particular patterns of early exit from work. Early retirement commonly has two meanings: withdrawal from employment prior to age 65 and the drawing of pre-retirement benefits until a statutory pension becomes available. It is thus a social practice that is related to and entails consequences for both social security programs and the labor market. During the first three postwar decades, retirement at statutory pension age (in most countries at age 65, albeit 260

Bernhard Ebbinghaus

261

with some significant exceptions) became a social institution of modern welfare states that regulated the later part of the life course (Atchley, 1982). Since the 1970s, early withdrawal from work before age 65 has become increasingly widespread, leading to a destandardization of the retirement age (Kohli and Rein, 1991): Increasing numbers of older people have expected to leave work early, while the age at exit is becoming less predictable. Although the decision to retire early depends upon the individual circumstances of older workers, the strategies of firms and other co-fellow workers, these micro-level decisions occur in the context of particular institutional environments. The welfare state regime, the production system and labor relations all play important roles in explaining the specific trajectory and cross-national differences in the rise and reversal of early exit from work. Early exit from work has been studied from two often-competing perspectives: protection-oriented analyses of pull factors that impact labor supply or production-oriented studies of push factors that affect labor demand (Kohli and Rein, 1991). Therefore we expect that both crossnational regime differences in welfare states and also varieties of capitalist systems should have an impact in shaping pull and push factors affecting the early exit from work. Arguments about the role of social partners in both perspectives remain implicit, although I will argue that employers and workers’ organizations play a crucial role in mediating between welfare incentives (pull) and economic contingencies (push). The social partners are involved in the arena of social policy and collective bargaining, and management–labor relations shape the ways in which firm-level actors abstain from or utilize early exit under the given constraints and opportunities. Informed by these three complementary perspectives of protection-related pull, production-related push, and partnershiprelated mediation, I adopt here an encompassing regime approach that combines insights from comparative welfare states research, political economy and labor relations analysis. In addition to providing a cross-national analysis of a widespread trend and specific practice with major repercussions for welfare states and employment systems, this contribution highlights two particular methodological approaches. First, I will present a comparative analysis that highlights differences between as well as within regimes, discussing three typologies of welfare states, production systems, and labor relations that have attracted much debate in comparative political economy. Early exit from work is a prime case to analyze the potential impact of macro-institutional contexts on micro-level social processes as well as the interaction and even “institutional complementarities” (Hall


and Soskice, 2001) between different societal subsystems. The second contribution to the dialogue between methods and substance of this volume is the adoption of a dynamic perspective and the development of process-oriented indicators for macrocomparative analysis. Crossnational analyses of employment indicators, such as the level of activity among older workers, are often static aggregate comparisons of contemporary levels without taking into account the long-term developmental trajectory and the often cohort-specific nature of social change. I will first describe the comparative regime approach applied here, in particular the case-oriented analysis of institutional configurations and the selection of a medium number of ten cases, carefully selected from different regime configurations. In the next section, I will describe the main decline in employment rates for men and women aged 55 to 64. The following section turns to cohort-adjusted early exit rates that allow a better analysis of early retirement trends over time and across countries. The next section summarizes the main variations in early exit regimes. The protection-related pull thesis – that is, the crossnational variations in welfare state regimes and the different multiple pathways – will be then discussed. The subsequent section then turns to the production-related push thesis and the role of social partners, discussing the different economic governance modes and their impact on labor-shedding strategy.

Comparative regime analysis Adopting a variation-finding comparative design (Tilly, 1984), I seek to explain cross-national variations in early exit patterns with particular regime constellations. The leading research question is thus: Under which production, protection, and partnership regimes do we find early exit from work to be most common? The inter-regime comparison, using the most-dissimilar-country design (Przeworski and Teune, 1970), helps to single out the impact of specific institutional configurations on the development of particular early exit trajectories. While the inter-regime comparison helps to account for the path-dependent trajectories of early exit from work, it cannot unravel path departures through policy and institutional change. Embracing an additional most-similar-country design (Dogan and Pelassy, 1990), the intra-regime comparison enables analysis of more subtle variations from the dominant model as well as the potential for institutional change within similar regime configurations. Outliers that do not fit prior expectations given a regime comparison

Bernhard Ebbinghaus

263

can then be used to explore the particular circumstances in within-case studies. As my dependent variable, I use longitudinal but cohort-adjusted data on employment changes as indicators of early exit from work before age 65 for men and women. Aggregate labor force data was collected mainly from OECD sources (with additions from EU and national sources) to compare early exit patterns over time (1970–2004) and across ten selected OECD countries. This study applies cohort-adjusted early exit rates for both men and women that are informed by the life course approach (Settersten and Mayer, 1997). Especially regarding women, exit indicators that are not cohort-adjusted would be misleading due to the oftensubstantial increases in female labor force participation from cohort to cohort. With cohort-adjusted exit rates, we can see that not only among older men but also among their female peers, early withdrawals from work before age 65 had increased during the three decades since the early 1970s. For practical reasons, the empirical analysis was limited to macro-level data due to the focus on longitudinal developments across ten OECD countries and the scarcity of cross-nationally comparable micro-level data (for recent micro-level studies using national surveys see Gruber and Wise (1999) and Blossfeld, Buchholz, and Hofäcker (2006) or European household panel data see Schils (2005)). The analysis adopts a qualitative case-oriented comparison instead of using cross-national pooled time-series analysis since this method exhibits serious limitations (Ebbinghaus, 2005; Kittel, 1999), given the highly auto-correlated but nonstationary processes due to long-term diffusion processes and path-dependent trajectories of early exit from work. This study uses longitudinal macro-indicators to describe the specific national trajectories in early exit from work that will be then explained by different macroinstitutional configurations. Since the independent variables are difficult to quantify, therefore institutional regime typologies as qualitative holistic classifications are used and their configurations are related to the more or less extensive early exit patterns. The comparative strategy thus resembles what Mahoney (2003) calls “ordinal comparison.” In order to allow systematic inter- and intra-regime comparisons, I chose a selected group of ten OECD countries that share not only enough commonalities to be comparable, but also sufficient differences on both dependent and independent variables. The selected countries represent cases from the main configurations along the conceptually defined dimensions of protection, production, and partnership regimes (see chapter 3 in Ebbinghaus, 2006). For pragmatic reasons, the study


is limited to a manageable number of countries for which crucial quantitative indicators were available and additional qualitative case studies already existed and could be used for secondary analysis. I opt for a smaller set of countries than usual in OECD studies, yet this medium N set of ten countries allowed the combination of intensive within-case studies based on secondary analyses (not reported here) with enough cross-national variations to systematically explore differences in institutional configurations. My comparative method thus follows Ragin (1987) in analyzing countries as theory-relevant cases that allow us to explore context-dependent, process-oriented analyses and to check for alternative hypotheses (Mahoney, 2003). They are not observations of a larger country sample to gain statistical leverage, thus the aim is not to extend beyond the cases at hand but to systematically analyse the findings from intensive within-case studies in a cross-case comparison. This study applies an institutionalist approach. The decisions of actors at the workplace level or in the social policy and bargaining arenas are embedded in institutional environments that shape actors’ orientations and interests as well as the opportunity structures for the actor constellations. Following Esping-Andersen (1990), I use “regime” to refer to the ways in which institutions hang together and interact in a systemic way, using the term as an analytical construct of the interrelations between institutions and their interaction with their environment. It is a heuristic tool to conceptualize complex institutional arrangements as a holistic system. A regime approach can help us understand how social protection is institutionalized, production systems are organized, and labor relations are governed. The regime approach is particularly useful in comparative analyses in order to conceptualize distinct regime typologies in which to classify empirical similarities and differences. While regimes should be theoretically grounded, representing ideal-types, much comparative work nevertheless seeks to use typologies to classify empirical cases or real-types. Seen from the protection-oriented pull perspective, social transfer programs provide incentives and opportunities to retire from employment before the statutory pension age (around age 65). There are often multiple exit pathways or “a combination of different institutional arrangements that are sequentially linked to manage the transition process, that is, the period between exit from work and entry into the normal old-age pension system” (Kohli and Rein, 1991, p. 6). Different social policy programs provide alternatives that facilitate early withdrawal from work: Flexible old age pensions, disability pensions, special pre-retirement schemes, long-term unemployment as well as

Bernhard Ebbinghaus

265

partial pension benefits (Kohli et al., 1991). Concentrating on only one social security program is thus insufficient to fully grasp pull factors because there are several alternative pathways, even when they are not necessarily complete functional equivalents. The welfare regime analysis cannot confine itself to the public programs of the welfare state, but must acknowledge the importance of the public–private mix (EspingAndersen, 1999; Rein and Rainwater, 1986). Not only public policy, but also private occupational benefits – provided by employers or negotiated by the collective bargaining partners – may offer additional opportunities or supplement insufficient public benefits for preretirement. Early retirement thus presents a prime case of the regime approach’s utility (Esping-Andersen, 1990), as it emphasizes the systemically interwoven web of institutions and their interaction within particular environments. In comparative perspective, we can expect that different welfare regimes provide varying opportunities of income support for early exit from work. On the other hand, in order to fully understand early retirement, we should take into account the reasons why firms shed older workers – the production-related push factors. In addition to institutionalized exit pathways and their incentive structures that affect labor supply, there are economic forces at work that influence the labor demand side. Most importantly, labor shedding or retaining of older workers will depend on firms’ age-related hiring, training, and firing policies. Early retirement is one major socially acceptable response to these pressures and constraints (Naschold and de Vroom, 1994). As institutional complementarities, pre-retirement benefits help socially buffer firms’ labor-shedding strategies. Thus the public–private welfare mix is particularly relevant for the study of early exit from work (Casey, 1992). What seem to be – in the protection-oriented “pull” perspective – income transfers to individuals that provide strong incentives to withdraw from work are, when seen from the firm-oriented “push” perspective, economically-motivated labor-shedding measures. In this case, public or private welfare policies are not politics against markets, enforcing redistributive social rights on free market economies, but politics for markets, enhancing the adaptability of social market economies (Ebbinghaus and Manow, 2001). However, neither protection-oriented nor production-oriented accounts sufficiently explain early exit. The labor supply perspective assumes that incentives determine the decision of older workers to retire, while the labor demand perspective perceives early exit as the outcome of firms’ human resource strategies. Protection systems provide


the pathways and the incentives for early retirement; the production systems induce pressure to shed older workers. Yet the partnership institutions are crucial in shaping the ways in which the social actors react to push and pull factors. At workplace level, the main actors – management, worker representatives, and the older workers – interact to find adequate responses given constraints and opportunities provided by the protection and production systems. Organized labor and capital, together with the government, influence and implement policies affecting early exit from work. The social partners not only regulate wages, employment conditions, and workplace relations through collective bargaining, they also influence social policy making and implementation through their political channels and their involvement in social insurance or occupational welfare programs. We should thus expect that the national traditions of partnership, that is, the institutionalized relations between the state, organized labor, and employer associations as well as the workplace relations between management, workers, and their representatives will have a major influence on national exit policies and on the everyday social practice of early retirement. In this study, I use regime typologies from three different fields to map the main differences across countries with respect to protection, production, and partnership institutions (Ebbinghaus, 2006). Although these regime typologies were developed largely independently and deal primarily with different social systems, they share a similar systemic view of institutions.

• First, for a regime typology of protection systems that provide the pull towards early exit, I rely on Esping-Andersen’s well-known welfare regime typology (1990, 1999). Three regime clusters are distinguished: Social-democratic universalist welfare states in Nordic countries, Christian-democratic conservative social insurance states in Continental Europe, and liberal-residual basic social security systems with substantial private pensions in the United Kingdom, Ireland, the United States, and Japan. • Secondly, for a classification of production systems and their economic governance, I borrow from similar typologies (Hall and Soskice, 2001) that juxtapose two political economy models: Anglo-American liberal (uncoordinated) market economies and coordinated (non-liberal) market economies (Germany and its Rhenish neighbors, Scandinavia, and Japan). • Finally, for the analysis of partnership traditions, I draw on comparative neo-corporatist studies of labor relations and organized interests

Bernhard Ebbinghaus

267

(Crouch, 1993). These delineate three different management–labor modes: voluntarist (or “give-and-take”) bargaining traditions in AngloAmerican labor relations, contentious labor relations in Latin Europe, and cooperative labor relations in the remaining countries. There is no clear one-to-one relationship between these institutional configurations; nevertheless, there are some intriguing institutional affinities between welfare regimes, production systems, and labor relations. The Liberal Market Economies (the UK, the US, and Ireland) all combine residual welfare and voluntarist labor relations. Among the Coordinated Market Economies (CMEs), there are several combinations between welfare regimes and labor relations. In fact, we may need more subtle distinctions for these CMEs: Central coordination in Nordic countries, sectoral coordination for Germany and the Netherlands, and state-coordination for Latin Europe (Kitschelt et al., 1999). The purpose of locating countries in these analytical typologies is to provide a conceptual map (Rokkan, 1999), which explains particular outcomes based on the institutional configurations. Thus, no one “master variable” is sufficient on its own to explain the divergent early exit trajectories; rather, it is the particular interaction of protection (pull) and production (push), mediated by specific partnership relations. The comparative regime typologies also help in delineating the institutional obstacles and opportunities for policy reversal as they provide the basis for evaluating the degree of path departure from regime-specific trajectories. Thus, the purpose of this encompassing comparison (Tilly, 1984) is to generate hypotheses about institutional macro-configurations and confront these with more process-oriented within-case studies. For this study, I selected eight member states of the European Union, representing four regime configurations, and added two major nonEuropean OECD countries, the United States and Japan (see Table 8.1). These two global players are not only major economic competitors to these European economies, but also prime examples of opposing political economy models: The United States is a case of an (uncoordinated) Liberal Market Economy, Japan of a (non-liberal) Coordinated Market Economy (Hall and Soskice, 2001). Explaining early exit from a production-oriented view, these two juxtaposed political economy models pose a puzzle. Because these countries with opposing production systems both have low exit rates, the differences in production regime (that is, the push factor) alone cannot explain such a similar outcome. Conversely, taking into account the pull factor, the liberal-residual welfare regimes cannot be the sufficient cause for low early exit since

268 Method and Substance in Macrocomparative Analysis Table 8.1 Conceptual map of protection, production, and partnership regimes Cluster countries

Protection (“pull”)

Production (“push”)

Partnership (mediation)

Center GERMANY NETHERLANDS

Conservative

Coordinated

Cooperative

Latin FRANCE ITALY

Conservative

Coordinated

Contentious

Nordic SWEDEN DENMARK

Universalist

Coordinated

Cooperative

Anglo-American UK IRELAND USA

Liberal-residual

Liberal

Voluntarist

Asian JAPAN

Liberal-residual

Coordinated

Cooperative

Note: see Ebbinghaus 2006: chap. 3 for details on these three typologies.

Sweden has also had relatively low early exit from work, despite sharing a relatively generous social security system with high exit Continental European welfare states.

Declining employment rates A first indicator of the rise in early retirement is the decline in employment; we should use employment rates which exclude those that are not active since unemployment benefits for older workers allow de facto early retirement. A drop in the employment rate indicates that fewer older people remain in gainful employment, while the others – non-working people – are most likely to be dependent on pension, unemployment, other welfare benefits, or – especially in the case of housewives – their spouse. For more detailed analysis we also need to disaggregate the early exit trends into at least two age groups: early exit (age 60–64), for which many pre-retirement options exist, and very early exit (age 55–59), for which few pre-retirement opportunities, other than unemployment benefits, exist. The most significant decline in employment rates occurred among men in the age group 60–64. With the exception of Italy, which already

Bernhard Ebbinghaus 100

1970

1985

2000

269

15–64

90 80 70 60 50 40 30 20 10

Men 55–59

USA

Japan

Ireland

UK

Sweden

Denma rk

Italy

France

Nethe rlands

Ge rma ny ( W est)

USA

Japan

Ireland

UK

Sweden

Denma rk

Italy

France

Nethe rlands

Ge rma ny ( W est)

0

Men 60–64

Figure 8.1 Employment rates for men aged 55–59/60–64/15–64, 1970–2000 Source: Ebbinghaus 2006: Table 4.4; OECD Labour Force Statistics, 1970, 1985, 2000.

had low employment rates (above 50 percent), all other countries experienced a long-term decline from the employment level of the 1960s (above 70 percent). The drop was most pronounced in the cases of Germany, France, and the Netherlands during the 1970s; more gradual thereafter, but plunging in the 1980s – to even below Italy’s traditionally low level. All four Continental welfare states stand out as having the lowest levels of active employment among men aged 60–64: less than one in three West Germans or Italians, less than one in four Dutchman, and one in every six Frenchmen or East German. Early retirement among men aged 60–64 is less common in the Nordic and Anglo-American countries (see Figure 8.1). Nevertheless, the United Kingdom, the United States, and Ireland have seen a drop to medium employment levels: Only every second man aged 60–64 is in work, despite a later statutory retirement age of 65. Sweden maintained a higher level until the surge in unemployment during the early 1990s that led to a drop from 60 percent to 50 percent in employment rates. Neighboring


Denmark saw an even more severe drop – to an employment rate of less than 40 percent in 2000, coming close to Germany around the turn of the century (30 percent). In terms of the timing of labor shedding, Sweden and Ireland experienced their major declines in the 1990s; Denmark and Britain in the 1980s; and the United States even earlier. Finally, Japan stands out with a more gradual decline during the mid-1980s and again since the mid-1990s. More than 70 percent of Japanese men still work after the age of 60 when they can draw the second-tier contributory pension, which they may combine with a part-time job. While most of the larger Japanese companies enforce mandatory retirement by the age of 60 (Kimura et al., 1994), these workers seek re-employment in order to supplement their pension and severance pay income. During the 1960s, nearly all societies had high levels of employment among men aged 55–59 (above 90 percent), the exceptions being France (80 percent), the United States (85 percent) and, most notably, Italy (only around 75 percent). During the 1970s, all countries experienced a decline in employment rates in this age group, but the Nordic, Anglo-American countries, and Japan went through the decline more slowly than Continental Europe. After relatively slow decline throughout the 1970s and 1980s, with high unemployment in the 1990s, Swedish employment among men 55–59 also dropped rapidly from 86 percent in 1990 to 76 percent in 1995. The downward trend in Denmark already started in the 1980s and continued until the mid-1990s. Britain and Ireland have had a more rapid decline since the late 1970s, due to high and fluctuating unemployment in this age group: every third man aged 55–59 was inactive by the mid-1990s. In comparison, the United States performed somewhat better, starting from a lower level and experiencing a slower decline: one in every four American men aged 55–59 was not (or no longer) employed in the 1990s. The Continental European welfare states are distinct in their significant drops in employment rates in this early age group. Italy always had a low employment level: Until the mid-1970s only three out of four Italian men aged 55–59 were working and by the 1990s it was only every second man. France and the Netherlands had higher employment rates in the early 1970s, but thereafter witnessed a major drop to only 60 percent by the mid-1990s. West Germany’s decline was somewhat less dramatic than in the Netherlands but by the late 1990s both countries had reached the same level of inactivity: Every third man aged 55–59 was not working (in East Germany it was 40 percent). Analysing early retirement among women with the help of employment rates produces the same shortcoming as previously discussed for

Bernhard Ebbinghaus

271

100 1970

90

2000

1985

15–64

80 70 60 50 40 30 20 10

Women 55–59

USA

Japan

Ireland

UK

Sweden

Denmark

Italy

France

Netherlands

Germany (West)

USA

Japan

UK

Ireland

Denmark

Italy

Sweden

France

Netherlands

Germany (West)

0

Women 60–64

Figure 8.2 Employment rates for women aged 55–59/60–64/15–64, 1970–2000 Sources: Ebbinghaus 2006: Table 4.5; based on OECD Labour Force Statistics, 1970, 1985, 2000.

participation rates. Nonetheless, the disaggregation into two age groups provides some additional information (see Figure 8.2). In 1970, nearly the majority (around 40–50 percent) of women aged 55–59 was working in all countries, except Germany (37 percent), Ireland (below 20 percent), the Netherlands (18 percent) and Italy (14 percent). The most dramatic increase in employment occurred in Sweden, reaching a level beyond 70 percent in the mid-1980s, followed by gradual increases in Denmark, the United Kingdom, the United States, and Japan; all these countries had reached levels between 50 percent and 60 percent by the 1990s. The Continental European countries and Catholic Ireland showed stagnating levels until the 1990s. During this time, female employment rates particularly improved in the Netherlands, Ireland and Italy. A comparison with the employment rates for the later age group (those aged 60–64) shows considerably lower levels and often falling employment rates, this indicates that early retirement supersedes the trend of increasing female participation in this age group. In particular, where


women can draw on pensions earlier, employment rates are considerably lower: British women receive the basic pension at the age of 60, while German, Danish, Italian, and Japanese women can, under some conditions, draw pensions earlier than men, though they will often receive lower benefits than if they worked their remaining years until statutory retirement. In Sweden, employment levels still increase from cohort to cohort; at least they did before the unemployment crisis of the 1990s. In Japan, with the second highest employment rates (short under 40 percent), there is no visible change over time. The United States and, at a lower level, the United Kingdom and Ireland have experienced some increase in recent years, while Denmark has a much lower (and unstable) employment level among women aged 60–64 due to the use of unemployment insurance as a bridging pension. In all Continental European countries, the level of employment has been falling, as in Germany and France, or remains very low, as in the Netherlands and Italy. However, falling employment rates are an incomplete means to detect the scope of early retirement, particularly among women since each female cohort tends to have a higher likelihood to be working at the age of 55. In order to take cohort-specific effects into account, we need to adopt a perspective that studies employment patterns over the life course and across cohorts.

Early exit from work Seen from a life course perspective, we should measure exit from work by cohort-adjusted employment rates, particularly in the case of women with rising participation levels and in countries where early retirement has lowered participation levels for several age groups. Following a life course perspective, this study compares the impact of cohort and historical changes on early retirement patterns with the help of cohort-adjusted exit rates or “net withdraw rate” (Blöndal and Scarpetta 1998). It uses the relative exit rate (percentage change) to measure cohort-adjusted withdrawal rate as a proportion of the population “at risk” of exiting work, that is, those in the same birth-cohort who were employed five years earlier (for example, aged 55–59 in 1990). The relative exit indicator is better than measuring absolute changes in employment rate in the cases where employment rates in previous age groups are relatively low, this holds particularly for women or countries with very early exit. Early exit among men aged 60–64 follows similar cross-national differences as those discussed for the decline in employment rates, with some minor differences. The Continental European countries show the same

Bernhard Ebbinghaus Continental Europe

Other countries

80

80

70

70

60

60

50

50

40

40

30

30

20 10

D (West) D (East) NL F I*

273

20 10

S UK USA

DK IRL JAP

0

0

1970 1975 1980 1985 1990 1995 2000 2005

1970 1975 1980 1985 1990 1995 2000 2005

Figure 8.3 Relative exit rates for men aged 60–64, 1970–2003 Notes: 5-year moving average of relative exit rates (see Ebbinghaus 2006); I∗ : Italy 1965–72: 55–59 partly estimated. Sources: OECD Labour Force Statistics 1966–2004, except IRL: Ireland 1983-, S: Sweden 1997-: Eurostat Labour Force Surveys, and own calculations.

pronounced trend of rapidly increasing early exit and high levels of early retirement, while the trajectories in the other countries are more gradual and remain at a lower exit level (see Figure 8.3). Given declining or low employment levels for the previous age group, the two withdrawal measures diverge over time: absolute exit rates, the share of the age group 60-64 leaving work, fall behind the relative exit measure, which shows the propensity of those previously (age 55–59) employed to leave work upon reaching ages 60–64. During the first growth period (from 1970 to the first peak in 1985), the Continental European countries witnessed acceleration (with annual growth rates of 8–9 percent) in relative early exit from work (see Table 8.2). An exception is Italy, which had a more gradual annual increase (2.6 percent) due to an already high level of early retirement in 1970 (30 percent). By the 1980s, more than 40 percent of the age group 60–64 retired early and more than half of those initially employed before 60 stopped working within the next five years. During the second period, beginning in the mid-1980s, we also see some stagnation and short-term

274

Table 8.2

Relative exit rates, men and women aged 60–64, 1970–2003 Men aged 60–64

Germany (West) (East) Netherlands France Italy Sweden Denmark UK Ireland USA Japan

Women aged 60–64

1970

1985

2003

1970–1985

– 18.2 – a 19.8 19.7 31.0 15.8 a 13.3 15.9 a 6.8 18.4 11.8

– 57.9 – 63.4 62.0 45.2 28.8 45.2 41.3 27.6 32.6 23.7

51.5 49.9 58.6 52.1 76.3 40.8 24.2 33.9 23.4 19.0 28.5 29.0

– +8.00 – +8.67 +7.95 +2.74 +4.10 +8.49 +6.55 +5.10 +3.89 +5.14

1985–2003

1970

1985

2003

1970–1985

−0.90 −0.81 c −0.83 −0.47 +1.11 +0.35 +0.74 +0.69 −2.07 −1.81 −0.81 +1.13

– 38.4 – a 27.0 19.2 22.4 24.4 a 34.4 37.3 a 7.1 24.4 b 23.3

– 71.2 – 66.1 60.8 40.6 36.7 44.6 66.1 31.1 31.3 24.1

63.5 60.6 74.7 52.8 72.8 54.2 27.9 53.9 48.2 17.4 27.1 34.6

– +4.20 – +6.15 +7.97 +4.04 +2.75 +1.74 +3.89 +10.31 +1.68 b +0.23

c

1985–2003 −1.20 −0.89 c −1.07 −1.24 +1.01 +1.62 −1.50 +1.06 −1.74 −3.18 −0.80 +2.04

c

Notes: relative exit rate: decline in cohort-specific employment rate in % previous employment rate,; a 1970/75 estimated based on participation rates (ILO); b 1971, c 1991 (estimated), d 1996; 1970–85, 1985–2003: annual natural growth rate. Sources: OECD Labour Force Statistics 1965–2004, except: Germany: 1991–2003 German Statistical Office internal data; Italy 55–59 (1966–72 estimated based on 50–59, 1973–), Sweden 1997–2003, Denmark 1975–83, and Ireland: Eurostat Labour Force Surveys 1973–2004; and own calculations.

Bernhard Ebbinghaus

80

Continental Europe

80

70

70

60

60

50

50

40

40

30

30

20 10

D (West) D (East) NL F I*

275

Other countries

20 10

S UK USA

DK IRL JAP

0

0

1970 1975 1980 1985 1990 1995 2000 2005

1970 1975 1980 1985 1990 1995 2000 2005

Figure 8.4 Relative exit rates for women aged 60–64, 1970–2003 Notes: 5-year moving average of relative exit rates (see Ebbinghaus 2006); I∗ : Italy 1965–72: 55–59 estimated. Sources: OECD Labour Force Statistics 1966–2004, except IRL: Ireland 1983-, S: Sweden 1997–: Eurostat Labour Force Surveys, and own calculations.

fluctuations, with the exception of France, where early retirement continues to increase. Given the considerable labor shedding among older male workers occurring throughout Continental Europe, one can refer to these countries as “welfare states without work” (Esping-Andersen 1996). They all show a long-term growth trajectory that follows an S-curve for both men (see Figure 8.3) and women (see Figure 8.4), suggesting a diffusion process that first excels during the 1970s and since the 1980s has reached saturation at a very high level of “penetration”: early retirement sometime between 60 and 64 has become the dominant social norm in Continental Europe. In contrast, the other countries on average show less dramatic increases and reach lower levels of early exit among men aged 60–64, though they also go through more pronounced cyclical fluctuations (see Figure 8.3). During the first period (1970–85), growth in early exit was slower (4–5 percent) and reached a much lower level (below 33 percent in 1985) than on the Continent (above 45 percent) with two exceptions. Denmark and Britain show rapid growth rates (7–8 percent), albeit starting from a somewhat lower level (about 15 percent in 1970) but (nearly) reaching the


Italian level in net withdrawal (45 percent in 1985). Sweden, the United States, Ireland, and Japan had relatively low early exit rates; between one-fifth and one-quarter of men aged 60–64 left work early. While early retirement declined somewhat during the late 1980s, the early 1990s brought another cyclical upturn, most dramatically in Sweden and Denmark. Interestingly, despite high levels of old age employment, more than a quarter of Japanese men aged 60–64 had left employment in the 1990s. Differences between the two measures are relatively minimal, given the high employment rates among men aged 55–59. We draw a much clearer picture of early retirement when we analyze early exit rates and adjust for cohort effects than if we look only at employment changes in aggregate. We find three distinct groups of countries for early retirement among men aged 60–64: the Continental European high early exit countries, the in-between cases of the United Kingdom and Denmark, with medium-level early retirement, and the remaining countries (Sweden, Ireland, the US, Japan) with oscillating, but overall lower levels (see Figure 8.3). In contrast to employment rates, the analysis of cohort-adjusted exit rates indicate a very similar increase in early retirement for older women aged 60–64 in Continental Europe, with the exception of Italy, which maintained a medium level until the early 1990s (see Figure 8.4). In general, all four countries show parallel trends for women and men with only a few exceptions: Germany’s exit rates among women exceed those for men; Italy’s gender gap has increased since the 1980s; there is no large difference in the Netherlands; and, in France women are less prone to retire early in the age group 60–64. Yet Germany and France have higher levels of female employment compared to the Netherlands and Ireland and thus nearly every fourth woman aged 60–64 withdraws from work. The high relative exit rates in the Netherlands and Italy are less important in absolute numbers because fewer women were employed at all before age 60. The other countries show lower early exit rates for women too, again with the partial exception of Denmark and Britain. Such different societies as Sweden, the United States, and Japan show very similar early retirement patterns for women aged 60–64, with cyclical ups-and-downs around a low level of early retirement (between 20 percent and 35 percent) in the 1980s. Yet, in the early 1990s, female exit rates increased in Sweden and Japan as a result of more severe labor market conditions. Another exception is Catholic Ireland, where, despite low overall levels of female participation, a substantial share of older women withdrew from work during the unemployment crisis of the 1980s, a trend that was reversed with improved labor market conditions

Bernhard Ebbinghaus

277

in the 1990s. The two major outliers are Denmark and the United Kingdom. Given the statutory female pension age of 60, British women tend to withdraw at rates comparable to Continental Europe (66 percent in 1985, declining to 52 percent in 2000). Since the late 1970s, Danish women aged 60–64 withdrew from work at an increasing rate, such that by 1995 two-thirds had left employment, this has been largely possible through disability pensions that allowed retirement long before the normal pension age of 67.

Early exit regimes The analysis of long-term trends in early exit from work for the age group 60–64 revealed two different early exit trajectories within the overall trend: (1) Continental European countries showed an S-curve-like diffusion process from low to high early retirement over the past three decades (with the partial exception of Italian men, who maintained an already high level of early exit); whereas (2) in the other countries early exit from work grew less rapidly and continued fluctuating during the 1980s and 1990s with the exception of early exit among British and Danish women that came close to Continental levels during the 1980s and 1990s, respectively. By the mid-1990s, we can distinguish three different worlds of early exit: (1) the Continental European high early exit countries (Germany, the Netherlands, France, and Italy); (2) the British and Danish medium exit levels (particularly among women); and (3) the other countries (Sweden, Ireland, the United States, and Japan) with lower levels of early exit. Would the inclusion of early exit before age 60 alter these findings? Not significantly. Indeed, the difference between the Continent and the other countries might even be enhanced (see Ebbinghaus, 2006). All four Continental European countries have high exit rates that are rising, while the other countries show more cyclical patterns due to unemployment waves that remain below the Continental European level. Very early exit (age 55–59) follows similar patterns as exit after age 60, though the groups of very early exit clearly stand out: Italians, French, and Eastern Germans. Moreover, earlier exit before age 55 occurs occasionally (with the exception of Italy), but remains limited to some occupational groups (such as miners) and to cyclical unemployment due to the absence of other institutionalized public exit pathways for this age group. The three worlds of exit regimes – high exit Continental Europe, medium-level outliers Denmark and Britain, and low exit countries – are still observable in the 1990s.


Cohort-adjusted early exit rates provide a better tool to study early withdrawal from work, particularly for women. Continental Europe’s main trend in early exit for the age group 60–64 shows an S-shaped diffusion curve with fast growth in the 1970s and early 1980s and a leveling off thereafter. In the other countries, however, early exit from work increased more gradually and cyclically, with the exception of relatively significant trends in early exit among women in the United Kingdom and Denmark. Although the level of very early exit before age 60 remains considerably lower, it increased rapidly during the 1980s and remained substantial in the 1990s in Continental Europe, while the other countries show a lower but cyclical trend. Premature early exit before age 55 remains rare, with Italy a major exception. Based on the early exit trajectories in the age groups 55–64 (see Ebbinghaus, 2006), there are clear country clusters that partly overlap with the regime typologies discussed thus far (see Table 8.3). The four Continental European welfare states show highest levels of early exit from work for both men and women. While Italy traditionally has had very early exit (age 50–59) and France showed increasing exit from work in the age group 55–59, very early exit before age 60 by and large remains limited to unemployment in Germany and the Netherlands (see Figures 8.3 and 8.4). With the exception of France, the rise in early exit has leveled off in recent years, and there has even been some improvement, particularly in the Netherlands. The remaining countries – universalist and residual-liberal welfare states – have moderate or lower levels of early exit. Denmark and the United Kingdom are outliers among the nonConservative welfare states with relatively high levels of early exit among older women (age 60–64). They also stand out due to relatively high levels of male early exit during the 1980s, continuing in Denmark into the 1990s. Sweden and the United States have moderate levels of early exit for men and women, while Japan and Ireland have comparatively low levels of early retirement. The trend among the non-Continental countries is less marked by a general diffusion curve, following a cyclical pattern instead (see Figures 8.3 and 8.4), indicating that early retirement results more from changing labor market situations than solely because it has become a socially accepted role. This holds also for Denmark and Sweden where exit increased during periods of high unemployment in the 1990s, but then receded. While British and Irish early exit trends declined with labor market improvements during the 1990s, Japan, after long periods of low early exit, experienced an unusual increase with the economic downturn in the late 1990s. Can the “push” and “pull” theses explain these early exit patterns?

Table 8.3

Index of pathways ranked by exit opportunities

Italy France Germany Netherlands Denmark UK Sweden USA Ireland Japan

Statutory pension

Flexible pension

Special scheme

Unemployment

Disability

Index of pathways

Exit trend

∗∗∗

∗∗

∗∗

∗∗

∗ ∗∗

∗∗∗

∗∗

∗

{∗∗ }

∗∗

∗∗

10 {12} 9 7 {9} w: 8 8 5 {6} w: 6 {7} m: 3{5} w: 6 3 {5} 3 3 3

early, high early, high high high m: mod.w: high m: mod.w: high moderate moderate low low

∗∗∗ ∗

w: ∗∗

∗∗

∗∗∗ ∗

w: ∗∗∗

∗∗ ∗

∗∗ ∗∗

{m: ∗∗ }

∗

∗ ∗∗

∗∗∗

{∗∗∗}

∗∗ ∗

∗

w: ∗∗∗

∗ ∗∗ ∗

∗∗

{ }

∗ ∗

{ }

Note: opportunities for early exit (see Ebbinghaus 2006: Table 5.5 for details): #: with labor market consideration; Index of pathways: ∗∗∗ major pathway (3 points); ∗∗ conditional pathway (dismissal, unemployment, disability); (2 points); ∗ limited pathway (means-tested, medical-test only, partial pension, actuarial reduction) (1 point); exit trend for men (m) and women (w) (see Ebbinghaus 2006: Table 4.8).

279


The protection-pull thesis Seen from the pull perspective, the combination of multiple pathways – whether explicitly intended for early exit or not – shapes the overall pull towards early exit from work. By the mid-1980s, early exit trends had peaked in most countries. Different early exit regimes emerged that reflect considerable cross-national differences in the availability of early exit pathways (see Table 8.3). The Continental European welfare states provide the most generous and largest set of pre-retirement options, though there are intra-regime differences. Most importantly, the French and Italian public pension schemes provide relatively early pension benefits for older workers (at age 60 or even earlier) and except for disability pensions provide relatively open exit pathways. The German system grants some early “normal” pensions (largely to women aged 60 with few working years), while the Dutch basic pensions are paid out only from age 65. Although all Continental European welfare states have had high levels of early exit from work since the late 1970s, the Latin welfare states show particularly high early exit rates (before age 60) in comparison with Germany and the Netherlands, this is also reflected in the index of pathway availability. The two “outlier” countries, Denmark and Britain, only partially follow the patterns of the Scandinavian universalist and Anglo-American liberal models, respectively (see Table 8.3). Denmark provides more multiple pathways (5–6 index points for men and 6–7 points for women depending on the period) than does Sweden, which provided mainly the disability-unemployment pathway until the early 1990s. In the Danish case, pre-retirement pay, in particular, turned out to be a major exit route, as did disability pensions, especially for women. Early exit for both men and women around age 60 became increasingly important during the 1990s. Sweden has, at least thus far, relied on part-time work and partial benefits, increasing the share of older working men who combine income from work with pension benefits. In Britain during the 1980s, early retirement was common for men due to the high level of unemployment, the Job Release Scheme (JRS), and employer policies, but it is no longer common before age 65. Due to an earlier standard retirement age, British women do retire earlier than men, though insufficient means lead many to continue working, often part-time up to age 65. Indeed, an increasing share of older working men combine work and public or private pension benefits (OECD 2001, p. 36). In respect to availability of pathways, British women have more opportunities to retire early then men (not least because of the earlier

Bernhard Ebbinghaus

281

normal pension age), yet the available pathways has been larger for men during the 1980s (Table 8.3). In comparison, Ireland has less institutionalized pathways than Britain. During the 1990s, old age unemployment before 65 was common for Irish men, while for Irish women 65 was the “norm” (Blöndal and Scarpetta, 1998). Thus relatively similar welfare states, Denmark and Sweden but also the United Kingdom and Ireland, show significant intra-regime differences in early retirement pathways and actual exit from work patterns, at least with respect to particular time windows and gender variations. These differences are not merely the outcome of dissimilar government policies, but the consequence of different strategies by the social partners and variations in the production regimes. Finally, the United States and Japan are two welfare regimes with a liberal-residual orientation that allow companies significant scope in determining occupational welfare, while both provide limited access to disability and unemployment benefits. In addition, pensions can be drawn earlier but these are insufficient, requiring additional occupational benefits. Moreover, in both countries, employers have used mandatory retirement rules in the past (Kimura et al., 1994). However, there are still marked differences in employment rates between both countries that reflect very different employer strategies and particular private–public mixes: American workers may be induced to retire early through employer-provided plans or may be let go through general downsizing, while Japanese employers provide reemployment opportunities for their “retired” workers. The United States has less developed public pathways and leaves a larger role for private pathways (see Table 8.3). In Japan, early retirement increases slowly up to age 60, when some Japanese men and women exit, but most continue working until at least 65. Japan and the United States are also the two countries in which older workers (aged 60–69) tend to combine work and some form of public or private pension benefits (OECD, 2001, p. 36). Together with Ireland, the United States and Japan rank lowest on the index of pathway availability (see Table 8.3), and indeed range also among the countries with the lowest or medium level of early exit.

The production-push thesis The protection-oriented pull thesis does not explain why the social partners played such an active role in bringing about and using early exit; we thus need to turn to the production-related push factors. Employers were thus far willing to co-finance early exit from work


in all countries (see Ebbinghaus, 2006), but particularly in liberalresidual welfare regimes that lack generous public pathways. Most of the larger British, Irish, American, and Japanese employers (in both private and public sectors) provide favorable early occupational pensions, topping-up of flexible pensions or special “window” plans. However, these employer-led pathways remain limited to larger (often unionized) companies, and firms have increasingly retreated from defined benefit pensions and long-term employment commitments. Private occupational pensions play less of a role than the publicly provided early exit pathways in the Continental European and Scandinavian welfare states, but also because social partners run schemes beyond the firm. Among the push factors that explain the trend toward early exit from work are structural changes: deindustrialization and the growth to limits of public employment. However, these general shifts cannot explain the cross-national variations in early exit trajectories in a systematic way, although the particular expansion and stagnation of the public sector in Denmark and Sweden provides one reason for the specific Nordic pattern. Although some special early retirement schemes were introduced to facilitate industrial restructuring, early exit from work has become a much broader social right, common across nearly all private industry and public service sectors. However, the analysis of age-related skill levels shows that older workforces tend to be less skilled, and that those with lower skills have been shed at a higher rate than others. Although all firms seem to have an interest in using early exit, there are differences according to the particular partnership traditions, labor market regulations, production strategies, and economic governance structures (see Table 8.4). Taking the ideal-typical production models, we can derive two different sets of push forces. Under Liberal Market Economies (the United Kingdom, Ireland, the United States), Fordist mass production tends to rely more heavily on general skills, while hiring and firing is largely unregulated. As labor turnover is more common, employers use occupational pensions to retain skilled workers. Furthermore, in unionized firms, unions defend seniority rights (“last in–first out” rules) and employers thus have to buy out older workers with “golden handshakes.” Given liberal corporate and financial governance, short-termism puts additional pressure on these firms to be numerically flexible during downturns. Therefore, early exit patterns in LMEs tend to be cyclical, as some companies used firm-sponsored early retirement plans to downsize during economic downturns, while others shed older workers in largely unregulated labor markets, pushing the risks of finding work (or income support) onto individuals.

Bernhard Ebbinghaus

283

Table 8.4 Early exit from work and regime configurations Women: Men: High and very early High

Moderate

Low

High and very early

High

Moderate

Low

France* Italy* Germany, Netherlands Denmark

Sweden

UK**

USA** Ireland** Japan

Notes: welfare regimes: conservative; universalist; liberal-residual; production system (underlined): liberal market economy; non-liberal market economy; labor relations (type face): cooperative; ∗ contentious; ∗∗ voluntarist.

In sharp contrast, Coordinated Market Economies tend to be characterized by firms with specialized or functionally flexible production methods, requiring industry or company-specific skills and well-developed vocational training (Estevez-Abe et al., 2001). Labor rights are more institutionalized: Employment is highly regulated, collectively negotiated wages tend to be high and more egalitarian, and workers have co-decision rights, particularly in employment matters. Corporate governance follows a stakeholder model that gives some co-decision rights to worker representatives at company level. Furthermore, financial and corporate governance has thus far consistently provided long-term patient capital. Therefore, during economic downturns, companies could hoard skilled workers, instead of downsizing immediately. However, given the seniority wage system and the need for costly skill upgrading, employers seek to induce timely retirement in order to enhance efficiency and maintain internal labor markets. Given the institutionalized workers’ rights and more favorable public benefits, early retirement is a socially acceptable means for restructuring; it is likely to receive the support of workers and workplace representatives. Nevertheless, both early exit practices are not in equilibrium. They follow either a downward or an upward spiral. In the case of the shareholder model in LMEs, financial market pressure has increasingly pushed companies to withdraw from their commitment to defined benefit pensions.


Moreover, unions have lost in bargaining power and can hardly enforce seniority rights. Thus, the burden of continued downsizing is placed on individuals and this cost has been amplified by retrenchment in liberal welfare states. On the other hand, the stakeholder employment model in CMEs has led to a self-defeating, self-reinforcing spiral of early exit from work. Unions are still strong enough to defend social rights, welfare states still provide public exit pathways that allow externalization of costs, and employers themselves find labor shedding of older workers the easiest solution to maintain the internal labor market. Employers’ belief in the diminished productivity of older workers leads to the selffulfilling prophecy of ever shorter employment tenure: Older workers are less productive; therefore, they are shed earlier, but because employers expect workers to retire early, they stop investing in training at earlier stages, and thus older workers have obsolete skills, which indeed make them less productive. However, the country case studies show two exceptions to the vicious circle of the internal labor market model: Sweden and Japan maintained higher levels of activity in older age groups, at least until the 1990s. In Sweden, the combination of partial pensions and part-time work allowed firms to retain older workers and their expertise. The Swedish work integration strategy has been highly contingent on the generosity of the public benefits, the willingness of employers to reorganize work, the support of local unions at workplace level, and activation policies by public employment agencies. In fact, this model came under severe pressure with the end of full employment in the 1990s and the fiscal crisis of the Swedish welfare state, which cut back on partial pension benefits. In recent years, the gradually phased-in pension reforms and future skill shortage may once again lead to a return to the prolonged working life model (Wadensjö, 2002). A very different social practice is at the heart of the Japanese partial exit model. Larger firms enforce mandatory retirement around age 60, but they provide reemployment at a lower wage or “secondment” to smaller supplier firms. In addition, firm-sponsored benefits are provided as income supplements to the lower earnings, until public pension age. This model also came under pressure in the 1990s, as Japanese employers were more reluctant to fulfill their “social responsibility” and the pension reforms gradually postponed pensions to cope with the country’s rapidly aging population. The importance of a long working life remains, but it may entail further segmentation into a primary core workforce (“company men”) and a secondary labor market made up of women, older men, and the unemployed (Brinton, 1998).

Bernhard Ebbinghaus

285

The analysis of push factors complements and fine-tunes the analysis of protection-induced pull factors. The basic claim of the varieties of capitalism (Hall and Soskice, 2001) approach seems to be valid: The interactions of the different institutional arrangements pose specific incentive structures and follow different institutional logics at firm-level. Firms in Liberal Market Economies are more likely to suffer cyclical downsizing pressure and early exit from work remains a partly internalized firm strategy or individualized risk of unemployment and poverty in old age. In contrast, firms in Coordinated Market Economies face the contingencies of seniority wage, high skill, highly regulated, and cooperative workplace relations. They seek to buy out older workers in a socially acceptable way, supported by multiple possibilities to externalize these restructuring costs (Naschold and de Vroom, 1994). The analysis of economic push factors also suggests that a reversal in early exit trends cannot be achieved merely by cutting back on public pathways. As long as firms are compelled to downsize or restructure, labor shedding will continue – a particular challenge to public policy that seeks to reverse the trends of early exit from work.

Conclusion This comparative historical analysis of early exit from work trajectories has shown the interaction of specific institutional configurations in respect to protection, production and partnership. In an “ordinal comparison” (Mahoney, 2003) we found a rank order of early exit from work patterns ranging from labor-shedding Continental Europe (France and Italy with high and very early exit, Germany and the Netherlands with high early exit) to intermediate levels in Liberal and Scandinavian welfare states with particularly higher exit levels for women in Denmark and UK, somewhat lower levels in Sweden and the USA, and relatively low levels in Ireland and Japan. In this cross-case comparison presented here, these variations in early exit from work can be explained as a combination of both “pull” and “push” factors, welfare state regimes and productions systems, while from an actor constellation perspective process-oriented within-case analysis would add the mediating factor of employers and worker representatives. The particular welfare states provided different numbers of pathways to early retirement; the “pull” argument holds for the major difference between Continental and Liberal welfare regimes, but this perspective fails to account for the intra-regime differences and the late or medium level in early exit patterns of the generous Scandinavian welfare regimes.


The integrative policies of the Swedish welfare state do not follow the incentive-pull argument, but relies on the cooperation by workplace actors in using gradual retirement and reintegration strategies to postpone exit from work. The Danish and British case also deviated at times from the respective regime, requiring a more detailed intra-regime analysis of the general unions in promoting early exit for unskilled workers in Denmark and gendered social policies, facilitating earlier exit for women in both countries. The “push” thesis has also proven a partial explanatory factor for the more market-driven cyclical exit patterns in the LME countries, and the use of early retirement to meet the internal labor market problems of CME firms. Yet the Varieties of Capitalism thesis needs amendment to account for the high Japanese and medium Scandinavian activity rates among older workers. Japanese and Scandinavian firms face similar problems as their German counterpart due to internal labor markets with high seniority wages, but they rely much less on an externalization of costs onto public early retirement policies than in Continental Europe. The Japanese re-employment of older workers is a particular arrangement that is consistent with both early retirement from career-jobs and a high employment level among older workers. The role of employers and social partners in providing additional pathways also requires the consideration of labor relations at national and workplace level. More detailed case studies (see Ebbinghaus, 2006) indicate the crucial role of employers and workers’ organizations in bringing about and maintaining early retirement practices at both national and workplace levels. Moreover, policy reversal is hardly possible without the consent of trade unions and workplace representatives. Comparing different institutional configurations thus allowed an analysis of “pull” and “push” factors across countries and over time, showing that both perspectives can be utilized to explain early exit patterns. In fact, I have argued that they are often complementary, thus while we can find evidence for the support of a “pull” of more or less generous welfare states on early exit, it would be shortsighted to ignore that public pathways are used by employers and workplace actors to mediate the economic “push.” Thus, these inter- and intra-regime comparisons allow a more systematic analysis that summarizes the findings from within-case analysis and sheds new insights on the interpretation of cases in light of the cross-case analysis. The case-oriented analysis requires taking “outliers” more seriously than in quantitative large-N studies, thus it points at the need to study deviations from the expected regime logic by more intensive within-case analysis.

Bernhard Ebbinghaus

287

An analysis of reform processes (see Ebbinghaus, 2006) reveals the importance of intra-regime analysis, highlighting interesting cases of path departure of some reform processes from the “impasse” of path-dependent inertia. Although past practices provide major obstacles for reform as actors at various levels have grown accustomed to early retirement, recent reform efforts have led to a slow change in employment rates. Some countries have accelerated their way out of the impasse (most notably the Netherlands and Denmark), yet some still remain stuck in an undecided switch of direction. A major reason for this difficulty in reversing the course of early exit from work is the institutionalization of early retirement practices in welfare state and production systems as well as the interest coalitions of workers and employers supporting these.

References Atchley, Robert C. 1982. “Retirement as a Social Institution.” Annual Review of Sociology 8: 263–87. Blöndal, Sveinbjörn and Stefano Scarpetta. 1998. “The Retirement Decision in OECD Countries.” OECD Economic Working Papers 202. Blossfeld, Hans-Peter, Sandra Buchholz, and Dirk Hofäcker, eds. 2006. Globalization, Uncertainty and Late Careers in Society. London: Routledge. Brinton, Mary C. 1998. “Institutional Embeddedness in Japanese Labor Markets.” Pp. 181–207 in The New Institutionalism in Sociology, edited by Mary C. Brinton and Victor Nee. New York: Russell Sage Foundation. Casey, Bernard. 1992. “Redundancy and Early Retirement: The Interaction of Public and Private Policy in Britain, Germany and the USA.” British Journal of Industrial Relations 30: 425–43. Crouch, Colin. 1993. Industrial Relations and European State Traditions. Oxford: Clarendon Press. Dogan, Mattei and Dominique Pelassy. 1990. How to Compare Nations: Strategies in Comparative Politics. London: Chatham House. Ebbinghaus, Bernhard. 2005. “When Less is More: Selection Problems in Large-N and Small-N Cross-National Comparison.” International Sociology 20: 133–52. Ebbinghaus, Bernhard. 2006. Reforming Early Retirement in Europe, Japan and the USA. Oxford: Oxford University Press. Ebbinghaus, Bernhard and Philip Manow, eds. 2001. Comparing Welfare Capitalism: Social Policy and Political Economy in Europe, Japan, and the USA. London: Routledge. Esping-Andersen, Gøsta. 1990. Three Worlds of Welfare Capitalism. Princeton, NJ: Princeton University Press. Esping-Andersen, Gøsta. 1996. “Welfare States without Work: The Impasse of Labour Shedding and Familialism in Continental European Social Policy.” Pp. 66–87 in Welfare States in Transition: National Adaptations in Global Economies, edited by Gøsta Esping-Andersen. London: Sage. Esping-Andersen, Gøsta. 1999. Social Foundations of Postindustrial Economies. Oxford: Oxford University Press.

288 Method and Substance in Macrocomparative Analysis Estevez-Abe, Margarita, Torben Iversen, and David Soskice. 2001. “Social Protection and the Formation of Skills: A Reinterpretation of the Welfare State.” Pp. 145–83 in Varieties of Capitalism: The Institutional Foundations of Comparative Advantage, edited by Peter A. Hall and David Soskice. New York: Oxford University Press. Gruber, Jonathan and David A. Wise, eds. 1999. Social Security and Retirement around the World. Chicago: University of Chicago Press. Hall, Peter A. and David Soskice. 2001. “An Introduction to Varieties of Capitalism.” Pp. 1–68 in Varieties of Capitalism: The Institutional Foundations of Comparative Advantage, edited by Peter A. Hall and David Soskice. New York: Oxford University Press. Kimura, Takeshi, Ikuro Takagi, Masato Oka, and Maki Omori. 1994. “Japan: Shukko, Teinen and Re-employment.” Pp. 247–307 in Regulating Employment and Welfare: Company and National Policies of Labour Force Participation at the End of Worklife in Industrial Countries, edited by Frieder Naschold and Bert de Vroom. Berlin: W. de Gruyter. Kitschelt, Herbert, Peter Lange, Gary Marks, and John Stephens. 1999. “Convergence and Divergence in Advanced Capitalist Democracies.” Pp. 427–60 in Continuity and Change in Contemporary Capitalism, edited by Herbert Kitschelt, Peter Lange, Gary Marks, and John Stephens. New York: Cambridge University Press. Kittel, Bernhard. 1999. “Sense and Sensitivity in Pooled Analysis of Political Data.” European Journal of Political Research 35: 225–53. Kohli, Martin and Martin Rein. 1991. “The Changing Balance of Work and Retirement.” Pp. 1–35 in Time for Retirement: Comparative Studies on Early Exit from the Labor Force, edited by Martin Kohli, Martin Rein, Anne-Marie Guillemard, and Herman van Gunsteren. New York: Cambridge University Press. Kohli, Martin, Martin Rein, Anne-Marie Guillemard, and Herman van Gunsteren, eds. 1991. Time for Retirement: Comparative Studies on Early Exit from the Labor Force. New York: Cambridge University Press. Mahoney, James. 2003. “Strategies of Causal Assessment in Comparative Historical Analysis.” Pp. 337–72 in Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. New York: Cambridge University Press. Mahoney, James and Dietrich Rueschemeyer. 2003. “Comparative Historical Analysis: Achievements and Agendas.” Pp. 3–38 in Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer. New York: Cambridge University Press. Naschold, Frieder and Bert de Vroom, eds. 1994. Regulating Employment and Welfare: Company and National Policies of Labour Force Participation at the End of Worklife in Industrial Countries. Berlin: W. de Gruyter. OECD. 2001. Ageing and Income: Financial Resources and Retirement in 9 OECD Countries. Paris: OECD. Przeworski, Adam and Henry Teune. 1970. The Logic of Comparative Social Inquiry. New York: Wiley. Ragin, Charles C. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. Berkeley, CA: University of California Press. Rein, Martin and Lee Rainwater, eds. 1986. Public/Private Interplay in Social Protection: A Comparative Study. Armonk, NY: M.E. Sharpe.

Bernhard Ebbinghaus

289

Rokkan, Stein. 1999. State Formation, Nation-Building and Mass Politics in Europe: The Theory of Stein Rokkan. Oxford: Oxford University Press. Scharpf, Fritz W. 2001. “Employment and the Welfare State: A Continental Dilemma.” Pp. 270–83 in Comparing Welfare Capitalism: Social Policy and Political Economy in Europe, Japan, and the USA, edited by Bernhard Ebbinghaus and Philip Manow. London: Routledge. Schils, Trudie. 2005. Early Retirement Patterns in Europe: A Comparative Panel Study. Amsterdam: Dutch University Press. Settersten, Richard A. and Karl Ulrich Mayer. 1997. “The Measurement of Age, Age Structure, and the Life Course.” Annual Review of Sociology 23: 233–61. Tilly, Charles. 1984. Big Structures, Large Processes, Huge Comparisons. New York: Russell Sage. Wadensjö, Eskil. 2002. “Active Strategies for Older Workers in Sweden.” Pp. 381– 402 in Active Strategies for Older Workers, edited by Maria Jespen, David Foden, and Martin Hutsebaut. Brussels: ETUI.

9 Identifying the Causal Effect of Political Regimes on Employment Adam Przeworski

9.1 Introduction The question studied here is whether political regimes, dichotomized as democracies and autocracies, affect the rate of growth of employment. But broader issues are at stake. The central claim of “new institutionalism” is that institutions are the primary cause of economic development. The theoretical program has been laid out by North (1997, p. 224, italics supplied): “To make sense out of historical and contemporary evidence, we must rethink the whole process of economic growth . . . The primary source of economic growth is the institutional/organizational structure of a political economy . . .” (For similar assertions, see Rodrik, Subramanian, and Trebbi, 2002 and Acemoglu, 2003.) Yet the new institutionalism also recognizes that institutions are endogenous. As already North and Thomas (1973, p. 6) observed, “new institutional arrangements will not be set up unless the private benefits of their creation promise to exceed the costs.” The embarrassingly obvious thought is that if endogeneity is sufficiently strong, causal effects of institutions cannot be identified. Imagine that only some particular institutions exist under the given conditions. Then the effects of institutions cannot be distinguished from the effects of the conditions under which these institutions are found. Consider the substantive question posed above in the context of the OECD countries. Since almost all of them had democratic regimes between 1950 and 1990 – the period studied here – it is not possible to determine whether the slow rate of growth of labor force in these countries, on the average 0.97 per annum as contrasted with 2.32 in the rest of the world, is the effect of democracy or of the high productivity of their labor force, on the average $19,257 per worker as opposed to $5,931 in other countries,1 or perhaps of the already higher levels of participation, on the average 44 percent as compared to 40 percent elsewhere. 290

Adam Przeworski

291

Whether the effect of political regimes can be identified in the world as a whole is the question pursued below. It is important, however, to keep in mind that identification is not a matter of sample size but of endogeneity. The reason causal effects of political regimes are next-toimpossible to identify among the OECD countries is not a small number of observations, but the fact that history has mischievously eliminated autocracy in developed countries. The logic entailed in identifying causal effects does not depend on N (see Fearon, 1991). Even if we are analyzing a single observation, we need to distinguish the effect of a cause from the effect of the conditions that activated this cause. Did the French Revolution generate little social change, as Tocqueville (1964 [1856]) would have it, because revolutions result in little change or because they occur only in countries resistant to change? The generic problem in identifying causal effects is how to answer the counterfactual question: what would have occurred had the cause been absent? But to engage in counterfactual inferences we need some systematic criteria to choose among several plausible candidates (Hawthorn, 1991). For example, the argument that colonialism had a positive effect on economic development of the colonies is based on the counterfactual hypothesis that these colonies would not have developed without foreign penetration, while claims that colonialism had a pernicious economic effect are based on the premise that they would have developed had they been left alone.2 Whether we can successfully solve such problems is, in my view, largely a matter of luck, namely whether history has been kind enough to generate observations that can be used to inform us about the plausible counterfactuals. Hence, some causal effects may be identifiable, while others may not be.3 Since this is mainly a methodological chapter, the theory is introduced rather briefly in section 9.2, only to motivate the statistical model to be estimated. Section 9.3 emphasizes that to identify causal effects it is necessary to make assumptions about counterfactuals. Section 9.4 presents different biases that may be present due to non-random assignment (“selection”) of causes to exogenous conditions. Section 9.5 is a review of estimators designed to avoid some of these biases. In section 9.6 these estimators are applied to the substantive problem at hand. Finally, section 9.7 focuses on the effects of globalization.

9.2 Growth in the labor force Assume a Cobb-Douglas economy with constant returns to scale, of the form Yt = At F(Kt , Lt ) = At Ktα L1−α . t

(9.1)

2 1.5

Rate of growth of labor force

2.5


1950

1960

1970

1980

1990

Year Lowess smooth. Source: PWT5.6 Figure 9.1 Average rate of growth of employment in the world, 1950–1990

The demand for labor in this economy is L∗t =

(1 − α)A w

1/α K,

(9.2)

where w is the wage rate per unit of L. The rate of growth in the labor force is thus given by L˙ K˙ 1 = + L K α

˙ ˙ A w − , A w

(9.3)

where the dots indicate time derivatives. One way to read this expression is that labor force grows at the same rate as the capital stock as long as increases in wages follow exactly the increases in Hicks-neutral productivity, Å/A. In turn, if wages grow slower than productivity, the growth of employment is faster than the growth of capital stock. The average rate of growth of employment in the world between 1950 and 1990 is presented in Figure 9.1.4 Given (9.3), we can use the series for labor force and capital stock to compare the rate of growth of productivity to that of wages. Figure 9.2 shows that until the mid-1970s average increases of wages largely outpaced average increases of productivity, but this difference was rapidly reduced so that by 1990 wages and productivity grew at the same pace.

293

⫺4 ⫺8

⫺6

Difference

⫺2

0

Adam Przeworski

1950

1960

1970

1980

1990

Year Lowess smooth. Source: PWT5.6 Figure 9.2 Average difference between productivity and wage growths

To introduce the effect of political regimes, assume that autocracies pay lower wages than democracies. The prima facie evidence for this assumption is based on Rodrik (1998) as well as Przeworski et al. (2000), who found that labor shares are lower in autocracies than in democracies at the same income levels. These data are reproduced in Figure 9.3. The labor share data, however, are scarce, cover only the manufacturing sector, and are highly unreliable.5 Hence, I will think in reduced form terms, assuming that the growth of wages is higher in democracies: ˙ w = θ ∗ REG, w

(9.4)

θ > 0, where REG = 1 if the political regime at time t is a democracy and ˙ ˙ ˙ REG = 0 otherwise. Letting LL ≡ γL , KK ≡ γK , and AA ≡ γA , we get γL = γK +

1 θ γA − REG, α α

(9.5)

where θ/α is the causal effect of political regimes on the growth of employment. We seek to identify this causal effect using data from 135 countries between 1950 and 1990.6 Assume that the rate of technical progress is

40

United States Democracies

30

Labor share

50

60


20

Autocracies

0

10000

20000

30000

40000

Product per worker fpfit. Shaded areas are 95% confidence intervals. Figure 9.3 Labor share as a function of product per worker, by regime

constant over time (but it may vary across countries or across regimes). Then we can write (9.5) as γL (it) = β0 + β1 γK (it) + β2 REG(it) + e(it),

(9.6)

where β0 = 1α γA (or 1α γA (i)), β1 = 1/α, and β2 = −θ/α. The question is whether it is possible to identify β2 when regimes are endogenous.

9.3 The problem First, we need some notation. Let T stand for the (potential) cause, where T = 1 indicates “treatment” and T = 0 “control” (or a different treatment).7 Without a loss of generality, we will think of democracy (REG = 1) as the treatment and of autocracy (REG =) as the control. X and V are “covariates,” that is, traits of an individual unit prior to the application of the treatment. X is the vector of covariates observed by the researcher, V are covariates not observed. Y = {Y0 , Y1 } is the variable subject to the potential effect of the cause, where Y0 stands for states of the units not exposed to treatment and Y1 of those exposed to it, so

Adam Przeworski

295

that for each unit i we observe either Y1 or Y0 : Yi = Ti Y1i + (1 − Ti )Y0i .

(9.7)

A “unit” is an opportunity for the cause to operate. It may be an individual, a country, or what not. Moreover, it may be the same individual or a country in a different state: say Sweden in 1950 and in 1951. Hence, the “unit” is a full set of observable and unobservable covariates: i is coextensive with the vector of “background conditions” (xi , vi ). Now, what is the causal effect of treatment on the particular unit i, the Individual Treatment Effect? This effect is defined as the difference between the states of an individual unit when it is subjected and not subjected to the operation of the cause, in our case between the rate of growth of labor force of a particular country at a particular time under democracy and autocracy. Formally,8 ITEi = y1i − y0i ≡ βi

(9.8)

But even if the assignment of regimes to countries were random, this question could not be answered without making some assumptions about hypothetical situations that would have occurred had a country that did not get treatment (had not been exposed to the potential causes) received it or had a country that did receive treatment not received it. Since these states did not occur, they are contrary to fact, counterfactual.9 And since counterfactuals cannot be observed, assumptions about counterfactuals cannot be directly tested.10 Hence, the effect of a cause on an individual unit cannot be determined without making assumptions about counterfactuals. These assumptions cannot be tested. What assumption would identify the individual treatment effect under random assignment? Assumption 1: Unit homogeneity (Holland 1986). For any i, j ∈ N, if {xi , vi } = {xj , vj }, then y0i = y0j and y1i = y1j . This assumption says that if any two units have the same values of covariates, they would have the same states under control and the same states under treatment. When this assumption is true, the process of selection can be ignored: it does not matter which of two identical units is subject to treatment and which serves as control.


This assumption identifies the causal effect of treatment. Assume that we observed i in state 1 and j in state 0. Applying the homogeneity assumption yields ITEi = y1,i − y0,i = y1,i − y0,j , where now both y1,i and y0,j are observed. What does “identify” mean? Intuitively “to identify” is to be able to infer relations among variables (or the parameters of multivariate distribution) on the basis of all the possible observations (Koopmans, 1949; in Manski, 1995, p. 6). But very often this is possible only by assuming something that may or may not be testable. As Manski (1995, p. 18) observed, “Theories are testable where they are least needed, and are not testable where they are most needed. Theories are least needed to determine conditional distributions P(y|x) on the support of P(x). They are most needed to determine these distributions off the support.” We have seen that since each unit can be observed only in one state at one time it is not possible to identify the individual causal effect without making some assumptions. Hence, we need identifying assumptions, such as unit homogeneity. This assumption is not testable. But it seems reasonable. Now we can ask about the Average Treatment Effect (ATE). Specifically, under what assumptions ¯ βATE = E(Y1 − Y0 |X) = E(β|X) = y¯ 1 − y¯ 0 = β, so that the observed mean difference identifies the average treatment effect? The answer is “conditional mean independence”: Assumption 2: Conditional Mean Independence. E(Y1 |X, T = 1) = E(Y1 |X, T = 0) = E(Y1 |X) E(Y0 |X, T = 0) = E(Y0 |X, T = 1) = E(Y0 |X) This assumption says that conditional on observed covariates we can expect the units not exposed to treatment to react to it identically to those observed under treatment and the units exposed to treatment not to differ in their control state from those observed under control.11 Under random assignment this assumption is trivially true. And it implies that the observed difference identifies the average causal effect:12 β¯ = E(Y1 |X, T = 1) − E(Y0 |X, T = 0) = E(Y1 − Y0 |X).

Adam Przeworski

297

Hence, if the assignment to treatment is random, then the difference of the observed means identifies the average causal effect of treatment. Now, let U stand for the effect of V on Y and assume linear separability. Then E(Y|X, V ) = E(Y|X) + U .

(9.9)

Substituting into (9.7) (and dropping the i subscript) yields Y = E(Y0 |X) + T [E(Y1 − Y0 |X)] + {T (U1 − U0 ) + U0 } = β0 (X) + β(X)T + U ,

(9.10)

where β(X) = E(Y1 − Y0 |X) is the average causal effect, discussed further below. To identify the causal effect, we need to ensure that U = T (U1 − U0 ) + U0 = 0, where U is the impact of unobserved factors in Y = β0 (X) + β(X)T + U and β(X) is the average causal effect conditional on X. The basic concern in identifying causal effects is thus whether E(U ) = 0.

9.4 Potential biases Baseline bias Note first that the causal effect of interest need not be the effect on the average unit but on those units that are actually observed as treated.13 This estimand is typically referred to as the Average effect of Treatment on the Treated (ATT ), defined as βATT = E(Y1 − Y0 |X, T = 1).

(9.11)

The value of this parameter tells us how the treatment changes the outcome for those unit that were observed as treated. Note that E(Y1 |T = 1) is observed, while E(Y0 |T = 1) is the missing counterfactual. Now consider ¯ as an estimator of βATT : the bias of the observed difference, β, β¯ − βATT = E(Y1 |X, T = 1) − E(Y0 |X, T = 0) − E(Y1 − Y0 |X, T = 1) = E(Y0 |X, T = 1) − E(Y0 |X, T = 0) = E(U0 |T = 1) − E(U0 |T = 0),

(9.12)


where the last expression is the difference in the control state between those units that were treated and those that were not, typically referred to as the “baseline bias.” Suppose, for example, that an omitted variable, say human capital, H, is correlated with the treatment and it affects the employment prospects of a country, so that E(U0 |H = high, T = 1) > E(U0 |H = low, T = 0) . Since countries in countries observed under T = 1 employment would have grown faster under T = 0 than those actually observed under T = 0, the observed difference overestimates the causal effect of T . This bias is sometimes referred to as “the” selection bias, but we will see that there are other potential selection biases than the baseline bias. Self-selection bias Now, return to ATE. The bias of β¯ as the estimator of βATE is β¯ − βATE = E(Y1 |X, T = 1) − E(Y0 |X, T = 0) − E(Y1 − Y0 |X).

(9.13)

Adding and subtracting E(Y0 |T = 1) yields β¯ − βATE = {E(Y0 |X, T = 1) − E(Y0 |X, T = 0)} + {E(Y1 − Y0 |X, T = 1) − E(Y1 − Y0 |X)} = {E(U0 |T = 1) − E(U0 |T = 0)}

(9.14)

+ {E(U1 − U0 |T = 1) − E(U1 − U0 )}. The term in the first curly brackets is the by now familiar baseline bias. The term in the second brackets, in turn, is best thought of as “selfselection” bias. This term is the difference between the effect of treatment on those who were actually treated and on the average unit. But why would the effect of the treatment on the treated differ from its effect on those who are not? One reason is that recruitment to treatment depends on something not observed by the researcher but anticipated by the unit. This will occur if units seek treatment for some reasons other than the X’s observed by the researcher or if they comply differently with the treatment depending on the X’s. Suppose – I am not asking you to believe it – that political elites which opt for democracy also know how to make employment grow faster. Then the effect of democracy on the growth of employment for the countries observed as democracies will differ from the effect on the average country: a self-selection bias.

Adam Przeworski

299

Post-treatment bias: manipulability and attributes Thus far we have assumed that the X s and the V s, called here “covariates,” do not change with treatment. The assumption was that causes can be manipulated one-at-a-time. But suppose that some of the covariates – call this subset A for “attributes” – change as the effect of treatment: this is called “post-treatment effect” by King and Zeng (2002). Now the treatment may have two effects: a direct one and an indirect via A. We need some identification assumptions to tell these two effects apart. Can we always make such assumptions? Here we enter into a complex and subtle issue. According to Holland (1986), to qualify as a potential cause, the particular variable must be vulnerable to (potential) manipulation. The critical feature of the notion of cause is that different values of the cause can be realized under the same background conditions. This is why attributes, such race or gender, cannot be causes. “Causes,” Holland says, “are only those things that could, in principle, be treatments in experiments” (1986, p. 954). What distinguishes statistical association from causation is manipulability: “the schooling a student receives can be a cause, in our sense, of the student’s performance on a test, whereas the student’s race or gender cannot.” It makes no sense to say “Joe earns $500 less than Jim because Joe is black,” since skin color (called “race” in the United States) cannot be manipulated. Causal inference is concerned with the effect of causes under specific background conditions (“on specific units”) and attributes cannot be manipulated without changing these conditions. This arguments confounds two propositions: (1) T cannot be manipulated and (2) T cannot be manipulated without changing A. The first one says that we cannot change the skin color of an individual. The second says that we can change it but if we change it, we will also change other characteristics of this individual (or the treatment of this individual by others). The confusion becomes apparent when we read that “An attribute cannot be a cause in an experiment, because the notion of potential exposability does not apply to it. The only way for an attribute to change its value [so it can be changed!] is for the unit to change in some way and no longer be the same unit” (Holland 1986, p. 954). Now, if (1) holds, it may still be true that there are other units that have the same background conditions but a different value of T and we can use the conditional mean independence assumption to identify the causal effect. Only if (9.2) is true, does identification become impossible. Consider an example closer to our practice: the location of a country in Africa, which in many analyses appears to affect civil strife and economic


growth. Does it make sense to say that “the effect of Africa on growth is β”? “Africa” is clearly an attribute by Holland’s definition, a set of related unobserved characteristics. If history had placed Zimbabwe in Latin America, it would have no longer been Zimbabwe: it would differ in various ways that make Africa distinct from Latin America. Hence, relying on the Africa dummy to generate counterfactuals would generate a “post-treatment bias.” King and Zeng (2002, p. 21) emphasize that controlling (matching) for variables that are endogenous with regard to treatment generates bias. This can be seen as follows. For simplicity, suppose that assignment is random, so that there is no baseline or self-selection bias, but X1 = X0 + δT . Then conditioning on X, E(Y1 − Y0 |X) = E(Y1 |X0 + δT ) − E(Y0 |X0 ) = E(Y1 − Y0 |X0 ) + {E(Y1 |X0 + δT ) − E(Y1 |X0 )},

(9.15)

where the last term is the “post-treatment bias.” For example, suppose that capital stock grows slower under dictatorships. Conditioning on the growth of growth of capital stock would then generate posttreatment bias. Non-independence bias: SUTVA One final implicit assumption concerns independence of the Y variables across units. This assumption is called SUTVA, for “stable unit treatment value.” Suppose that the units are individuals and that they learn from one another, so that yi = f (yj ). This means that the performance of the treated may affect the performance of the untreated, or vice versa. In Lucas’s (1988) growth model, a young plumber learns from the experienced one. Hence, if we take the difference in their productivity as the effect of experience, it will be underestimated because of the externality. Or take T to be ‘export-oriented’ strategy. South Korea adopted this strategy early and had high growth rates. Brazil adopted it late. But suppose that Brazil had adopted it early: would the growth rate of Korea have been the same? If it would not have been the same, the values observed for Korea under treatment depend on the realization of the treatment variable for Brazil: hence the Korean values are not “stable.” In our context, this assumption is particularly dubious. In an open economy, the rate of growth of employment in one country depends on its growth in other countries. Hence, if country i that is an autocracy in which wages grow slowly and employment quickly (say China) were

Adam Przeworski

301

to become a democracy in which employment would grow slower, the rate of growth of employment in other countries would accelerate. One needs some kind of a world equilibrium model to identify the causal effect when this assumption is violated.

9.5 Types of estimators How can we identify causal effects when the assignment is not random?14 Basically, we can adopt two approaches: drop the observations that are not “comparable,” restricting identification of causal effects to those that are, or keep all the observations and generate hypothetical matches for each of them. Matching procedures would eliminate (or give almost zero weights to) all the observations that do not have close matches, while procedures generating hypothetical counterfactuals would fill all the growth cells for which history did not generate the information. Matching One way to proceed is to match on observables.15 Say we want to examine the effect of guaranteed income programs on labor supply. We observe some wealthy countries with such programs (Revenue minimum d’insertion in France) and many countries, rich and poor, without them. We would not want to match the wealthy treatment cases with controls from poor countries. Hence, we use as controls countries with comparable per capita income, and restrict our causal inference to such countries. Matching takes the assignment of causes as given and calculates causal effects conditional on the assignment of causes realized by history, relying on the conditional mean independence assumption E(Yj |X, T = j) = E(Yj |X)

∀j,

(9.16)

which says that the value of Y in any state j does not depend on the state T in which a unit is observed once it is conditioned on the observed covariates. This is the same assumption as conditional mean independence introduced above, but written more generally to emphasize that the cause may assume any set of values. Matching estimators are vulnerable to two problems: (1) Dropping observations reduces the scope of generality. Sometimes, as in the example of minimum income programs, this is not a loss. It is not a loss because the probability that a poor country would institute these programs is zero: poor countries cannot afford such programs,

1.5

Autocracies

1

Democracies

0

.5

Labor force growth

2

2.5


0

5000

10000

15000

20000

Per capita income Shaded area is the 95% confidence interval Figure 9.4 Growth of employment as a function of per capita income, by regime

so that the question how these programs would affect labor supply in poor countries is moot. But how should we proceed when this probability is positive under all conditions, yet very differently distributed with regard to these conditions, as in the case of political regimes? What to do with observations without a close match? Figure 9.4 shows a semi-parametric (fractional polynomial) regression of labor force growth on per capita income, by regime. Since some democracies are located in the range where there are no autocracies, different matching algorithms will either drop these observations of democracies or assign to them very low weights.16 In either case, we have to worry whether the causal effect is the same for those observations with close matches and those without them. Moreover, as King and Zeng (2002) emphasize, extrapolations out of range of common support are highly sensitive to the form of the function. (2) We can match on observables. But should we not worry about unobservables? Suppose that leaders of some countries go to study in Cambridge, where they absorb the ideals of democracy and learn how to promote employment. Leaders of other countries, however, go to the School for the Americas, where they learn how to repress and nothing about economics. Autocracies will then generate lower growth because of the quality of the leadership, which is not observed. Since this is a variable we could not observe systematically,

Adam Przeworski

303

we cannot match on it. And it may matter. Conditional mean independence – the assumption that unobserved factors do not matter – is very strong, and likely to be often false in cross-national research. All that was said about matching applies to regression models that control for the observables. Matching is just a non-parametric regression: both generate means of Yconditional on X and T . Moreover, as observed respectively by Manski (1995) and Achen (1986), both matching and parametric regressions that control for observables may in fact exacerbate the biases due to selection on unobservables. Both matching and parametric regression estimates can be subjected to sensitivity analysis. Given assumptions about the unobservables, one can calculate the range of estimates that are compatible with the observed data (Manski, 1995). Rosenbaum (2002, chapter 4) presents methods for quantifying the sensitivity of the estimates of causal effects under different assumptions. Obviously, the more plausible the assumption and the narrower the bounds, the more credible is the estimate. Instrumental variables Instrumental variables estimator is based on the assumption of conditional mean independence in the form: E(Yj |X, Z, T = j) = E(Yj |X, Z)

∀j.

(9.17)

The idea is the following. Suppose that after conditioning on X, Yj still depends on j, in other terms that cov(T , U ) = 0. Now, suppose that there is a variable Z, called an “instrument,” such that cov(Z, T ) = 0

(9.18)

cov(Z, U ) = 0.

(9.19)

and

Then conditioning on X and Z satisfies (9.11). Thinking in regression terms, let Yˆ = f (Z) and Tˆ = g(Z). Then, by assumption (9.17), β in Yˆ = βTˆ is that part of the causal effect of T on Y which is independent of U . To qualify as an instrument, a variable must be related to the cause and only to the cause, so that its entire effect is transmitted by the cause. Note that while the assumption that the instrument is related to the cause (conditional on all exogenous variables) can be and should be tested,


the assumption that it is independent of the conditions that also shape the effect is not testable. Instruments must be correlated with the cause. Weak instruments (those weakly correlated with the treatment) can generate biased estimates even with very large samples. But instruments cannot be too strongly correlated with the cause. In the limit, if the instrument and the cause are the same, the instrument is as endogenous as the cause: this is “the curse of strong instruments.” The causal effect cannot be identified, because it is impossible to separate the impact of the cause from that of the conditions that give rise to it. In turn, the “exclusion restriction” (9.19) requires that the instrument have no effect that is not mediated by the cause. Moreover, given that U = T (U1 − U0 ) + U0 , cov(Z, U ) = cov(Z, U0 ) + cov(Z, T (U1 − U0 )).

(9.20)

Hence, the exclusion restriction has two parts, and Heckman (1996, 2004) repeatedly makes the point that, even if cov(Z, U0 ) = 0 , in the presence of unobserved self-selection the second covariance will not be zero. Selection on unobservables Both matching and instrumental variables estimators condition on observed covariates and both are vulnerable to the influence of unobserved variables that are correlated with the treatment. Another approach conditions on unobserved as well as on observed covariates. One way to think of these estimators is that they emulate experiments, but differently than matching: not by eliminating observations that do not have an observed match but by creating observations to match all the observed values. The assumption is that if the conditioning is correct, then the resulting data have the same structure as if history had performed a random experiment assigning different values of treatment to each unit. Since the conditional mean independence of the form E(Yj |X, Z, V, T = j) = E(Yj |X, Z, V )

∀j

(9.21)

holds whenever assignment is random, the only issue with regard to these estimators is whether they correctly emulated random assignment. The basic idea is the following. We first describe the process by which the observed assignment of causes was generated by history: T ∗ = Zα + V, T = 1(T ∗ > 0), V ∼ (0, 1).

(9.22)

Adam Przeworski

305

This equation says that the propensity toward being observed under treatment depends on observable variables Z and unobserved factors Vand that we observe T = 1 if T ∗ > 0. Secondly, we exploit the possibility that cov(V , U ) = 0 , by expressing E(Uj |T = j) in E(Yj |X, T = j) = E(Yj |X) + E(Uj |T = j),

(9.23)

E(Uj |T = j) = θj E(V|T = j),

(9.24)

as

where the latter expectation can be estimated from (9.22). Finally, we substitute, to obtain E(Yj |X, T = j) = E(Yj |X) + θj E(V |T = j),

(9.25)

which can be now estimated by least squares. The OLS coefficients of E(Yj |X) = Xβj can be then used to generate counterfactual values of Yj for the cases in which it is not observed, thus filling all the missing matches. Finally, for j = {0, 1}, βˆ ATE = E(Y1 |X) − E(Y0 |X) = (βˆ 1 − βˆ 0 )X, is the estimator of the average causal effect. Note that we still have to be concerned about strong endogeneity of treatment. In principle, it has to be true that 0 < Pr (T = 1|Z) < 1 ∀Z. Otherwise, the counterfactuals cannot be realized given the mechanism by which history assigns treatments, so that the entire exercise is moot. The main vulnerability of this class of estimators stems from the untestable assumption about the joint distribution (V , U1 , U0 ).

9.6 Political regimes and the growth of employment With this background, we return to the effect of political regimes on the growth of employment between 1950 and 1990. We observe 1,595 democratic years and 2,396 autocratic ones. The mean rate of growth of labor force under democracy was 1.59 (s.d. = 1.20) and under autocracy 2.28 (s.d. = 1.94), for a difference of −0.69. Obviously, comparing means is the same as regression, so it generates the same result, with a standard error of 0.05. Yet we already know that the difference of observed means

.0003

Density

0

0

.0001

.0002

.0003 .0002 .0001

Density

.0004

.0004

.0005

.0005


0

5000 10000 15000 20000

0

5000 10000 15000 20000

Level Democracies

Level Autocracies

Figure 9.5 Density of per capita income, by regime

is a biased estimator of the average causal effect if the assignment of regimes is not random, and it is easy to see that it is not. Figure 9.5 shows the density of per capita incomes by regime. As one would expect, autocracies tend to be poor, while democracies can be found at all income levels. Indeed the wealthiest autocracy in the data set, Singapore in 1990 with per capita income of $11,698, was poorer than 200 years of democracies, with the US in 1989 leading the list at $18,095 in 1989. Moreover, Figure 9.6 shows that democracies were somewhat more frequent during the years when the average rate of growth of labor force in the world was lower. To identify the causal effects of regimes, we must, therefore, distinguish it from the effect of the conditions under which these regimes were found. To do so, we will augment the theoretically derived specification given in (9.6) by adding some controls. These include the lagged proportion of the population that is employed (since labor force cannot grow when everyone is employed), the average rate of growth of labor force in the world during a particular year (as a crude attempt to take into account the world equilibrium effects), and per capita income (as a crude attempt to control supply effects, on the assumption that preference for

Density

.5

1

1.5

2 1.5 1 0

0

.5

Density

307

2

Adam Przeworski

1

1.5

2

2.5

3

1

1.5

lfg_world Democracies

2

2.5

3

lfg_world Autocracies

Figure 9.6 Density of average world employment growth, by regime

leisure increases in income). Hence, we will be estimating models of the form: lfg = β0 + β1 ksg_lag + β2 lfprop_lag + β3 lfg_world + β4 level + βATE REG + e, (9.26) where lfg stands for labor force growth, ksg_lag is the lagged value of capital stock growth,17 lfprop_lag is the lagged value of labor force proportion in the population, lfg_world is the mean rate of growth of labor force in the world in a particular year, and level is income per capita. The parameter of interest is βATE . In addition, in the instrumental variables and the selection on unobservables models, we will augment (9.26) by a selection equation of the form Pr(REG = 1) = Pr(Zα + V > 0) = F(Zα).

(9.27)

The probit model uses the specification based on Przeworski et al. (2000). It includes per capita income (levlag), proportion of countries in the world that are democracies in a particular year (odwp), the number of completed spells of democracy in the history of the country (stra), where all these values are lagged one year, and interactions of these variables with the lagged regime (autocracy = 1).

308 Method and Substance in Macrocomparative Analysis Table 9.1 Different estimates of the average treatment effect Estimator

βATE

s.e.

OLSa Fixed effects Match-Kernelb Match-Neighborb Match-Stratab Match-Imbensc Heckmand Heckmane 2SLSf

−0.27 −0.15 −0.29 −0.30 −0.29 −0.18 −0.26 −0.29 −0.29

0.110 0.093

−2.56 −1.63

0.010 0.104

0.074 0.015 0.070 0.070

−2.44 −17.33 −4.14 −4.13

0.015 0.000 0.000 0.000

t

p

Notes: a Panel corrected standard errors. Other OLS results are similar. b Standard errors are not given for these matching estimators since the average treatment effect was calculated as the weighted average of ATT and ATC (see below). c Imbens nnmatch with 5 matches. d Heckman two-steps estimator, with separate regressions for each regime. e Heckman estimator with all the observations considered together. f With propensity score as the instrument. Using all the instruments separately generates an almost identical result.

Table 9.2 Estimates of the effect of the treatment on the treated and on the control group Estimator

Democracies βATT

Kernel Neighbor Strata Heckman

0.43 0.48 0.39 0.44

as s.e. 0.11 0.49 0.08 0.02

Autocracies z

Autocracies βATC

3.93 0.98 4.74 27.7

−0.20 −0.19 −0.23 −0.02

as s.e. 0.05 0.12 0.06 0.03

Democracies z −3.62 −1.59 −3.92 −0.81

The results are surprisingly robust.18 Here is a table that summarizes the estimates of βATE . Similar conclusions apply to the estimates of the effect of the treatment on the treated (ATT) and the effect of the treatment on the control (ATC). Since we took the treatment to be democracy, the first estimates tell us what would have been the difference in the growth of employment for the units observed as democracies had they been autocracies under the identical conditions, while the second inform us what would have been the difference for the countries observed as autocracies had they been democracies (The signs are inverted to facilitate the interpretation.) These estimates indicate that countries observed as democracies (which tend to be more developed) would have had a much faster employment growth had they been autocracies. In turn, the countries

Adam Przeworski

309

observed as autocracies would have had a somewhat slower employment growth as democracies, but the difference is about a half of that for the observed democracies. Thus, from the methodological point of view, this exercise turned out to be disappointing. There appear to be no selection biases with regard to the growth of labor force, so that all the estimators generate similar results.19 It seems safe to conclude that the growth of employment is somewhat slower in democracies by some amount between 0.15 and 0.30.

9.7 Globalization Yet all the estimators we used are based on the assumption of stable unit treatment value, which is unlikely to be satisfied in a globalized economy. Note that all the parametric analyses indicate that the rate of growth of labor force in each country is positively affected by its rate of growth in the world as a whole. Suppose that a country experienced a transition to democracy. Since employment grows slower in democracies, the world average would become lower, thus affecting the rate of growth in each country. This is clearly a violation of SUTVA, even if the bias it generates may be small. To analyze the consequences of the mobility of capital and of commodities, one would have to study a world economic equilibrium, which I will not do. Instead, we can analyze the effect of the average growth of employment in autocracies in a particular year on the growth of employment in each of the democracies during this year. The estimates are again robust for all parametric models (different versions of OLS, IV, and Heckman), so I do not enter into details. Re-estimating all the parametric models with the effect of the mean growth of employment in autocracies increases the estimate of the effect of democracy on the growth of employment from a ball park number of −0.27 to about −0.43. With only minor variations, these analyses show that (1) labor force in democracies grows when employment increases in autocracies, indicating that autocracies and democracies respond similarly to fluctuations in world demand, but (2) this effect interacts negatively with per capita income. Since (using estimates from Heckmantwo) the effect of growth in autocracies on the growth in democracies is +0.52 and the effect of interaction with per capita income of each of the democracies is −0.0393 per thousand, in a poor democracy like India, with per capita income of about $1,000, the rate of growth of labor force increases by 0.48, while in a wealthy democracy such as the


United States, with an income of $18,000, employment growth slows down by about 0.19. when employment in autocracies increases by 1%. Democracies with per capita income of 0.52/0.0393 = 13,343, the income of Switzerland in 1971, neither benefit nor lose when employment in autocracies increases. Note that in 1990 the unweighted average of per capita incomes in the OECD countries was $13,650. Thirteen countries with incomes equal or higher to that of Iceland were net employment losers because of competition with autocracies. Here, then, is the story: At each income level, capital stock grows at about the same rate in the two regimes. Yet autocracies repress wages; hence, they employ workers with lower marginal product; hence, their employment grows faster. In turn, this implies that democracies employ only workers with higher marginal product; hence, their employment grows slower. Since, as Figure 9.1 shows, the gap in wages opens up with per capita income, the effect of competition with autocracies is greater in the more developed democracies.

Notes 1. All the dollar numbers are in 1985 purchasing power parity dollars from Penn World Tables, release 5.6. 2. For a thoughtful discussion of this issue, see Kaniyathu (2006). 3. For a more extensive discussion of these issues, see Przeworski (2006). 4. The labor force series is obtained by dividing product per worker by product per capita from PWT 5.6. According to the ratings of data quality provided by PWT, the reliability of the product series varies greatly across countries. Przeworski et al. (2000: Appendix 3.1) calculated that this quality is much higher in democracies but did not find data quality to be a source of bias. 5. Indeed, the World Bank stopped publishing them. 6. This particular data set is used because this is the only period during which the information about capital stock is available. Economic data are from PWT5.6 and political data from Przeworski et. al. (2000). 7. Although for simplicity I assume that the cause is a binary variable, everything said here holds for any discrete or continuous values of T . 8. For simplicity, I will ignore time in the theoretical dicusssion. 9. The idea of counterfactuals goes back to Pascal (1669, sec. 162): “Le nez de Cléopâtre: s’il eût été plus court, toute la face de la terre aurait changé.” On the distinctions among different types of conditional propositions, see Edgington (2001). On the logical problems with counterfactuals, see Quine (1953), Lewis (1973), Mackie (2002 [1973]), Goodman (1979), and Stalnaker (1987). 10. For a statistical view of causality without counterfactuals, see Dawid (2000), who rejects them as metaphysical. 11. To help with the notation, E(Y1 |T = 1) is to be read as “the expected value of the outcome under treatment, given that the units have been observed

Adam Przeworski

12.

13.

14.

15. 16.

17. 18.

19.

311

as treated,” while E(Y1 |T = 0) as “the expected value of the outcome under treatment, given that the units have been observed as not treated.” According to a theorem by Rosenbaum and Rubin (1983), if the conditional mean independence holds in the form specified in the text, then it also holds in the form in which p(X) = Pr(T = 1|X) is substituted for X, where p(X) is the “propensity score.” This effect is of particular interest in remedial policy programs. As Heckman repeatedly points out, it makes no sense to ask what would be the effect of manpower training program on millionaires. In turn, we want to know the effectiveness of such programs for the people who need them and get them. For overviews of estimators see Angrist and Krueger (1999), Berk (2004, Chapter 5), Dufflo (2002), Persson and Tabelini (2003, chapter 5), or Winship and Morgan (1999). For reasons of space, I do not discuss difference-indifference estimators, for which see Woolridge (2002) and Bertrand, Duflo, Mullainathan (2004). On matching estimators, see Rosenbaum (2002), Imbens (2002), Becker and Ichino (2002), and, more critically, Heckman (2004). Depending on the algorithm, matching estimators treat differently observations that cannot be matched exactly. When matching is restricted to common support or when it is confined to balanced strata, observations without a match are ignored. When some kind of distance measure is employed, distant matches obtain weights approaching zero. Przeworski et al. (2000) performed various tests and found that the growth of capital stock is exogenous with regard to regimes. The parametric estimators are not sensitive to the specification of the selection equation but matching estimates become lower when the variable stra is dropped from this equation. In some other contexts, different estimators generate highly disparate conclusions. See Przeworski (2006).

References Acemoglu, Daron. 2003. “Root Causes: A Historical Approach to Assessing the Role of Institutions in Economic Development.” Finance and Development 27–30. Achen, Christopher. 1986. The Statistical Analysis of Quasi-Experiments. Berkeley, CA: University of California Press. Amemyia, Takeshi. 1994. Introduction to Statistics and Econometrics. Cambridge, MA: Harvard University Press. Angrist, Joshua D. and Alan B. Krueger. 1999. “Empirical Strategies in Labor Economics.” Chapter 23 in The Handbook of Labor Economics, volume III, edited by O. Ashenfelter and D. Card. Amsterdam: North-Holland. Angrist, Joshua D. and Alan B. Krueger. 2001. “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments.” Journal of Economic Perspectives 15: 69–85. Becker, Sascha O. and Andrea Ichino. 2002. “Estimation of Average Treatment Effects Based on Propensity Scores.” The Stata Journal 7: 1–19. Berk, Richard A. 2004. Regression Analysis: A Constructive Critique. Thousand Oaks, CA: Sage.

312 Method and Substance in Macrocomparative Analysis Bertrand, Marianne, Esther Duflo, and Sendhill Mullainathan. 2004. “How Much Should we Trust Differences-in-differences Estimates?” Quarterly Journal of Economics 119: 249–75. Dawid, A.P. 2000. “Causal Inference without Counterfactuals.” Journal of the American Statistical Association 95: 407–24. Duflo, Esther. 2002. “Empirical Methods.” Class notes. Department of Economics, MIT. Edgington, Dorothy. 2001. “Conditionals.” Pp. 385–414 in The Blackwell Guide to Philosophical Logic, edited by Lou Goble. Oxford: Blackwell. Fearon, James. 1991. “Counterfactuals and Hypothesis Testing in Political Science.” World Politics 43: 169–95. Goodman, Nelson. 1979. Fact, Fiction, and Forecast, 4th edition. Cambridge, MA: Harvard University Press. Hawthorn, Geoffrey. 1991. Plausible Worlds: Possibility and Understanding in History and the Social Sciences. Cambridge: Cambridge University Press. Heckman, James J. 1996. “Instrumental Variables: A Cautionary Tale.” Technical Working Paper No. 185. Cambridge, MA: National Bureau of Economic Research. Heckman, James J. 1997. “Instrumental Variables: A Study in Implicit Behavioral Assumptions Used in Making Program Evaluations.” Journal of Human Resources 32: 441–62. Heckman, James J. 1992. “Randomization and Social Policy Evaluation” in Evaluating Welfare and Training Programs, edited by C. Manski and I. Garfinkel. Cambridge, MA: Harvard University Press. Heckman, James J. 2004. “The Scientific Model of Causality.” Working Paper. Department of Economics, University of Chicago. Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81: 945–60. Imbens, Guido W. 2002. “Semiparametric Estimation of Average Treatment Effect under Exogeneity: A Review.” Working Paper. Department of Economics, University of California at Berkeley. Kaniyathu, Sunny. In progress. The Balance Sheet of Colonialism: Economic Development in the Colonial Period. PhD Dissertation. Department of Politics, New York University. King, Gary, and Langche Zeng. 2002. “When Can History be Our Guide? The Pitfalls of Counterfactual Inference.” Available at gking.harvard.edu. Lewis, David. 1973. Counterfactuals. Cambridge, MA: Harvard University Press. Mackie, J.L. 2002 [1973]. “The Logic of Conditionals.” Pp. 106–14 in Philosophy of Science: Contemporary Readings, edited by Yuri Balashov and Alex Rosenberg. London: Routledge. Manski, Charles F. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press. North, Douglass C. 1997. “Some Fundamental Puzzles in Economic History/ Development.” In The Economy as an Evolving Complex System II, edited by W. Brian Arthur, Steven N. Durlauf, and David A. Lane. AddisonWesley. North, Douglass C. and Robert Paul Thomas. 1973. The Rise of the Western World: A New Economic History. Cambridge: Cambridge University Press.

Adam Przeworski

313

Przeworski, Adam. 2006. “Is the Science of Comparative Politics Possible?” In Oxford Handbook of Comparative Politics, edited by Carles Boix and Susan C. Stokes. New York: Oxford University Press. Przeworski, Adam, José Antonio Cheibub, Fernando Limongi, and Michael E. Alvarez. 2000. Democracy and Development. New York: Cambridge University Press. Quine, W.V. 1953. From the Logical Point of View. Cambridge, MA: Harvard University Press. Rodrik, Dani. 1998. “Democracies Pay Higher Wages.” Working Paper 6364. Cambridge, MA: National Bureau of Economic Research. Rodrik, Dani, Arvind Subramanian, and Francesco Trebbi. 2002. “Institutions Rule: The Primacy of Institutions Over Geography and Integration in Economic Development.” Unpublished. Rosenbaum, Paul R. 2002. Observational Studies, 2nd edition. New York: SpringerVerlag. Rosenbaum, Paul R. and D.B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies.” Biometrika 70: 41–55. Stalnaker, Robert C. 1987. Inquiry. Cambridge, MA: MIT Press. Tocqueville, Alexis de. 1964 [1856]. L’ancien Régime et la Révolution. Paris: Gallimard. Winship, Christopher and Stephen L. Morgan. 1999. “The Estimation of Causal Effects from Observational Data.” Annual Review of Sociology 25: 659–707. Woolridge, Jeffrey M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT.

This page intentionally left blank

Index

affirmative action 6 analytical variables 31 annual data 35–6 anti-discrimination laws 6 Australia employment change 3, 71, 83 employment level 3 employment protection 73 high wage increases 73 low earnings inequality 73 men’s employment 5 payroll/consumption taxes 73 public employment 73 unemployment benefit 73, 84 women’s employment 5 Austria childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 employment change 3 employment levels 3 extended leave 153 family policies 214 left cabinet incumbency 148 maternity leave 150, 212 men’s employment 5, 198 public sector employment 213 state benefits 154 women’s employment 5, 154, 198, 199 women’s working hours 247 autocorrelation 44 Durbin’s M test 44 average effect of treatment on the treated 297 average treatment effect 296 bad employment performance baseline bias 297–8

Belgium childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 employment change 3, 71, 83 employment levels 3 employment protection 73 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199 women’s working hours 247 between-country variation 41 see also individual countries and parameters Boolean analysis 1 breadwinner state 229–35 part-time employment 235–41 British Household Panel Survey 227 Canada childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 employment change 3, 71, 83 employment levels 3

69

315

316 Index Canada – Continued employment protection 73 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198 payroll/consumption taxes 73 public sector employment 213 state benefits 154 women’s employment 5, 154, 198, 199 case studies 14 causal complexity 95, 120 causal conditions 72–4 fuzzy-set scores vs raw values 73 causal configurations 67 causal effects 169 political regimes 290–313 causal necessity 150, 160 causal pathways 14, 82 causal relations 161–3, 169 causal sufficiency 9, 10, 150, 160 family policies 198–202 childcare provision 5, 92, 142, 151, 152, 204, 210, 211 and women’s educational attainment 203 and women’s employment 142–4, 200 see also individual countries children, and employment rate 222 civilian government employment 149 clearly conforming cases 82 clearly not conforming cases 82 Cobb-Douglas economy 291 coefficient of determination 13 comparative analysis 35 comparative employment performance 2–6 fuzzy-set analysis 67–90 compliers average causal effects (CACE) 20, 149, 156 estimation of 177–9 women’s employment 164–6 conceptual map 267

conditional mean independence 296–7 consistency scores 13, 76, 79 consumption taxes 72, 86, 88 coordinated market economies 267, 283–4 counterfactual cases 77 country dummies 208, 209 covariates 299 coverage score 13, 79–80, 82 raw coverage 79 unique coverage 79–80 cross-sectional analysis 48–57 cultural factors in women’s employment 114–19 cumulative indices 41 cumulative left cabinet incumbency 138, 148, 151, 152, 155, 158, 160, 163, 174 day care see childcare provision Denmark childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 268 employment change 3, 71, 83 employment levels 3 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199, 271

Index deterministic relationships detrending 36 Durbin’s M test 44

8

early-exit rate 272–4 men 273, 274 women 274, 275 early-exit regimes 22, 260–89 comparative analysis 262–8 partnership (mediation) 266–7, 268 production (push) 261, 262, 281–5 protection (pull) 261, 264, 280–1 earnings inequality 86 economic growth 32 economic outcomes 31 economic structure, and women’s employment 94 educational attainment of women 201–2, 203 employment change 3, 71 by causal configuration 83, 84 cross-country variation 71–4 fuzzy-set scores vs raw values 71 low-end private sector services 70 poor performance 86 employment population ratio 230 employment protection regulations 42, 72, 88 private sector consumer services 50–1 and replacement rate 51, 56 employment rate 2–6, 32 decline in 268–72 effect of political regime 305–9 low-wage sector 38–41 men 5, 198, 269 and presence of children 222 private sector consumer services 42–61 women see women’s employment encompassing comparison 267 estimand 297 estimators 301–5 European Community Household Panel 227 European Employment Strategy 224, 246

317

European Union Directive 97/81/EC 233 Part-Time Directive 246 exit pathways 279 extended leave 153 family policies 4–5, 102–7, 139–46, 214 causal sufficiency 198–202 and women’s employment 20, 91–134, 196–220 see also individual countries female friendliness 135 Finland childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 employment change 3, 71, 83 employment levels 3 employment protection 73 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199 women’s working hours 247 first differencing 36 Fisher test for nonstationarity 39, 41 fixed unit effects 46, 209 France childcare provision 102–3, 151, 152, 210, 211 children and employment rate 222 civilian government employment 149

318 Index France – Continued Comité du Travail Féminin 108, 122 cultural factors 114–15 dénatalité 114 dépopulation 114 early-exit rate 274 early-exit regimes 268 economic factors 107–8 employment change 3, 71, 83 employment levels 3 exit pathways 279 extended leave 153 family policies 214 family policy 102–4 high wage increases 73 labor market policies 108–10 left cabinet incumbency 148 loi Roudy 109 maternity leave 102, 150, 212 men’s employment 5, 198, 269 Ministere des Droits de la Femme 122 Mouvement Démocratique Féminin 108 parental care leave 103, 121 part-time employment 99 public employment 73 public sector employment 213 single salary allowance 102 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 97, 98, 99, 107–8, 154, 198, 199, 271 women’s working hours 247 work and family values 101 free choice 115 full-time work for women 98, 99 fuzzy-set qualitative comparative analysis 9, 12, 18, 20, 67–90 advantages of 67–8 goodness-of-fit tests 160, 161, 176–7 membership scores 173–4 outcome 69–71 women’s employment 156–63 gender employment gap 221–2, 223 gender equality 136, 141

gendered job creation 136, 138 gendered roles 117, 140 German Socio-Economic Panel 227 Germany childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 268 employment change 3, 71, 83 employment levels 3 employment population ratio 230 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199, 221–59, 271 women’s labor market status 236 women’s working hours 247 globalization 309–10 global variables 31 golden handshakes 282 goodness-of-fit tests 160, 161, 176–7, 179–87 Greece children and employment rate 222 women’s working hours 247 heteroskedasticity

43, 49

ideals of care 94 identifying assumptions 296 institutional complementarity

265

Index institutionalist approach 264 institutional variables 33, 34 institutions 31 instrumental variables 303–4 intention-to-treat analysis 153, 155–6 Ireland childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 268 employment change 3, 83 employment levels 3 exit pathways 279 extended leave 153 family policies 214 left cabinet incumbency 148 maternity leave 150, 212 men’s employment 5, 198, 269 public sector employment 213 state benefits 154 women’s employment 5, 154, 198, 199, 271 women’s working hours 247 Italy childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 268 employment change 3, 71, 83 employment levels 3 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269

319

payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199, 271 women’s working hours 247 Japan early-exit rate 274 early-exit regimes 260–89 employment change 3, 71, 83 employment levels 3 employment protection 73 exit pathways 279 low earnings inequality 73 men’s employment 5, 269 partial exit model 284 payroll/consumption taxes 73 public employment 73 women’s employment 5, 271 labor force growth 291–4 labor market institutions 4, 29–66 labor market policies 108–13 labor market rigidities 4 labor market status of women 236, 238–40 labor share 294 last in-first out rules 282 left cabinet incumbency 148 left governance 148, 163, 164 high levels 167 impact on policy 159–61 low levels 165 liberal market economies 266, 267, 282 lock-in effects 34 low earnings inequality 72 low-end wages 4 low-wage sector employment 38–41 autoregression 40 cross-sectional and time variance components 40 main variables 39 nonstationarity 41

320 Index macrocomparative analysis 6 macro-comparison 1–2 macro-level variables 30–8 manipulability 299–300 market outcomes 46 matching 301–3 maternity leave 139, 140, 144–5, 150, 204, 212 and women’s educational attainment 203 and women’s employment 200 see also individual countries membership scores for fuzzy-set analysis 173–4 men early-exit rate 273, 274 employment 5, 198, 269 method of agreement 16 method of difference 15 methodology 6–18 Mill, John Stuart method of agreement 16 method of difference 15 motherhood and employment 22 lone mothers 112–13 reinforcement of role 117 see also women’s employment multicollinearity 54 multiple pathways 68 nearly always sufficient condition 10, 11 necessary condition 16 necessity 9, 10 Netherlands Adjustment of Hours Act 112 Breed Platform 116 childcare provision 104–6, 113, 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 cultural factors 115–19 early-exit rate 274 early-exit regimes 268 Emancipatiekommissie 116, 122

employment change 3, 71, 83 employment levels 3 employment population ratio 230 employment protection 73 Equal Treatment Act 111 exit pathways 279 extended leave 153 family policies 104–7, 214 high wage increases 73 labor market policies 110–13 left cabinet incumbency 148 low earnings inequality 73 maternity leave 102, 106, 150, 212 men’s employment 5, 198, 269 parental sharing 107 part-time clause 232 part-time employment 99 payroll/consumption taxes 73 public employment 73 public sector employment 213 single mothers 112–13 state benefits 154 Stimulation Measure on Childcare 105 unemployment benefit 73, 84 Wassenaar agreement 232 welfare restructuring 111 welfare system 104 women’s employment 5, 97, 98, 99, 154, 198, 199, 221–59, 271 women’s labor market status 236 women’s working hours 247 Work and Care Act (2001) 121 work and family values 101 new institutionalism 290 New Zealand employment change 3 employment levels 3 men’s employment 5 women’s employment 5 non-independence bias 300–1 nonstationarity 38–9, 41, 44, 46 Nordic countries see Denmark; Finland; Norway; Sweden Norway childcare provision 151, 152, 210, 211

Index children and employment rate 222 civilian government employment 149 employment change 3, 71, 83 employment levels 3 employment protection 73 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199 omitted variable bias 86 ordinal comparison 14, 263, 285 OSA Labor Supply Panel 227 panel data 29, 63–4 parental care leave 5, 103, 121, 144, 145–6 see also maternity leave parental sharing 107 partial exit model 284 partnership (mediation) regimes 266–7, 268 part-time employment 6 breadwinner determinants 235–41 as coping strategy 241–6 women 98, 99, 100, 223–4, 226 pay equality 42 payroll taxes 72, 86, 88 pension schemes 264–5 public-private mix 265 period-demeaning 36 policy variables 31, 32, 34 political economic variables 38–42 employment regulation 42 low-wage sector employment 38–41

321

pay equality 42 reservation wage 41–2 political regimes effect on employment 305–9 and per capital income 306 pooled regression 7–9, 18 limitations of 8–9 see also regression analysis Portugal children and employment rate 222 men’s employment 5 women’s employment 5 women’s working hours 247 post-treatment bias 299–300 potential exposability 299 potentially conforming cases 82 Prais-Winston transformation 46 pre-school childcare see childcare provision principal components analysis 204 principal factor analysis 204 private sector consumer services 42–61 annual variation 47, 62 cross-sectional analysis 48–57 employment protection regulations 50–1, 56 long-time changes 48, 55 low-end, employment rate 70 pooled analysis 42–8, 57–9 replacement rate 43, 45, 56, 59, 60, 61 process tracing 14, 95 production (push) regimes 261, 262, 265–6, 268, 281–5 productivity, and wage growth 293 protection (pull) regimes 261, 264, 265, 266, 268, 280–1 public childcare see childcare provision public sector employment 6, 72, 73, 88, 204 expansion of 137–46 women 94, 137–9, 162, 200, 213 and women’s educational attainment 203 see also individual countries

322 Index qualitative comparative analysis 1, 7, 9–13, 196 crisp-set 9 fuzzy-set 9, 12, 18, 20, 67–90 limitations 12 ratchet effects 34 raw coverage 79 regression analysis 196–220 bivariate 205–8 multivariate 208–17 see also pooled regression relative risk ratio 229 remainders 77 replacement rate 43, 59, 60, 61 and employment regulations 51, 56 panel models 45 scores 59 reservation wage 41–2 school scheduling 92 selection bias 23 self-selection bias 298 single mothers 100–1, 112–13 single salary allowance 102 small-N analysis 1, 7, 13–18 social care 135 social democratic governance 139–41 social policy 93 solution sets 77–8 Spain children and employment rate 222 employment change 3 employment levels 3 men’s employment 5 women’s employment 5 women’s working hours 247 stable unit treatment value 300–1 state benefits 154 structural variables 31 subsidiarity 96 sufficient condition 16 Sweden childcare provision 140, 151, 152, 210, 211

children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 268 employment change 3, 71, 83 employment levels 3 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199 women’s working hours 247 Switzerland employment change 3 employment levels 3 men’s employment 5 women’s employment 5 taxation policies 6 and women’s employment 93–4 tendential relationships 8 The OECD Jobs Study 4 threshold effects 34 time series 36 time-series regression 1 transitional labor market 224 truth tables 74–6 UK childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149

Index early-exit rate 274 early-exit regimes 268 employment change 3, 71, 83 employment levels 3 employment population ratio 230 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73 Job Release Scheme 280 left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269 payroll/consumption taxes 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199, 221–59, 271 women’s labor market status 236 women’s working hours 247 Working Hours Adjustment Act 235 unemployment benefits 73, 84, 88 unemployment rate 32 unique coverage 79–80 unit homogeneity 295–6 unobservables, selection on 304–5 USA childcare provision 151, 152, 210, 211 children and employment rate 222 civilian government employment 149 early-exit rate 274 early-exit regimes 260–89 employment change 3, 71, 83 employment levels 3 employment protection 73 exit pathways 279 extended leave 153 family policies 214 high wage increases 73

323

left cabinet incumbency 148 low earnings inequality 73 maternity leave 150, 212 men’s employment 5, 198, 269 payroll/consumption taxes 73 public employment 73 public sector employment 213 state benefits 154 unemployment benefit 73, 84 women’s employment 5, 154, 198, 199, 271 vector space 74 voluntarist bargaining systems

267

wage growth 72, 86 and productivity 293 wage levels 4 women 93–4 welfare systems 93, 104 see also family policies Why We Need a Welfare State 135 women childcare role 22, 221 early-exit rate 274, 275 educational attainment 201–2, 203, 216 employment preferences 201, 246–52 labor market status 236, 238–40 marginalization of 224–5 public sector jobs 6 wage levels 93–4 working hours 247 women’s employment 5, 20, 136, 141–6, 271 CACE analysis 164–6 causal relations 161–3, 200 childcare provision 142–4, 200 cross-country comparisons 154 cultural factors 114–19 and economic structure 94 and educational attainment 216 explanatory model of 120 factors shaping 92–5 family policies affecting 20, 91–134, 196–220 full-time 98, 99

324 Index women’s employment – Continued fuzzy-set analysis 156–63 maternity leave 200 mothers see mothers part-time 98, 99, 100, 223–4, 226 patterns of 94, 197–8, 221–59 with pre-school children 142 public sector 94, 137–9, 162, 200

single mothers 100–1, 112–13 taxation policies 93–4 see also individual countries work and family values 101 importance of 117–18 work–family conflicts 135, 226

Method and Substance in Macrocomparative Analysis

Method and Substance in Macrocomparative Analysis (Research Methods Series)

Substance and Individuation in Leibniz

Substance and Individuation in Leibniz

Substance

Substance

Substance and Separation in Aristotle

Substance and Separation in Aristotle

Self and Substance in Leibniz

Substance

Discourse Analysis as Theory and Method

Cognitive Reliability and Error Analysis Method

Sameness and Substance Renewed

Structural Analysis: The Analytical Method

Substance Use and Abuse

Critical Realism and Substance

Sameness and Substance Renewed

Substance and Function

Self and Substance

I Substance and Form in Aristotle

Method and Appraisal in Economics

Method and Appraisal in Economics

Method and Appraisal in Economics

Substance Mort

Substance Mort

Divine substance

Substance Mort

Soul Substance

Narrative, Expression, and Mental Substance

Data Analysis Using the Least-Squares Method

Method for 3-D eddy current analysis

Method and Substance in Macrocomparative Analysis