Mismatch and Labour Mobility

High and persistent unemployment has been experienced by most developed countries during the 1980s, and inflationary pressures have recently emerged at rates of unemployment far higher than those experienced in the 1960s and 1970s. This suggests that there has been an increase in the natural rate of unemployment. Many researchers have sought to explain this development in terms of'mismatch', arguing that the economies that have suffered most from persistently high unemployment are those that been least flexible in matching their unemployed with the available employment opportunities. This book reports the proceedings of a conference on 'Mismatch and Labour Mobility', sponsored jointly by the Centre for Economic Policy Research, the Centre for Economic Performance (formerly the Centre for Labour Economics) at the London School of Economics and the Centro Interuniversitario di Studi Teorici per la Politica Economica (STEP). The contributors to this volume examine the evidence on sectoral wage differentials, labour mobility and the ratio of unemployment to job vacancies, in detailed studies of seven countries with a wide variety of labour market and macroeconomic structures: the United States and Japan, three North European economies (West Germany, Sweden, and the United Kingdom), and two in Southern Europe (Italy and Spain). They analyse the variations in unemployment rates across regions, occupations and demographic groups, and investigate whether these help to explain the growth and persistence of unemployment. The volume also includes a cross-country study of skills mismatch in relation to the effectiveness of training programmes.

Mismatch and labour mobility

Centre for Economic Performance The Centre for Economic Performance is part of the London School of Economics. It studies the reasons for economic success amongfirmsand nations. The Centre's staff are drawn from a variety of disciplines and include, besides the staff at LSE, important groups from Sheffield and Oxford universities. The Centre incorporates the former Centre for Labour Economics at LSE. It is an ESRC research centre but also receives income from other bodies. It currently has grants from the Esmee Fairbairn Trust and the Alfred P. Sloan Foundation, and research contracts with the Department of Employment, the Department of Trade and Industry, the Commission of the European Communities and London Buses. The research programmes of the Centre for Economic Performance are corporate performance and work organisation; industrial relations; human resources; entrepreneurship; national economic performance; and post-Communist reform. Director

Richard Layard

30 June 1990

STEP STEP (Centro Interuniversitario di Studi Teorici per la Politica Economica) is a joint research centre of the economics departments of the universities of Bologna and Venezia and the institute of economics of Bocconi University, Milan. The centre promotes research in the area of economic policy and, through its collaboration with the Centre for Economic Policy Research, provides another STEP in furthering the Italian contribution to European economics. Directors

Giorgio Basevi, Mario Monti, Gianni Toniolo Scientific Advisory Board

Fiorella Padoa Schioppa, Richard Portes, Luigi Spaventa 30 June 1990

Centre for Economic Policy Research The Centre for Economic Policy Research is a network of 140 Research Fellows, based primarily in European universities. The Centre coordinates its Fellows' research activities and communicates their results to the public and private sectors. CEPR is an entrepreneur, developing research initiatives with the producers, consumers and sponsors of research. Established in 1983, CEPR is already a European economics research organisation, with uniquely wide-ranging scope and activities. CEPR is a registered educational charity. Grants from the Leverhulme Trust, the Esmee Fairbairn Charitable Trust, the Baring Foundation, the Bank of England and Citibank provide institutional finance. The ESRC supports the Centre's dissemination programme and, with the Nuffield Foundation, its programme of research workshops. None of these organisations gives prior review to the Centre's publications nor necessarily endorses the views expressed therein. The Centre is pluralist and non-partisan, bringing economic research to bear on the analysis of medium- and long-run policy questions. CEPR research may include views on policy, but the Executive Committee of the Centre does not give prior review to its publications and the Centre takes no institutional policy positions. The opinions expressed in this volume are those of the authors and not those of the Centre for Economic Policy Research. Executive Committee Chairman

Vice-Chairmen

Anthony Loehnis

Sir Adam Ridley Jeremy Hardie

Professor Giorgio Basevi Dr Paul Champsaur Honor Chapman Guillermo de la Dehesa Romero Michael Emerson

Sarah Hogg Kate Mortimer Sheila Drew Smith Sir Douglas Wass

Officers Director

Assistant Director

Professor Richard Portes

Stephen Yeo

Director of Finance and Research Administration

Wendy Thompson 30 June 1990

Mismatch and labour mobility Edited by

FIORELLA PADOA SCHIOPPA

The right of the University of Cambridge to print and sell all manner of books was granted by Henry VIII in 1534. The University has printed and published continuously since 1584.

CAMBRIDGE UNIVERSITY PRESS Cambridge New York Port Chester Melbourne Sydney

CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, Sao Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521402439 © Cambridge University Press 1991 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 1991 A catalogue record for this publication is available from the British Library Library of Congress Cataloguing in Publication data Mismatch and labour mobility / edited by Fiorella Padoa Schioppa. p. cm. Proceedings of a conference held in Venice on Jan. 4—6, 1990, sponsored by the Centre for Economic Policy Research and others. ISBN 0 521 40243 3 I. Labor market - Congresses. 2. Unemployment - Congresses. 3. Labor mobility - Congresses. I. Padoa-Schioppa, Fiorella, 1945II. Centre for Economic Policy Research (Great Britain) HD5701.3.M57 1990 331.12'7-dc20 90-2674 ISBN 978-0-521-40243-9 hardback Transferred to digital printing 2008

Contents

List of figures List of tables Preface Acknowledgements List of conference participants 1

2

page

A cross-country comparison of sectoral mismatch in the 1980s Fiorella Padoa Schioppa 1 Foreword and summary 2 Short-run and long-run sectoral shocks 3 Equilibrium and disequilibrium unemployment 4 Equilibrium unemployment and maximum aggregate hirings 5 Equilibrium unemployment and the minimum NAIRU 6 A more eclectic approach to mismatch 7 Empirical evidence on industrial mismatch in Europe 8 Is unemployment in Europe really high and persistent? Mismatch: a framework for thought R. Jackman, R. Layard and S. Savouri 1 The structure of unemployment: some facts 2 How the structure of unemployment is determined 3 How mismatch is related to the NAIRU 4 Evidence on sectoral wage behaviour and on mobility 5 Policy implications 6 Mismatch and the unemployment/vacancy relationship 7 Conclusions Appendix: Mismatch and substitution between types of labour Discussion Sherwin Rosen

xiv xviii xxiii xxiv xxvi 1 1 6 7 11 13 17 20 33 44 45 61 67 74 81 87 93 95 101

x Contents 3

Match and mismatch on the German labour market Wolfgang Franz 1 Introduction 2 ulv analysis 3 Lessons from a rationing model 4 An examination of possible causes 5 The SURE and the Beveridge curve reconsidered 6 Concluding remarks Discussion Renato Brunetta 1 Analytical structure 2 Causes of mismatch 3 Concluding remarks

4

5

6

Mismatch in Japan Giorgio Brunello 1 Introduction 2 Some stylised facts 3 The dispersion of local unemployment rates 4 The distribution of vacancies 5 Mismatch and the macro ulv curve 6 Conclusions Appendix Data Appendix Discussion SushiI Wadhwani Mismatch and internal migration in Spain, 1962-86 Samuel Bentolila and Juan J. Dolado 1 Introduction 2 Stylised facts of Spanish unemployment and mismatch indices 3 Analysis of migration flows 4 Conclusions Appendix 1: Grouping of regions into 5 aggregate regions Appendix 2: Sources and definitions Appendix 3: Migration and wage equations Discussion Nicola Rossi Regional inequalities, migration and mismatch in Italy, 1960-86 Orazio P. Attanasio and Fiorella Padoa Schioppa 1 Introduction

105 105 106 111 117 128 130 135 136 137 138 140 140 142 145 153 166 171 172 173 179 182 182 185 195 226 228 228 231 234

237 238

Contents xi 2 Regional imbalances: some basic facts 3 Reservation and net real wages; productivity and unit labour costs 4 The aggregate unemployment level and other factors limiting migration flows 5 Interregional migration rates 6 Migration rates and individual characteristics 7 Conclusions Data Appendix Discussion Giuseppe Bertola 7

Skill shortages and structural unemployment in Britain: a (mis)matching approach Charles R. Bean and Christopher A. Pissarides 1 Some broad facts about the structure of British unemployment 2 Some preliminary evidence on skill mismatch 3 An unemployment model with skill differentiation 4 Econometric evidence 5 Summary and some policy considerations Data Appendix Discussion Ugo Trivellato

243 253 267 270 286 301 302 321

325

325 328 333 340 349 351 354

1 2 3 4

Main points of the study 354 The basic model 355 Econometric evidence 356 Microeconomic evidence on job search behaviour would help 357 5 Stability of relative unemployment rates 358 6 The term 'mismatch' 358

8

Labour market tightness and the mismatch between demand and supply of less-educated young men in the United States in the 1980s Richard B. Freeman 1 Earnings and unemployment 2 Migration and area unemployment-wage locus 3 Changes in labour utilisation by education, 1970s—1980s 4 The effect of area unemployment 5 Employment of recent male school leavers

360 362 364 365 368 371

xii Contents 6 Area unemployment and earnings 7 Conclusion Appendix Discussion Michael Bur da 9

Skill mismatch, training systems and equilibrium unemployment: a comparative institutional analysis David Soskice 1 Introduction 2 Mismatch, effectiveness of ET systems and equilibrium unemployment in a simple Layard-Nickell open economy framework 3 Comparative education and training systems 4 Conclusions: problems of mismatch in GJS Discussion Leonardo Felli 1 2 3 4

A missing question A screening interpretation Cooperative vs. non-cooperative institutions Conclusion

10 Unemployment, vacancies and labour market programmes: Swedish evidence Per-Anders Edin and Bertil Holmlund 1 Introduction 2 Background 3 Macroevidence on matching 4 Microevidence on labour market transitions 5 Concluding remarks Appendix: Data description and some additional estimates Discussion Dennis J. Snower 11

Mismatch and labour mobility: some final remarks Katharine G. Abraham 1 Introduction 2 Mismatch as a suspect in the case of the rising unemployment rate 3 Measurement issues 4 What have we learned about trends in mismatch?

375 378 379 382

386 386

388 392 397 400 401 401 403 404

405 405 407 416 422 437 438 449

453 453 456 460 467

Contents xiii 5 Mobility and labour market adjustment 6 Conclusions

474 478

S. J. Nickell 1 Introduction 2 Short-run mismatch and turbulence 3 Long-run mismatch or dispersion 4 Summary and conclusions

481 482 483 484

Index

486

Figures

1.1 1.2

1.3 1.4 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9

page The aggregate disequilibrium (S) and the Beveridge curve (B) 18 Differences between the EC observed (y/uy, the EC constructed weighted average of 19 sectors' (yt/u$ and the OECD (v/uy official data 22-6 The EC constructed weighted average of 19 sectors' {vt/uiy and the industrial dispersion index (M4y 28-32 The EUR8 (v/u) ratios and the intercountry dispersion index (M4) 36-7 Fluctuations in mismatch and turbulence: Britain, 1963-87 55 Industrial turbulence index, 1900-90 57-8 Employment and wages in a single sector: labour force given 63 Employment and wages in a single sector: labour force endogenous, zero migration 65 Introductory presentation of mismatch and the NAIRU 68 The unemployment frontier: wages responding to own-sector unemployment 69 The unemployment frontier: wages responding to leadingsector unemployment 73 Skilled and unskilled labour markets: Lu L2 fixed 80 Skilled and unskilled labour markets: Lu L2 variable 82 The u/v curve of a group 88 Stylised Beveridge curve 106 Beveridge curve: official data for vacancies 110 Beveridge curve: corrected data for vacancies 110 Share of firms being in different regimes, 1961-86 116 SURE and UR, 1961-85 116 SURE, SUCEL and UC, 1961-85 117 Migration and unemployment, 1966-83 120 Share of long-term unemployment, 1966-88 122 Duration of vacancies, weeks, 1973-88 124 xiv

List of figures xv 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4N.1 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 6.1 6.2 6.3 6.4 6.5 6.6 6.7

Mobility among regions, 1970-86 Real wage changes and index of net labour inflow, Tokyo, 1975-85 Real wage changes and index of net labour inflow, Kagoshima (Kyushu), 1975-85 Mismatch: JR (1987) style, 197^87 Regional mismatch: common and heterogeneous hiring function, 1975-87 Labour force share by age: the young and the old, 1968-87 Lilien's sigma, with and without agriculture, 1970-87 Employed women: share of total employment, 1968-87 Employment share: service sector, 1968-87 Unemployment insurance: share of insured unemployed, 1968-87 Residential land price/gross wage, large cities, 1970-87 National unemployment rate, 1962-89 Regional unemployment inequality index, 1962-89 MM mismatch indices, 1977-89 MM regional mismatch index, 1962-89 Estimated mismatch from disequilibrium model (1/p) 1964^87 Turbulence, 1965-89 Interregional migration rate, 1962-87 Regional structure and aggregate regions Relative per capita real GDP, 1962-86 Regional unemployment rates, 1962-86 Gross outmigration rates, 1962-87 Gross inmigration rates, 1962-87 Net inmigration rates, 1962-87 Real wage inequality index, 1962-86 Regional system: adjustment path, total sample Regional system: adjustment path, first sub-sample Regional system: adjustment path, second sample Italy: administrative regions, ISTAT partitions and geographical areas Real per capita value added at factor cost and constant prices, 1961-86 Male unemployment rates, 1961-86 Female unemployment rates, 1961-86 Male employment rates, 1961-86 Female employment rates, 1961-86 Agricultural employment rates, 1960-86

151 153 155 158 159 159 162 168 168 169 175 184 190 191 192 193 194 197 198 199 200 201 201 202 207 224 225 225 239 244 248 249 251 252 253

xvi List of figures 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20 6.21 6.22 6.N1 6.N2 7.1 7.2 7.3 7.4 7.5 7.6 8.1 8.2 8.3

8.4

8.5

Male activity rates, 1960-86 Female activity rates, 1961-86 Net real wage in the aggregate economic system, 1961-86 Proportion of public sector employees relative to total employees, 1961-86 Ratio between public and private wages, 1961-86 Real wage of the private sector, 1961-86 Unit labour cost, 1961-86 Real productivity: value added per employee deflated by the product price, 1961-86 Ratio betweeen disability pensions and value added, 1961-86 Relative housing price in each area compared to the relative housing price in NO, 1961-85 Within-area migration rates, 1960-86 Gross outmigration rates, 1960-86 Gross immigration rates: NO, CE and LZ, and gross outmigration rates: SE, SO and NE, 1960-86 Net migration rates, 1960-86 Net migration rates and male unemployment rates: (NO, CE and SO), 1960-86 Birthrate, 1960-86 Ratio between average disability pensions and the wage rate in the private sector, 1960-86 Mismatch indices, 1963-84 Percentage of firms reporting labour shortage, 1965-89 Ratio of non-manual to manual wages, 1970-88 Phase-plane diagram Effect of a technology shock that lowers skilled employment in equilibrium Estimated bias in technical change, 1972-88 Unemployment rates and unemployment/population ratios for male workers, 25-64, 1970-90 Rates of unemployment in 205 MSAs in 1987 and in 48 MSAsin 1983 and 1987 Estimated effect of 1987 area unemployment on the probability of employment for less-experienced men with high school or less education, 1987 and 1983 Estimated effect of 1987 area unemployment on the In earnings for less-experienced men with high school or less education, 1987 and 1983 Estimated effect of 1987 area unemployment on In earnings for males with college or greater education

254 255 256 258 259 261 263 264 266 268 271 272 274 275 287 314 318 331 332 333 338 339 343 367 369

374

377 378

List of figures xvii 9.1 10.1 10.2 10.3 10.4 10.5 10.6 10.7

B and wp = wb schedules The Swedish unemployment rate, 1962-88 Workers unemployed and in relief jobs, 1970-88 Workers unemployed and in training programmes, 1970-88 The duration of vacancies and the rate of unemployment, 1970-88 The Swedish Beveridge curve, 1969-88 Kaplan-Meier survivor function estimate: Stockholm youth sample Kaplan-Meier survivor function estimate: displaced worker sample

391 408 409 410 413 414 426 427

Tables

1.1 1.2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 3.1 3.2

Criteria identifying when mounting mismatch's uniquely responsible for the unemployment rate increase page 19 Unemployment rate, percentage of civilian labour force 34-5 Unemployment by occupation: Britain, 1985 46 Unemployment by occupation: United States, 1987 47 Unemployment rate by occupation: various countries, 1987 48 Dispersion of occupational unemployment rates, 1973-87 49 Unemployment by occupation: inflow and duration, 1984 and 1987 50 Unemployment rate by highest educational level, 1988 51 Unemployment by region: Britain, Summer 1988 52 Unemployment by region: United States, 1988 52 Dispersion of regional unemployment rates, 1974—87 53 Regional turbulence indices 56 Unemployment by industry, age, race and sex, 1984 and 1987 59 Dispersion of industrial unemployment rates, 1973-87 60 Industrial turbulence indices 61 Determinants of regional wage rates, Britain 75 Determinants of regional wage rates, United States 78 Non-manual wages relative to manual wages, 1970-86 80 Unemployment rates and registered vacancy rates by occupation, region and industry: Britain, 1982 90 u/v mismatch: time series, Britain, 1963-88 91 Differences between occupations in vacancy flows and stocks: Britain, 1988 92 Estimates of the Beveridge curve, 1967-88 112 Interindustrial dispersion of employment growth, 1960-83 118 xviii

List of tables xix 3.3 3.4 3.5 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5A. 1 5A.2 6.1 6.2

Mismatch indicators, 1967-88 119 Structure of unemployment, 1987 125 Duration of unemployment by sex and receipt of unemployment benefits, 1980 and 1988 127 Unemployment and vacancies by skill, 1985 143 Unemployment by age, sex, industry and region, 1985 144 Unemployment average duration, by age, sex, and industry, 1985 145 Dispersion of unemployment rates: Japan, 1972-87 146 Regional dispersion of unemployment: spatial equilibrium and mismatch, 1975-87 149 Local wage dynamics, 1977-86 154-5 Ratio of placements by public employment agencies to total engagements, 1987 156 Regional mismatch, JR (1987) style, 1975-87 158 Age mismatch, JR (1987) style, 1972-88 160 The relation between Lilien's sigma, unemployment and vacancies, 69:3-87:4 163 Percentage of firms which have entered or are planning to enter new lines of business 164 Reasons for entry into new lines of business 165 Methods of filling vacancies in the new lines of business 165 IV estimate of the macro ulv curve, 1969-87 167 Augmented Dickey-Fuller (ADF) tests, 1968-87 169 Cointegrating vector and error correction model, 1969-87 170 Composition of unemployment, 1976, 1985 and 1989 187 Regional unemployment rates, 1962, 1976, 1985 and 1989 188 M M mismatch indices, 1977-89 189 M M regional mismatch indices, 1961-70 191 Migration, wages and unemployment by region, 1962-86, 1962-75 and 1976-86 206-7 Sample correlation coefficients, 1964—86 208 Regressions: net migrations and wages 210-11 Migration, 1964-75 and 1976-86 214-15 Wages, 1964-75 and 1976-86 218-19 Regional system dynamic adjustment path 222-3 Migration equations 229-30 Wage equations 231 Coefficients of variation, 1960-86 246-7 Regressions on net migration rates in the six geographical areas 277

xx List of tables 6.3 6.4

6.5 6.6

6N. 1 6N.2 6N.3 6N.4 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8.1 8.2 8.3 8.4 8.5

10.1 10.2 10.3 10.4 10.5

Pooled regressions on gross outmigration rates in the six geographical areas 284-5 Within-area migration rates, gross outmigration rates, gross inmigration rates and net migration rates, all ages, 1970 and 1985 289-92 Gross outmigration rates by sex, working condition and age, 1970 and 1985 293^ Within-area migration rates, gross outmigration rates, gross inmigration rates and net migration rates, 20-29 age group,1970 295-8 Resident population growth, 1951-85 312 Crime rates in the Italian regions, 1986 313 Dynamics of employment by status, 1980-7 315 Weights on the consumer basket of different items in the six geographical areas 316 Composition of the unemployed, selected years 326 Unemployment by demographic group, 1984 327 Unemployment by skill, 1984 327 Unemployment by region, 1988 328 Total factor productivity growth by industry, selected years 329 Vacancies by skill, 1988 334 Manual wage equations 346 Non-manual wage equations 347 Unemployment rates and employment/population rates for white male workers, 1974-88 366 Rates of unemployment and employment/population rates for 16-24-year-old males, 1973-86 368 Employment/population rates for men with 0-5 years of potential labour market experience, 1987 371 Regression estimates for the effect of area unemployment on men who left school within 0-5 years, 1987 and 1983 372 Regression estimates for the effect of 1987 and 1983 area unemployment on In earnings of less- and moreeducated men with less than 5 years of work experience 376 Unemployment and labour market programmes: Sweden, 1970-88 411 Search effort among unemployed, programme participants and employed 412 Unemployment outflow, by destination, 1984-8 412 The Swedish Beveridge curve, 70:2-86:4 415 Estimates of aggregate matching functions: Sweden, 1970-88 419

List of tables xxi

10.6 10.7 10.8 10.9 10.10 10.11 10.12 10A.1 10A.2 10A.3

10A.4

10A.5 10A.6 11.1 11.2

Hirings in Swedish manufacturing, 1969-87 420 Matching and labour market programmes: Sweden, 1970-88 421 Characteristics of the samples 424 Recorded transitions out of unemployment and out of relief jobs, by destination 425 Weibull estimates of re-employment equations for unemployed and relief workers: Stockholm youth sample 429 Weibull estimates of re-employment equations for unemployed and relief workers: displaced worker sample 431 Weibull estimates of re-employment equations for unemployed with controls for previous programme participation: Stockholm youth sample 432-3 Weibull estimates of unemployment and relief job duration 440 Weibull estimates of re-employment equations for unemployed and relief workers: Stockholm youth sample, spells longer than 20 weeks treated as censored 441 Weibull estimates of re-employment equations for unemployed and relief workers: Stockholm youth sample, restricted to spells of individuals with both unemployment and relief job spells 442 Weibull estimates of re-employment equations for unemployed and relief workers: displaced worker sample, restricted to spells of individuals with both unemployment and relief job spells 443 Weibull estimates of transitions from temporary and relief jobs to unemployment: Stockholm youth sample 444 Weibull estimates of transitions from temporary and relief jobs to unemployment: displaced worker sample 445 Evidence concerning the trend in skill mismatch, selected OECD countries 468-9 Evidence concerning the trend in geographical mismatch, selected OECD countries 472-3

Preface

This volume contains the proceedings of a conference on 'Mismatch and Labour Mobility', held in Venice on 4-6 January 1990, sponsored jointly by the Centre for Economic Policy Research, the Centre for Labour Economics (now incorporated into the Centre for Economic Performance) at the London School of Economics, and the Centro Interuniversitario di Studi Teorici per la Politica Economica (STEP). Financial support for the conference was provided by Directorate General V (Employment, Social Affairs and Education) of the Commission of the European Communities, and the German Marshall Fund of the United States contributed to the travel costs entailed. Financial support for the production of the present volume was provided by the UK Department of Employment. I am very grateful to Gianni Toniolo for co-organising the Conference and to the Economics Department of the University of Venice for their efficiency and warm hospitality during the Conference proceedings. I thank Richard Portes, Stephen Yeo and Ann Shearlock for encouraging and enabling us to organise the Conference. Particular thanks go to Sarah Wellburn, CEPR Publications Officer, and to Barbara Docherty, Production Editor, for their outstanding professionalism. I hope this book will acquaint the reader with the current state of debate on mismatch and connected labour-market problems: not all questions will find a ready-for-use answer, but we hope to have stimulated fresh debate, while providing some answers to the problems considered. Fiorella Padoa Schioppa 15 June 1990

Acknowledgements

The editors and publisher wish to thank the following for permission to reproduce copyright material. General Household Survey, for data in Tables 2.1 and 2.4 and Figure 2.1. Employment and Earnings, for data in Tables 2.2, 2.4, 2.5, 2.8, 2.11, 2.13 and 2.15. ILO, for data in Tables 2.3, 2.4 and 2.12 and Figure 2.1. CPS, for data in Tables 8.1, 8.2, 8.3, 8.4 and 8.5. Labour Force Survey, for data in Tables 2.5, 2.11, 2.17 and 2.19. Department of Employment, for data in Tables 2.7, 2.10, 2.14, 2.17, 2.18, 7.1 and 7.3. OECD, for data in Tables 1.2, 2.6, 2.9, 2.10 and 2.13, and Figure 1.2. Employment and Training Report to the President, for data in Table 2.10. US Bureau of Labor Statistics, for data in Tables 2.10, 8.2 and 8.3 and Figures 2.2 and 8.1. HMSO, for data in Table 2.10 and Figure 2.2. CSO, for data in Table 2.17 and Figure 2.1. EUROSTAT, for data in Tables 1.2 and 2.16. CBI, for data in Table 2.19 and Figure 7.2. IFF Research Limited, for data in Table 2.19. Sachverstdndigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, Jahresgutachten 1988189, for data in Table 3.4. Amtliche Nachrichten der Bundesanstalt fur Arbeit, for data in Table 3.5. Ministry of Labour (Japan), for data in Tables 4.1, 4.2, 4.7,4.11,4.12 and 4.13 and Figure 4.1. Office of the Prime Minister (Japan), for data in Tables 4.3 and 4.4. Fondazione Giacomo Brodolini and Centro Europa Ricerche, for data in Table 6N.3. ISTAT, for data in Tables 6N.2 and 6N.4. NBER Macroeconomics Annual 1989, for data in Table 7.5. xxiv

Acknowledgements xxv FIEF, for data in Table 10.2. National Labour Market Board (Sweden), for data in Table 10.3. Oxford Bulletin of Economics and Statistics, for data in Figure 2.1. Historical Statistics of the United States, for data in Figure 2.2. Jahrbuchfur Regionalforschung, for data in Figure 3.7. CEC, Monthly Bulletin, for data in Figure 1.2. Isco-mondo Economica, Monthly Bulletin, for data in Figure 1.2. Institut fiir Arbeitsmarkt und Berufsforschung, for data in Figure 3.9. Brookings Institution, Washington, for data in Table 3.2. Duncker and Humboldt, for data in Table 3.2. Vandenhoeck and Ruprecht, Gottingen, Mannheim, for data in Figures 3.4-3.6.

Conference participants

Katharine Abraham, University of Maryland Orazio Attanasio, Stanford University and CEPR Charles Bean, London School of Economics and CEPR Samuel Bentolila, Banco de Espana Giuseppe Bertola, Princeton University and CEPR Giorgio Bodo, Banca d'ltalia Giorgio Brunello, Universitd di Venezia Renato Brunetta, Fondazione Brodolini, Roma Michael Burda, Institut Europeen dyAdministration des Affaires and CEPR Bruno Contini, Universitd di Torino Per-Anders Edin, Uppsala University Leonardo Felli, Massachusetts Institute of Technology Wolfgang Franz, Universitdt Konstanz Andrea Gavosto, Banca d'ltalia Bertil Holmlund, Uppsala University Zmira Hornstein, United Kingdom Department of Employment Richard Jackman, London School of Economics Guy Laroque, Institut National de la Statistique et des Etudes Economiques Richard Layard, London School of Economics and CEPR John Martin, OECD Karl Moene, University of Oslo Anthony Murphy, Nuffield College, Oxford Stephen Nickell, Institute of Economics and Statistics, Oxford, and CEPR Fiorella Padoa-Schioppa, Universitd di Roma 'La Sapienza , LUISS, and CEPR Makis Potamianos, Commission of the European Communities Sherwin Rosen, University of Chicago Nicola Rossi, Universitd di Venezia Savvas Savouri, London School of Economics xxvi

List of conference participants

Dennis Snower, Birkbeck College, London, and CEPR David Soskice, University College, Oxford Gianni Toniolo, Universita di Venezia and CEPR Ugo Trivellato, Universita di Padova Sushil Wadhwani, London School of Economics and CEPR

Stephen Yeo, CEPR

xxvii

1

A Cross-country Comparison of Sectoral Mismatch in the 1980s1 FIORELLA PADOA SCHIOPPA

1

Foreword and summary

The idea on which the January 1990 Venice Conference on 'Mismatch and Labour Mobility' premised its proceedings was to verify whether the persistently high unemployment rates - observed in most countries since the first oil shock - could be explained by the growth of the frictional/ structural component of unemployment, due to increasing mismatch. Similar thoughts, inspiring many studies in this volume, have been widespread, especially in Europe, as indicated by the two following quotations. The 1990 Annual Report of the CEPS Macroeconomic Policy Group (Danthine et al, 1990, 20) states: 'a common view is that Europe's unemployment problem is, to a significant degree, the result of a structural mismatch between the supply of, and the demand for, different skill types . . . Previous reports of this group have called attention to the need for greater differentials in labour costs, both regionally and across occupations if unemployment is to be kept at acceptable levels'. In turn, Burda and Wyplosz (1990, 1) add that 'high unemployment remains a highly visible feature of the European economic landscape. The conventional wisdom that has emerged over the past fifteen years is that high unemployment rates in Europe are symptomatic of insufficient economic activity or malfunctioning labor markets. Regardless of the cause, persistence or sluggish behavior of the stocks of unemployment and employment in European countries - in contrast to the United States, Canada and Japan - is taken as prima facie evidence of declining gross hiring and firing activity and deteriorating worker mobility'. These being the presumptions on which this volume was based, it is only natural that it attempts to tackle the problem of sectoral imbalances under several different perspectives. First of all, in an international perspective, the book compares the experiences of four main EC member states (two Northern - the United

2

Fiorella Padoa Schioppa

Kingdom and Germany - and two Southern - Spain and Italy), of a non-EC European country (Sweden), and of two non-European countries (the United States and Japan). Second, with regard to the content, the volume deals with at least four distinguished subjects. A first group of studies (Chapters 2, 3 and 4, respectively by Jackman, Layard and Savouri, by Franz and by Brunello) is devoted to exploring the nature of mismatch from different analytical and empirical viewpoints. A second group of studies (Chapters 5 and 6, respectively by Bentolila and Dolado and by Attanasio and Padoa Schioppa) aims at examining the connected problem of labour mobility and of interregional migration flows. Two contributions (Chapters 7 and 8, respectively by Bean and Pissarides and by Freeman) study the effect of technological shocks on employment and wage differentials of skilled vs. unskilled workers. A fourth set of studies (Chapters 9 and 10, respectively by Soskice and by Edin and Holmlund) attempts to assess whether private and public training and retraining programmes are effective in reducing the negative implications of sectoral shifts. The book ends with two excellent overview studies by Abraham and by Nickell (in Chapter 11), which makes any summary by me of each author's essential arguments superfluous. I will try only to highlight what I think to be the book's main achievements, as well as its main limitations. The major result of this volume, in my opinion, consists in highlighting the looseness of the 'mismatch' concept - even though the term is frequently used both by experts and laymen - which explains why different mismatch definitions lead to such widely varying judgements on the same observable facts. This volume's lesson being as much destructive as constructive, one must state that the book also demonstrates its major limitation in failing to find a unified interpretation of the 'mismatch' phenomenon. At least four approaches to mismatch emerge from the volume's studies. Approach (1) (described in section 2 below) associates mismatch with short-run sectoral shocks, which usually balance out at the aggregate level but temporarily raise both unemployment and vacancies: the turbulence index (in Lilien's, 1982, style) reveals this kind of mismatch. The remaining three approaches view mismatch as a more permanent phenomenon: the differences between them correspond to divergences on the concept of equilibrium unemployment relative to which mismatch is evaluated. Approach (2) identifies mismatch within a disequilibrium (or rationing) model, under the basic assumption that the short side of each micro market determines its own employment level. At the aggregate level, given that the binding constraint is not the same in every micro market, unfilled

Sectoral mismatch in the 1980s 3 vacancies in some sectors coexist with unemployment in others, and employment is lower than the minimum between aggregate labour demand and supply. In this framework, the equilibrium unemployment rate is the one which would arise if at the aggregate level the (notional) labour demand equalled supply: it is at the same time frictional and structural and, corresponding to the vacancy rate, is attributed to mismatch (section 3 below). Approach (3) stems from the idea that frictional unemployment is unavoidable. It, then, defines mismatch as the distance of the unemployment rate from an optimal rate, proved to maximise aggregate hirings under the conditions specified in section 4 below. This optimal rate is obtained when the vacancy/unemployment rates ratio coincides across all micro markets. The corresponding mismatch indexes measure the intersectoral dispersion of the vacancy/ unemployment rates ratios. Approach (4) defines mismatch in terms of the NAIRU, as the distance of the unemployment rate from a minimum rate, compatible with price stability. This minimum is reached - under certain assumptions described in section 5 below - when all unemployment rates are identical in every micro market. The corresponding mismatch index measures the variance of the relative unemployment rates in the economic system. Along with some elements of great interest, all these stands also present considerable shortcomings. This being the state of the mismatch theory, it is for the moment perhaps advisable to take up a cautious, rather eclectic approach, trying to combine the most relevant suggestions of the analyses undertaken (section 6 below). In my view, the existence of the frictional/structural unemployment rate should be detected by the coexistence, at the aggregate level, of both unemployment and vacancies, even when aggregate labour demand equals labour supply: the benchmark equilibrium unemployment rate should therefore be equal to the vacancy rate (as in approach (2)). One would then say that a rise in frictional/structural unemployment relative to the observed unemployment rate requires a mounting vacancy/ unemployment ratio. But the observed increase in this ratio might stem, ceteris paribus, from three causes: a reduction in the labour market aggregate disequilibrium (an upward movement along a 'Beveridge curve') or an outward shift of the 'Beveridge curve', the latter due to misplacement, either because frictions are growing within each micro market or - as a third possibility - because larger intersectoral discrepancies of unemployment and vacancies (and hence mismatch) are arising. The growth of the vacancy/unemployment ratio at the aggregate level is thus a necessary, but not a sufficient, condition to make us suspect that

4

Fiorella Padoa Schioppa

the increasing mismatch is responsible for the rising unemployment rate. To identify whether, ceteris paribus, the growth in the vacancy/ unemployment ratio depends on higher misplacements or on falling aggregate disequilibria, one has to observe the unemployment rate: it has to increase in the former case and decrease in the latter. Finally, to distinguish whether higher misplacements are due to mismatch or to other reasons, one has to verify the existence of a growing intersectoral dispersion in the vacancy/unemployment ratios (accepting, on this point, the most important message contained in approaches (3) and (4)). I therefore propose to adopt a threefold criterion to assess whether a growing mismatch uniquely explains a worsening in the unemployment rate dynamics: the vacancy/unemployment rate ratio, the unemployment rate, and finally the intersectoral dispersion of the vacancy/unemployment rates ratios have all to increase. There are other cases in which a growing mismatch can partly contribute to explaining the unemployment dynamics. In these cases - which arise when shifts of the 'Beveridge curve' are combined with movements along the 'Beveridge curve' - the intersectoral dispersion of the vacancy/unemployment ratios has to grow, with or without a parallel rise in the observed unemployment rate or in the vacancy/unemployment ratio. Through this methodology, I analyse (in section 7 below) the industrial mismatch in the 1980s of 8 European countries for which data on 19 sectors, homogeneously defined across countries, have been regularly collected through EC business surveys ever since 1980. It then appears that, almost everywhere, the vacancy/unemployment ratio declined from 1980 to 1982-3, presented a cyclical positive trend up to 1987 and a sharp rise thereafter. In the meanwhile, almost everywhere, the dispersion index of the 19 sectors' vacancy/unemployment ratios was cyclically fluctuating, showing a general, year by year, negative correlation with the rate of change of the vacancy/unemployment ratio and a mildly negative trend after 1983. It therefore seems most unlikely that industrial mismatch and structural imbalances supply a unique, or even an important, explanation of the European unemployment in the 1980s: the information contained in the country studies in this volume - concerning both the EC countries I have examined through the business survey data and other European and non-European countries - confirm (through different indicators) that nowhere in the 1980s has the industrial dispersion index been positively trended. I should also point out that everywhere in Europe, in Japan and in the United States the unemployment performance has not been globally unsatisfactory in the 1980s. Reporting standardised cross-country data, I

Sectoral mismatch in the 1980s 5 suggest (in section 8 below) that, though long-term comparisons between the average unemployment rates in the 1980s and in the 1970s (and a fortiori in the 1960s) reveal a worsening in all countries, almost everywhere the rate of change of unemployment rates within the 1980s has been positive only up to the mid-1980s, while becoming continuously negative thereafter. The 1980s clearly trace a downturn in all countries' unemployment rates, in a time interval running from 1983 (Belgium, Luxemburg, the Netherlands, the United States and Sweden) to 1985 (Germany, Spain, Ireland, Portugal, the United Kingdom) and 1986 (Italy and Japan), with no subsequent uptrend. Exceptions are France, showing a late downturn in 1987 and Denmark, whose unemployment rate turned down in 1982 but has moved up again since 1987. Looking at all this empirical evidence, one therefore gets the impression that there exists a consistent story for the 1980s' unemployment rates. Contrary to the layman's opinion, unemployment steadily increased only up to the mid-1980s and then decreased almost everywhere. In the meantime, the vacancy/unemployment rates ratio declined almost everywhere up to about 1983 and then rose again: consequently, misplacements are not likely to explain the unemployment dynamics of the 1980s. As the intersectoral dispersion of the vacancy/unemployment ratios has been cyclical and has grown nowhere in the second half of the 1980s, one may apparently conclude that neither frictional nor structural changes - due to industrial mismatch - are important components of the initial rise and the subsequent fall in unemployment rates. Industrial mismatch possibly played a downward minor role in the second half of the 1980s, when it was slightly falling while unemployment rates were also decreasing. Aggregate disequilibria (due to lack of aggregate labour demand relative to labour supply) probably bear the major responsibility for the unemployment rate increase of the first half of the 1980s and its reduction in the second half. Similar reasoning seems to apply both to the countries included in the EC business survey data set and to the others under consideration in this volume (Spain, Sweden, Japan and the United States). Though no study in this volume apparently contradicts these conclusions, most authors would suggest that industrial mismatch is not really relevant, because geographical location more than other components has contributed to labour mismatch. To test this hypothesis, I have utilised the EC business survey information, not to measure the intersectoral dispersion within each country but to evaluate the 'regional' dispersion between the 8 European countries (the national states being 'regions' of EUR8), sector by sector and in industry as a whole. In fact, my data indicate that 'regional' mismatch in Europe has been positively correlated

6

Fiorella Padoa Schioppa

to the vacancy/unemployment ratios of EUR& and has been positively trended after 1982 in most intermediate and consumer good sectors and in industry as a whole. One should state here with regard to the European unemployment in the 1980s that aggregate disequilibria have brought about a rise and then a fall in unemployment, while 'regional' imbalances within Europe have first decreased and then grown, increasingly explaining the declining rate of aggregate unemployment. This suggests the value of few policy interventions, perhaps at a supernational level, particularly through training, retraining and manpower programmes; through some sort of deregulation in the labour market so as to enable it better to signal the relative labour scarcities while increasing the proper wage differentials; through a wise mix of subsidies and taxation in order to favour labour and capital mobility, whenever there emerge clear externalities or whenever market adjustments appear too slow. 2

Short-run and long-run sectoral shocks

As recalled in section 1, there are essentially two (implicit or explicit) assumptions in the studies in this volume: 1. that there is a high and persistent unemployment rate to be explained; 2. and that this rate possibly - and at least partly - depends on structural imbalances or labour mismatch (by skill, by occupation, by region or by sector). While I shall return to thefirstaspect in section 8 below, I will devote the next few pages to a problem connected with the second aspect, notably the definition of 'mismatch'. There are four main discrepancies emerging in the various mismatch concepts (hereafter labelled (l)-(4)) utilised by the authors and the discussants of the studies in this volume. The first regards the short- or long-term nature of the phenomenon analysed, as is outlined by Nickell (Chapter 11 in this volume: hereafter, the citation of an author, if not otherwise stated, will refer to his or her contribution to this book). According to approach (1), mismatch is associated with short-run sectoral shocks which (usually) balance out at the aggregate level and raise both unemployment in the contracting sectors and vacancies in the expanding ones, given that it takes time to reach the steady-state adjustment obtained through wage-priceflexibilityand factors' mobility. This approach is apparently the one adopted by Freeman in his study (Chapter 8) on American labour market tightness, when he says: The simplest interpretation of a mismatch is in terms of shifts in the supply

Sectoral mismatch in the 1980s 7 and demand schedules that in the long run induce offsetting long-term changes in labour supply'. This is also Brunello's point of view, expressed in his analysis of mismatch in Japan (Chapter 4): 'Let [the] economy be displaced from its long-run equilibrium by (temporary) sector-specific shocks that do not alter the aggregate relation between the demand and supply. In a frictionless economy the long-run equilibrium is instantaneously recovered. With frictions, however, the original displacement persists over time as the economy goes through a sequence of short-run equilibria. Because of relative wage rigidities, incomplete information and costly labour mobility, the sectoral distribution of unemployment (and vacancies) is altered and aggregate unemployment could be reduced by reallocating labour among different sectors. There is mismatch'. It is certainly no accident that the only two studies exclusively embracing this approach in the present volume concern the only two non-European economies examined. Indeed, as Nickell states, the short-run stand, endorsed by many American economists,2 'is not taken to be very important by most European economists who are searching for explanations of the secular rise in unemployment over the last two decades'. The shortrun approach emerges, however, as a minor ingredient also in other studies in the book, when looking at the industrial or the regional turbulence index (as in Lilien, 1982). In no country of our concern (EC, Sweden, the United States and Japan) do the regional and industrial turbulence indexes, reported for 7 countries by Jackman, Layard and Savouri (Chapter 2, hereafter JLS), by Bentolila and Dolado (Chapter 5) and by Brunello, rise in the 1980s as against the previous decades: a mild exception is found in the United Kingdom and Sweden (for the regional turbulence) and in the United Kingdom and the Netherlands (for the industrial turbulence). The three other approaches to mismatch adopted in this volume, sometimes as an alternative to, sometimes in combination with, the first approach (and often in combination with each other) concern long-term phenomena: the difference between them essentially depends on the different definitions of equilibrium unemployment. 3

Equilibrium and disequilibrium unemployment

In approach (2), followed by Franz (Chapter 3) for Germany and by Bentolila and Dolado for Spain, mismatch is identified within the disequilibrium (or equilibrium with rationing) model, developed in the European Project on Unemployment (see Dreze and Bean, 1990). This model is based on the assumption that the short side of each micro market /

8 Fiorella Padoa Schioppa determines the level of transacted labour in /: the existence of rationing thus implies that there exists, in each i, either unfilled vacancies or unemployment. At the aggregate level, however, given that not all micro markets are rationed on the same side (for some markets, the binding constraint is labour demand, while for others it is labour supply), vacancies and unemployment coexist and aggregate employment is lower than the minimum between aggregate labour demand and supply. The larger the variance between micro markets, the smaller is aggregate employment relative to the minimum between aggregate labour demand and supply, and the more spread is mismatch said to be. This simple idea is conveyed by the following aggregate employment equation, utilised within the European Project on Unemployment and here adopted by Franz and Bentolila and Dolado, N=(LD-p

+ LS-p)-'/p

:

(1)

TV = aggregate employment; LD = aggregate (notional) labour demand, being LD = N+V

(2)

with V = unfilled vacancies; LS = aggregate labour supply, being LS = N + U

(3)

with U= aggregate unemployment; 1/p^O, indicating the variance between micro markets, represents the mismatch index. Indeed, if \/p=0 and equation (1) is transformed into N = min(LZ),LS), mismatch is said to be non-existent; the larger is \/p, the higher is the equilibrium (or structural) unemployment rate arising at full employment, defined (Beveridge, 1955, 77) 'as a state of affairs in which the number of unfilled vacancies is not appreciably below the number of unemployed persons'. In fact, substituting equations (2) and (3) into equation (1), the latter is transformed into the following 'Beveridge curve' 1 =(1 + v)- p + (l + u)~p,

(4)

where v = V/N\s the aggregate vacancy rate and u = U/N is the aggregate unemployment rate. Therefore, if the equilibrium rate of unemployment, w*, is defined, as before, at the full employment level where u = v, w* = v i.e., in equation (4),

(5)

Sectoral mismatch in the 1980s 9 which increases when the mismatch parameter, 1/p, rises. Obviously, the observed aggregate unemployment rate, u, is bigger than u* if and only if there is aggregate disequilibrium - i.e., if LD < LS. The ideal decomposition of u is therefore U =

(LS-LD\ (M -

ILD-N\

IU -

v) + v

The last component of the unemployment rate in the RHS of equation (5"), (v), is the equilibrium or structural or mismatch component of the unemployment rate; the first component, (w - v), is the disequilibrium component of the unemployment rate, due to insufficient aggregate labour demand relative to aggregate labour supply and is labelled us\ Us = u-v

(5'")

Both Franz for Germany and Bentolila and Dolado for Spain refer to the fact that the estimation of \/p shows an outward shifting of the 'Beveridge curve' through time. Generally speaking, \/p seems to be upward trended in all the countries where the same model3 as in equations (l)-(5) has been estimated within the European Project on Unemployment - i.e., Austria, Belgium, Denmark, France, Italy and the Netherlands, the United States - while other forms of 'Beveridge curve' also show an outward shifting in the United Kingdom (see Jackman, Layard and Pissarides, 1984) and in Japan (Brunello), but not in Sweden (according to Edin and Holmlund in Chapter 10). Approach (2) deserves four comments: (a)

First, the min condition at the micro market level rules out by definition the coexistence of unemployment and vacancies in each micro market. This is a very specific assumption: even in the absence of rationing (see, among others, Pissarides, 1985, 1986 and Blanchard and Diamond, 1990), the matching within each sector is a time-consuming process which is longer the more scattered is the information, the more limited is the search intensity, the higher is the choosiness on the part of employers and employees. Thus u, and v, coexist in each micro market, /, along a sectoral 'Beveridge curve'. Geographical, occupational and sectoral differences between idle workers and available jobs, which in this more general model are properly responsible for mismatch account for only a part of what might be generally called misplacement (following Dow and DicksMireaux, 1958). Only a fraction of the unemployment rate, w*, existing at full

10 Fiorella Padoa Schioppa

(b)

employment, depends on mismatch and should be labelled structural unemployment, while the other portion, corresponding to frictional unemployment, depends on other causes of misplacement. In what follows we will adopt the definition given by Jackman and Roper (1987, hereafter JR, 10): 'it is customary to attribute the co-existence of unemployment and unfilled vacancies within a sector to labour market "frictions" (time taken over job search or recruitment due to imperfect information), while if there is unemployment (in excess of frictional) in some sectors of the economy and vacancies (in excess of frictional) in others, there is said to be structural imbalance and this category of unemployment is described as structural'. 4 As suggested by Hansen's (1970) pioneering study, even within a disequilibrium model it is possible to relax the assumption that, at the micro market level, v, and ut cannot coexist and still come out with the equilibrium concept of unemployment, w*, now redefined as frictional/structural. In this case, however, the growth of 1/p, observed through time, may not be caused by rising mismatch, and the corresponding increase in w* cannot be said to depend exclusively on increasing structural imbalances, even though this does remain a correct formulation within the model of the European Project on Unemployment. Though Franz and Bentolila and Dolado do not discuss this point anaytically, their estimates, aimed at explaining the growth in 1/p, shed light on this element at an empirical level. Indeed, Franz states that the fact that the 'higher degree of fixity of labour due to legislative employment protection . . . and higher investments in firm-specific human capital undertaken by the firm . . . [has a] positive impact . . . on the rise of the [so-called] structural rate of unemployment supports our suspicion of a shift in hiring patterns in the sense that employers appear to have become choosier': we know by now that this kind of rise in w* is due to frictions within micro markets rather than to mismatch and structural imbalances between micro markets. A similar comment is partially appropriate for Bentolila and Dolado when, in replicating an econometric exercise produced in Padoa Schioppa (1990) within the disequilibrium model adopted in the European Project on Unemployment, they declare that they have 'successfully explained . . . the trend of 1/p through . . . the unweighted standard deviation of regional unemployment rates, . . . the gross interregional migration as a proportion of total population . . . the proportion of long-term unemployed (one year or more) in the labour force . . . [and] . . . finally, the turbulence index for total employment'.

Sectoral mismatch in the 1980s 11 (c) There is furthermore, a considerable difference between showing the growth in the frictional/structural unemployment rate, w*, and explaining through this increase the observed rise in the aggregate unemployment rate, u: the fact that w* rises through time does not necessarily imply that the share of the observed unemployment due to frictional/structural reasons (u*/u) also grows through time. To this end, it is interesting - and indeed illuminating - to verify from the data reported by Dreze and Bean (1990) concerning u* and u that, though w* increases between the early 1960s and the mid-1980s in all the ten countries analysed, only in Italy and France is u*/u higher in 1986 compared to the beginning of the period. (d) Finally, even though the determination of the equilibrium rate of unemployment, w* at u = v, is very common and appears in models close to Dreze and Bean (as Hansen, 1970 or Malinvaud, 1986) and in completely different models (such as Abraham, 1983 or Edin and Holmlund), it is also questioned by many people. For example, Jackman, Layard and Pissarides (1984, 4) propose to 'replace the commonly used criterion of u = v, which has no theoretical basis' (without explaining why this is so) and Abraham (1983, 722) states that it is not necessarily optimal to have 'the same number of jobs vacant as there are persons unemployed . . . The optimal vacancy rate/unemployment rate combination (along a 'Beveridge curve') will depend upon the marginal social costs associated with unemployment and with job vacancies'. 4

Equilibrium unemployment and maximum aggregate hirings

The critical comments about approach (2), referred to above, lay the basis for the third approach to the mismatch problem, which is largely adopted in the studies in this volume. The basic idea is that mismatch should be measured in terms of the distance from an equilibrium unemployment rate, w**, different from w* because it assumes that frictional unemployment is unavoidable within each micro market /. As JR (1987, 11) put it, 'it is thus necessary to measure the extent of sectoral imbalances relative to the existing aggregate levels of unemployment and vacancies in the economy, rather than to some hypothetical, but probably unattainable, state where the unemployment rate equalled the vacancy rate in each sector'. This equilibrium rate can be proved to be The equilibrium aggregate rate of unemployment has the property that its ratio to the aggregate vacancy rate equals the unemployment/vacancy rate ratio in each micro market /.

12

Fiorella Padoa Schioppa

The intuition behind this result is simple. Given the frictions within each /, a sectoral 'Beveridge curve' - supposed identical everywhere - arises. Hence, the aggregate 'Beveridge curve' has the same shape: its convexity implies that the minimum unemployment rate is reached when the sectoral (w,-/v/) ratios are equalised. The demonstration produced by JR (1987) goes along the following lines. Let the hiring function in each micro market be //, and assume that it is a linear homogeneous function of Uf and V,-. Then /z, = Hj/N,- is a linear homogeneous function of it, and v, and can be written as ft* = V/A^z/v/).

(7)

The maximum of aggregate hirings (2//,-), subject to 1U,: = U and to a given pattern of vacancy rates, is obtained at h'iuj/vi) = constant, if and only if /z, = h

V/

(8)

Let us stress that the optimum unemployment/vacancy rate ratio, identified in equation (8) as the one which is equalised across micro markets, requires as a strong assumption that the hiring functions (hi) be the same (equal to h) in each micro market. Indeed, if ht were different in different /, the maximum of 2 / / , would not be reached by equalising (w//v/) across micro markets, and thus the unemployment rate, w**, as defined in equation (6), would no longer be optimal. Though the necessary assumptions to obtain the result in equation (6) are strong, all the mismatch synthetical indexes utilised in approach (3) are based on them. They inevitably show that mismatch is higher the higher the intersectoral dispersion of the unemployment/vacancy rate ratios. The two most commonly used indicators, 5 as noted by Abraham, are:

where u, is the share of unemployment in /, relative to aggregate unemployment, and v, is the share of vacancies in /, relative to aggregate vacancies; and

M\ measures (JR, 1987, 13) 'the number of unemployed workers who need to be moved from one sector to another in order to achieve structural balance - i.e., w**; M2 identifies (JR, 1987, 14) the 'contribution of structural imbalances to overall unemployment' (i.e., M2 = {u - u**)/u), provided some further assumption on /z, = h is added, namely that the hiring function is a Cobb-Douglas with equal elasticity (of ^), relative to vacancies and unemployment.

Sectoral mismatch in the 1980s 13 Looking at the evidence on regional and occupational mismatch produced by the indexes M, and M2, computed in the studies in this volume and in JR (1987), one would conclude that no country shows a positive trend in the occupational mismatch, while the regional one appears to increase in Germany and in Japan but not in the United Kingdom and in Sweden: these results are synthesised in Abraham's Tables 11.1 and 11.2. 5

Equilibrium unemployment and the minimum NAIRU

Approach (4) measures mismatch in terms of the NAIRU, as the distance between the unemployment rate and an equilibrium unemployment rate, w***, which would be the minimum compatible with price stability. JLS, who first introduce this concept, followed by many authors in this volume, show that the minimum unemployment rate, w***, is obtained when all unemployment rates are equalised across micro markets, /: w***

= u.

v/.

(9)

The corresponding mismatch indicator is proved to be M3 = \ V2LT(Ui/u).

The latter has three nice features. First, it is a frequently used dispersion index in descriptive statistics (being nothing but half of the squared coefficient of variation of unemployment rates, w,-). Second, it requires no knowledge of the vacancy rates by sectors - a piece of information which is unavailable for some countries (like Italy and Ireland) and is usually biased when officially available. Finally, it has the appealing property in the JLS model of being equal to log(w/w***), thus indicating the percentage of aggregate unemployment due to structural imbalances. The latter property, however, is based on three strong assumptions which may or may not be verified in different countries and in different disaggregations of each country's labour market. These are: (a) the convexity of the sectoral wage function; (b) the dependence of every sector's wage-setting on the unemployment rate of that sector; (c) the equality of the wage-setting functions across micro markets, apart from a sectoral fixed effect, appearing in the constant of the equations. For a better understanding of these three hypotheses, their analytical implications and their empirical validity, let us recall the functional form used by JLS for their wage equation in each micro market, w,: logW/ = # • - ylogw/,

(10)

where (a) holds true because d2 W;/du2 > 0; condition (b) is self-evident; and assumption (c) is verified because y is not sector-specific.

14 Fiorella Padoa Schioppa

Combining the price function6 log/? = 5/2 log w>/ + constant

(11)

with equation (10), the latter implies, at stable prices, /?, a convex relation between the unemployment rates of the various sectors /. This convexity is crucial to reach the result that the minimum aggregate unemployment rate, w***, requires the equalisation of all ut across micro markets. Whether or not the assumptions contained in equation (10) are correct is merely an empirical matter. 7 JLS supply some evidence that equation (10) is not contradicted by few English and American data. Freeman's study (Chapter 8) provides further support to the idea that there is an 'inverse relation between area unemployment and the earnings of young less-educated men. No relation [however, exists] between area unemployment and the earnings of young more-educated men'. Bean and Pissarides (Chapter 7), in examining the determinants of industrial wages, look in particular for the 'role played by firm-/industryspecific factors vis-a-vis general economic factors'. Their econometric results show that both manual and non-manual wages (in log) depend on an industry-specific labour tightness variable (in log), but the latter is not unemployment.8 More than that, aggregate unemployment appears to have a strong influence on industrial wages, albeit with a positive sign. Hence, 'both manual and non-manual wages are influenced by firm/industry-specific factors as well as economy-wide developments . . . The skill shortage variable appears to be a better indicator of labour market pressures than the unemployment rate'. Therefore, following Bean and Pissarides and Freeman, we should conclude that in the United Kingdom and the United States an index like M3 would not be the most appropriate to measure skill mismatch. The suspicion that the assumptions embodied in equation (10) have a rather weak empirical foundation is even stronger for all the countries for which a sectoral regional wage equation has been analysed in the studies in this volume, or, as far as I know, elsewhere.9 Let us refer to our volume's results on regional wage-settings. As for Spain, Bentolila and Dolado state that 'the common view is that local supply and demand conditions play only a limited role in the determination of regional wages . . . In our estimates there is a well defined (albeit small) positive relationship between a region's relative wage and its unemployment differential'. But their estimated wage equation (being in semi-log form) does not lead to the convex relation between regional unemployment rates that is necessary to assign to M3 the meaning of a mismatch index. In Italy, as reported by Attanasio and Padoa Schioppa, regional wages

Sectoral mismatch in the 1980s 15 do not seem to react negatively to regional unemployment rates. This occurs because contractual wages are determined at the national level, regional effective wages mostly depend on the leading sector's (NorthCentre) unemployment rate (as shown by Bodo and Sestito, 1989) and the local net real wage dynamics seems to compensate the path of local unemployment, so that expected returns tend to equalise across regions (in a Harris-Todaro, 1970-style model). The estimation of regional wage equations for Japan leads Brunello to conclude in Chapter 4 that 'regional unemployment rates do not significantly affect regional hourly real wages. Notice that the irrelevance of regional unemployment is quite robust to variations in the specification of [the] equation . . ., including the JLS regional wage equation. Overall, this evidence points to the stabilising role of Japanese local wages'. The empirical evidence on regional wage-setting provided for Spain, Italy and Japan in this volume leads one to object to the general validity of equation (10), thus leading one to reject the idea that the minimum unemployment rate, w***, is obtained in these countries where all u, are equalised across regions. Consequently, M 3 loses its ability to measure geographical mismatch, though it remains a useful dispersion index of relative unemployment rates. As such, it has to be interpreted when it is used, among others, by Bentolila and Dolado, Attanasio and Padoa Schioppa and Brunello. There is a final reason to believe that equation (10), with its three fundamental assumptions, is not generally confirmed by the data on most OECD countries, for which there exists a multiplicity of estimated aggregate wage equations. Notice that, if assumption (c) held true in each sectoral wage-setting - i.e., if y were constant across micro markets - the wage function estimated at the aggregate level should also have the same form as equation (10) - a double log form. Indeed, if we call w the average wage rate (calculated as a geometric mean of sectoral w) and u the aggregate unemployment rate (calculated as a geometric mean of sectoral «/), provided y is constant, equation (10) is transformed into log w = 2 at log Wt = Xj8ya/ - y 2 a, log ut = constant - ylog u

(12)

Apparently, there exists overwhelming OECD empirical evidence contradicting equation (12) at the aggregate level. I will refer to three experiments, carried out by Grubb (1985), Bean, Layard and Nickell (1986) and Dreze and Bean (1990). Grubb estimates in various pooled regressions the wage equation for 15 OECD countries, and shows that the best estimation implies in the long run the semi-log form log w = constant — yu + other push factors

(13)

16 Fiorella Padoa Schioppa

Grubb does not forget to try other types of wage equations - called Phillips curves - with steady implications different from equation (13), particularly with regard to the linear effect of u on log w. He concludes, however (17), that 'a simple test for non-linearity in the response to unemployment is to include log u in the Phillips curve . . . [in which case the estimates] show a tendency for u to be more significant than log u in the Phillips curve, so that the average response to unemployment is not strongly non-linear'. Bean, Layard and Nickell (1986) tried a similar experiment and estimated the wage-setting equation of about 20 OECD countries, with the following long-term properties log w = constant H

log(l - u) + other push factors

(13')

Although the estimates are quite sensible for most aspects, they are not very successful in terms of the parameters yx y2, that should be of equal sign to confirm the double-log form of equation (12) - necessary but not sufficient for the validity of equation (10). Extracting from the group of countries they analyse all those belonging to the EC, plus the three other countries involved in this volume (the United States, Japan and Sweden), the estimation of Bean, Layard and Nickell (1986) show that only Denmark and France present an equal and significant sign for yx and y2. The estimations carried out in ten countries within the European Project on Unemployment supply our third test on the greater empirical robustness at the aggregate level of equation (13) than (12): Dreze and Bean (1990) survey the estimated wage equations - presumably the preferred ones at each country level - and indicate that, though these equations are different from many points of view, they all have a long-run solution similar to equation (13), with the exception of France (where u appears linearly in a A\ogw equation without a steady state10), and the Netherlands (where unemployment does not appear as a regressor). If the semi-log form for the wage-setting were preferred even at the sectoral level - a subject still to be studied in depth - then the convexity in the u, function would be lost and the minimum variance in relative unemployment rates would no longer represent the identifying condition of the equilibrium unemployment rate w***; M 3 could still be calculated, but should not be interpreted as a mismatch index. All this empirical evidence leads me to consider the index M 3 as a useful piece of information on the dispersion of unemployment rates across micro markets, rather than as a mismatch indicator; Nickell, too, seems to share this assessment. If that is so, there is no presumption that M 3 should

Sectoral mismatch in the 1980s 17 provide indications similar to those of Mx or M2 on the mismatch path in the various countries. This is why Abraham is, in my opinion, only partly correct when, referring to Mu M2 and M 3 , she states that 4[t]he disturbing feature of the results . . . is that trends in measured skill mismatch within individual countries appear to be quite sensitive both to the measure used and to the occupational groupings employed in their construction . . . [while] the geographic mismatch measures . . . appear to be somewhat more robust, in the sense that the movements in different mismatch measures for a particular country seem generally to be similar': there is no reason to be disturbed when two indicators of two different phenomena are uncorrelated. But Abraham is right when she stresses that the comparisons between various mismatch indexes are affected by spurious discrepancies in occupational or regional groupings; she is also right when she states that the meaning of these indicators is weakened by the presence of quite different hiring functions in different micro markets, and by the absence of sufficiently disaggregated data, truly corresponding to distinct labour markets. With all these caveats, it is finally interesting to look at the indexes M3, as computed in the studies in this volume, because the dispersion of unemployment rates by occupation seems to have increased wherever it has been calculated (in Sweden, in Germany and probably in Spain and in the United States - up to 1983); only in the United Kingdom has it not been trended. The regional dispersion of unemployment rates has grown in Italy, in the United States and probably in Japan, while it has decreased (up to 1985) in the United Kingdom and in Spain, and has shown no definite trend in Sweden. 6

A more eclectic approach to mismatch

What has been stated so far on the various definitions of the equilibrium unemployment rate - w*, w**, w*** - and on the corresponding mismatch indicators, brings us to the conclusion that all these concepts present, along with interesting aspects, considerable shortcomings. This being the state of the theory, it is worth adopting a rather eclectic approach to mismatch. My own, which I will use in providing some further crosscountry comparison of the sectoral mismatch dynamics, is as follows. The identification of the presence of the frictional/structural unemployment rate should be based on the coexistence, at the aggregate level, of both unemployment and vacancies, even when aggregate labour demand equals labour supply: the benchmark equilibrium unemployment rate should therefore remain w* = v. One would then say that a rise in fric-

18 Fiorella Padoa Schioppa

Figure 1.1 The aggregate disequilibrium (S) and the Beveridge curve (B)

tional/structural unemployment relative to the observed unemployment rate requires a mounting (V/M) ratio. But the observed increase in (V/M) might derive, ceteris paribus, from three causes: a reduction in the labour market aggregate disequilibrium (an upward movement along a 'Beveridge curve') or an outward shift of the 'Beveridge curve' (for given aggregate disequilibrium in the labour market), the latter due either to growing frictions within each micro market (lower search intensity, higher choosiness) or - as a third possibility - to larger intersectoral discrepancies of unemployment and vacancies, that is to mismatch. My reasoning is very simple and, closely following Malinvaud (1986), may be graphically expressed on the u — v plane (Figure 1.1). The aggregate disequilibrium, recalling equations (2) and (3), is described by the straight line S, which draws relation (5'") - i.e., v = u — us: S coincides with the diagonal when the disequilibrium unemployment, us, is zero. Curve B (standing for the 'Beveridge curve') indicates all possible combinations of the aggregate unemployment and vacancy rates compatible with steady conditions in the labour market (/?,- = given in equation (7)). The intersection between B and S identifies the observed unemployment rate, M, and the (V/M) ratio (equal to the tangent of angle e). A reduction in the labour market aggregate disequilibrium leads, ceteris paribus, to a parallel upward shift in S, hence to a rise in the (v/u) ratio: if aggregate disequilibrium does not exist, the intersection between B and the 45° line S implies that the observed unemployment rate (it) equals the equilibrium one (v). Also an outward shift of the B curve, for given S, increases the (V/M) ratio, and hence the misplacement percentage of the unemployment rate. Obviously, what distinguishes one case from the other is that in the

Sectoral mismatch in the 1980s

19

Table 1.1. Criteria identifying when mounting mismatch is uniquely responsible for the unemployment rate increase Ceteris paribus causes of change

Outward shift of B:

Upward shift of S:

Indexes

Higher mismatch

Lower aggregate disequilibrium

(v/u) M4 u

Higher frictions +

former the observed unemployment rate declines, while in the latter it rises. But not all outward shifts of the 'Beveridge curve' are due to mismatch - i.e., to a growing dispersion of the vacancy/unemployment rates ratios across micro markets. In this approach, dispersion is measured in a rather standard way, by half of the weighted variation coefficient of the (v//w/) ratios, where the weights, rjh should be equal11 to the share of unemployment in / relative to aggregate unemployment, ut. This dispersion index, labelled as M 4 , 12 is therefore

MA = \

/ ,

^"

, , - l = U / y

^

Ti,-!

In synthesis, my rather eclectic approach to assessing whether a growing mismatch uniquely explains the worsening in the unemployment rate dynamics is based on three building blocks: the (v/u) growth at the aggregate level is a necessary, but not a sufficient, condition to make one suspect that mismatch is rising relative to the overall unemployment; an increasing degree of mismatch also requires a higher dispersion index, M4 and, under the ceteris paribus condition, a higher level of the unemployment rate. Table 1.1 summarises the criteria identifying the presence, ceteris paribus, of a mounting mismatch responsible for the unemployment rate increase: (4-) implies a growth; ( - ) a decrease; (/) shows a constant index. Of course, there are other cases in which a growing mismatch can partly contribute to explaining the unemployment dynamics. In all these cases which arise when both the B and the S curves move together - the dispersion index, M 4 , has to rise.

20

7

Fiorella Padoa Schioppa

Empirical evidence on industrial mismatch in Europe

I use this methodology in a European data set concerning 8 countries 13 and 19 industrial sectors,14 homogeneously defined across countries, for the period 1980-9. The empirical evidence consists of yearly averages of quarterly data gathered through EC business surveys, which report (by country and by sector) the percentage of firms whose production plans are hindered by insufficient demand, by lack of equipment or by shortage of labour force. In the wake of what has been done within the European Project on Unemployment, the (Vj/uiy ratio (i.e., the ratio between vacancies and unemployment in sector / and country j) is proxied by the ratio of firms constrained by lack of labour force relative to all other constrained firms in sector / and country j ; the (v/uy ratio - i.e., the vacancy/unemployment rate ratio of industry as a whole in country y - is constructed similarly. The single and basic idea underlying this approximation - a more elaborate version of which may be found in Bean and Gavosto (1989), while some implications are derived in Bean and Pissarides - is the following: for each / and j there is a one-to-one correspondence between the number of firms declaring themselves to be constrained by shortage of labour force and the number of their unfilled vacancies; there exists a similar correspondence between the number of firms declaring themselves to be hindered by lack of demand or lack of equipment and the number of the 'effective' unemployed in / and j . This procedure does not contain the shortcomings typical of data sets on job vacancies where, as Abraham notes 'the numbers reported are derived from administrative records rather than from surveys designed for statistical purposes'; neither is it affected by the usual bias on unemployment data by sector, when job searchers are classified according to their latest job, as it is well known that 'individuals cannot easily be assigned to a single occupational or even a single [individual] geographic category. Any one individual's previous experience might have prepared him or her for employment in a number of occupations. Indeed, . . . a substantial number of job changers also report changes in occupation'. My approximations, however, present another limitation: I assume that the 'effective' labour supply in each sector / consists, apart from the employed in /, only of those workers the firms in sector / would be ready to hire, if there were no productive capacity or final demand constraints (the lack of labour force being excluded by assumption). The unemployment pool does not therefore include those who would never get an offer from the existing firms: 'effective' unemployment refers only to the firms' point of view. Although this strong assumption has been already successfully adopted

Sectoral mismatch in the 1980s 21

within the European Project on Unemployment, I have tried to verify its validity through a control solution provided by the OECD official data on unemployment and vacancies - knowing, however, that this control solution is itself imperfect, for the reasons pointed out by Abraham.15 I have drawn a comparison on the (v/uy industrial ratios for the years and for the countries for which I had both the EC business survey observations and the more traditional OECD empirical evidence: the corresponding plots16 (see Figures 1.2a—i) indicate that, with the exception of Denmark, whose official data on vacancies are obviously downward biased, the paths of the two (v/uy series are rather well correlated. According to these sources, almost everywhere in Europe, in the United States and in Japan the (V/M) ratio first decreases in the beginning of the 1980s, and then rises. I have consequently computed the (vt/uiy ratios for each of the 19 sectors and for industry as a whole, of every European country, j , belonging to EURS: notably, Belgium (£), Germany (/)), Denmark (DK), France (F), Ireland (IRL), Italy (IT), the Netherlands (NL), the United Kingdom (UK). The computation was tedious because these EC data, being used here for the very first time - unlike the corresponding national data which have been analysed many times before - contained inconsistencies and mistakes which I have patiently (and provisionally) tried to overcome. With regard to the existing inconsistencies, an indicator was offered whenever the observed (v/uy ratio in industry as a whole of they country in a given year did not correspond to the weighted average of the 19 sectors' (vt/u^ ratios of the same country in the same year.17 In these cases, I was confronted with a threefold choice, (a) If I could resort to the national statistical sources from which the EC data originated, the correction was immediate.18 When that was impossible (notably for all countries except for Italy), I looked for outliers, quarter by quarter and sector by sector. Then, (b) if there emerged a blatant outlier in a specific quarter and sector, in computing the yearly average I abandoned that observation so as to eliminate the relevant discrepancy.19 (c) If no blatant outlier was specifically found, I preferred to avoid 'discretional interventions'.20 This is why Figure 1.2 shows few persisting differences between the observed (v/uy and the constructed weighted average of the 19 sectors' (Vj/uiy': they are small but disturbing and hopefully in the future the EC DG2, which is responsible for these data, will introduce the necessary corrections with the collaboration of the corresponding National Statistical Institutes. After some data excavation work on the European business survey information, I suspect that other errors remain in my graphs. They are difficult to discover, however, because they are not revealed by inconsist-

22

Fiorella Padoa Schioppa

(a) U.UJOU

(vjufOECD

_ Belgium (= B) 0.0530 -

jj

0.0480 0.0430

jl

\ \

fl1

0.0380 0.0330 0.0280 0.0230 0.0180 0.0130

-: W - \ \ ^

B.S

\V \ /

/

A

\

f

T i l l

i

i

,

.

1

,

1

,

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Sample period is 1980-8

(b)

0.450

Denmark {= DK)

0.400

\ i 1

0.350 0.300 0.250 0.200 0.150 0.0100 0.050

:

\

s\

1 (v/ a y«B.s \ N \

7 \ \\ (v/u)DK OECD

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Sample period is 1980-8 Figure 1.2 (see p. 26)

Sectoral mismatch in the 1980s 23 (c) 0.500

Germany (= D)

0.450 0.400 0.350 0.300 0.250 0.200 0.150 0.100

(v/u)D OECD

0.050 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Sample period is 1980-8

(d) 0.0700 France (= F) 0.0600 0.0500 0.0400 0.0300 0.0200 J 1980 1981 1982 1983 1984 1985. 1986 1987 1988 1989 Sample period is 1980-8 Figure 1.2

(see p. 26)

24

Fiorella Padoa Schioppa

(e) 0.220

Ireland (= IRL)

0.170

0.120

0.070

1980 1981 1982 1983 19841985 1986 1987198819891990 Sample period is 1980-9

(f) 0.160

Italy (= IT)

0.140 0.120 0.100 0.080 0.060 0.040

B.S.

0.020 1980 1981 1982 1983 1984 1985 1986 19871988 1989 1990 Sample period is 1980-9 Figure 1.2

(see p. 26)

Sectoral mismatch in the 1980s 25 (g) 0.700

Netherlands (= NL) \

0.600

i \

0.500 0.400 0.300

w

\

0.200

.

0.100

(v/u)NL B.S. (v/u)NL OECD

i|

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Sample period is 1980-8

(h) 0.400

United Kingdom (= UK)

0.350 0.300

,(v/u)UK

0.250 0.200 0.150 0.100 0.050 J

i

I

i

I

•

I

•

I

•

I

•

I

i . i

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Sample period is 1980-8 Figure 1.2

(see p. 26)

B.S

26

Fiorella Padoa Schioppa

(0 1 .40 r1.20

Sweden (= SW) United States (= USA)

i

(v/u)sw USA

(v/u)

JAP

y{vlu)

OECD OECD

OECD

(v/u)E OECD

0.0000