Migration and Culture

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MIGRATION AND CULTURE

FRONTIERS OF ECONOMICS AND GLOBALIZATION 8

Series Editors: HAMID BELADI University of Texas at San Antonio, USA E. KWAN CHOI Iowa State University, USA

FRONTIERS OF ECONOMICS AND GLOBALIZATION VOLUME 8

MIGRATION AND CULTURE Edited by

Gil S. Epstein Department of Economics, Bar-Ilan University, Ramat Gan, Israel

Ira N. Gang Department of Economics, Rutgers University, New Brunswick, NJ, USA

United Kingdom – North America – Japan India – Malaysia – China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2010 Copyright r 2010 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-85724-153-5 ISSN: 1574-8715

Emerald Group Publishing Limited, Howard House, Environmental Management System has been certified by ISOQAR to ISO 14001:2004 standards Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print

To Ayelet, Laura my mother and to my precious children Ofir, Noga, Inbal, and Eytan, with love, Gil. To Gail and to my precious children, Joshua and Eli, with love, Ira. To our ancestors, who understood the forces of migration and culture.

ABOUT THE SERIES: FRONTIERS OF ECONOMICS AND GLOBALIZATION

This series is aimed at economists and financial economists worldwide and will provide an in-depth look at current global topics. Each volume in the series will focus on specialized topics for greater understanding of the chosen subject and provide a detailed discussion of emerging issues. The target audiences are professional researchers, graduate students, and policy makers. It will offer cutting-edge views on new horizons and deepen the understanding in these emerging topics. With contributions from leading researchers, each volume presents a fresh look at today’s current topics. This series will present primarily original works, and employ references appropriate to the topic being explored. Each volume will bring a set of highly concentrated articles that will provide in-depth knowledge to a target audience, while the entire series will appeal to a wide audience by providing them with deeper knowledge on a broad set of emerging topics in the global economy. The Frontiers of Economics and Globalization series will publish on topics such as: – – – – – – – – – – – – – – – –

Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers Frontiers

of of of of of of of of of of of of of of of of

Trade Negotiations Derivative Pricing International Lending and Debt Problems Economics Integration Trade and Environment Foreign Exchange International Finance Growth of Open Economies Futures Pricing International Financial Markets Investment Banking Mergers and Acquisitions Government Policy and Regulations Multi-Sector Growth Models Intellectual Property Rights Fragmentations and Outsourcing Hamid Beladi E. Kwan Choi Series Editors

ABOUT THE VOLUME: MIGRATION AND CULTURE

About the editors Gil S. Epstein is a professor of economics at the Department of Economics at Bar-Ilan University, Israel. He is an associate editor of the Journal of Population Economics and e-conomics. In addition, he is a research fellow in IZA and CReAM. He is the author of Endogenous Public Policy and Contests (with Shmuel Nitzan); his papers on migration, public policy, public choice, political economy, and labor economics have been published in leading journals in economics. Ira N. Gang is a professor of economics at the Department of Economics at Rutgers University. Ira Gang is an associate editor of the Journal of Population Economics and on the board of editors of the Journal of International Trade and Economic Development and one of the founding editors of Review of Development Economics. In addition he is a research fellow in IZA and CReAM. His papers on tax reform, development trade liberalization, corruption, migration, and lobbying have been published in the leading journals in economics. About this volume Culture is not new to the study of migration. It has lurked beneath the surface for some time, occasionally protruding openly into the discussion, usually under some pseudonym. The authors of the chapters in this volume bring culture into the open. They are concerned with how culture manifests itself in the migration process for three groups of actors: the migrants, those remaining in the sending areas, and people already living in the recipient locations. The topics vary widely. What unites the authors is an understanding that though actors behave differently, within a group there are economically important shared beliefs (customs, values, attitudes, etc.), which we commonly refer to as culture. Culture and identity play a central role in our understanding of migration as an economic phenomenon, but what about them matters? Properly, we should be looking at the determinants of identity and the determinants of culture (prices and incomes, broadly defined). But this is not what is done. Usually, identity and culture appear in economics articles as a black box. The authors of the chapters in this volume begin to break open the box.

LIST OF CONTRIBUTORS

Randall K. Q. Akee

Department of Economics, Tufts University, Medford, MA, USA

Arnab K. Basu

Department of Economics, College of William and Mary, Williamsburg, VA, USA

Michele Battisti

Department of Economics, Simon Fraser University, Burnaby, BC, Canada

Howard Bodenhorn

The John E. Walker Department of Economics, Clemson University, Clemson, SC, USA

O¨rn B. Bodvarsson

Department of Economics, Department of Management, St. Cloud State University, St. Cloud, MN, USA; Institute for the Study of Labor (IZA), D-53113 Bonn, Germany

Nancy H. Chau

Department of Applied Economics and Management, Cornell University, Ithaca, NY, USA

Barry R. Chiswick

Department of Economics, University of Illinois at Chicago, IL, USA; IZA-Institute for the Study of Labor, Bonn, Germany

Sarit Cohen-Goldner

Department of Economics, Bar-Ilan University, Ramat Gan, 52900, Israel; Institute for the Study of Labor (IZA), Bonn, Germany

Joseph Deutsch

Department of Economics, Bar-Ilan University, Ramat Gan, 52900, Israel

Don DeVoretz

Department of Economics, Simon Fraser University, Burnaby, BC, Canada

Gil S. Epstein

Department of Economics, Bar-Ilan University, Ramat Gan, 52900, Israel; CReAM, London, UK; Institute for the Study of Labor (IZA), Bonn, Germany

Giovanni Facchini

Dipartimento di Scienze Economiche, Aziendali e Statistiche, Universita´ degli Studi di Milano, Milano, Italy; Department of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands

xii

List of Contributors

Riccardo Faini (Deceased)

Faculty of Economics, Centre for International and Economic Studies, University of Rome Tor Vergata, Rome, Italy; CEPR-Center for Economic and Policy Research, London, UK; IZA-Institute for the Study of Labor, Bonn, Germany

Michael Fertig

Institut fu¨r Sozialforschung und Gesellschaftspolitik, Ko¨ln, Germany; RWIRheinisch-Westfa¨lisches Institut fu¨r Wirtschaftsforschung, Essen, Germany; Institut zur Zukunft der Arbeit (IZA), Bonn, Germany

Ira N. Gang

Department of Economics, Rutgers University, New Brunswick, NJ, USA; Institute for the Study of Labor (IZA), Bonn, Germany; CReAM, London, UK

T. H. Gindling

Department of Economics, University of Maryland Baltimore County, Baltimore, Maryland, USA

Yitchak Haberfeld

Department of Labor Studies, Tel-Aviv University, Tel Aviv, Israel

Robert Kaestner

Institute of Government and Public Affairs, University of Illinois at Chicago, IL, USA

Martin Kahanec

Department of Economics, Central European University (CEU), Budapest, Hungary Institute for the Study of Labor (IZA), Bonn, Germany

Shirit Katav-Herz

School of Management and Economics, Tel-AvivYaffo Academic College, Tel Aviv, Israel

Neeraj Kaushal

School of Social Work, Columbia University, New York, NY, USA

Melanie Khamis

Institute for the Study of Labor (IZA), Bonn, Germany

Sajal Lahiri

Department of Economics, Southern Illinois University Carbondale, Carbondale, IL, USA

Anna Maria Mayda

Department of Economics and School of Foreign Service, Georgetown University, Washington, DC, USA

Yosef Mealem

The School of Banking and Finance, Netanya Academic College, Netanya, Israel


xiii

Paul W. Miller

School of Economics and Finance, Curtin University, Perth, WA, Australia

Carolyn M. Moehling

Department of Economics, Rutgers University, New Brunswick, NJ, USA

Kusum Mundra

Department of Economics, Rutgers University, Newark, NJ, USA

Anne Morrison Piehl

Department of Economics, Rutgers University, New Brunswick, NJ, USA

Matloob Piracha

School of Economics, University of Kent, Canterbury, Keynes College, Kent, UK

Sara Z. Poggio

Department of Modern Languages and Linguistics, University of Maryland Baltimore County, Baltimore, Maryland, USA

Francisco L. Rivera-Batiz

Economics and Education, Teachers College and International and Public Affairs, Teachers College, Columbia University, New York, NY, USA

Christoph M. Schmidt

Rheinisch-Westfa¨lisches Institut fu¨r Wirtschaftsforschung, Essen, Germany; Ruhr-Universita¨t Bochum

John G. Sessions

Department of Economics, University of Bath, Bath, UK

Moshe Semyonov

Department of Sociology, Tel-Aviv University, Ramat Aviv, Tel Aviv, Israel

Erez Siniver

School of Economics, The College of Management Academic Studies, Rishon Letzion, Israel

Florin Vadean

Centre for Economic and International Studies University of Rome Tor Vergata, Rome, Italy; School of Economics, University of Kent, Canterbury Kent, UK

Alessandra Venturini

IZA-Institute for the Study of Labor, Bonn, Germany; Department of Economics, University of Torino, Torino, Italy; European university Institute, Florence 50014 Fiesole, Italy

Yan Xing

Department of Sociology, University of Illinois, Chicago, IL, USA

xiv


Mutlu Yuksel

Department of Public Policy, Dalhousie University, Halifax, NS, Canada

Myeong-Su Yun

Institute for the Study of Labor (IZA), Bonn, Germany; Department of Economics, Tulane University, New Orleans, LA, USA

CONTENTS

LIST OF CONTRIBUTORS

xxvii

PREFACE CHAPTER 1 MIGRATION AND CULTURE Gil S. Epstein and Ira N. Gang 1 2 3 4 5

Enclaves and location choice Production, earnings, and competition Assimilation struggles Family issues and the effects of remittances Selection, attitudes, and public policy Acknowledgment References

PART I:

xi

ENCLAVES AND LOCATION CHOICE

1 2 6 8 12 14 16 16 23

CHAPTER 2 INFORMATIONAL CASCADES AND THE DECISION TO MIGRATE Gil S. Epstein

25

1 Introduction 2 The model 2.1 The background 2.2 A one-signal model 2.3 An illustration 2.4 Multiple signaling 3 Network externalities 4 Concluding remarks Acknowledgment Appendix. Proof of Proposition 1 References

25 28 28 29 31 32 33 39 41 41 43

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Contents

CHAPTER 3 THE MEASUREMENT OF INCOME POLARIZATION BY ETHNIC GROUPS: THE CASE OF ISRAEL POPULATION Joseph Deutsch 1 Introduction 2 Measuring polarization when income groups do not overlap 2.1 The case of two groups of equal size 2.2 The case of three nonoverlapping income groups 3 Measuring polarization when income groups do overlap 4 An empirical illustration 4.1 The case of nonoverlapping groups 4.2 The case of overlapping groups Appendix A. On the concept of Shapley decomposition References

CHAPTER 4 THE EFFECTS OF SCHOOL QUALITY IN THE ORIGIN ON THE PAYOFF TO SCHOOLING FOR IMMIGRANTS Barry R. Chiswick and Paul W. Miller 1 2 3 4

Introduction Methodology Country-level data Empirical assessment 4.1 Aggregate-level analyses 4.2 The role of age at migration 4.3 Reference education, overeducation and undereducation, and PISA scores 5 Conclusion Acknowledgments Appendix A. Definitions of variables Appendix B. Analyses using the Hanushek and Kimko data B.1 Analyses of Hanushek and Kimko using full sample of 73 countries B.2 Analyses of Hanushek and Kimko indices using subset of countries with both PISA and Hanushek and Kimko measures B.3 Analyses of PISA scores using subset of countries with both PISA and Hanushek and Kimko measures Appendix C. Supplementary results References

45 46 46 46 48 50 53 53 57 62 65

67 68 70 72 78 80 85 86 92 93 93 95 95

96

98 102 102

Contents

CHAPTER 5 DEVELOPMENT AND MIGRATION: LESSONS FROM SOUTHERN EUROPE Riccardo Faini and Alessandra Venturini 1 Introduction 2 The pervasiveness of home bias 3 A simple migration model 3.1 The ‘‘home bias’’ model 3.2 The role of financial constraints 4 Trends in Southern European migrations 5 Econometric analysis 5.1 The estimating equation 5.2 The data 5.3 Estimation methods and the results 6 Conclusions and policy implications Acknowledgments Appendix. Data and variables appendix A1 Methodology A2 More information on European migration References CHAPTER 6 GEOGRAPHIC DISPERSION AND INTERNAL MIGRATION OF IMMIGRANTS Neeraj Kaushal and Robert Kaestner 1 Introduction 2 Theoretical considerations 3 Empirical models 3.1 Current location choice 3.2 Internal migration 4 Data 5 Results 5.1 Current location choice: Descriptive analysis 6 Current location choice: Multivariate analysis 6.1 Internal migration: Descriptive analysis 6.2 Internal migration: Multivariate analysis 6.3 Decomposition analysis 6.4 Dispersion due to changes in immigrant composition 7 Conclusion Appendix A. Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth Appendix B. Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth and year

xvii

105 106 107 110 111 113 115 119 119 121 123 129 130 131 131 132 133

137 138 139 142 142 145 146 147 147 149 155 157 160 164 166 168

169

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Contents

Appendix C. Logitistic estimates of the effect of individual characteristics and location attributes on the inter-state migration of foreign-born persons, by country of birth References PART II:

PRODUCTION, EARNINGS AND COMPETITION

CHAPTER 7 UNDERSTANDING THE WAGE DYNAMICS OF IMMIGRANT LABOR: A CONTRACTUAL ALTERNATIVE Christoph M. Schmidt 1 2 3 4 5

Introduction The orthodoxy: Country-specific human capital The contractual model Optimal contracts Policy implications Acknowledgments References

CHAPTER 8 INTERACTIONS BETWEEN LOCAL AND MIGRANT WORKERS AT THE WORKPLACE Gil S. Epstein and Yosef Mealem

170 171

175

177 177 179 181 184 187 189 189

193

1 Introduction 2 The model 3 Concluding remarks Acknowledgment Appendix References

193 195 200 201 201 202

CHAPTER 9 ETHNIC COMPETITION AND SPECIALIZATION Martin Kahanec

205

1 Introduction 2 The model 2.1 Demand 2.2 Supply 2.3 The equilibrium 3 Specialization of ethnic groups 4 Discussion and conclusions Acknowledgments Appendix A.1 Derivation of equilibrium properties using specific functional forms References

205 209 209 212 213 215 222 223 224 224 226

Contents

CHAPTER 10 NATIONALITY DISCRIMINATION IN THE LABOR MARKET: THEORY AND TEST + B. Bodvarsson and John G. Sessions Orn 1 Introduction 1.1 Nationality discrimination: Meaning and previous literature 2 A theory of nationality discrimination 2.1 The problem setting 3 A test case: Major League Baseball 3.1 Description of the test case 3.2 Empirical analysis 3.3 Decomposition analysis 4 Concluding remarks Acknowledgments Appendix. Summary of studies providing information about ceteris paribus native/immigrant earnings differences References CHAPTER 11 CULTURE, INVESTMENT IN LANGUAGE AND EARNINGS Erez Siniver 1 2 3 4 5 6

xix

231 232 233 238 238 245 245 246 257 261 262 263 266

269

Introduction Review of the literature Data Method Results The effects of networks on the decision to invest in learning the host country’s language 7 Summary and conclusion References

284 289 290

PART III:

293

ASSIMILATION STRUGGLES

CHAPTER 12 IMMIGRATION: AMERICA’S NINETEENTH-CENTURY ‘‘LAW AND ORDER PROBLEM?’’ Howard Bodenhorn, Carolyn M. Moehling and Anne Morrison Piehl 1 2 3 4 5 6

The first major wave of immigration Pennsylvania prison data Immigrant arrivals and prison commitments Aggregate incarceration experience: immigrants and natives Exploring the differences in immigrant and native incarceration Variation across immigrant groups: British, Irish, and Germans

269 271 272 272 274

295

296 301 303 306 311 316

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Contents

7 Concluding remarks Acknowledgments References CHAPTER 13 A POLITICAL ECONOMY OF THE IMMIGRANT ASSIMILATION: INTERNAL DYNAMICS Gil S. Epstein and Ira N. Gang 1 Introduction 2 The model 2.1 The absolute ranking 2.2 The relative ranking 2.3 Comparing the investment of effort of the groups under both situations 3 Conclusion Acknowledgment Appendix References CHAPTER 14 ASSIMILATING UNDER CREDIT CONSTRAINTS: PUBLIC SUPPORT FOR PRIVATE EFFORTS Sajal Lahiri 1 2 3 4

Introduction The theoretical framework Public support and private assimilation Conclusion Acknowledgment References

CHAPTER 15 IMMIGRANT NETWORKS AND THE U.S. BILATERAL TRADE: THE ROLE OF IMMIGRANT INCOME Kusum Mundra 1 Introduction 2 Immigrant and the Heckscher–Ohlin model 2.1 Assumptions 2.2 Analysis 2.3 Sufficient condition 3 Immigrants’ income and demand 4 Empirical model 4.1 Data 5 Results

320 320 321

325 325 328 330 331 334 335 336 336 337

341 341 344 348 354 355 355

357 358 360 360 361 363 364 365 367 368

Contents

xxi

6 Concluding remarks Appendix A Appendix B References

370 371 371 371

CHAPTER 16 THE SOCIETAL INTEGRATION OF IMMIGRANTS IN GERMANY Michael Fertig

375

1 2 3 4 5

Introduction Economic and societal integration Empirical strategy and data Results Conclusions Acknowledgments Appendix References

CHAPTER 17 WHO MATTERS MOST? THE EFFECT OF PARENT’S SCHOOLING ON CHILDREN’S SCHOOLING Ira N. Gang 1 2 3 4

Introduction Data Empirical Results Conclusions References

CHAPTER 18 INTERGENERATIONAL TRANSFER OF HUMAN CAPITAL UNDER POST-WAR DISTRESS: THE DISPLACED AND THE ROMA IN THE FORMER YUGOSLAVIA Martin Kahanec and Mutlu Yuksel 1 Introduction 2 Literature review 3 Background on internally displaced people and Roma population in Europe 4 Data and descriptive statistics 5 The results 5.1 Income and employment 5.2 Education and intergenerational transfer of human capital 6 Conclusions and policy recommendations Acknowledgment References

375 378 380 383 391 392 392 399

401 401 403 405 412 413

415 416 417 419 422 432 432 438 441 442 442

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PART IV:

Contents

FAMILY ISSUES AND THE EFFECTS OF REMITTANCES

445

CHAPTER 19 HOUSEHOLD STRUCTURE OF RECENT IMMIGRANTS TO ISRAEL Sarit Cohen-Goldner

447

1 Introduction 2 Data analysis 3 Conclusions Acknowledgments References

447 451 464 465 465

CHAPTER 20 CIRCULAR MIGRATION OR PERMANENT RETURN: WHAT DETERMINES DIFFERENT FORMS OF MIGRATION? Florin Vadean and Matloob Piracha 1 2 3 4 5 6

Introduction Framework for analysis Background and data Econometric specification Empirical results Conclusions Acknowledgments References

CHAPTER 21 LABOR MIGRATION, REMITTANCES, AND ECONOMIC WELL-BEING: A STUDY OF HOUSEHOLDS IN RAJASTHAN, INDIA Yan Xing, Moshe Semyonov and Yitchak Haberfeld 1 2 3 4 5

Introduction Labor migration and the role of remittances Remittances in India Data and variables Analysis and findings 5.1 The multiple use of remittances 5.2 Descriptive overview – comparing households with and without labor migrants 5.3 Multivariate analysis 6 Conclusions

467 467 470 472 478 480 491 494 494

497

497 498 500 500 502 502 504 506 512

Contents

Appendix. Consumption assets and the mean of SDLV of households with previous overseas workers, with current overseas workers and with no overseas workers References CHAPTER 22 PROMOTING THE EDUCATIONAL SUCCESS OF LATIN AMERICAN IMMIGRANT CHILDREN SEPARATED FROM PARENTS DURING MIGRATION Sara Z. Poggio and T.H. Gindling 1 Introduction 2 Literature review 3 Policy recommendations from parents and teachers 3.1 Parents 3.2 Teacher survey 4 Conclusions Acknowledgments Appendix References CHAPTER 23 CULTURAL DIFFERENCES IN THE REMITTANCE BEHAVIOUR OF HOUSEHOLDS: EVIDENCE FROM CANADIAN MICRO DATA Don DeVoretz and Florin Vadean 1 Introduction 2 Theoretical considerations 2.1 The demand system 2.2 Demographic controls, immigration entry and assimilation 2.3 Weak separability 3 Data and descriptive statistics 3.1 Family expenditure survey (FAMEX) 3.2 Prices 4 Empirical results 4.1 Homogeneity and symmetry 4.2 Weak separability 4.3 Expenditure elasticities 4.4 Demographic controls 4.5 Immigration entry and assimilation effects 5 Conclusions Acknowledgments References

xxiii

513 514

517 518 518 523 523 524 530 533 534 539

543 543 545 546 547 548 548 548 551 553 553 555 558 563 570 572 573 573

xxiv

PART V:

Contents

SELECTION, ATTITUDES AND PUBLIC POLICY

CHAPTER 24 FSU IMMIGRANTS IN CANADA: A CASE OF POSITIVE TRIPLE SELECTION? Don DeVoretz and Michele Battisti 1 Introduction 2 Literature review 3 Data 3.1 Data source 3.2 Construction of our dataset 3.3 Data selection 4 Regressions results 4.1 OLS results 4.2 Labour force activity 5 Two-stage models 5.1 FSU vs. Canadian born 5.2 USSR/FSU immigrants versus all immigrants 6 Simulations 6.1 Decomposition analysis 7 Conclusions Acknowledgments Appendix A References CHAPTER 25 WHAT DRIVES IMMIGRATION POLICY? EVIDENCE BASED ON A SURVEY OF GOVERNMENTS’ OFFICIALS Giovanni Facchini and Anna Maria Mayda 1 Introduction 2 Political economy model of migration policy 2.1 What drives individual attitudes toward immigration? 2.2 From individual preferences to migration policy 3 Governments’ views and policies toward immigration 3.1 Governments’ views toward immigration 3.2 Governments’ policies toward immigration 4 Individual attitudes toward immigrants 5 Individual opinions and immigration policy 6 Conclusions Acknowledgments Appendix References

577

579 579 583 587 587 588 588 589 589 593 594 594 596 598 598 601 602 603 604

605 606 607 608 611 614 614 616 622 630 637 638 639 646

Contents

CHAPTER 26 CHANGES IN ATTITUDES TOWARD IMMIGRANTS IN EUROPE: BEFORE AND AFTER THE FALL OF THE BERLIN WALL Ira N. Gang, Francisco L. Rivera-Batiz and Myeong-Su Yun 1 2 3 4 5 6

xxv

649

Introduction The determinants of anti-immigrant attitudes The eurobarometer survey and the empirical model Results Changes in attitudes: a decomposition analysis Summary and conclusions Acknowledgments References

649 653 655 660 667 672 674 674

CHAPTER 27 THE IMPLICATIONS OF SOCIAL NORMS ON IMMIGRATION POLICY Shirit Katav-Herz

677

1 Introduction 2 The model 2.1 The choice of the median voter in a one-period model 2.2 Anti-immigrant actions 2.3 The number of immigrants in the second period 2.4 A far-sighted median voter 3 The distribution of immigration over time 4 Conclusions Acknowledgment Appendix References

677 680 682 683 684 684 685 687 688 688 688

CHAPTER 28 ETHNIC FRAGMENTATION, CONFLICT, DISPLACED PERSONS AND HUMAN TRAFFICKING: AN EMPIRICAL ANALYSIS Randall K.Q. Akee, Arnab K. Basu, Nancy H. Chau and Melanie Khamis

691

1 2 3 4

Introduction Data Empirical methodology and results Conclusion Appendix Acknowledgment References

692 697 701 709 709 714 714

AUTHOR INDEX

717

SUBJECT INDEX

725

PREFACE

For some time we have been concerned with the use of the concept of culture in the economics migration literature and in our own work. Migrants, the places they leave and the places they go, are complex elements that are linked in multiple and multidimensional ways. Yet in our search for simple models, we often summarize this information in single categorical variables such as country of origin, just as in the discrimination literature we often summarize the differences in gender or ethnic/racial achievements solely by a shift in the intercept. Occam’s razor is sometimes too sharp. We know culture matters; but what about it matters? Usually culture appears in economics articles as a black box. While useful, this is not satisfying. This is why when the editors of this series offered us the opportunity to edit this volume, we jumped. We invited a range of scholars who in their work had touched on this theme to submit papers. Each submission was subjected to two referees and our own readings. Each accepted paper was accordingly revised. What we ended up with is an excellent heterogeneous collection of papers that address the links between migration and culture – some directly, some indirectly. This book has two alternative functions. It can be used as a text for a course on migration and culture for advanced undergraduate or graduate students. It can also be used (partly or as a whole) as part of an advanced undergraduate or graduate course in migration or labor economics. This volume consists of 28 chapters, broken into an introduction and five parts. There is a mixture of both theoretical and empirical studies presented in this volume. The introduction provides a brief overview of the literature and of the chapters presented in this volume. Part I deals with enclaves and the locational choices migrants make, part II deals with production, earnings, and competition between migrants and local population, part III deals with the assimilation struggles among migrants and the reaction of the local population, part IV deals with family issues and the effect remittances have on the population left in the home country, and finally part V deals with the issues of selection, attitudes of the local population, and public policy. The ideas presented in this book are new and fresh and are those of the authors of each chapter. We thank the series editors, Hamid Beladi and E. Kwan Choi, for offering us this opportunity; and the people at Emerald for helping us carry it out. We thank, of course, the contributors to this volume and those who helped us referee and comment. We especially thank Geoffrey Williams, a

xxviii

Preface

rare gem of a graduate student for taking on the task of reading every chapter and offering unique and superior insights. Part of this project was completed while we were on one of our frequent visits to the IZA. We are most grateful to the institute for their warm hospitality and supporting research environment.

CHAPTER 1

Migration and Culture Gil S. Epsteina,b,c and Ira N. Gangb,c,d a

Department of Economics, Bar-Ilan University, Ramat Gan, 52900, Israel E-mail address: [email protected] b Institute for the Study of Labor (IZA), Bonn, Germany c CReAM-Center for Research and Analysis of Migration, London, UK d Department of Economics, Rutgers University, New Brunswick, New Jersey, 08901-1248, USA E-mail address: [email protected]

Abstract Culture is not new to the study of migration. It has lurked beneath the surface for some time, occasionally protruding openly into the discussion, usually under some pseudonym. The authors bring culture into the open. They are concerned with how culture manifests itself in the migration process for three groups of actors: the migrants, those remaining in the sending areas, and people already living in the recipient locations. The topics vary widely. What unites the authors is an understanding that though actors behave differently, within a group there are economically important shared beliefs (customs, values, attitudes, etc.), which we commonly refer to as culture. Culture and identity play a central role in our understanding of migration as an economic phenomenon; but what about them matters? Properly, we should be looking at the determinants of identity and the determinants of culture (prices and incomes, broadly defined). But this is not what is done. Usually identity and culture appear in economics articles as a black box. Here we try to begin to break open the black box. Keywords: Migration, Culture, Discrimination, Assimilation, Mobility Jel classifications: F22, O15, R23, J61, J71 Migrants are quite diverse. The discussion here is on the distinctions in culture among migrants, the families they left behind, and the local population in the migration destination. The new interactions directly affect all three groups. Assimilation is one result; separation is also a possibility. Location choice, workplace interaction, enclave size, the opportunity for the migrant obtaining credit in their new country, the local

Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008007

r 2010 by Emerald Group Publishing Limited. All rights reserved

2

Gil S. Epstein and Ira N. Gang

population’s reaction to migrants, the political culture of the migrants and local population, links to the country-of-origin, and the economic state of the host country, all contribute to the classic conflict between assimilation and separation. Papers examining the working of the assimilation process on the migrants themselves, on the local population, on the families left at the home country, and others can be divided into five nonexclusive areas: (1) enclaves and location choice; (2) production, earnings, and competition; (3) assimilation struggles; (4) family issues and the effects of remittances; (5) selection, attitudes, and public policy. 1. Enclaves and location choice A characteristic of international migration is the clustering of immigrants in ethnic communities. Prominent examples are the concentration of Turks in Germany, Tamils in Switzerland, Moroccans in the Netherlands and Belgium, Italians in Argentina, Greeks in Australia, and Ukrainians in Canada. Clustering may be very narrow, such as when immigrants from a town or region are concentrated in a specific foreign town or region. For example, Macedonians from Skopje have come to make up a notable part of the population of Gothenburg, Sweden. In the United States, noticeable clusters of Mexican immigrants exist in California, Texas, Florida, and Chicago. Three-fourths of migrants from Guanajuato, the Mexican state with the highest emigration rate to the United States, go to California or Texas. The prevailing explanation for immigrant clusters is the existence of beneficial network externalities when previous immigrants provide shelter and work, assistance in obtaining credit, and/or generally reduce the stress of relocating to a foreign culture (see Gottlieb, 1987; Grossman, 1989; Marks, 1989; Church and King, 1983; Carrington et al., 1996; Chiswick and Miller, 1996; Epstein, 2003; Munshi, 2003). Ethnic networks, however, might also be associated with negative externalities. Disadvantageous network externalities may arise if immigration is subject to adverse selection, or if increase in immigrant concentration increases competition for jobs and lowers immigrants’ wages. Under certain conditions the tendency to cluster may lower incentives to learn the language of the host country, which in turn may ‘‘trap’’ migrants in poverty (Bauer et al., 2009). These negative network externalities limit the benefits immigrants can obtain from clustering. A growing literature investigates the determinants of location choice by immigrants. The first significant study on this, Bartel (1989), finds that post-1964 migrants to the United States tend to locate in cities with a high concentration of immigrants of similar ethnicity. She further shows that highly skilled migrants are less geographically concentrated and rely less on the location of fellow compatriots. Similarly, Jaeger (2007), who differentiates between immigrants of different admission statuses, finds that immigrants tend to locate where former immigrants of the same ethnicity are concentrated.

Migration and Culture

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Migration enclaves may be naturally limited in size. Migrants often choose to live together in enclaves, and to carry out a relatively large share of their transactions with other parties of the same enclaves – people who share a language, origin, and history. Such an enclave gives the migrants a clear benefit, particularly if they are more likely to encounter a cooperative environment in such a setting. However, enclaves can feed xenophobia and make natives hostile toward the migrants. Such hostility can be expected to increase as the minority grows in size. This mitigates the benefit from the enclave, as the hostility harms the migrants. Thus, we expect to see numerous enclaves of migrants spread throughout the receiving country rather than concentrated in a single location (Weiss and Rapoport, 2003). Migrant and local populations interact. Each can invest in activities promoting or hindering assimilation. Migrants may want to assimilate, or they may want to hold onto their cultural identities. The local population may welcome or not. A major site for these interactions is within the firm – the proverbial ‘‘shop floor.’’ As with enclaves, here also the size of the groups is important. Migrants consider several factors in making their decisions about where to move, including the clustering of compatriots and similar folk in various localities. Ties of kinship, friendship, and village link migrants, former migrants, and nonmigrants in the home and host country. Stock factors measure the degree to which migrants may view a location as (ethnically) hospitable and the availability of information about specific locations. Stock factors may have an ethnic goods component and include village migration history. Flow factors measure the tendency of migrants to follow the paths of very recent migrants from their own villages. These factors offer different information to a potential migrant. The ethnic goods component sends signals to the migrant about the possibility of living in a culturally similar environment, that is, speaking one’s native language, listening to his music, reading his own newspapers, and eating ethnic food. The ethnic goods factor reduces the monetary and psychic costs of migrating. The village migration history component largely captures information about the host region received in the home village. This includes, for example, information on the labor and housing market, and information on specific employers in a region. In addition, the migrant may be able to count on contacts in a specific location established by former migrants from the same village. This factor reflects the probability of receiving help from compatriots. The flow factor represents potential herd behavior by migrants, a sort of ‘‘peer emulation effect.’’ Following the argument by Epstein (2010), migrants may choose a location on the supposition that recent migrants had information that he does not have. Until the appearance of the paper by Polachek and Horvath (1977) much of migration theory treated migration as an individual investment

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decision. Family members other than the household head are not always explicitly considered. However, other members are clearly influential in migration decisions. Polachek and Horvath (1977) established the foundations for models of location choice that take into consideration all the different type of considerations. They do so by adopting a life-cycle approach used in human capital theories of earnings accumulation, accounting for household considerations in both a general theoretical and empirical model. More importantly, migration is analyzed within a nonstochastic framework and remigration is endogenously explained. Bauer et al. (2009) examine the determinants of a current migrant’s location choice emphasizing the relative importance and interaction of migrant stocks and flows. They show that both stocks and flow have significant impacts on the migrant’s decision of where to locate. The significance and size of the effects vary according to legal status and whether the migrant is a ‘‘new’’ or a ‘‘repeat’’ migrant. A different aspect of locational outcomes considers how extensive is polarization based on wages and other economic indicators. Deutsch (2010) takes a multidimensional approach to the measurement of wellbeing, checks whether there has been a change in the degree of (group) polarization in the distribution of well-being in Israel. Deutsch (2010) shows how it is possible to decompose by population subgroups the polarization index. This polarization index is related to the Gini index and its components so that previous results on the decomposition of the Gini index may be incorporated. Two main cases are examined, that of nonoverlapping groups and overlapping groups. Using Israeli data he shows decreasing polarization from 1990 to 2004. Polarization has many aspects; one is education. The payoff to schooling among the foreign-born in the United States is only around one-half of the payoff for the native-born. Chiswick and Miller (2010) examines whether this differential is related to the quality of the schooling immigrants acquired abroad. They use the over-education/required education/under-education specification of the earnings equation to explore the transmission mechanism for the origin-country school quality effects. They also assess the empirical merits of two alternative measures of the quality of schooling undertaken abroad. Their results suggest that a higher quality of schooling acquired abroad is associated with a higher payoff to schooling among immigrants in the U.S. labor market. This higher payoff is associated with a higher payoff to correctly matched schooling in the United States, and a greater (in absolute value) penalty associated with years of under-education. A set of predictions is presented to assess the relative importance of these channels, and the over-education channel is shown to be the more influential factor. This channel is linked to greater positive selection in migration among those from countries with better quality school. In other words, it is the impact of origin-country school quality on the immigrant selection process, rather than the quality


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of immigrants’ schooling per se, which is the major driver of the lower payoff to schooling among immigrants in the United States. Another aspect of locational choice is migrant mobility. Policy-makers in OECD countries appear to be increasingly concerned about growing migration pressure from developing countries. At the same time, at least within Europe, they typically complain about the low level of internal labor mobility. Faini and Venturini (2010) try to shed light on the issues of both internal and external labor mobility. They investigate the link between development and migration and argue, on both theoretical and empirical grounds, that it is likely nonlinear. More precisely, they find that, in a relatively poor sending country, an increase in income has a positive impact on the propensity to migrate, even if we control for the income differential with the receiving country, because the financial constraint of the poorest become less binding. Conversely, if the home country is relatively better off, an increase in income may be associated with a fall in the propensity to migrate even for an unchanged income differential. Econometric estimation for Southern Europe over the period 1962–1988 provides substantial support to this approach. They estimate first the level of income for which the financial constraint is no longer binding, around $950, and then the level of income for which the propensity to migrate declines, which is around $4,300 in 1985 prices. They, therefore, predict a steady decline in the propensity to migrate from Southern European countries. Similarly, their results highlight the possibility that the pressure to migrate from Northern African countries and other developing countries may increase with further growth. Taking a broader view Kaushal and Kaestner (2010) study the correlates of immigrant location and migration choices to address the following questions: What location-specific, economic, and demographic factors are associated with these choices? Does the influence of these factors differ by immigrant characteristics? What explains the observed increase in immigrant geographic dispersion during the 1990s? Their analysis suggests that: (1) there is significant heterogeneity in the correlates of immigrant location and migration choices; associations vary by immigrant birthplace, age, gender, education, and duration of residence in the United States. (2) Economic factors are, for the most part, weakly associated with immigrant location decisions. (3) Immigrants appear to be more attracted to states with large (growing) populations; less attracted to states with a high proportion of other foreign-born persons; more attracted to states with high unionization, and less attracted to states with high crime. (4) The association between location-specific characteristics and immigrant location choices changed between 1990 and 2000 for some immigrant groups and this explains most of the increase in geographic dispersion during the nineties. In contrast, changes in location attributes and changes in immigrant composition explain relatively little of the increase in dispersion.

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2. Production, earnings, and competition The analysis of immigrants’ contributions to the economy has concentrated on immigrants’ impact on native’s employment and wages (Baldwin-Grossman, 1982; Gang and Rivera-Batiz, 1994; Friedberg and Hunt, 1995; Borjas, 2003; Card, 2005; Ottaviano and Peri, 2008). Immigration affects relative supply of workers with different characteristics and effects workers differently depending on their characteristics. The debate has generally turned on the degree of substitutability or complementarity of immigrants and the native-borns: if immigrants tend to cluster into jobs requiring mostly manual work and little education or experience, and the native-borns hold jobs requiring higher levels of education and/or experience, how would increased immigration affect the wages of the native-borns? The answer is, of course, directly related to whether low-skilled and high-skilled labors are substitutes or complements. This is very nicely laid out in Bodvarsson and Van den Berg (2009). The chapters in this book push beyond the scope of the received tradition. The classic confrontation between immigrants and the local population takes place in the labor market. While many papers deal with labor market concerns, the chapters in this book tackle key issues head on, providing new insights to well-worn subject matter. For example, it is very clear that otherwise similar-looking immigrants and locals earn different amounts and have different jobs. The question is whether these differences constitute discrimination or is something else going on. If it is discrimination, what is at the root of it? In part, immigrant earnings are the outcome of the friction between the migrants and the local population. The willingness of the local population to accept the migrants also plays a role here. In terms of assimilation, the effect of the borrowing constraint facing new immigrants on the process of their assimilation in the new society is important. Those who succeed enjoy a higher level of productivity and therefore wages in the future. The level of investment is endogenously determined. Thus, an important assimilation issue is the possibility of borrowing. On this issue, migrants and the local population differ. Empirical evidence on the labor market performance of immigrants shows that migrant workers suffer from an initial disadvantage compared to observationally equivalent native workers, but that their wages subsequently tend to increase faster than native earnings. Economists usually explain these phenomena by spot markets for labor and investments into human capital. By contrast, Schmidt (2010) proposes a contract theoretic model. This alternative has important implications for integration policy, since it suggests investing into the transparency of foreign educational credentials. Also contrasting human capital theory, the model suggests that permanent migrants never earn higher wages than equally skilled temporary migrants.


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One should not ignore the interaction between the local and foreign workers. Epstein and Mealem (2010) consider the interaction between local workers and migrants in the production process of a firm. Both local workers and migrants can invest effort in assimilation activities in order to increase the assimilation of migrants into the firm, and by doing so increase their interaction and production activities. They consider the effect, the relative size (in the firm) of each group, and the cost of activities has on the assimilation process of the migrants. One of the outcomes of this model is specialization in production. If this is the outcome then the question that comes to mind is: are ethnic specialization and thus a downward sloping labor demand curve fundamental features of labor market competition among ethnic groups? In a general equilibrium model, Kahanec (2010) argues that spillover effects in skill acquisition and social distances between ethnic groups engender equilibrium regimes of skill acquisition that differ in their implications for ethnic specialization. Specifically, fundamental relationships through which relative group sizes determine whether ethnic specialization arises and to what degree are established. Thus, his paper theoretically justifies a downward sloping labor demand curve and explains why some ethnic groups earn more than others, ethnic minorities underperforming or outperforming majorities. As presented above, migrants are many times paid differently than the local population. Bodvarsson and Sessions (2010) focus on immigrant workers paid differently than their equally productive native-born counterparts (‘‘nationality discrimination’’). Constructing a theory and test of nationality discrimination is particularly challenging because: (a) foreign- and native-born workers in the same occupation are very likely to be imperfect substitutes in production, owing to the former group’s imperfectly transferable human capital; but (b) the literature offers models only where majority and minority workers are perfect substitutes. In the theory section, a generalized Leontief production function where native and immigrant workers are distinct inputs is articulated. In the empirical section, a U.S. test case is available: Major League Baseball (MLB). The dataset consists of 1,093 hitters and 1,204 pitchers for four seasons during 1992–1998, a period during which the industry expanded. Salary, experience, player performance, and team performance data come from the Lahmann Baseball Database and race and nationality are inferred from Topps baseball cards. Estimates of nationality discrimination against immigrant players in both job categories are obtained. Culture is intimately linked to pecuniary incentives – to earnings and productivity. This is brought out by Siniver (2010) who shows immigrants do, in fact, respond to economic incentives in acquiring proficiency in the language of the host country, particularly immigrants with 13þ years of schooling.

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3. Assimilation struggles Some migrants stay in their new country and some go back home. Those who return home bring with them experience and, perhaps, higher human capital. To what extent do the socioeconomic characteristics of circular/repeat migrants differ from migrants who return permanently to their home country after their first trip (i.e., return migrants)? What determines each of these distinctive temporary migration forms? What happens to those who do not return, though they continue sending remittances home? What effect does this have on the migrants and those left at home? Minority ethnic group participation in labor markets is quite complex and in many ways different from that of citizens belonging to a nation’s majority ethnicity. Studies of minorities around the world show, with few exceptions, that they tend to earn wages substantially below those of comparable majority workers (Altonji and Blank, 1999; Blau and Kahn, 1997, 2006, 2007; Smith and Welch, 1989; Bhaumik et al., 2006). Partly, this reflects a failure on the part of the minority group to undertake the effort to assimilate with the majority (Constant et al., 2009). ‘‘Lack of effort’’ can arise from the desire to maintain a cultural heritage or separate identity that would be lost or reduced if the group assimilated. The failure to take active steps to assimilate can also arise in the face of high adjustment costs, such as inadequate language skills, intergenerational familial conflicts, and, in the case of immigrants, lack of knowledge about the host country labor market (Chiswick and Miller, 1995, 1996; Bauer et al., 2005). Yet for immigrants and their descendants, as length of time in the host country increases, assimilation generally creeps in and various immigrant labor market indicators approach those of comparable majority workers. On occasion, minority workers outperform majority workers (Chiswick, 1977; Deutsch et al., 2006). Efforts made to assimilate, and time, are two elements working to bring minorities into line with the majority. A third element, the degree to which the majority welcomes the minority, also plays a role. Often, the majority is less than welcoming, blaming the minority for depressing wages and displacing majority workers – that is, causing majority unemployment. This presumption has very strong policy implications and is implicit, for example, in the calls for increased regulation of immigration heard worldwide. Yet, there is mixed evidence on the impact of minorities on majority wages and employment – it depends on whether they are substitutes or complements with respect to the skills and other attributes they bring to the labor market (Gang and Rivera-Batiz, 1994; Gang et al., 2002). Whether minorities actually lower the wages and increase employment or not, the perception exists that they do so. Because of this perception, the majority may take active steps to discourage minority assimilation – discrimination, isolation, and so on.


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Often the efforts of the minority and the majority are mediated through political institutions. These institutions exist in both the minority and majority worlds. They could be, for example, political parties, trade organizations, unions, or thugs. These are organizations that are able to overcome the free-rider problem individual members of each group have in moving from the actions they desire to take, to actually taking the actions. Yet, while an organization’s purpose may be to represent the members of their group, the interests’ of the organization and that of its members do not always coincide. The work here adds to the blossoming literature on majority–minority conflict and resolution, assimilation, and the reestablishment of cultural identity (see, e.g., Alesina and La Ferrara, 2000; Anas, 2002; Bisin and Verdier, 2000; Dustmann et al., 2004; Kahanec, 2006, and Lazear, 1999). Epstein and Gang (2009) are interested in why minorities are so often at a disadvantage compared to the majority, the circumstances under which their status changes or stagnates over time, and role public policy can play. Assimilation efforts by the minority, harassment by the majority and time are the three elements that determine how well the minority does in comparison to the majority. They examine the consequences for these increases in the numbers of members of the minority, time, and the role of the political entity. They construct a model in which there are four actors: the members of the majority and the organization that represents them, and members of the minority and the organization that represents them. Over time, the political entity representing the minority and the members of the minority exhibit different interests in assimilating and in maintaining their cultural identity. They discuss how this affects the minority’s position over time and discuss the public policy implications of the model. Some view migration and crime as dependent. The Bodenhorn et al. (2010) study provides a fresh look at the question of immigration and crime by looking at mid-19th century data created from the records of Pennsylvania’s state prisons from the 1830s to the 1870s. These records provide information on the birthplace, age, prior occupation, country of conviction, crime, and sentence of all individuals entering the prisons. With these data we can examine the share of immigrants in prison commitments as well as in the prison population on a given date. These data, when combined with data on the general population, allows them to determine whether immigrants were disproportionately incarcerated in general and for violent crimes in particular, and whether immigrant incarceration patterns changed over time as immigrants assimilated to life in the United States. The use of micro-level data that allows analysis by type of crime and age provides a much tighter and much richer understanding of immigrant participation in crime. Impressions of immigrants as a source of violence and disruption are longstanding. Furthermore, they underlie many of the theories of culture conflict and

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assimilation. Modern empirical methods and detailed population data allow revisiting these age-old research questions with a sharper focus. Within immigrant society there is often a conflict between those arguing for assimilation and those demanding an independent identity for the group. Of course there are many shades to this discussion; immigrant societies are multilayered and multidimensional with many viewpoints. One point of view may come into conflict with others because of the development of rivalrous strategies, at least partly overlapping followers, and/or the necessity of laying claim to having the bigger impact. Supporters of each point of view invest resources and effort into convincing the general body of immigrants of the virtue of their point of view and, therefore, having an effect. Epstein andGang (2010) develop economic theory that considers how such a competition affects the resources invested by the supporters and how beneficial it is to the immigrant group. Fertig (2010) investigates whether and to what extent immigrants in Germany are integrated into German society by utilizing a variety of qualitative information and subjective data collected in the 1999 wave of the German Socio-Economic Panel (GSOEP). To this end, leisure-time activities and attitudes of native Germans, ethnic Germans, and foreign immigrants of different generations are compared. The empirical results suggest that conditional on observable characteristics the activities and attitudes of foreign immigrants from both generations differ much more from those of native Germans than the activities/attitudes of ethnic Germans. Furthermore, the attitude of second-generation immigrants tends to be characterized by a larger degree of fatalism, pessimism, and self-doubt than those of all other groups, although their activities and participation in societal life resemble more those of native Germans than those of their parents’ generation. Whose role in helping the second generation to assimilate and get along in their new country is more important, the mother’s or the father’s? Gang (2010) examines the differential effects of mother’s schooling and father’s schooling on the acquisition of schooling by their offspring. The context is ‘‘cross-cultural,’’ comparing results across three countries: Germany, Hungary, and the Former Soviet Union (FSU). Within these countries, it looks at differences by gender and by different ethnic subgroups. The evidence is, generally, that father’s schooling is more important than mother’s schooling, but this does vary by ethnic group. Moreover, mother’s schooling plays a relatively larger role for females. Kahanec and Yuksel (2010) investigate the effects of vulnerability on educational outcomes in Croatia, Bosnia and Herzegovina, Montenegro, and Serbia using a unique 2004 UNDP dataset. Treating the collapse of the former Yugoslavia as a natural experiment, they compare educational achievement and intergenerational transfer of human capital for three groups that have been differently affected by the wars and postwar distress: the majority as the benchmark, the ex-ante and ex-post vulnerable


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Roma people, and the ex-ante equal but ex-post vulnerable internally displaced people (IDPs). Their findings reveal significant negative effects of vulnerability on educational attainment. IDPs seem to be more negatively affected than Roma and both groups exhibit significant inertia in intergenerational transfer of human capital. They find evidence that this inertia is stronger for the Roma. Their findings highlight the need for policies that not only tackle vulnerability as such but also address the spillover effects of current vulnerability on future educational attainment. In the struggle for assimilation credit markets may play an important role. Lahiri (2010) examines the effect of borrowing constraints facing new immigrants in the process of their assimilation in their new society. He does so in two-period model. In period 1, the immigrants invest, with some costs to them, in trying to assimilate. The probability of success in this endeavor depends on the amount invested and on the level of the provision of a ‘‘public’’ good paid for by lump-sum taxation of the ‘‘natives.’’ Those who succeed enjoy a higher level of productivity and therefore wages in period 2. The level of investment is endogenously determined. Given this framework, Lahiri (2010) characterize the optimal level of the public good provision. This is done under two scenarios regarding the credit market facing new migrants. In the first, they can borrow as much as they want in period 1 at an exogenously given interest rate. In the second scenarios, there is a binding borrowing constraint. Lahiri (2010) compares the equilibrium level of ‘‘assimilation’’ under the two scenarios. There is a well-established high quality literature on the role of networks, particularly ethnic networks, in international trade. Ethnic networks are a way of overcoming informal barriers (information costs, risk, and uncertainty) to trade by building trust and substituting for the difficulty of enforcing contracts internationally. Networks form between migrants and natives in the host country and between migrants and their home country. Ethnic networks exist when assimilation is not complete. Epstein and Gang (2006) consider the struggle of migrants to assimilate and, at the same time, the struggle of the local population to prevent such assimilation. These activities affect trade possibilities. Moreover, they show that it may well be in the interest of migrants who specialize in trade to, at some point in time, turn from investing in assimilation activities and instead invest in anti-assimilation activities in order to preserve immigrants’ preferences for home-country goods. There is increasing evidence in empirical trade that the immigrant population provides the social and co-ethnic networks that facilitate trade with their home country by removing some informal trade barriers and lowering transactions cost to trade. Immigrants’ carry home-country information that helps in matching buyers and sellers and enforcement of trading contacts (information effect) and immigrants affect imports by demanding goods from their home countries (demand effect). Usually, the size of immigrant

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stock – both older cohorts and new entrants – captures network size. However, as immigrants stay longer in their host country their information and demand effects may weaken or strengthen. This varies across immigrant groups and type of goods. Trade flows between the host and the home country change in response. Mundra (2010) focuses on the role of immigrants’ economic assimilation on the U.S. bilateral trade using a panel data for 63 trading partners as well as immigrant sending countries over the period 1990–2000. She examines whether the immigrants’ assimilation effect on trade varies across the homogenous goods and differentiated products.

4. Family issues and the effects of remittances Migration is not generally a purely individual decision; most frequently it takes place in a family context. One or two members of the family migrate; the others stay in their home country. For example, for those from Central America and Mexico it is not uncommon for a mother or father (or both) to migrate to the United States and leave their children behind. After the parent(s) have achieved some degree of stability in the United States, the children follow. There are many important questions. Are children separated from parents during migration more likely to fall behind others their age in school? Are they more likely to drop out of high school? Does the impact of separation for children differ when separated from their mothers or fathers? Migration may change family structure in the host country as they interact with the local economy and new culture. This may have strong and important effects on migrant identity and socialization and their willingness to assimilate (Gang and Zimmermann, 2000). The growth perspectives of European Union member countries are seen to be crucially related to the challenge of mobilizing people to work. One issue is that noneconomic migrants have more difficulties in economic performance and labor market integration, and are a larger potential burden to the social security systems than economic migrants. Recent work in Denmark and Germany (see Tranaes and Zimmermann, 2004; Schultz-Nielsen and Constant, 2004; Constant and Zimmermann, 2005; Constant et al., 2009) provides new evidence indicating that an ever-rising number of immigrants are unavailable to the labor force. Instead, migrants arrive as refugees, asylum seekers, or for family reunification purposes. Differences in labor market attachment might be due to differences in individual characteristics across ethnicities and within ethnicities. The effect of migration and remittances on nonmigrating family members has long attracted attention. Migration and remittances can increase investment in human and physical capital (Cox Edwards and Ureta, 2003; Hildebrand and McKenzie, 2005; Mesnard, 2004), reduce poverty and alter inequality in the home country (Adams, 1992;


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Taylor and Wyatt, 1996). It can also induce chain migration (Dimova and Wolff, 2009). Recent research links migration, transfers, and child labor, showing in the aftermath of migration and the transfers sent by emigrating parents may enable the children and other family members to stop working (Epstein and Kahana, 2008). In recent years, both the structure of families and household composition changed dramatically. For example, more and more young people leave the house of their parents before the establishment of their own family, more and more young couples live together without marriage, etc. Cohen Goldner (2010) explores immigrant family structure in Israel and follows the dynamics of immigrants’ households as a function of time in the new country and labor market performance. Upon arrival a typical immigrant household consists of more than one family. This pattern reflected the economic constraints that immigrant faced upon arrival and the need to save additional costs, as well as a sociological need of immigrants to ‘‘stick together.’’ However, as immigrants are integrated in the labor market and time passes, the share of households consisting of more than one family diminishes. To what extent do the socioeconomic characteristics of circular/repeat migrants differ from migrants who return permanently to the home country after their first trip (i.e., return migrants)? What determines each of these distinctive temporary migration forms? Piracha and Vadean (2010) using Albanian household survey data and both a multinomial logit model and a maximum simulated likelihood (MSL) probit with two sequential selection equations find that education, gender, age, geographical location, and the return reasons from the first migration trip significantly affect the choice of migration form. Compared to return migrants, circular migrants are more likely to be male, have primary education, and originate from rural, less developed areas. Moreover, return migration seems to be determined by family reasons, a failed migration attempt and also the fulfillment of a savings target. Remittances have long been viewed as a means to combat poverty, to improve consumption, to raise standard of living. Remittances, however, can also enable investment in human capital resources (especially education) of the next generation. Haberfeld et al. (2010) examines the impact of remittances sent by labor migrants from India on the standard of living (as a proxy of consumption) and on the education of young children (as a proxy of investment in human capital) on nonmigrating family members. The analysis is conducted on a randomly selected representative sample of households in Rajasthan. Three types of households are distinguished: 575 having labor migrants, 162 without current migrants, and 232 not having migrants at present but sent migrants in the past. Analysis of the data reveals meaningful differences among the types of households. Those having current labor migrants are characterized by the highest standard of living but at the same time by a low level of children’s education. Further analyses suggest that remittances are likely

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to increase consumption and improve standard of living but have very little effect on children’s education. Earlier research found that children separated from parents during migration are more likely to lag behind others their age in school and are more likely to drop out of high school. The negative impact of separation during migration on educational success is largest for children separated from their mothers (in contrast to fathers), for those whose parents have lived in the United States illegally, and for those who reunited with parents as teenagers (rather than at younger ages). Poggio and Gindling (2010) suggest public policies to help immigrant children separated from parents during migration to succeed in U.S. schools. The policies are based on focus group discussion with parents separated from their children during migration, interviews with psychologists and school administrators, and an online survey of elementary and high school teachers. DeVoretz and Vadean (2010) analyze the effect of cultural differences among ethnic groups on the remittance behavior of native and immigrant households in Canada. In contrast to the literature that examines remittance motivation in the framework of extended family agreements, they embed remittances in a formal demand system, suggesting that they represent expenditures on social relations with relatives and/or friends and contribute to membership in social/religious organizations, respectively. The results indicate strong ethnic group cultural differences in the remittance behavior of recent Asian immigrant households and highlight the importance of differentiating with respect to cultural background when analyzing the determinants of remittances.

5. Selection, attitudes, and public policy Cost, benefits, and the local population’s reaction affect public policy. We see this in the different policies toward migration as reported by governments to the United Nations Department of Economic and Social Affairs between 1976 and 2007. Preliminary evidence shows that most governments have policies aimed at either maintaining the status quo or at lowering the level of migration. The UN dataset also allows us to document variation in migration policies over time and across countries of different regions and incomes. Battisti and DeVoretz (2010) investigate the economic performance of immigrants from the FSU countries in Canada. The contribution of their paper lies in its use of a natural experiment to detect possible differential labor market performances of Soviet immigrants prior to and after the collapse of the Soviet Union. In short, the collapse of the FSU allows an exogenous supply change in the number and type of FSU immigrants potentially destined to enter Canada. For this purpose, Census micro-level data from the 1986, 1991, 1996, and 2001 Canadian Census are utilized to


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estimate earnings and employment outcomes for pre- and post-FSU immigrants. The first goal of Facchini and Mayda (2010) is to measure the restrictiveness of policies toward migration as reported by governments to the United Nations Department of Economic and Social Affairs between 1976 and 2007. Preliminary evidence shows that most governments have policies aimed at either maintaining the status quo or at lowering the level of migration. The UN dataset also allows them to document variation in migration policies over time and across countries of different regions and income levels. Finally, it makes it possible to examine patterns in different aspects of destination countries’ migration policies, such as policies toward family reunification, temporary versus permanent migration and highly skilled migration. This analysis leads to an investigation of the politicaleconomy determinants of destination countries’ migration policy. Facchini and Mayda (2010)’s goal is to develop a framework in which voters’ attitudes represent a key component and to examine the link between these attitudes and governments’ policy decisions. To that end, they merge the information contained in the UN migration-policy dataset with crosscountry data on individual attitudes toward immigrants. They use data on public opinion from the International Social Survey Programme, National Identity Module, for the years 1995 and 2003. The merged datasets allow us to investigate whether – within a median voter framework (Benhabib, 1996; Ortega, 2005; Facchini and Testa, 2009) – voters’ migration attitudes are consistent with migration-policy decisions as reported by governments. The link between ethnic conflicts and international trafficking is an issue that has recently received a surge in international attention. The main argument is that internal conflicts encourage the internal displacement of individuals from networks of family and community, and their access to economic and social safety nets. These same individuals are particularly vulnerable to being trafficked, by the hopes of better economic prospects elsewhere. Akee et al. (2010) take this link between ethnic fragmentation and international trafficking to the data for the first time, making use of a novel dataset of international trafficking. They conduct a two-stage estimation, which highlights the ultimate impact of ethnic fragmentation and conflict on international trafficking, both directly and indirectly, through their impacts on the scale of internal displacements. From a different angle, Gang et al. (2010) explores the determinants of the attitudes of European citizens toward non-European Union foreigners using samples from the Eurobarometer Surveys. They carry out a probit analysis of some of the key factors influencing the attitudes of European Union citizens toward foreigners and their changes over time. They study the roles of labor market, concentration of immigrants in neighborhoods, racial prejudice, and education on anti-foreigner sentiment. Implementing the Oaxaca-type decomposition analysis based on probit estimates show a generally rising trend toward greater racial prejudice, and the decline in the

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strength of educational attainment in reducing negative attitudes toward foreigners, contributes to the increased anti-foreigner attitudes. Along the same line, Katav-Herz (2010) examines how social norms affect a local population’s attitudes toward immigration. A model is set out showing how a trade-off can arise between the contribution of immigration to the welfare of the local population and the concerns about changes in social norms. The chapter addresses three questions. The first question concerns the determination of immigration policy through majority voting when a population differs in attitudes to changes in social norms. The second question concerns how social norms can impede the realization of the benefits of immigration as a solution for financing intergenerational transfers to retired people in an ageing population. The third question concerns the timing of immigration when immigration affects social norms. Acknowledgment Financial support from the Adar Foundation of the Economics Department of Bar-Ilan University is gratefully acknowledged. References Adams, R. (1992), The impact of migration and remittances on inequality in rural Pakistan. Pakistan Development Review 31 (4), 1189–1203. Akee, R.K.Q, Basu, A.K., Chau, N.H., Khamis, M. (2010), Ethnic fragmentation, conflict, displaced persons and human trafficking: an empirical analysis. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 691–716. Alesina, A., La Ferrara, E. (2000), Participation in heterogeneous communities. Quarterly Journal of Economics 847–904. Altonji, J.G., Blank, R.M. (1999), Race and gender in the labor market. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics Vol. 3C. Elsevier Science B.V., Amsterdam, pp. 3143–3259. Anas, A. (2002), Prejudice, exclusion and compensating transfers: the economics of ethnic segregation. Journal of Urban Economics 52 (3), 409–432. Baldwin-Grossman, J. (1982), The substitutability of natives and immigrants in production. The Review of Economics and Statistics 4, 596–603. Bartel, A.P. (1989), Where do the new U.S. immigrants live? Journal of Labor Economics 7 (4), 371–391. Battisti, M., DeVoretz, D. (2010), FSU immigrants in Canada: a case of positive triple selection? In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 579–604. Bauer, T., Epstein, G.S., Gang, I.N. (2005), Enclaves, language, and the location choice of migrants. Journal of Population Economics 18 (4), 649–662.


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Bauer, T., Epstein, G.S., Gang, I.N. (2009), Measuring ethnic linkages between immigrants. International Journal of Manpower 30 (1/2). Benhabib, J. (1996), On the political economy of immigration. Economic European Review 40, 1737–1743. Bhaumik, S.K., Gang, I.N., Yun, M.-S. (2006), Ethnic conflict and economic disparity: Serbians and Albanians in Kosovo. Journal of Comparative Economics 34 (4), 754–773. Bisin, A., Verdier, V. (2000), Beyond the melting pot: cultural transmission, marriage, and the evolution of ethnic and religious traits. Quarterly Journal of Economics, 955–988. Blau, F.D., Kahn, L.M. (1997), Swimming upstream: trends in the gender wage differential in the 1980s. Journal of Labor Economics 15 (1), 1–42. Blau, F.D., Kahn, L.M. (2006), The U.S. gender pay gap in the 1990s: slowing convergence. Industrial and Labor Relations Review 60 (1), 45–66. Blau, F.D., Kahn, L.M. (2007), The gender pay gap. The Economists’ Voice 4 (4), (http://www.bepress.com/ev/vol4/iss4/art5). Bodenhorn, H., Moehling, C.M., Piehl, A.M. (2010), Immigration: America’s nineteenth century ‘law and order problem?’ In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization (Vol. 8). Emerald, Bingley, UK, pp. 295–323. Bodvarsson, O¨.B., Van den Berg, H.F. (2009), Economics of Immigration: Theory and Policy. Springer-Verlag, Berlin & Heidelberg. Bodvarsson, O¨.B., Sessions, J.G. (2010), Nationality discrimination in the labor market: theory and test. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization (Vol. 8). Emerald, Bingley, UK, pp. 231–268. Borjas, G.J. (2003), The labor demand curve is downward sloping: reexamining the impact of immigration on the labor market. The Quarterly Journal of Economics 118 (4), 1335–1374. Card, D. (2005), Is the new immigration really so bad? Economic Journal 115 (507), F300–F323. Carrington, W.J., Detragiache, E., Vishwanath, T. (1996), Migration with endogenous moving costs. American Economic Review 86 (4), 909–930. Chiswick, B.R., Miller, P.W. (1995), The endogeneity between language and earnings: international analyses. Journal of Labor Economics 13, 246–288. Chiswick, B.R. (1977), Sons of immigrants: are they at an earnings disadvantage? American Economic Review: Papers and Proceedings 67 (1) 376–380. Chiswick, B.R., Miller, P.W. (1996), Ethnic networks and language proficiency among immigrants. Journal of Population Economics 9 (1), 19–35. Chiswick, B.R., Miller, P.W. (2010), The effects of school quality in the origin on the payoff to schooling for immigrants. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 67–103.

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Church, J., King, I. (1983), Bilingualism and network externalities. Canadian Journal of Economics 26 (2), 337–345. Cohen-Goldner, S. (2010), Household structure of recent immigrants to Israel. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 447–465. Constant, A., Zimmermann, K.F. (2005), Immigrant performance and selective immigration policy: a European perspective. National Institute Economic Review 194, 94–105. Constant, A., Gataullina, L., Zimmermann, K.F. (2009), Ethnosizing immigrants. Journal of Economic Behavior and Organization 69 (3), 274–287. Cox Edwards, A., Ureta, M. (2003), International migration, remittances and schooling: evidence from El Salvador. Journal of Development Economics 72 (2), 429–461. Deutsch, J. (2010), The measurement of income polarization by ethnic groups: the case of Israel population. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 45–66. Deutsch, J., Epstein, G.S., Lecker, T. (2006), Multi-generation model of immigrant earnings: theory and application. Research in Labor Economics, 217–234. DeVoretz, D.J., Vadean, F.P. (2010), Cultural differences in the remittance behaviour of households: evidence from Canadian micro data. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 543–575. Dimova, R., Wolff, F.-C. (2009), Remittances and Chain Migration: Longitudinal Evidence from Bosnia and Herzegovina. IZA Discussion Papers 4083, Institute for the Study of Labor (IZA), Bonn, Germany. Dustmann, C., Fabbri, F., Preston, I. (2004). Ethnic Concentration, Prejudice and Racial Harassment of Minorities, CReAM Discussion Paper 05/04 (www.econ.ucl.ac.uk/cream/). Epstein, G.S. (2003), Labor market interactions between legal and illegal minorities. Review of Development Economics 7 (1), 30–43. Epstein, G.S., Gang, I. (2006), Ethnic networks and international trade. In: Federico Foders, I., Langhammer, R.J. (Eds.), Labor Mobility and the World Economy. Springer, Berlin Heidelberg, pp. 85–103. Epstein, G.S., Gang, I.N. (2009), Ethnicity, assimilation and harassment in the labor market. Research in Labor Economics 79, 67–90. Epstein, G.S., Gang, I.N. (2010), The political economy of the immigrant assimilation: internal dynamics. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 325–339.


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Epstein, G.S., Kahana, N. (2008), Child labor and temporary emigration. Economics Letters 99 (3), 545–548. Epstein, G.S., Mealem, Y. (2010), Interactions between local and migrant workers at the workplace. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 193–203. Epstein, G.S. (2010), Informational cascades and the decision to migrate. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 25–44. Facchini, G., Mayda, A.M. (2010), What drives immigration policy? Evidence based on a survey of government officials. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 605–648. Facchini, G., Testa, C. (2009), Who is against a common market? Journal of the European Economic Association 7, 1068–1100. Faini, R., Venturini, A. (2010), Development and migration: lessons from Southern Europe. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 105–136. Fertig, M. (2010), The societal integration of immigrants in Germany. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 375–400. Friedberg, R.M., Hunt, J. (1995), The impact of immigrants on host country wages, employment, and growth. Journal of Economic Perspectives 9 (2), 23–44. Gang, I.N. (2010), Who matters most? The effect of parent’s schooling on children’s schooling. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 401–414. Gang, I.N., Zimmermann, K.F. (2000), Is child like parent? Educational attainment and ethnic origin. Journal of Human Resources 35, 550–569. Gang, I.N., Rivera-Batiz, F. (1994), Labor market effects of immigration in the United States and Europe: substitution vs. complementarity. Journal of Population Economics 7, 157–175. Gang, I.N., Rivera-Batiz, F., Yun, M.-S. (2002), Economic Strain, Ethnic Concentration and Attitudes Towards Foreigners in the European Union, IZA Discussion Paper 578 (www.iza.org). Gang, I.N., Rivera-Batiz, F.L., Yun, M.-S. (2010), Changes in attitudes towards immigrants in Europe: before and after the fall of the Berlin Wall. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 649–676. Gottlieb, P. (1987), Making their own way: shorthorn blacks’ migration to Pittsburgh, 1916–30. University of Illinois Press, Urbana.

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Grossman, J.R. (1989), Land and hope: Chicago, black Southerners, and the Great Migration. University of Chicago Press, Chicago. Hildebrand, N., McKenzie, D. (2005), The effects of migration on child health in Mexico. Economia 6, 257–289. Jaeger, D.A. (2007), Green cards and the location choices of immigrants in the United States, 1971–2000. Research in Labor Economics 27, 131–184. Kahanec, M. (2006), Ethnic Specialization and Earnings Inequality: Why Being a Minority Hurts but Being a Big Minority Hurts More, IZA Discussion Paper 2050, (www.iza.org). Kahanec, M. (2010), Ethnic competition and specialization. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 205–229. Kahanec, M., Yuksel, M. (2010), Intergenerational transfer of human capital under post-war distress: the displaced and the Roma in the Former Yugoslavia. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 415–443. Katav-Herz, S. (2010), The implications of social norms on immigration policy. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 677–689. Kaushal, N., Kaestner, R. (2010), Geographic dispersion and internal migration of immigrants. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 137–173. Lahiri, S. (2010), Assimilating under credit constraints: public support for private efforts. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 341–356. Lazear, E.P. (1999), Culture and language. Journal of Political Economy 107 (6, pt. 2), S95–S126. Marks, C. (1989), Farewell – we’re good and gone: the Great Black Migration. Indiana University Press, Bloomington. Mesnard, A. (2004), Temporary migration and capital market imperfections. Oxford Economic Papers 56, 242–262. Mundra, K. (2010), Immigrant networks and the U.S. bilateral trade: role of immigrant income. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 357–373. Munshi, K. (2003), Networks in the modern economy: Mexican migrants in the U.S. labor market. Quarterly Journal of Economics 118, 549–599. Ortega, F. (2005), Immigration quotas and skill upgrading. Journal of Public Economics 89, 1841–1863.


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Ottaviano, G.I., Peri, G. (2008), Immigration and national wages: clarifying the theory and the empirics. NBER Working Papers 14188, National Bureau of Economic Research (NBER), Cambridge, Massachusetts. Available at http://www.nber.org/papers/w14188 Poggio, S., Gindling, T.H. (2010), Promoting the educational success of Latin American immigrant children separated from parents during migration. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 517–541. Polachek, S.W., Horvath, F.W. (1977), A life cycle approach to migration: analysis of the perspicacious peregrinator. In: Ron Ehrenberg (Ed.), Research in Labor Economics, Vol. 1. JAI Press, Greenwich, Conn., 103–149. Schmidt, C.M. (2010), Understanding the wage dynamics of immigrant labor: a contractual alternative. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 177–191. Schultz-Nielsen, M.L., Constant, A. (2004), Employment trends for immigrants and natives. In: Tranaes, T., Zimmermann, K.F. (Eds.), Migrants, Work, and the Welfare State. University Press of Southern Denmark, Odense, pp. 119–146. Siniver, E. (2010), Culture, investment in language and earnings. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 269–292. Smith, J.P., Welch, F.R. (1989), Black economic progress after Myrdal. Journal of Economic Literature 27 (2), 519–564. Taylor, J.E., Wyatt, T.J. (1996), The shadow value of migrant remittances, income and inequality in the household-farm economy. Journal of Development Studies 32 (6), 899–912. Tranaes, T., Zimmermann, K.F. (2004), Migrants, work, and the welfare state: an introduction. In: Tranaes, T., Zimmermann, K.F. (Eds.), Migrants, Work, and the Welfare State. University Press of Southern Denmark, Odense. Vadean, F., Piracha, M. (2010), Circular migration or permanent return: what determines different forms of migration? In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 467–495. Weiss, A., Rapoport, H. (2003), The optimal size for a minority. The Journal of Economic Behavior and Organization 51 (1), 27–45. Xing, Y., Semyonov, M., Haberfeld, Y. (2010), Labor migration, remittances, and economic well-being: a study of households in Rajasthan, India. In: Epstein, G.S., Gang, I.N. (Eds.), Migration and Culture: Frontiers of Economics and Globalization, Vol. 8. Emerald, Bingley, UK, pp. 497–516.

PART I

Enclaves and Location Choice

CHAPTER 2

Informational Cascades and the Decision to Migrate Gil S. Epsteina,b,c a

Department of Economics, Bar-Ilan University, Ramat-Gan 52900, Israel CReAM-Center for Research and Analysis of Migration, London, UK c Institute for the Study of Labor (IZA), Bonn, Germany E-mail address: [email protected] b

Abstract We introduce the idea that informational cascades can explain the observed regularity that emigrants from the same location tend to choose the same foreign location. Thus, informational cascades generate herd behavior. Herd behavior is compared with the network-externalities explanation of the same phenomenon of migration clustering. Keywords: Migration, informational cascade, herd behavior, xenophobia, network-externalities JEL classifications: F22, J61

1. Introduction Consider yourself living in a small town in a low-income country, and you have decided to emigrate. Where would you go? You might prefer a country because of your familiarity with its language. That may leave you with a number of alternatives. Or you may choose a foreign location because of the presence of people there from your own home community. You may have a relative or a friend of the family in the foreign location. Or, at least the name and address of somebody who knows your family and who will treat you sympathetically, assisting you with housing and finding a job, and perhaps in explaining the rules of neighborhood.1 As with language proficiency, in all likelihood a number of foreign locations can provide such networks of people to help you. Language itself, and the 1 See Church and King (1983), Gottlieb (1987), Grossman (1989), Marks (1989), and Chiswick and Miller (1996).



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presence of people ready and willing to help, are not sufficient to provide a basis for a decision. A choice remains to be made from among the available alternatives where language and/or personal connections are present. Help in adjusting to a new environment such as finding jobs and accommodations are provided by network externalities. The ability to speak his language in a foreign country is in itself helpful. The network allows the immigrant to preserve his traditions and history in the new environment. Network externalities are not always positive. An increase in the number of foreigners in the host country increases competition for jobs that the immigrants can work at, thus decreasing the immigrants’ wages. Moreover, as the number of immigrants increase, the local population may become xenophobic. There are, therefore, two different contradicting effects to network externalities. The immigrant must weigh one against the other and decide which dominates. If full information were available about local conditions, migrants would choose the location where there are net benefits from network externalities. If such full information is not available, a choice is made under conditions of uncertainty. If you have imperfect information, which decision rule should you adopt? In the face of uncertainty, a common decision rule is to randomize, but here you confront an indivisible location decision. You may not know all that much about life in a particular location. You observe, however, that other people who are like you have recently been favoring this location. You might have a personal feeling that the location people have been choosing is not the best from among the available alternatives. You might, however, decide to discount this feeling that is based on your private information, and to proceed on the assumption that others have been making decisions based on better information than you have. That is, you may take the position that so many other people cannot be wrong. If you behave in this way and discount your private information or your feelings to follow the decisions of others, you are adopting a decision rule that gives rise to herd behavior. In order for a population of immigrants to produce network externalities that will attract other migrants, the population of immigrants must be sufficiently large. In many situations it is not clear how this critical mass of people arrived at a certain location. Informational cascades – herd behavior – help us understand the creation of the critical mass that creates network externalities. In order to create a herd in a certain location, the number of immigrants needed is relatively small. Thus, herd behavior may be an explanation for the creation of the mass of immigrants that is sufficient to attract others to join and enjoy the positive externalities of the network. Informational cascades also help us understand why we observe immigrants deciding to emigrate to destinations where the negative externalities are stronger than the positive externalities of the network. The reason for this phenomenon is that individuals are uncertain regarding the effect of the network externalities and decide to follow the flow of

Informational Cascades and the Decision to Migrate

27

immigrants rather than the stock of immigrants. Finally, herd behavior enables us to understand how an individual makes a decision when there is more than one country that provides the immigrant with the same level of network externalities. The immigrants will decide to follow the flow (informational cascade or herd behavior) rather than the stock (network externalities) of previous immigrants’ destinations. Several empirical studies investigate the determinants of location choice of immigrants in the United States.2 Bartel (1989) finds that post-1964 US immigrants tend to locate in cities with a high concentration of immigrants of similar ethnicity. Bauer et al. (2007) distinguish between two types of networks and herd effects, using data from the Mexican Migration Project with individual-level data on Mexican-US migration (based at the University of Pennsylvania and the University of Guadalajara). One of their network variables captures the general variables that describe the type of origin-specific consumption products that migrants wish to consume in a US location. This variable is generally called ethnic goods, which describes the availability of products that are unique to specific groups from specific origins. Their other network variables capture origin, village connections, and the history of a village in US locations. Using these two variables helps to distinguish a generalized network effect from the village-specific links. The herd variable describes the flow of migrants during the year. Their empirical results show that both network externalities and herds have significant effects on the migrant’s decision about where to migrate. Moreover, the significance and size of the effects vary according to the legal status of the migrant and whether the migrant is a ‘‘new’’ or a ‘‘repeat’’ migrant. Bauer et al. (2009), based on the same data, use three different measures of ethnic networks in order to investigate different channels through which ethnic networks affect the location choice of migrants. The empirical evidence shows that the availability of ethnic goods, the information provided by return migrants to potential migrants in their origin village, and the number of current migrants from an origin village living in the host location compared to other locations are significant and important for the location choice of migrants. Using Israeli data, Epstein and Cohen (2006) investigated the case where migrants from the Soviet Union decided to live in Israel. They show that both network and herd behavior determined the location choice of the migrants. Moreover, it was shown that the network effect has an inverted U shape and the herd effect is linear. It was also discovered that there is an inverted U-shape effect of the size of the network on language proficiency and the probability of finding a job. In this paper we set out a formal framework introducing informational cascades and generating a theory of herd behavior as an explanation of

2

For a more detailed description of empirical results and the literature, see Epstein (2008).

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migration. In the model emigrants may have some private information but are imperfectly informed about the attributes of alternative foreign locations, and they observe previous emigrants’ decisions. Behavior is rational on the supposition by impending emigrants that previous emigrants had information that they do not have. The outcome is that emigrants discount private information and duplicate a location that previous emigrants have been observed to choose.3 The consequence is immigrant clustering in foreign locations, but based on a decision rule that does not internalize all true information. Since individuals are discounting private information that may be accurate and making decisions based on the perception that other people’s information is accurate when others are also likewise discounting private information, we can have no expectation that outcomes will have desirable properties. The paper proceeds as follows. We set out the model of herd behavior in the following section, and then analyze herd behavior together with network externalities.

2. The model 2.1. The background We consider a country where potential emigrants are identical other than in age and information, and are uncertain about conditions in the rest of the world. We do not wish to attribute aspects of behavior to risk aversion, and so take emigrants to be risk neutral (although we realize in practice they may not be). An emigrant’s utility U(.) is increasing in income, and in other parameters that we shall subsequently introduce. From among the alternative foreign locations for emigration (legal or illegal), one location objectively offers better conditions than others. Emigrants do not know the identity of this best foreign location. They have a uniform prior over foreign locations. An individual may decide not to emigrate, which is encompassed by viewing one of the locational options as the home country. Individuals may receive information regarding the standard of living and their opportunities in a host country. This information could be obtained from watching TV, reading newspapers, etc. This information is hereafter referred to as a signal. After observing the different information, the individual must make a decision if and where to immigrate. If the individual cannot make a decision based on this information (signal), then there is no difference between an individual who did not receive a signal and this individual – this 3

The theory of information cascades or herd effects has been applied to the explanation of behavior in a number of contexts. See Scharfstein and Stein (1990), Banerjee (1992), and Bikhchandani et al. (1992).


29

individual is, therefore, seen as an individual who didn’t receive any information. We now demonstrate the formal structure of emigration decisions that follow herd behavior using one- and two-signal models. 2.2. A one-signal model Imperfect private information provides a signal, with probability p, regarding the identity of the best foreign country. With probability q, the signal providing this private information is true. If a signal is false, it does not provide information regarding the true signal. Also, to simplify, we assume that, for two locations, qW0.5 (or qW1/m where m is the number of foreign locations). Otherwise, there is a better chance of choosing the preferred country by randomizing than by using the information provided by the signal. Emigration decisions are made sequentially, with people contemplating emigration at a given age or stage in their lives. In the sequential decision process, people of different ages make decisions regarding immigration at different times. Someone may have received a signal, and he or she can also observe the behavior of previous emigrants. Potential emigrants cannot however observe the information signal that was the basis for previous emigrants’ decisions. While potential new immigrants know the choices made by past emigrants, they do not have to know the latter’s position in the queue. Given the information available, each person chooses a location to which to emigrate. The structure of the game and Bayesian rationality are common knowledge. Three assumptions govern individuals’ actions4: (a) A person who does not receive a signal and observes that everybody else has chosen to stay home will also choose not to emigrate. (b) Someone who is indifferent between following his or her own signal and copying someone else’s choice will follow his or her own signal. (c) Someone who is indifferent between copying previous emigrants’ decisions will make a decision by randomizing with equal probabilities assigned to the different alternatives. These assumptions, which minimize the likelihood of herd behavior, give rise to the following different possibilities: The first person making a decision: This person fails to receive a signal with probability (1p) and receives a signal with probability p. In the first case, by assumption a, individual will not emigrate. In the second case, individual will follow his signal, and will emigrate. The probability that emigration is to the correct country is q. 4

See Epstein (2008).

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The second person: If person 2 has received no signal, then she follows person 1. If only person 2 has a signal, she of course will follow her signal. If the two people have different signals (person 1 chose to emigrate and thus had a signal), then person 2 is indifferent between following her own signal and copying person 1, as the both persons’ signals have the same probability of being true. In this case, by assumption b, person 2 will follow her own signal. The third person: If neither of the two previous persons chose to emigrate, this means that neither received a signal. Person 3 will copy them if and only if he does not receive a signal, otherwise will follow the signal he receives. If one of the previous persons chose not to emigrate and the other chose to emigrate, person 1 did not receive a signal and person 2 did receive a signal. If person 3 then receives a signal that indicates emigration to the country to which the second person has emigrated, person 3 will join the second emigrant. Otherwise, if a signal different from that of person 2 is received, person 3 follows his own signal. If persons 1 and 2 have chosen to emigrate to different countries, and person 3 does have a signal, then person 3 will base his emigration decision on his own private information as conveyed by the signal he receives. This can be shown formally in the following way: Assume that person 1 emigrated to country j, person 2 emigrated to country k, and person 3 has a signal to emigrate to country j. Using the Bayesian rule, person 3 can calculate the probability that the true signal is j out of m possible countries5: Prðjj j; k; jÞ ¼

p3 q2 ð1 qÞ1=m Prðk; j; jÞ

(1)

In the same way, person 3 could calculate the probability that the true signal is k: Prðkj j; k; jÞ ¼

p3 qð1 qÞ2 1=m Prðk; j; jÞ

(2)

For qW0.5, Prðjj j; k; jÞ4Prðkj j; k; jÞ

(3)

from which it follows that person 3 will choose to follow his own signal. There is one further possibility: the first two persons choose to emigrate to country j and the third person receives a signal to emigrate to country k. This last possibility brings us to a general proposition. First, however, 5

By definition, the probability q is normalized with regard to the two different locations.


31

to simplify, we add the following assumption: 1p 2 Assumption d : qð1 qÞ40:5 p That is, we place a lower bound on the probability that an individual receives a signal. The assumption is relaxed in Proposition 2. PROPOSITION 1. If at a point in time the number of emigrants in country j is greater than emigrants in all the other countries by at least two persons, then from that time on, all persons, regardless of their signal, will emigrate to country j, and so we have herd behavior. For the proof, see the Appendix. The proposition is true for any number of countries, as the choice is always whether to follow one’s signal or to follow the herd; that is, the problem is always a binomial decision. In order for herd behavior to occur after a difference between two individuals, we require a bound on the probability that a signal is received: p2 qð1 qÞ40:5ð1 pÞ2 Thus, as q increases, in order for herd behavior to occur, a higher value of p is required. As the probability of receiving a signal decreases, more emigrants are required to create herd behavior, and we can conclude that: PROPOSITION 2. For a given probability q that a signal is true, as the probability p that an individual receives a signal decreases, the number of emigrants required to evoke herd effect increases. A person who has chosen to emigrate does not immediately know the quality of life in the new location. Suppose that a person has emigrated, and after some time a clustering of immigrants occurs in a different country. As the emigrant continues to confront uncertainty regarding future income and the future quality or standard of life in the new country, he or she once again calculates the probability regarding the best country. The propositions above indicate that such a person will decide against going to the country of initial choice and join the herd. 2.3. An illustration We now present an illustration. We have established that if the first two persons emigrate to the same location, all subsequent persons will emigrate to this same location. The probability that the two first persons will emigrate to the same location is (where we assume that j is

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the best location): Prðclustering in one countryÞ ¼ Prðclustering in country jj j is the right countryÞPrðj is the right countryÞ þPrðclustering in country jj k is the right countryÞPrðk is the right countryÞ þPrðclustering in country kj k is the right countryÞPrðk is the right countryÞ þPrðclustering in country kj j is the right countryÞPrðj is the right countryÞ (4)

Using the values of the different probabilities, we obtain: Prðclustering in one countryÞ ¼ ðp2 q2 þ pð1 pÞqÞ1 þ ðp2 ð1 qÞ2 þ pð1 pÞð1 qÞÞ0 þðp2 q2 þ pð1 pÞqÞ0 þ ðp2 ð1 qÞ2 þ pð1 pÞð1 qÞÞ1 ¼ p2 q2 þ pð1 pÞq þ p2 ð1 qÞ2 þ pð1 pÞð1 qÞ

(5)

In the case where q ¼ 0.51 and p ¼ 1 (all people obtain a signal), we calculate this probability to be 0.5002. More generally, as q increases, for any p, the probability of clustering in one of the countries increases: @PrðclusteringÞ ¼ 2p2 ð2q 1Þ40 @q

(6)

Herd behavior thus occurs with positive probability. Simple Markovian reasoning tells us that, with an infinite population size, the probability of any event occurring is 1. Thus, if the population size is infinite, after some point in time, with probability 1, there will be clustering of immigrants in one location.6 2.4. Multiple signaling In a multiple-signaling version of the model, a person can receive two types of signals: a general signal, a specific signal from previous emigrants, and also can observe the behavior of previous emigrants. Again, he or she cannot however observe the signal that was the basis of the decisions of past emigrants, and, given the information available, each person proceeds to choose a country of emigration. We retain assumptions a, b, c, and d and add the assumption e: individuals value a specific signal from former emigrants, qi, more than a general signal, q, that is, q qi 8 i.7 6

For a similar result, see Bikhchandani et al. (1992). One of the key determinants of the location of immigration is past colonial relationships. The general signal can be interrupted accordingly. We can view the specific signal as evidence that an immigrant has gone to the wrong country and chooses another location at the next period. Here the individual’s change of his/her decision can be seen as a specific signal telling the individual not to immigrate to a location. The signal is not true with probability 1 as it is not clear why the emigrant changed his decision, and it may well be that the location may not suit him while this is the correct choice for other emigrants.

7


33

Notice that a person can receive a specific signal to go to a particular country only if there has been a prior emigrant to that country. An immigrant who receives opposite general and specific signals must determine which to follow. It is clear that the probability that an individual will choose to emigrate against his specific signal is smaller than against his general signal. However, the presence of a greater number of emigrants already located in the host country against the specific signal increases the probability that the emigrant follows the herd. We summarize the results in the following proposition8: PROPOSITION 3. With multiple signals, if two initial persons have emigrated to the same country, subsequent emigrants copy them regardless of their own signals; otherwise, herd behavior will occur when the difference between the number of emigrants in two countries is large enough. As the probability that a person’s own signal is true increases, the difference decreases between the number of emigrants in alternative locations required for herd behavior. 3. Network externalities As we observed in the introduction, herd behavior is conceptually different and distinguishable from migration that is motivated by network externalities (see also the concluding section).9 There is also no reason why herd effects and network externalities should not be simultaneously present to influence emigration location decisions. When there is simultaneous presence, there is also interaction. In this section we place net externalities within our model of herd behavior and show the nature of the interactions between the two phenomena.10 To introduce network externalities, we follow the representation of Carrington et al. (1996). A potential emigrant calculates the present discounted value of income for staying at home versus emigrating. The present value of income for emigrants is a function of the number of immigrants in the host country. The number of immigrants thus has an effect on the migration decision. Wages in the host country are assumed to decrease with the number of immigrants, while the wage in the home country is an increasing function of the number of people who emigrate. With endogenous moving costs that decrease with the size of the network, the impetus for emigration develops gradually over time. Emigration, once 8

The proof is available on request. When a migrant immigrates as a result of network externalities, he wishes to benefit from the externalities that other migrants can provide him with. On the other hand, in migration as a result of herd behavior, the migrant wishes to maximize the probability of choosing the correct destiny and has uncertainty regarding the information available to him and others. 10 See also Choi (1997). 9

34

Gil S. Epstein

it begins, gains momentum, and the number of people who migrate can well increase even as differences in wages between the country of emigration and immigration decline. In a network-externalities model, costs of relocation thus decrease with the number of immigrants, which encourages more emigration, and leads to immigrant clustering – but some immigrant clustering must already have been present to provide the externalities. Let us assume for now full information. The utility of an immigrant, Uj (.), is a function of two main variables: first is the relative wage (relative to his home country) that the immigrant will receive from immigrating to the new location wf (wf is the immigrants earnings at the new location), and second is the number of immigrants from the same origin that previously immigrated to that location, N. From the above discussion, the immigrant’s utility increases with the immigrant’s earnings and increases with the number of immigrants that have already immigrated before him to the same location. Thus, ð@U j ðwj ; NÞ=ð@wj ÞÞ40 and ð@U j ðwj ; NÞ=ð@NÞÞ40. For a given utility level, an iso-utility locus (indifference curve) is described by the following: dU j ðwj ; NÞ ¼

@U j ðwj ; NÞ @U j ðwj ; NÞ dwj þ dN ¼ 0 @wj @N

(7)

which has a negative slope as there is a trade-off between the wage level (earnings) in the host country and the total number of immigrates from the same origin country that have already immigrated before immigrant j: dwj ð@U j ðwj ; NÞ=ð@NÞÞ ¼ o0 dN ð@U j ðwj ; NÞ=ð@wj ÞÞ

(8)

In other words the iso-utility is downward sloping. Moreover, as we increase the wage and the number of previous immigrants from the same origin, the utility of the new immigrant increases. Assume a normal downward sloping demand function for workers in the host country: qd ðwf Þ such that ðqd ðwf Þ=ð@wf ÞÞo0, and an upward sloping supply function of workers: qs ðN L ; NÞ, where NL is the size of the local population such that ðqs ðN L ; NÞ=ð@NÞÞ40. In equilibrium qd ðwf Þ ¼ qs ðN L ; NÞ, thus we obtain that the wages in equilibrium are given by wf ðNÞ. In other words, the wage in equilibrium is a function of the number of immigrants in the country. More specifically we can easily show that ð@wf ðNÞ=ð@NÞÞo0; namely, as the number of immigrants increase, the equilibrium wage decreases. To illustrate this let us look at specific demand and supply functions for immigrants and the immigrants’ utility function: Denote demand for immigrants in country j by qD j ¼ b0 b1 wj where qj denotes the number of immigrants and wj the wage. The supply function is qsj ¼ a0 þ a1 wj þ N j ,


35

where Nj is the number of immigrants in the country.11 In equilibrium s qD j ¼ qj and the equilibrium wage is: wj ¼

b0 a0 N j . a1 þ b1

(9)

It is clear that, as the number of migrants increases, the wage decreases. Let wjWwk for all k 6¼ j. Denote utility of a representative emigrant by UðC; N; LÞ ¼ Cd1 N d2 Ld3 where C is consumption and L is the size of the local population, with d1 ; d2 ; d3 o1. Externalities are reflected in the size of the local population and in the number of immigrants. All income is spent on consumption, so that C ¼ wj, and d b 0 a 0 N 1 d2 d3 N L (10) UðC; N; LÞ ¼ a1 þ b1 The condition for utility to be increased by more immigrants is: Noðb0 a0 Þ

d2 ¼ N0 d1 þ d2

(11)

which shows that the wage and consumption decrease as immigration increases, but that the loss in utility is offset by network-externality benefits.12 Thus, as the number immigrants in the host country increase until N0, the probability of a new immigrant choosing the same country increases. Increasing the number of immigrants beyond N0 will decrease the probability of an individual choosing that host country – see Figure 1. Now to herd behavior: let one person emigrate to a designated country when a second person receives a positive signal indicating emigration to a different country. If this latter person chooses to follow the first migrant, then she knows that all successors will follow, for informational and payoff reasons (herd behavior and positive externalities). If she chooses the other country, there is a positive probability that she will end up alone. So, while she may think that the basic payoff or utility from moving to the alternative country is as good as for the first country, the awareness of the positive network payoff will induce her to choose the location chosen by this first emigrant. Herd behavior is therefore more pronounced than when externalities are absent, and with high probability the first emigrant will be followed by everyone. In disregarding network externalities to focus on herd behavior, we took it to be the case that an emigrant had no information regarding expected utility, and received signals regarding the probability that a

11

See Brezis and Krugman (1996) for an argument that this is so in the short run, but not necessarily in the long run. 12 Notice that ðb0 a0 Þ40 and ðd2 =ðd1 þ d2 ÞÞo1.

36

Probability Of Immigration

Gil S. Epstein

0

No Number of Immigrants

Fig. 1.

N

Network externalities.

particular country offered the best location.13 In the presence of beneficial externalities, the utility from emigration to a country depends on (1) the number of immigrants who have previously immigrated and (2) how many people will immigrate in the future. So even if the wage in a country is relatively low, the positive externalities may make that country an attractive location. For example, suppose n people have emigrated to country j and one person has emigrated to country k, and that utility of an immigrant in country j is higher than that of an immigrant in country k. It could however be that if n immigrants had immigrated to country k and one immigrant to country j, utility in country k would have been higher than in country j (if n immigrants had immigrated to that country). With herd behavior, the probability that a signal received by an individual is true is a function of the number of both previous immigrants who have immigrated to the same country and immigrants who have chosen other countries. We can define the probability in the following way: Suppose an individual has received a signal that country j is best, and has to choose between country j and country k. Given the number of individuals who have already emigrated to country j and k, the probability that this signal 13

If an individual were able to calculate expected utility in the foreign country, the combined herd effects and positive externalities could be easily established.


37

is true is given by: q0j ð:Þ ¼ q0j

nj nk

while

q0j ðnj =nk Þ 40 @ðnj =nk Þ

(16)

The probability q0j ð:Þ represents the normalized probability that the right thing to do is follow the signal. Thus q0j ð:Þ is a function of all the information, that is, the number of emigrants who have already emigrated to the different countries, and the basic probability that the signal is true q (q0j ð:Þ) is calculated in a similar way as in (1). Thus, the benefits from network externalities influence the probability that a signal is true via the relative number of immigrants who previously emigrated to the different countries. When we now recompute the probabilities of Section 2, we find that herd effects are more pronounced because of the externalities, and we conclude: PROPOSITION 4. The probability of herd behavior increases in the presence of positive externalities. As argued above, given that the immigrant is already in the host country, he prefers that the total number of immigrants will be equal to N 0 ¼ ðb0 a0 Þðd2 =ðd1 þ d2 ÞÞ. However, when this individual makes his decision whether to immigrate to this county, he will compare the expected utility from the different countries and chose the one with the highest value. We therefore may see immigrants deciding to immigrate to a country where the number of immigrants has already exceeded N0. Thus, the probability that an individual will chose to immigrate to a country where the number of immigrants already exceeding N0 is positive. This probability, however, will decrease as the number of immigrants already in the host country increases. We conclude: PROPOSITION 5. Given network externalities, the probability an individual will immigrate to a certain country has an inverse U-shape relationship with regard to the number of immigrants already in the host country. Herd effects are less pronounced when externalities are negative.14 Consider a general signal received by an individual to move to country j rather than country k. The probability associated with this signal increases with the relative number of immigrants who already chose j, only if the total number of past emigrants is less than a bound, determined by the number at which negative externalities set in. 14

When disadvantageous externalities are present, incentives arise to move to new locations, in the course of which individuals tend to reveal private information – as they will only emigrate to another location if warranted by private information.

38

Gil S. Epstein

Denote this number by mj. Then: nj q0j ð:Þ ¼ q0j nk q0j ðnj =nk Þ while 40 for nj omj and nk omk @ðnj =nk Þ and q0j ðnj =nk Þ o0 @ðnj =nk Þ

for nj 4mj and nk omk

ð17Þ

The probability that the private signal is true is independent of the number of emigrants who previously chose a country. If disadvantageous externalities are present, incentives arise to move to new locations, in the course of which individuals tend to reveal private information – as they will only migrate to another location if warranted by private information. Informational herd effects are therefore less pronounced in the case of negative externalities. A migrant may move to a country and find out that the marginal positive effect of the externalities is lower than the marginal negative effect of the wage. In other words, the stock of immigrants who have migrated to this host country has exceeded N0. A migrant who is leaving this host country, where the stock of migrants is higher than N0, will now send negative specific signals to his home-country people who are thinking about migrating to that country. The signals will be saying not to migrate to where he migrated. The local population at the home country receives these negative signals. However, the population at home knows that a lot of individuals have migrated to this country and may even receive other information that this place is the right place to which to immigrate. An individual who has to make the decision will weigh the information he received: the stock of previous individuals who migrated to that country (and to other countries), the general information he received while observing the flow of migrants, and the negative information he received from the migrants who have already immigrated to that country. This individual knows that there is a positive probability that the information he received from the migrants in the host country is true for them as they do not want other migrants to join them. However, it may be optimal for the migrant to join them even if there are negative signals. In order for the individual to follow the flow (herd), the proportion of negative signals relative to the stock of migrants must fall. Thus, if the stock of immigrants is sufficiently large in the host country, the new migrants may continue to follow the herd even though the network externalities are negative. Thus, under herd behavior one may observe that the probability of migrating to a certain location will increase over time when it should have decreased if one only takes into account network effects (see Figure 2).


39

Probability Of Immigration

Herd effect

0

Network externalities

No

N

Number of Immigrant

Fig. 2. Network externalities verses Herd behavior. 4. Concluding remarks Our purpose in this paper has been to draw attention to informational cascades and herd behavior as an influence on where migrants locate. Herd behavior offers an informational perspective on why emigrants from the same location make the same foreign relocation decision. Herd behavior complements network externalities in explaining foreign location decisions. Network externalities may not be sufficient to explain the foreign locational choice, since a number of alternative locations may all offer network externalities. Herd effects can explain which of the alternatives offering network externalities is chosen. There are a number of additional dimensions to differences between network externalities and herd-effect explanations for the choice of emigrants’ destinations. Positive network externalities tell a story of efficiency through the internalized benefits provided by the externalities. There are no mistakes. Herd behavior introduces the possibility of economic inefficiency through the discounting of accurate private information. Also, a prior critical presence of emigrants with the same cultural background or from the same location is required for network externalities. This is not a prerequisite for a herd-effects explanation of foreign location choice.

40

Gil S. Epstein

When the population of prior immigrants in a foreign location is small, network externalities are of course not present. Still, emigration decisions are made, generally under conditions of uncertainty. In such cases, we can only look to herd effects to explain initial immigrant clustering. After the immigrant population reaches a particular size, relations can become more impersonal, and the arrival of someone from ‘‘back home’’ may not evoke the same feeling of responsibility and benevolence. Network externalities can therefore be subject to diseconomies of size of the immigrant population. After a sufficiently long presence, a local individualistic culture can take hold (‘‘let the new arrival work hard and succeed by his own merits like I did’’). Thus, after a certain number of immigrants, it may be beneficial for the emigrant to join a different network. Herd behavior will lead immigrants to continue coming to the same location when network externalities no longer justify this decision. Network externalities appear to be more important than herd effects for illegal immigrants, or when legal immigrants convert to illegality (see Epstein et al., 1998 and Epstein, 2002), because of the greater need of illegal immigrants for surreptitious existence and protection. Herd behavior can however be expected to diminish in significance as prospective emigrants have access to more sophisticated and accurate information about conditions in foreign locations, since more weight is then placed by people on private information. Often host countries want migrants (see Epstein and Hillman, 2003). Trying to influence migration, even a limited migration, may result in a herd of migrants who are not wanted by the host country or by the local population in the host country. This scenario illustrates the complexity of national preferences and xenophobia regarding immigrant composition.15,16 The host country may have a clear objective of what it wants, 15

The national preferences reflect electoral outcomes, political popularity, and mass expression in various countries. Local indigenous populations have expressed discontent and uneasiness, and in cases have also become violent, because of immigration issues. In some European countries, parts of the local population have expressed anti-immigration preferences through the polls. Political parties taking explicit anti-immigrant positions have found significant support in France, Austria, Switzerland, and the eastern regions of Germany. In Norway, when foreign presence is low, immigration has been a major electoral issue, and also in Denmark, where the foreign presence is higher. There has also been antiimmigration sentiment in Sweden. Xenophobia and national ethnic preference have been found outside of Europe, in Indonesia, for example, the Chinese population suffered in the vast pogroms of the 1960s and again in 1998. Indians were expelled from Uganda. In Fiji the indigenous population revoked democracy when they became a minority. 16 Gang et al. (2002) provide a statistical analysis on the determinants of attitudes toward foreigners displayed by Europeans sampled in the Eurobarometer surveys in 1988 ands 1997. In general they show that those who compete against migrants in the labor market have more negative attitudes toward foreigners, and as the concentration of immigrants in the local population increases, the likelihood of negative attitude increases (a negative networkexternality effect).

41


however, as a result of informational cascades; a herd of migrants may arise and bring unexpected results. Empirically we can distinguish between network externalities and herd effects in stock and flow terms. If the flow of emigrants to different locations is related to the prior stock of emigrants, we can infer network externalities are important. If the flow of emigrants is related to prior flows, herd effects are important. Acknowledgment Financial support from the Adar Foundation of the Economics Department of Bar-Ilan University is gratefully acknowledged. Appendix. proof of Proposition 1 Denote by Prðjj nj; ðn 2Þk; kÞ the probability that j is the best country to which to emigrate, and let it be observed that n individuals have immigrated to country j; (n2) to country k; and an individual receives a signal to immigrate to country k. First consider the case of three persons: The two first persons have immigrated to country j and person 3 has received a signal to immigrate to country k. Given assumptions a and b, it is clear that the first person has received a signal to immigrate to country j and the second person either did not receive a signal or received a signal to immigrate to country j. We can calculate the probability that j(k) is the true signal. Using the Bayesian rule, given this information, the probability that the j signal is true out of m possible countries is: Prðjj2j; 0k; kÞ ¼

ðp3 q2 ð1 qÞ þ p2 ð1 pÞqð1 qÞÞ1=m Prðk; j; jÞ

(A.1)

In the same way we calculate the probability that k is the true signal: Prðjj2j; 0k; kÞ ¼

ðp3 qð1 qÞ2 þ p2 ð1 pÞqð1 qÞÞ1=m Prðk; j; jÞ

(A.2)

Given that qW0.5, Prðkj2j; 0k; kÞoPrðjj2j; 0k; kÞ. Since the decision is between two different locations (out of a larger set of locations), the conditional probability for q is thus greater than 0.5. Given that the conditional probability qW0.5 it holds that Prðkj2j; 0k; kÞoPrðjj2j; 0k; kÞ. We now consider the case where one person has immigrated to country k, three persons have immigrated to country j, and the fifth person has received a signal to immigrate to country k. Necessarily, the first individual received a signal for j and the second receives a signal to country k. The third individual receives a signal for j or randomly selects j. The fourth

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Gil S. Epstein

individual does not receive a signal for k. Finally, the fifth individual receives a signal for k. So: Prð3j; 1k; kjjÞ ¼ pqpð1 qÞðpq þ 0:5ð1 pÞÞðpq þ ð1 pÞÞpð1 qÞ (A.3) Likewise: Prð3j; 1k; kjkÞ ¼ pqpð1 qÞðpð1 qÞ þ 0:5ð1 pÞÞðpð1 qÞ þ ð1 pÞÞpq (A.4) By Bayes’ rule: Prðjj3j; 1k; kÞ ¼

Prð3j; 1k; kjjÞ Prð3j; 1k; kjjÞ þ Prð3 j; 1k; kjkÞ

(A.5)

which is larger than Prðkj3j; 1k; kÞ ¼ 1 Prðjj3j; 1k; kÞ if and only if Prðjj3j; 1k; kÞ40:5, which is equivalent to Prðjj3j; 1k; kÞ4Prðkj3j; 1k; kÞ. We now see that Prðjj3j; 1k; kÞ4Prðkj3j; 1k; kÞ if and only if ðpq þ 0:5ð1 pÞÞðpq þ ð1 pÞÞpð1 qÞ 4ðpð1 qÞ þ 0:5ð1 pÞÞðpð1 qÞ þ ð1 pÞÞpq

(A.6)

Thus, (A.6) holds if and only if p2 qð1 qÞ40:5ð1 pÞ2

(A.7)

holds by assumption d. The rest of the proof is by induction. We have shown that herd behavior occurs in the two cases. Assume that the country that has the largest number of immigrants, country j, has n (n1) immigrants. Denote by k the country with the second largest number of immigrants, with (n2)(n3) immigrants. We have shown that herd behavior holds true for n ¼ 2 and n ¼ 3. Assume that it holds for n and n1. We will show that it holds for nþ1 and nþ2. Assuming that: Prðjj nj; ðn 2Þk; kÞ4Prðkj nj; ðn 2Þk; kÞ

(A.8)

and Prðjj ðn 1Þj; ðn 3Þk; kÞ4Prðkj ðn 1Þj; ðn 3Þk; kÞ

(A.9)

Our aim is to show that Prðjj ðn þ 1Þj; ðn 1Þk; kÞ4Prðkj ðn þ 1Þj; ðn 1Þk; kÞ

(A.10)

and Prðjj ðn þ 2Þj; nk; kÞ4Prðkj ðn þ 2Þj; nk; kÞ

(A.11)

Using Bayes’ rule, (A.10) and (A.11) hold if and only if Prððn þ 1Þj; ðn 1Þk; kj jÞ4Prððn þ 1Þj; ðn 1Þk; kj kÞ

(A.12)

and Prððn þ 2Þj; nk; kj jÞ4Prððn þ 2Þj; nk; kj kÞ

(A.13)


43

Let us first consider the case where nþ1 people have emigrated to country j, n1 people have emigrated to country k, and an individual has received a signal to emigrate to country k: ððn þ 1Þj; ðn 1Þk; kÞ. Given (A.8) and (A.9) it is at most the case that n1 people have emigrated to country j and n1 people have immigrated to country k: ððn 1Þj; ðn 1ÞkÞ, otherwise we would have had a herd behavior when the event ðnj; ðn 2ÞkÞ occurred and the event ððn þ 1Þj; ðn 1Þk; kÞ would have never occurred: Prððn þ 1Þj; ðn 1Þk; kj j Þ ¼ Prððn 1Þj; ðn 1ÞkÞPrð3j; 1k; kj jÞ

1 p2 qð1 qÞ

and Prððn þ 1Þj; ðn 1Þk; kj kÞ ¼ Prððn 1Þj; ðn 1ÞkÞPrð3j; 1k; kj kÞ

1 p2 qð1

qÞ

ðA:14Þ

where Prððn 1Þj; ðn 1Þkj jÞ ¼ Prððn 1Þj; ðn 1Þkj kÞ ¼ Prððn 1Þj; ðn 1ÞkÞ. Given (A.6) and (A.7), it is clear that (A.14) holds. In the same way we prove (A.11). Q.E.D.

References Banerjee, A.V. (1992), A simple model of herd behavior. Quarterly Journal of Economics 107 (3), 797–817. Bartel, A.P. (1989), Where do the new US immigrants live? Journal of Labour Economics 7 (4), 371–391. Bauer, T., Epstein, G.S., Gang, I.N. (2007), The influence of stocks and flows on migrants’ location choices. Research in Labor Economics 26, 199–229. Bauer, T., Epstein, G.S., Gang, I.N. (2009), Measuring ethnic linkages between immigrants. International Journal of Manpower 30 (1/2), 56–69. Bikhchandani, S., Hirshleifer, D., Welch, I. (1992), A theory of fads, fashion, custom, and culture change as informational cascade. Journal of Political Economy 100 (5), 992–1026. Brezis, E.S., Krugman, P.R. (1996), Immigration, investment, and real wages. Journal of Population Economics 9 (1), 83–93. Carrington, W.J., Detragiache, E., Vishwanath, T. (1996), Migration with endogenous moving costs. American Economic Review 86 (4), 909–930. Chiswick, B.R., Miller, P.M. (1996), Ethnic networks and language proficiency among immigrants. Journal of Population Economics 9 (1), 19–35. Choi, P. (1997), Herd behavior, the ‘‘penguin effect’’ and the suppression of informational diffusion: an analysis of informational

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externalities and payoff interdependency. Rand Journal of Economics 28 (3), 407–425. Church, J., King, I. (1983), Bilingualism and network externalities. Canadian Journal of Economics 26 (2), 337–345. Epstein, G.S. (2002), Labor market interactions between legal and illegal immigrants. Review of Development Economics 7 (1), 30–43. Epstein, G.S. (2008), Herd and network effects in migration decisionmaking. Journal of Ethnic and Migration Studies 34 (4), 567–583. Epstein, G.S., Cohen, O. (2006), Immigrants during 1990’s from former Soviet Union: herd effect and network externalities. The Economic Quarterly (in Hebrew) 53 (1), 166–201. Epstein, G.S., Hillman, A.L. (2003), Unemployed immigrants and voter sentiment in the welfare state. Journal of Public Economics 87, 1641–1655. Epstein, G.S., Hillman, A.L., Weiss, A. (1998), Creating illegal immigrants. Journal of Population Economics 12, 3–21. Gang, I.N., Rivera-Batiz, F.L., Yun, M.-S. (2002), Economic strain, ethnic concentration and attitudes towards foreigners in the European Union. Mimeo. Gottlieb, P. (1987), Making Their Own Way: Shorthorn Blacks’ Migration to Pittsburgh 1916–30. University of Illinois Press, Urbana. Grossman, J.R. (1989), Land and Hope: Chicago, Black Southerners, and the Great Migration. University of Chicago Press, Chicago. Marks, C. (1989), Farewell – We’re Good and Gone: The Great Black Migration. Indian University Press, Bloomington. Scharfstein, D.S., Stein, J.C. (1990), Herd behavior and investment. American Economic Review 80 (3), 465–479.

CHAPTER 3

The Measurement of Income Polarization by Ethnic Groups: The Case of Israel Population Joseph Deutsch Department of Economics, Bar-Ilan University, Ramat Gan, 52900, Israel E-mail address: [email protected]

Abstract Income polarization is a relatively new concept introduced in the literature of the measurement of income inequality. It has essential properties that may be used to measure relative deprivation and it adds another dimension to the measurement of income inequality concerned mainly with the middle income class (Esteban and Ray, 1994). No study, however, seems to have tried to decompose by population subgroups any of the polarization indices that have appeared in the literature. This study introduces a methodology that decomposes the polarization index recently suggested by Deutsch et al. (2007) by population subgroups. This polarization index is related to the Gini index and its components so that previous results on the decomposition of the Gini index may be applied. Two main cases are examined, that of nonoverlapping groups and overlapping groups. The paper also includes an empirical analysis based on Israeli data for the period 1990–2004, which covers the case of nonoverlapping (income) groups as well as that of overlapping groups, the latter being either Jews of Western and Eastern origin or Jews and Non-Jews. The empirical analysis shows a decrease in polarization over the period 1990–2002 and an increase in polarization during the years 2002–2004. Using the Shapley methodology we analyze the contribution of the different factors to the trend in polarization observed over time. Keywords: Gini index, inequality decomposition, Israel, polarization, population subgroups Jel classification: D31



46

Joseph Deutsch

1. Introduction Numerous studies have appeared in the literature on the decomposition of income inequality by population subgroups. While certain authors preferred to use entropy-related inequality indices because they may be easily broken down into a between and a within groups inequality component (see, e.g., Bourguignon, 1979; Cowell, 1980, 1984; Mookherjee and Shorrocks, 1987; Shorrocks, 1980, 1984), others preferred using the very popular Gini index, despite the fact that the latter, in addition to between and within groups components, includes also a residual (see Bhattacharya and Mahalanobis, 1967; Dagum, 1980, 1987, 1997; Lambert and Aronson, 1993; Pyatt, 1976; Sastry and Kelkar, 1994; Silber, 1989; Yitzhaki and Lerman, 1991). It turns out, however, that it is possible to give intuitive interpretations to this residual and several have been proposed (see Dagum, 1997; Lambert and Aronson, 1993; Pyatt, 1976; Silber, 1989). No study, however, seems to have tried to decompose by population subgroups any of the polarization indices that have appeared in the literature. The purpose of this paper is precisely to show how it is possible to decompose by population subgroups the polarization index PG recently suggested by Deutsch et al. (2007). This polarization index is related to the Gini index and its components so that previous results on the decomposition of the Gini index may be applied. The paper is organized as follows. Section 2 shows how it is possible to decompose the polarization index PG when there are two nonoverlapping groups of equal size or three nonoverlapping groups, whatever their size. Section 3 extends the analysis to the case of overlapping groups while Section 4 presents an empirical analysis based on Israeli data for the period 1990–2004. This empirical illustration covers the case of nonoverlapping (income) groups as well as that of overlapping groups, the latter being either Jews of Western and Eastern origin or Jews and Non-Jews. 2. Measuring polarization when income groups do not overlap 2.1. The case of two groups of equal size In a recent paper, Deutsch et al. (2007) defined a new index PG of bipolarization as 2PG ¼ ðGB GW Þ=GT

(1)

where GT, GB, and GW refer, respectively, to the Gini index for the whole income distribution and the between and within groups Gini indices. Note that in the case of bipolarization we assume that the population is divided into two groups of equal size, the ‘‘poor’’ who are those whose income is

The Measurement of Income Polarization by Ethnic Groups

47

lower than the median income and the ‘‘rich’’ who are those with an income higher than the median income. Let now GP and GR refer, respectively, to the Gini index among the ‘‘poor’’ and the ‘‘rich.’’ It is then easy to see (cf. Kendall and Stuart, 1969, for a general definition of the Gini index) that the indices GT, GB, GP, and GR may be expressed as GT ¼ ð1=2ÞðD=yÞ

(2)

GB ¼ ð1=2ÞðDB =yÞ

(3)

GP ¼ ð1=2ÞðDP =yP Þ

(4)

GR ¼ ð1=2ÞðDR =yR Þ

(5)

where D, DB, DP, and DR represent, respectively, the overall mean difference, the between groups mean difference, the mean difference within the group of poor, and the mean difference within the group of rich. yR ; and yP are, respectively, the (arithmetic) mean incomes in Similarly y; the whole population, in the subgroup of rich, and in that of the poor. Finally, in the case of nonoverlapping groups, the overall Gini index may be expressed (see Silber, 1989b) as GT ¼ GB þ GW

(6)

The within groups Gini index GW may, however, be written (see Silber, 1989) as GW ¼ f P sP GP þ f R sR GR

(7)

where fP, fR, sP, and sR refer, respectively, to the population shares of the groups of poor and rich and to the income shares of these two groups. Since we assumed that f P ¼ f R ¼ 1=2

(8)

and since in our case sP ¼ ð1=2ÞðyP =yÞ

(9)

and sR ¼ ð1=2ÞðyR =yÞ

(10)

we end up, combining expressions (7)–(10), with P þ ð1=4ÞðyR =yÞG R GW ¼ ð1=4ÞðyP =yÞG

(11)

The between groups Gini index GB in (3) may, however, be also expressed (see Silber, 1989) as GB ¼ ½ð1=2Þ; ð1=2ÞG½sR ; sP 0

(12)

where [(1/2), (1/2)] is a row vector giving the population shares of the ‘‘rich’’ and the ‘‘poor,’’ ½sR ; sP 0 is a column vector giving the income shares

48

Joseph Deutsch

of these two groups and G is here a two by two square matrix, called G-matrix (see Silber, 1989) whose typical element gij is equal to 0 if i ¼ j, 1 if jWi, and þ1 if joi. Combining (9), (10), and (12) we end up, after simplifying, with GB ¼ ð1=2ÞðyR y P Þ=ðyR þ yP Þ

(13)

Note also that the overall mean income y may be written, in the case of two groups of equal size, as y ¼ ð1=2ÞðyR þ yP Þ

(14)

If we now combine expressions (1), (6), (11), (13), and (14) we will derive PG ¼ f½ð1=2ÞðyR y P Þ=ðyR þ yP Þ ½ðð1=4ÞðyR GR þ y P GP ÞÞ=ðð1=2ÞðyR þ yP ÞÞg=GT

ð15Þ

where GT ¼ GB þ GW ¼ f½ð1=2ÞðyR yP Þ=ðyR þ yP Þ þ ½ðð1=4ÞðyR GR þ yP GP ÞÞ=ðð1=2ÞðyR þ yP ÞÞg

ð16Þ

Combining (15) and (16) we end up, after simplifying, with PG ¼ ½ðyR yP Þ ðyR GR þ yP GP Þ=½ðy R yP Þ þ ðyR GR þ yP GP Þ

(17)

PG ¼ ½ððyR =yP Þ 1Þ ððyR =yP ÞGR þ GP Þ= ½ððyR =yP Þ 1Þ þ ððyR =yP ÞGR þ GP Þ

ð18Þ

so that PG may also be expressed as PG ¼ f ððyR =yP Þ; GR ; GP Þ

(19)

Let now DPG ; DðyR =DyP Þ; DGR ; and DGP refer, respectively, to the changes that took place between two periods, say 0 and 1, in the values of the polarization index PG, the ratio of the mean incomes yR and yP , and the Gini indices GR and GP, we can then derive from (19) that DPG ¼ hðDðyR =DyP Þ; DGR ; DGP Þ

(20)

Using the so-called Shapley decomposition (see Shorrocks, 1999 and Appendix A) it is easy to determine the contributions of the changes DðyR =DyP Þ, DGR, and DGP to the overall change DGP in the value of the polarization index PG. 2.2. The case of three nonoverlapping income groups Since the Gini index will evidently not include any residual term (also called overlap) when income groups do not overlap, there is no reason why the definition of polarization given in (1) could not apply to the case where there are more than two groups. This is so because the main assumptions

49


underlying (1) as well as the concept of polarization remain valid, that is, that polarization should increase with the between groups inequality and decrease with the within groups inequality (see, e.g., Esteban and Ray, 1994; Wolfson, 1994, 1997; Wang and Tsui, 2000, or Chakravarty and Majumder, 2001). The present section will therefore derive expressions similar to those given in Section 2, but for the case of three groups. Assume we divide the population into three nonoverlapping income groups. Let fP, fM, and fR refer, respectively, to the shares in the total population of the ‘‘poor,’’ the ‘‘middle class,’’ and the ‘‘rich.’’ Let sP, sM, and sR refer to the corresponding income shares of these three groups. Using the algorithm mentioned in (12) the between groups Gini index GB will now be expressed as GB ¼ ½ðf R ; f M ; f P ÞG½sR ; sM ; sP 0

(21)

where G is now a three by three G-matrix. Let y P ; yM ; and yR refer to the mean incomes of the ‘‘poor,’’ the ‘‘middle class,’’ and the ‘‘rich.’’ Remember now that for any group g (g ¼ R, M, or P), we can define the income shares sg as sg ¼ f g ðyg =yÞ

(22)

where fg and yg refer, respectively, to the population share and the mean income of group g and that the mean income y in the whole population may be expressed as y ¼ f R yR þ f M yM þ f P yP

(23)

We then end up, combining (21), (22), and (23), after simplifying, with GB ¼ f½f R ð1 f R ÞyR þ ½ð1 f R f P Þðf P f R Þy M ½f P ð1 f P Þy P g=½f R yR þ f M yM þ f P yP GB ¼ fð1=yP Þf½f R ð1 f R ÞðyR =yP Þ þ ½ð1 f R f P Þðf P f R ÞðyM =yP Þ ½f P ð1 f P Þgg=½f R ðyR =yP Þ þ f M ðyM =yP Þ þ f P ð24Þ However, since f M ¼ 1 ðf R þ f P Þ

(25)

GB ¼ kðf R ; f P ; ðyR =yP Þ; ðyM =yP ÞÞ

(26)

Now it is well-known that the within groups Gini index GW (see Silber, 1989) will be expressed in our case as GW ¼ ½ðf R sR GR Þ þ ðf M sM GM Þ þ ðf P sP GP Þ

(27)

50

Joseph Deutsch

Combining expressions (22), (23), (25), and (27), we end up with GW ¼ ½ðf 2R yR GR Þ þ ðf 2M yM GM Þ þ ðf 2P yP GP Þ=½f R yR þ f M yM þ f p yP (28) GW ¼ ½ðf 2R ðyR =yP ÞGR Þ þ ðf 2M ðyM =yP ÞGM Þ þ ðf 2P GP Þ=½f R ðyR =yP Þ þ f M ðyM =yP Þ þ f p

ð29Þ

so that we may also express GW as a function q, that is, as GW ¼ qðf R ; f p ; ðyR =yP Þ; ðyM =yP Þ; GR ; GM ; GP Þ

(30)

Since the polarization index PG, in the case of nonoverlapping groups, may be written as PG ¼ ðGB GW Þ=ðGB þ GW Þ

(31)

we conclude, combining (26) and (30) that PG may be expressed as a function r with PG ¼ rðf R ; f p ; ðyR =yP Þ; ðyM =yP Þ; GR ; GM ; GP Þ

(32)

Using, as in the case of two groups, the sign D to refer to the change of a variable between two periods, we end up expressing the change DPG in polarization as a function s, that is, as DPG ¼ sðDf R ; Df p ; DðyR =yP Þ; DðyM =yP Þ; DGR ; DGM ; DGP Þ

(33)

However, if we assume that there is no change in the relative size of the three groups we may, in fact, write that DPG ¼ sðDðyR =yP Þ; DðyM =yP Þ; DGR ; DGM ; DGP Þ

(34)

Using the so-called Shapley decomposition (see Shorrocks, 1999 and Appendix A), it is then easy to determine the contributions of the changes DðyR =yP Þ; DðyM =yP Þ, DGR, DGM, and DGP to the overall change DPG in the value of the polarization index PG. 3. Measuring polarization when income groups do overlap The definition of the polarization index PG given in (1) should also apply to the case of overlapping groups, that is, to the case where groups are not defined by their income level but by some other characteristics, for example, ethnicity and educational level. What, however, has to be remembered is that when there is overlap, the denominator of (1) will be expressed as GT ¼ GB þ GW þ OV

(35)

where OV, the third element on the R.H.S. of (35), is a measure of the degree of overlap between the distributions of income of the various


51

population subgroups. As shown by Silber (1989), OV may be defined as OV ¼ ½e0 Gs ½e0 Gv

(36)

In (36), eu is a 1 by n row vector of the population shares of the various individuals. Therefore, if the observations are at the individual level and there are n individuals, each element of the row vector eu will be equal to (1/n). The letters s and v on the R.H.S. of (36) refer to n by 1 column vectors. The elements of the column vector s are the individual income shares and they are assumed to be ranked by decreasing values of these shares (see Silber, 1989). The column vector v also includes income shares but here the individual shares are ranked first by decreasing values of the average income of the group to which the individuals belong, second within each group by decreasing values of the shares (see Silber, 1989). Finally G on the R.H.S. of (36) is an n by n G-matrix of which the definition was given previously. Using (1) the polarization index PG in the case of overlapping groups will be expressed as PG ¼ ðGB GW Þ=ðGB þ GW þ OVÞ

(37)

Note that as in the case of nonoverlapping groups, the polarization index PG will increase with the between groups inequality GB, decrease with the within groups inequality GW. In addition since the overlapping term OV appears only in the denominator of (37), the polarization index PG will also decrease with the amount of overlap, which also makes sense. It should, however, be stressed that the three components GB, GW, and OV are not independent. To understand this, we have to recall some results derived by Dagum (1980, 1997). Let m represent the number of population subgroups. The overall mean difference D may then be decomposed into the sum of two terms as D ¼ DA þ DW

(38)

where DA refers to what may be called the ‘‘across groups inequality’’ (see Dagum, 1960, 1997), while DW measures the within groups inequality, with DW ¼ ð1=n2 Þ

m XXX X

jyih yjk j

(39)

jyih yjk j

(40)

h¼1 i2h k¼h j2k

and DA ¼ ð1=n2 Þ

m XXX X h¼1 i2h kah j2k

the second subindex (h or k) in (39) and (40) referring to the group to which the individual belongs.

52

Joseph Deutsch

Let us now assume that the groups are ranked by decreasing values of the average of the income in each group so that yh ; the mean income in group h, is higher than yhþ1 , the mean income in group hþ1. Equation (40) may then be written as (41)

DA ¼ Dd þ Dp with Dd ¼ ð1=n2 Þ

m XXX X

jyih yjk j with

yih yjk

(42)

jyih yjk j with

yih yjk

(43)

h¼1 i2h kah j2k

and Dp ¼ ð1=n2 Þ

m XXX X h¼1 i2h kah j2k

Combining (42) and (43) we derive that Dd Dp ¼ ð1=n2 Þ

m XXX X ðyih yjk Þ

(44)

h¼1 i2h kah j2k

" # m X X X X Dd Dp ¼ ð1=n Þ ðnk ðyih ÞÞ ðnh ðyjk ÞÞ 2

h¼1 kah

Dd Dp ¼ ð1=n2 Þ

m X X

i2h

(45)

j2k

½nk nh ðy h y k Þ

(46)

h¼1 kah

where nh and nk represent, respectively, the number of individuals in groups h and k. Since the between groups mean difference DB is obtained by giving each individual the average value of the incomes of the group to which he belongs, we may define the index DB as DB ¼ ð1=n2 Þ

m X X ½nk nh ðyh yk Þ

(47)

h¼1 kah

and it is easy to observe, when comparing (46) and (47), that DB ¼ ðDd Dp Þ

(48)

Since (41) indicates that DA ¼ ðDd Dp Þ þ ð2Dp Þ

(49)

we conclude, combining (38) and (49), that D ¼ DW þ DB þ ð2Dp Þ

(50)

One should note that (50) indicates that (2Dp), the residual obtained in the traditional decomposition of the mean difference by population


53

subgroups, is expressed as a simple function of the ‘‘transvariations,’’1 which exist between all pairs of population subgroups. Combining (38), (49), and (50) we may now express the mean difference as D ¼ DW þ Dd þ Dp

(51)

The three elements on the R.H.S. of (51) are now independent from each other whereas those on the R.H.S. of (50) were not, given the definition of DB that appears in (48). Combining now (2), (3), (35), (37), (38), (41), and (51) we end up with PG ¼ ½Dd ðDW þ Dp Þ=½Dd þ ðDW þ Dp Þ

(52)

It is then easy to observe that PG will increase with Dd but decrease with Dp and Dw. All these three properties make sense and correspond certainly to our intuition of the concept of polarization. Finally using (52), we may express the polarization index PG as a function r with PG ¼ rðDW ; Dd ; Dp Þ

(53)

Let us now use the letter d to indicate a change in a given variable. The change in the degree of polarization between two periods may therefore be written as dPG ¼ rðdDW ; dDd ; dDp Þ

(54)

Applying to (54) the Shapley decomposition procedure (see Appendix A), we will be able to determine the marginal impacts of changes in DW, Dd, and Dp on the overall change DPG in polarization during the period examined. 4. An empirical illustration 4.1. The case of nonoverlapping groups The empirical analysis is based on the income surveys that are conducted each year in Israel. In the first stage we have limited our analysis to nonoverlapping groups. Three cases were distinguished. In the first one, we assumed that the population was divided in two groups of equal size, 1 Following Gini (1959) we may say that there exists a ‘‘transvariation’’ between two distributions {xi} and {yj} with respect to their (arithmetic, geometric, etc.) means mx and my when among the nxny possible differences (xiyj), the sign of at least one of them is different from that of the expression (mx my), nx, and ny being the number of observations in these two distributions. The importance of such a ‘‘transvariation’’ may be measured in several ways (see DeutschRand Silber, R1997). The reference here is to the moment m1 of order 1, which is defined þ1 y as m1 ¼ 1 gðyÞdy 1 ðy xÞf ðxÞdx where g(y) and f(x) are the densities of y and x.

54

Joseph Deutsch

those with incomes lower and higher than the median income. In the second case, we assumed that the poor were those belonging to the two poorest deciles of the income distribution, the richest those belonging to the two highest deciles of this distribution and the middle class those belonging to the other six deciles. Finally, in the third case, we defined the poor as those belonging to the two poorest deciles, the rich as those belonging to the richest decile, and the middle class those belonging to any of the other seven deciles. Three indicators were computed for each of the years (1990–2004) for which observations were available: the Gini index for the whole income distribution, the polarization PG defined previously and the polarization index KZ proposed by Zhang and Kanbur (2001). In the latter case we assumed that the inequality index selected was Gini rather than an entropy-related index. The results are given in Table 1. Let us first examine the case of two nonoverlapping groups of equal size. Table 1 indicates clearly that the polarization indices and the Gini index do not measure the same thing since the indices PG and KZ often move in a direction that is opposite to that observed for the Gini index. There have been fluctuations over time in the value of the polarization indices, but as a whole there seems to have been a decrease in polarization between 1990 and 2001 and an increase between 2002 and 2004.

Table 1. Year

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Yearly values of the Gini index GT and of the two polarization indices PG and KZ Case of two nonoverlapping groups of equal size

Case of three nonoverlapping groups, the poorest and richest each representing 20% of the population and the middle class 60%

Case of three nonoverlapping groups, the poorest representing 20%, the richest 10%, and the middle class 60% of the population

GT

PG

KZ

PG

KZ

PG

KZ

0.4193 0.4250 0.4389 0.4216 0.4441 0.4347 0.4330 0.4407 0.4379 0.4467 0.4413 0.4463 0.4503 0.4409 0.4412

0.3937 0.3845 0.3786 0.3853 0.3697 0.3684 0.3730 0.3603 0.3575 0.3506 0.3560 0.3463 0.3429 0.3580 0.3665

2.2988 2.2492 2.2186 2.2536 2.1733 2.1663 2.1898 2.1265 2.1131 2.0797 2.1056 2.0594 2.0438 2.1151 2.1570

0.5930 0.5987 0.6035 0.6005 0.6007 0.6109 0.6056 0.6066 0.6104 0.6102 0.6110 0.6114 0.6101 0.6030 0.6041

3.9146 3.9833 4.0435 4.0066 4.0087 4.1400 4.0705 4.0842 4.1332 4.1313 4.1419 4.1463 4.1300 4.0384 4.0519

0.3797 0.3832 0.3971 0.3858 0.3985 0.4039 0.3982 0.4176 0.4189 0.4236 0.4174 0.4282 0.4322 0.4217 0.4104

2.2245 2.2428 2.3170 2.2564 2.3248 2.3552 2.3233 2.4339 2.4418 2.4700 2.4330 2.4979 2.5223 2.4587 2.3923

55


In the case where three nonoverlapping groups are distinguished representing, respectively, 20%, 60%, and 20% of the population polarization seems here to have increased between 1990 and 2001 and decreased afterwards. Finally, in the third case where the share in the total population of the poor, the middle class, and the rich are, respectively, 20%, 70%, and 10% we also observe an increase in polarization between 1990 and 2001–2002 and a decrease between 2002 and 2004. To better understand the differences between the three cases examined, we have presented in Table 2, for each of the three cases, the between and within groups Gini index since the PG as well as the KZ polarization indices are simple functions of the between and within groups Gini indices. In the case where only two groups are distinguished, Table 2 indicates clearly that the increase in polarization observed between 2002 and 2004 is essentially the consequence of the decrease in the within groups component of the Gini index. On the contrary when three groups are distinguished, Table 2 indicates that the trend toward an increasing degree Table 2.

Yearly values of the overall Gini index (GT) and of the between and within groups Gini indices GB and GW

Year

Case of two nonoverlapping groups of equal size

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Case of three Case of three nonoverlapping nonoverlapping groups, the poorest groups, the poorest and richest each representing 20%, the representing 20% of richest 10%, and the the population and middle class 60% of the middle class 60% the population

Overall Gini index GT

Between Within groups Gini groups index GB Gini index GW

Between groups Gini index GB

Within groups Gini index GW

Between groups Gini index GB

Within groups Gini index GW

0.4193 0.4250 0.4389 0.4216 0.4441 0.4347 0.4330 0.4407 0.4379 0.4467 0.4413 0.4463 0.4503 0.4409 0.4412

0.2922 0.2942 0.3025 0.2920 0.3041 0.2974 0.2973 0.2997 0.2972 0.3016 0.2992 0.3004 0.3024 0.2994 0.3014

0.3340 0.3397 0.3519 0.3374 0.3554 0.3501 0.3476 0.3540 0.3526 0.3596 0.3555 0.3596 0.3626 0.3534 0.3539

0.0853 0.0853 0.0870 0.0842 0.0887 0.0846 0.0854 0.0867 0.0853 0.0870 0.0858 0.0867 0.0878 0.0875 0.0873

0.2893 0.2939 0.3066 0.2921 0.3105 0.3051 0.3027 0.3123 0.3106 0.3179 0.3128 0.3187 0.3225 0.3134 0.3111

0.1300 0.1311 0.1323 0.1295 0.1336 0.1295 0.1303 0.1283 0.1272 0.1287 0.1285 0.1276 0.1279 0.1275 0.1301

0.1271 0.1308 0.1364 0.1296 0.1399 0.1373 0.1358 0.1409 0.1407 0.1450 0.1421 0.1459 0.1480 0.1415 0.1398

56

Joseph Deutsch

Table 3.

Correlations coefficient between the Gini index GT and the polarization indices PG and KZ GT

PG

(1) The case of two nonoverlapping groups of equal size GT 1.0000 0.8975 0.8975 1.0000 PG KZ 0.9010 0.9997

KZ

0.9010 0.9997 1.0000

(2) The case of three nonoverlapping groups, the poorest and richest each representing 20% of the population and the middle class 60% GT 1.0000 0.7152 0.7127 0.7152 1.0000 0.9999 PG KZ 0.7127 0.9999 1.0000 3) The case of three nonoverlapping groups, the poorest representing 20%, the richest 10%, and the middle class 70% of the population GT 1.0000 0.8812 0.8761 0.8812 1.0000 0.9997 PG KZ 0.8761 0.9997 1.0000

of polarization between 1990 and 2001 or 2002 is mainly the consequence of an increase in the between groups Gini index, this being true for both cases where three groups have been distinguished. In Table 3, we computed the Pearson correlation coefficients over the years for which data were available between both polarization indices and the Gini index. As expected the correlation between the PG and KZ indices is extremely high (0.99). When only two groups are distinguished there is a strong negative correlation between the overall Gini index and the two polarization indices. However, when three groups are distinguished the correlation between the Gini index and the two polarization indices is positive and quite high (above 0.7). It is specially high when the rich are assumed to represent 10% of the population. In Tables 4–6, we present the results of the Shapley decomposition of the change over time in the value of the polarization index PG, the results being given separately for the 1990–2001 period and for the 2002–2004 period and for the three different divisions of the population (in nonoverlapping group) that have been selected. When only two groups of equal size are distinguished (Table 4), the decrease in polarization between 1990 and 2001 and the increase in polarization between 2002 and 2004 appear to be essentially the result of changes over time in the Gini index among the ‘‘rich.’’ The rise in the dispersion of incomes among the rich during the first period led to a decrease in polarization, while the decrease in this dispersion during the second period led to a rise in polarization. The picture is more complex when three groups are distinguished. In Table 5, where the rich and the poor represent each 20% of the population, the increase in polarization that occurred during the period


Table 4.

57

Shapley decomposition of the change in polarization: The case of two nonoverlapping groups of equal size

(1) The period analyzed: 1990–2001 (a) Ratio of the average income of the rich over that of the poor and Gini indices within the rich and poor subgroups GP GR Year yR =yP 1990 3.8113 0.2616 0.2523 2001 4.0100 0.2479 0.3027 (b) Decomposition of the polarization index PG 1990 2001 Component PG DPG DðyR =yP Þ 0.39364 0.34623 0.04741 0.01171

DGP 0.00443

DGR 0.06354

(2) The period analyzed: 2002–2004 (a) Ratio of the average income of the rich over that of the poor and Gini indices within the rich and poor subgroups GP GR Year yR =yP 2002 4.0603 0.2535 0.3064 2004 4.0359 0.2588 0.2846 (b) Decomposition of the polarization index DPG 2002 2004 Component DPG DðyR =y P Þ DPG 0.34292 0.36644 0.02352 0.00133

DGP 0.00160

DGR 0.02645

1990–2001 was first due to an increase in the ratios of the average incomes of the rich and the middle class when compared to that of the poor. There was, however, a counter force, that of the increase in the dispersion of incomes among the rich that per se would have led to a decrease in polarization. During the period 2002–2004, the decrease in polarization was also mainly the consequence of the rise in the dispersion of incomes among the rich. Finally when the rich are assumed to represent 10% and the poor 20% of the population (see Table 6), the rise in polarization during the period 1990–2001 is mainly the consequence of an increased gap between the average incomes of both the rich and the middle class and that of the poor. The decrease in polarization during the period 2002–2004 also seems to be the consequence of what happened to between groups inequality since the main contribution to the change in polarization comes from a decreased gap between the average incomes of the rich and the middle class and that of the poor. 4.2. The case of overlapping groups Two cases have been examined. We first limited the analysis to the Jewish population and made a distinction between ‘‘Easterners’’ and ‘‘Westerners.’’ ‘‘Easterners’’ are Jews who were born in Asia or Africa or Jews born

58

Joseph Deutsch

Table 5.

Shapley decomposition of the change in polarization

(1) The period analyzed: 1990–2001 (a) Ratio of the average income of the middle class and the rich over that of the poor and Gini indices within the rich middle class and poor subgroups GP GM GR yM =yP yR =y P 1990 0.17651 3.67907 0.22770 10.34450 0.17692 2001 0.18422 3.40772 0.22306 10.97907 0.23693 (b) Decomposition of the polarization index PG PG0 0.59312 0.61138 PG1 DPG 0.01826 0.01831 Dðy M =yP Þ DðyR =yP Þ 0.01655 0.00026 DGP DGM 0.00492 DGR 0.02126 (2) The period analyzed: 2002–2004 (a) Ratio of the average income of the middle class and of the rich over that of the poor and Gini indices within the rich middle class and poor subgroups GM GR GP yM =yP yR =y P 2002 0.19110 3.50555 0.22368 11.37355 0.24610 2004 0.18482 3.62592 0.22978 11.21478 0.21398 (b) Decomposition of the polarization index PG PG0 0.61013 PG1 0.60414 DGP 0.00599 0.00770 Dðy M =yP ÞM DðyR =yP Þ 0.00372 DGP 0.00020 0.00614 DGM DGR 0.01138

The case of three nonoverlapping groups, the poorest and richest groups representing 20% and the middle class 60% of the population.

in Israel whose fathers were born in Asia or Africa. ‘‘Westerners’’ are Jews born in Europe, America, South Africa, New Zealand, or Australia, or Jews born in Israel whose fathers were born in one of those areas. At the second stage, we divided the population into two groups, Jews and NonJews. Table 7 gives the values of the Gini index and of the polarization indices PG and KZ during the period 1990–2000 when a distinction is made between ‘‘Westerners’’ and ‘‘Easterners’’ and during the period 2001–2004 when a distinction is made between Jews and Non-Jews.2 2

The periods covered in the two cases are not the same because during the 1990–2000 period we could not make a distinction between Jews and Non-Jews, while during the period 2001– 2004 no detailed data were available on the continent of birth of the Jews.


Table 6.

59

Shapley decomposition of the change in polarization

(1) The period analyzed: 1990–2001 (a) Ratio of the average income of the middle class and of the rich over that of the poor and Gini indices within the rich middle class and poor subgroups GP GM GR yM =yP yR =yP 1990 4.27442 0.26527 12.84149 0.15045 4.27442 2001 4.00204 0.26914 14.38926 0.21793 4.00204 (b) Decomposition of the polarization index DPG PG0 0.37974 0.42824 PG1 DPG 0.04849 0.02007 DðyM =yP Þ DðyR =yP Þ 0.04094 0.00022 DGP DGM 0.00571 DGR 0.00668 (2) The period analyzed: 2002–2004 (a) Ratio of the average income of the middle class and of the rich over that of the poor and Gini indices within the rich middle class and poor subgroups GM GR GP yM =yP yR =yP 2002 0.19110 4.11418 0.26902 14.97818 0.23640 2004 0.18482 4.24924 0.27198 14.43992 0.18649 (b) Decomposition of the polarization index PG PG0 0.43218 PG1 0.41043 DPG 0.02175 0.00975 Dðy M =yP Þ DðyR =yP Þ 0.01289 DGP 0.00017 0.00421 DGM DGR 0.00509

The case of three nonoverlapping groups, the poorest representing 20%, the richest 10% and the middle class 60% of the population.

Table 7 indicates that during the period 1990–2000 the Gini index (when the sample is limited to the Jewish population) first increased, then decreased. No important change took place in the Gini index between 2001 and 2004 when its computation is based on the whole population. Table 7 also indicates that the polarization index PG was always negative, whatever the case and the year examined. This clearly indicates (see (54)) that the sum of the within groups mean difference (DW) and the component of the across groups mean difference that reflects overlapping (Dp) was greater in absolute value than the component of the across groups mean difference that does not correspond to overlapping (Dd). In the first case, when the analysis is limited to the Jewish population (‘‘Easterners’’ versus ‘‘Westerners’’), it appears that as a whole polarization was less important at the end than at the beginning of the period since the index PG is more

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Table 7. Year

Gini index GT

The case of overlapping groups

Polarization index PG

Component Dd

Component Dp

Component Dw

Case 1: Two nonoverlapping groups are distinguished: Jews of Western origin (‘‘Westerners’’) versus Jews of Eastern origin (‘‘Easterners’’). Period analyzed: 1990–2000. 1990 0.4234 0.4153 762 530 1,316 1991 0.4278 0.4255 883 626 1,564 1992 0.4427 0.4331 1,048 723 1,925 1993 0.4252 0.4816 919 811 1,815 1994 0.4477 0.4781 1,291 1,088 2,567 1995 0.4427 0.4596 1,568 1,205 3,028 1996 0.4412 0.4662 1,737 1,382 3,390 1997 0.4452 0.4768 2,161 1,803 4,296 1998 0.4382 0.4697 2,309 1,883 4,516 1999 0.4493 0.4495 2,638 1,930 5,018 2000 0.4387 0.4742 2,583 2,144 5,098 Case 2: Two nonoverlapping groups are distinguished: Jews versus Non-Jews. analyzed: 2001–2004. 2001 0.4463 0.7029 1,528 608 2002 0.4503 0.7114 1,506 504 2003 0.4409 0.6996 1,489 583 2004 0.4412 0.6989 1,511 506

Period 8,149 8,424 7,844 8,022

Yearly values of the Gini index GT, of the polarization index PG, and of the components Dd, Dp, and Dw.

negative around 2000 than around 1990. This is so because, as Table 7 shows, the increase between 1990 and 2000 in the components Dp and Dw was greater than that of the component Dd. In the second case, when a distinction is made between Jews and Non-Jews no clear conclusion may be drawn as far as changes in the polarization index PG are concerned. In Table 8, finally we give the ‘‘Shapley contributions’’ of the changes over time in the three determinants Dd, Dw, and DW to the overall change in the polarization index PG. Let us first examine the case where a distinction is made, within the Jewish population, between ‘‘Easterners’’ and ‘‘Westerners.’’ It appears that polarization decreased between 1990 and 2000 (the polarization index PG decreased by 0.059), but this decrease appears to be the consequence of two conflicting forces. On one hand the change over time in the component Dd would per se have led to an increase in polarization but variations in the components DW and Dp would have led to a decrease in polarization. This seems to indicate that all the mean differences increased and in particular the overlapping component, hence the decrease in polarization. In the second case, where the population is divided into Jews and NonJews, the changes during the period 2001–2004 were not very important but here again the important role is played by the components Dp and DW rather than by the component Dd.

Value of the polarization index in the initial year (P0G )

Value of the polarization index in the final year (P1G )

Change in the value Contribution of of the polarization the change in the index during the component d(CDd) period examined ðDPG Þ

Contribution of the change in the component p(CDp)

Contribution of the change in the within groups inequality GW(CDW)

The case of overlapping groups: Shapley decomposition of the change in polarization

Case 2: Two nonoverlapping groups are distinguished: Jews versus Non-Jews. Period analyzed: 2001–2004. 2001 2002 0.7029 0.7114 0.0085 0.0036 2002 2003 0.7114 0.6996 0.0118 0.0028 2003 2004 0.6996 0.6989 0.0007 0.0038 2001 2004 0.7029 0.6989 0.0040 0.0027

0.0029 0.0023 0.0023 0.0030

0.0078 0.0168 0.0054 0.0038

Case 1: Two nonoverlapping groups are distinguished: Jews of Western origin (‘‘Westerners’’) versus Jews of Eastern origin (‘‘Easterners’’). Period analyzed: 1990–2000. 1990 1991 0.4153 0.4255 0.0102 0.0602 0.0198 0.0506 1991 1992 0.4255 0.4331 0.0077 0.0697 0.0165 0.0609 1992 1993 0.4331 0.4816 0.0485 0.0519 0.0131 0.0165 1993 1994 0.4816 0.4781 0.0035 0.1303 0.0344 0.0924 1994 1995 0.4781 0.4596 0.0185 0.0758 0.0117 0.0456 1995 1996 0.4596 0.4662 0.0066 0.0403 0.0154 0.0315 1996 1997 0.4662 0.4768 0.0106 0.0847 0.0304 0.0649 1997 1998 0.4768 0.4697 0.0071 0.0257 0.0049 0.0137 1998 1999 0.4697 0.4495 0.0202 0.0526 0.0028 0.0297 0.0119 0.0044 1999 2000 0.4495 0.4742 0.0247 0.0084 0.3431 1990 2000 0.4153 0.4742 0.0590 0.4531 0.1690

Period

Table 8.

The Measurement of Income Polarization by Ethnic Groups 61

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Appendix A. On the concept of Shapley decomposition Let an index I be a function of n variables and let ITOT be the value of I when all the n variables are used to compute I. I could, for example, be the R-square of a regression using n explanatory variables, any inequality index depending on n income sources or on n population subgroups. Now, let I k=k ðiÞ be the value of the index I when k variables have been dropped so that there are only (nk) explanatory variables and k is also the rank of variable (i) among the n possible ranks that variable (i) may have in the n! sequences corresponding to the n! possible ways of ordering n numbers. We will call I k=k1 ðiÞ the value of the index when only (k1) variables have been dropped and k is the rank of the variable (i). Thus, I 1=1 ðiÞ gives the value of the index I when this variable is the first one to be dropped. Obviously there are (n1)! possibilities corresponding to such a case. I 1=0 ðiÞ then gives the value of the index I, when the variable (i) has the first rank and no variable has been dropped. This is clearly the case when all the variables are included in the computation of the index I. Similarly I 2=2 ðiÞ corresponds to the (n1)! cases where the variable (i) is the second one to be dropped and two variables as a whole have been dropped. Clearly I 2=2 ðiÞ can also take (n1)! possible values. I 2=1 ðiÞ gives then the value of the index I, assuming only one variable was eliminated and the variable (i) has the second rank. Here also there are (n1)! possible cases. Obviously I n=ðn1Þ ðiÞ corresponds to the (n1)! cases where the variable (i) is dropped last and is the only one to be taken into account. If I is an inequality index, it will evidently be equal to zero in such a case. But if it is, for example, the R-square of a regression it would give us the R-square when there is only one explanatory variable, the variable (i). Obviously I n=n ðiÞ gives the value of the index I when variable (i) has rank n and abcd variables have been dropped, a case where I will always be equal to zero by definition since no variable is left. Let us now compute the contribution of Cj(i) of variable (i) to the index I, assuming the variable bcad is dropped when it has rank j. Using the previous notations, we define Cj(i) as

Cj ðiÞ ¼ ð1=n!Þ

ðn1Þ! X

½I j=ðj1Þ ðiÞ I j=j ðiÞh

(A.1)

h¼1

where the superscript h refers to one of the (n1)! cases where the variable i has rank j.


63

The overall contribution of variable (i) to the index I may then be defined as CðiÞ ¼ ð1=n!Þ

n X

(A.2)

C k ðiÞ

k¼1

It is then easy to prove that I ¼ ð1=n!Þ

n X

(A.3)

CðiÞ

i¼1

Let us give a simple illustration where the index I depends on four determinants a, b, c, d. Table A1 below gives all the possible ways of ordering these four elements. As indicated previously the so-called Shapley decomposition looks at all possible elimination sequences. As Table A1 indicates, there are six cases where a appears in the first position, six where it appears in the second, etc. If we look at the various ways of eliminating a, we can say that if a is eliminated first its contribution to the indicator will be equal to the difference between the value of the indicator when all four determinants are different from zero and its value when a is equal to zero. In that case (a eliminated first) the contribution of a will be written as Cða=a is eliminated firstÞ ¼ ½Iðaa0; ba0; ca0; da0Þ Iða ¼ 0; ba0; ca0; da0Þ Clearly, looking at the first column of the table above, there are six such cases out of 24 possible orderings. The case where a is eliminated second is based on the orderings given in the second column of this table. There are three possibilities: b is eliminated first and a second, so that the contribution of a in this case will be written as Cða=a is eliminated second and b firstÞ ¼ ½Iðaa0; b ¼ 0; ca0; da0Þ Iða ¼ 0; b ¼ 0; ca0; da0Þ and this case appears twice in the second column of the table. Table A1.

The 24 ways of ordering four elements

a appears in the first position

a appears in the second position

a appears in the third position

a appears in the fourth position

abcd abdc acbd acdb adbc adcb

bacd badc cabd cadb dabc dacb

bcad bdac cbad cdab dbac dcab

bcda bdca cbda cdba dbca dcba

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c is eliminated first and a second so that the contribution of a in this case will be written as Cða=a is eliminated second and c firstÞ ¼ ½Iðaa0; ba0; c ¼ 0; da0Þ Iða ¼ 0; ba0; c ¼ 0; da0Þ and this case appears also twice in the second column of the table. d is eliminated first and a second so that the contribution of a in this case will be written as Cða=a is eliminated second and d firstÞ ¼ ½Iðaa0; ba0; ca0; d ¼ 0Þ Iða ¼ 0; ba0; ca0; d ¼ 0Þ and this case appears also twice in the second column of the table. The case where a is eliminated third is based on the orderings given in the third column of this table. There are again three possibilities: b is eliminated first, c second, and a third or c is eliminated first, b second, and a third. In both cases the contribution of a will be written as Cða=a eliminated third and b and c firstÞ ¼ ½Iðaa0; b ¼ 0; c ¼ 0; da0Þ Iða ¼ 0; b ¼ 0; c ¼ 0; da0Þ b is eliminated first, d second, and a third or d is eliminated first, b second, and a third. In both cases the contribution of a will be written as Cða=a eliminated third and b and d first or secondÞ ¼ ½Iðaa0; b ¼ 0; ca0; d ¼ 0Þ Iða ¼ 0; b ¼ 0; ca0; d ¼ 0Þ c is eliminated first, d second, and a third or d is eliminated first, c second, and a third. In both cases the contribution of a will be written as Cða=a eliminated third and c and d first or secondÞ ¼ ½Iðaa0; ba0; c ¼ 0; d ¼ 0Þ Iða ¼ 0; ba0; c ¼ 0; d ¼ 0Þ Finally, the cases where a is eliminated last (fourth) appear in the fourth column of the table above. There are six such cases but in each of these cases the contribution of a may be expressed as Cða=a eliminated fourth while b; c and d first; second or thirdÞ ¼ ½Iðaa0; b ¼ 0; c ¼ 0; d ¼ 0Þ Iða ¼ 0; b ¼ 0; c ¼ 0; d ¼ 0Þ


65

Taking all the 24 cases into account, we therefore conclude that the overall contribution of a to the indicator I may be expressed as CðaÞ ¼ fð6=24Þ½Iðaa0; ba0; ca0; da0Þ Iða ¼ 0; ba0; ca0; da0Þ þ ð2=24Þ½Iðaa0; b ¼ 0; ca0; da0Þ Iða ¼ 0; b ¼ 0; ca0; da0Þ þ ð2=24Þ½Iðaa0; ba0; c ¼ 0; da0Þ Iða ¼ 0; ba0; c ¼ 0; da0Þ þ ð2=24Þ½Iðaa0; ba0; ca0; d ¼ 0Þ Iða ¼ 0; ba0; ca0; d ¼ 0Þ þ ð2=24Þ½Iðaa0; b ¼ 0; c ¼ 0; da0Þ Iða ¼ 0; b ¼ 0; c ¼ 0; da0Þ þ ð2=24Þ½Iðaa0; b ¼ 0; ca0; d ¼ 0Þ Iða ¼ 0; b ¼ 0; ca0; d ¼ 0Þ þ ð2=24Þ½Iðaa0; ba0; c ¼ 0; d ¼ 0Þ Iða ¼ 0; ba0; c ¼ 0; d ¼ 0Þ þ ð6=24Þ½Iðaa0; b ¼ 0; c ¼ 0; d ¼ 0Þ Iða ¼ 0; b ¼ 0; c ¼ 0; d ¼ 0Þg

One can naturally derive in a similar way the overall contributions C(b) of b, C(c) of c, and C(d) of d to the value of the indicator I. Moreover, it is also easy to verify that CðaÞ þ CðbÞ þ CðcÞ þ CðdÞ ¼ Iðaa0; ba0; ca0; da0Þ.

References Bhattacharya, N., Mahalanobis, B. (1967), Regional disparities in consumption in India. Journal of the American Statistical Association 62, 143–161. Bourguignon, F. (1979), Decomposable income inequality measures. Econometrica 47, 901–920. Chakravarty, S.R., Majumder, A. (2001), Inequality, polarization and welfare: theory and applications. Australian Economic Papers 40, 1–13. Cowell, F.A. (1980), On the structure of additive inequality measures. Review of Economic Studies 47, 521–531. Cowell, F.A. (1984), The structure of American income inequality. Review of Income and Wealth 30, 351–375. Dagum, C. (1980), Inequality measures between distributions with applications. Econometrica 48, 1791–1803. Dagum, C. (1987), Measuring the economic affluence between populations of income receivers. Journal of Business and Economic Statistics 5, 5–12. Dagum, C. (1997), A new approach to the decomposition of the Gini income inequality ratio. Empirical Economics 22, 515–531. Deutsch, J., Hanoka, M., Silber, J. (2007), On the link between the concepts of Kurtosis and bipolarization. Economics Bulletin (36), 1–5.

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Deutsch, J., Silber, J. (1997), Gini’s transvariazione and the measurement of distance between distributions, Empirical Economics 22, 547–554. Esteban, J.-M., Ray, D. (1994), On the measurement of polarization. Econometrica 62, 819–852. Gini, C. (1959), Memorie de Metodologia Statistica: Volume Secondo – Transvariazione, Libreria Goliardica, Roma. Kendall, M.G., Stuart, A. (1969), The advanced theory of statistics. Charles Griffen, London. Lambert, P.J., Aronson, J.R. (1993), Inequality decomposition analysis and the Gini coefficient revisited. Economic Journal 103, 1221–1227. Mookherjee, D., Shorrocks, A.F. (1987), A decomposition of the trend in U.K. income inequality. Economic Journal 92, 886–902. Pyatt, G. (1976), On the interpretation and disaggregation of Gini coefficients. Economic Journal 86, 243–255. Sastry, D.V.S., Kelkar, U.R. (1994), Note on the decomposition of Gini inequality. Review of Economics and Statistics LXXVI, 584–585. Shorrocks, A.F. (1980), The class of additive decomposable inequality measures. Econometrica 48, 613–625. Shorrocks, A.F. (1984), Inequality decomposition by population subgroups. Econometrica 50, 1369–1385. Shorrocks, A.F. (1999), Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. University of Essex, Mimeo. Silber, J. (1989), Factor components, population subgroups and the computation of the Gini index of inequality. Review of Economics and Statistics 71, 107–115. Wang, Y.Q., Tsui, K.Y. (2000), Polarization orderings and new classes of polarization indices. Journal of Public Economic Theory 2, 349–363. Wolfson, M.C. (1994), When inequalities diverge. American Economic Review, Papers and Proceedings 84, 353–358. Wolfson, M.C. (1997), Divergent inequalities: theory and empirical results. Review of Income and Wealth 43, 401–421. Yitzhaki, S., Lerman, R.I. (1991), Income stratification and income inequality. Review of Income and Wealth 37, 313–329. Zhang, X., Kanbur, R. (2001), What difference do polarisation measures make? An application to China. Journal of Development Studies 37, 85–98.

CHAPTER 4

The Effects of School Quality in the Origin on the Payoff to Schooling for Immigrants Barry R. Chiswicka,b and Paul W. Millerc a

Department of Economics, University of Illinois at Chicago, IL 60607-7107, USA IZA-Institute for the Study of Labor, Bonn, Germany E-mail address: [email protected] c School of Economics and Finance, Curtin University, Perth, WA 6845, Australia E-mail address: [email protected] b

Abstract The payoff to schooling among the foreign born in the United States is only around one-half of the payoff for the native born. This paper examines whether this differential is related to the quality of the schooling immigrants acquired abroad. The paper uses the overeducation/required education/undereducation specification of the earnings equation to explore the transmission mechanism for the origin-country school-quality effects. It also assesses the empirical merits of two alternative measures of the quality of schooling undertaken abroad. The results suggest that a higher quality of schooling acquired abroad is associated with a higher payoff to schooling among immigrants in the US labor market. This higher payoff is associated with a higher payoff to correctly matched schooling in the United States, and a greater (in absolute value) penalty associated with years of undereducation. A set of predictions is presented to assess the relative importance of these channels, and the undereducation channel is shown to be the more influential factor. This channel is linked to greater positive selection in migration among those from countries with better quality schools. In other words, it is the impact of origin-country school quality on the immigrant selection process, rather than the quality of immigrants’ schooling per se, that is the major driver of the lower payoff to schooling among immigrants in the United States. Keywords: Immigrants, schooling, school quality, earnings, selectivity Jel classifications: I21, J24, J31, J61, F22



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Barry R. Chiswick and Paul W. Miller

1. Introduction Studies of immigrant economic adjustment have placed considerable emphasis on the less-than-perfect international transferability of immigrants’ human capital. Starting with Chiswick (1978), this has been linked to the lower payoff to schooling for immigrants than for the native born. Chiswick (1978, p. 919) concluded: The smaller partial effect of schooling on earnings in the United States is an important finding. y The smaller effect of preimmigration schooling may be ‘‘explained’’ by country-specific aspects of the knowledge acquired in school, by a lower quality of foreign schooling, or by the poorer information it provides employers who use schooling as a screen y. The weaker partial effect of schooling may in part reflect self-selection in migration in which only the most able and most highly motivated of those with little schooling migrate, while those with (or who subsequently acquire) higher levels of schooling came from a broader ability and motivation spectrum. Empirical assessment of this important finding has proceeded along a number of lines. Chiswick and Miller (2008) use insights from the overeducation/required education/undereducation (ORU) literature (Hartog, 2000) to assess the possible contribution of self-selection in migration and the less-than-perfect international transferability of immigrants’ human capital. This is done indirectly through linking these two aspects of the immigrant adjustment process to the patterns observed in the payoffs to overeducation and undereducation. Chiswick and Miller (2008, p. 1339) argue: ‘‘The analysis also suggests that the two related issues of selectivity in migration and the international transferability of skills are both relevant, but their relative importance will vary by country of origin and educational attainment.’’ Bratsberg and Terrell (2002) and Betts and Lofstrom (2000) provide direct evidence on the effect that characteristics of the immigrants’ country of origin might have on the payoff to schooling in the United States. Bratsberg and Terrell (2002) link the payoff to schooling that the foreign born receive in the United States to measures of the resources devoted to education (namely, the pupil–teacher ratio and relative expenditure per pupil in immigrants’ country of origin), a measure of the commitment to education (namely, years of compulsory education in the country of origin), and a number of other variables that cover differences in the transferability of immigrants’ schooling to the US labor market (e.g., English as an official language in the origin labor market). They report (p.193): that differences in the attributes of educational systems account for most of the variation in rates of return to education earned by immigrants applying their source-country education in the U.S. labor

Effects of School Quality in the Origin on Earnings

69

market. We find a particularly robust inverse relationship between the rate of return to education and the pupil–teacher ratio in primary schools in the source country, and similarly robust direct relationships between the rate of return and relative teacher wages and expenditures per pupil in the source country. Similar analyses by Betts and Lofstrom (2000, p. 102) led them to conclude: y the characteristics of the source country affect immigrants’ earnings substantially. Reductions in the pupil–teacher ratio and increases in the average level of educational attainment increase earnings of immigrants significantly, but only for the most highly educated workers y. GDP per capita affects earnings positively for all immigrants, although it is the least well educated immigrants for whom the effect is the largest. Sweetman (2004) extends this latter line of inquiry by focusing on an outcome measure, test scores from international standardized tests, rather than on input variables from the education production function. Thus, in his analysis of immigrant earnings in Canada, Sweetman relates the birthplace differences in the payoff to schooling to differences in the country-level average test scores compiled by Hanushek and Kimko (2000). Sweetman (2004) reports that the country of origin differences in the payoff to schooling are related to this measure of school quality, although the R2 in the country-level regressions (of less than 0.2) were much lower than those reported by Bratsberg and Terrell (2002) where multiple input variables were used (of up to 0.84).1 In this chapter we merge the approaches of Chiswick and Miller (2008) and Sweetman (2004). Thus we quantify birthplace differences in the payoff to schooling in the United States using both conventional and ORU models of earnings determination. These birthplace differentials are then related to measures of the quality of the immigrant source-country human capital provided by the OECD Programme for International Student Assessment (or PISA) and the Hanushek and Kimko (2000) data previously used by Sweetman (2004) in his analysis of immigrants’ earnings in Canada.2 1 Hanushek and Kimko (2000) impute the majority of their country scores using educational input variables, and hence utilizing both the country-level average test scores and input variables in a single estimating equation has little merit. 2 This relates standardized partial effects of education to standardized test scores. The partial effects of education are standardized in the sense noted by Bratsberg and Terrell (2002, p. 179) ‘‘because the index is constructed on the basis of returns to education in a single market economy, it supplies a productivity-based estimate of the quality of educational institutions in foreign countries.’’

70


The structure of this chapter is as follows. Section 2 provides a brief account of the methods that are employed in the statistical analysis. Section 3 reviews the PISA and Hanushek and Kimko (2000) data. Empirical findings are presented in Section 4. A summary and conclusion are provided in Section 5. 2. Methodology Analyses of the birthplace differentials in the payoff to schooling have estimated both the conventional schooling and experience earnings equation and the ORU earnings equation. The conventional earnings equation relates the natural logarithm of a measure of earnings (hourly, weekly, annual) to years of schooling (EDUC), years of labor market experience (EXP) and its square, and other variables that are generally held to affect earnings, such as marital status, official language skills, and location, and, among the foreign born, years since migration and citizenship. That is: ln Y i ¼ b0 þ b1 EDUCi þ þ ni

(1)

The ORU modification of this earnings equation disaggregates the measure of years of schooling into three terms, namely a term for the years of education which are usual or standard in the worker’s occupation, a term for any years of overeducation possessed by the worker, and a term for any years of undereducation. These terms for years of over- and undereducation are measured relative to the central tendency for education in the respondent’s occupation, which is what is referred to in the literature as the required, usual, or standard level of schooling. For simplicity, occupation is treated as exogenous. Specifically: ln Y i ¼ a0 þ a1 Over_Educi þ a2 Req_Educi þ a3 Under_Educi þ þ ui (2) where Over_Educ ¼ years of surplus or overeducation, Req_Educ ¼ the usual or reference years of education, Under_Educ ¼ years of deficit or undereducation, and EDUC ¼ Over_EducþReq_EducUnder_Educ. Note that for each individual, ‘‘Over_Educ’’ and ‘‘Under_Educ’’ cannot both be positive.3 Either one or both must be zero. There are various ways of compiling a measure of ‘‘Req_Educ’’ (see Hartog, 2000; Chiswick and Miller, 2008). The measure used below is the modal educational attainment of workers in each of the approximately 500 occupations identified in the 2000 US Census. 3

It will be apparent that the standard earnings equation in (1), ln Y i ¼ b0 þ b1 EDUCi þ þ ui , forces a1 ¼ a2 ¼ |a3|. As this condition does not hold, the ORU specification results in a higher R2 and a2Wb1.


71

When (1) and (2) are estimated on separate samples of the native born and foreign born, considerable interest had been focused on differences by nativity in the estimates of the payoff to schooling and the coefficients of the ORU variables. For the simple foreign-born/native-born dichotomy, the payoff to actual years of schooling for the foreign born is usually much less than the payoff to actual years of schooling for the native born. For example, in analyses of 2000 US Census data, Chiswick and Miller (2008) report that the payoff to schooling for the native born was 10.6 percent, while that for the foreign born was only 5.2 percent. They also show that this payoff varies appreciably by country of origin, being relatively high for immigrants from developed, English-speaking countries, and relatively low for immigrants from less developed, non-English-speaking countries. For example, the payoff to schooling was just 1.8 percent for immigrants from Mexico, 4.3 percent for immigrants from Cuba as well as those from Eastern Europe, but as high as 11 percent for immigrants from Canada. Chiswick and Miller (2008) also report that the payoffs to the ORU variables, though particularly the earnings effects of the undereducation and overeducation variables, also vary by country of origin. In the analyses that follow, these variations are linked to direct measures of the quality of schooling in the immigrants’ country of origin provided by the PISA and Hanushek and Kimko (2000) data. The country-level information on the quality of schooling is incorporated into the study of immigrants’ earnings using Card and Krueger’s (1992) two-step approach. This involves augmenting the usual regression model with birthplace-schooling interaction terms, and then relating the estimated birthplace differentials in the payoff to schooling to the PISA scores and Hanushek and Kimko’s (2000) human capital quality index in a second step or supplementary regression. The supplementary regressions may contain other country-level information, such as GDP per capita. This approach can be represented by two equations (for simplicity only the conventional schooling earnings equation and the PISA scores are considered here), namely: ln Y i ¼ b0 þ

J X

½b1j ðI j EDUCij Þ þ þ ni

i ¼ 1; . . . ; n

(3a)

j¼1

b1j ¼ a0 þ a1 PISAj þ þ Zj

j ¼ 1; . . . ; J

(3b)

where Ij is a vector of dichotomous variables with a value of 1 for each birthplace j, and zero otherwise, and b1j are the separate birthplace effects on the payoff to schooling. This model can be generalized through the inclusion of birthplace intercept shifts. That is, b0 can be replaced by PJ ½b I j¼1 0j j . The estimates in (3b) can be obtained using weighted least squares, where the weights are given by the sample sizes of workers from each country in the first-step regression, or the inverse of the variances of

72


the slope estimates in the first-step regression.4 Further details are provided in Section 5.

3. Country-level data Two measures of school quality are employed in the analyses that follow. The first is provided by the reading, mathematics, and science scores for countries in the PISA. The second is the human capital quality indices compiled by Hanushek and Kimko (2000). The PISA is an international standardized assessment, coordinated by the OECD, to measure the outcomes of education systems. This assessment mechanism is administered every three years (first conducted in 2000) to 15 year olds in schools of participating countries. Initially the assessment framework of the PISA covered performance only in reading, mathematics, and science.5 However, problem-solving skills, designed to assess cross-curriculum competencies, were also covered in the 2003 survey. The PISA covers both OECD (e.g., France, UK, Australia, and United States) and non-OECD (e.g., Brazil, Chile, Peru, and Thailand) countries. Further details are available from the PISA web site: www.pisa.oecd.org. The reading, mathematics, and science literacy scores from the 2000 PISA survey form the basis of the main set of analyses presented below. Reading literacy in the PISA is defined as the ability to understand, to use, and to reflect on written texts in order to fulfill one’s goals, to develop one’s knowledge and potential, and to use written information to function or participate effectively in complex modern societies. Mathematical literacy is defined in the PISA as the capacity to identify, understand, and engage in mathematics, as well as to use mathematical knowledge and skills in one’s life. These skills incorporate simple calculations, posing and solving mathematical problems in various situations, and being able to take a point of view and appreciate things expressed numerically. Scientific literacy is defined in the PISA as the capability to use scientific knowledge, to identify questions/issues, and to draw evidence-based 4

The use of weighted least squares in the second step mimics the more formal random parameters model, a single equation representation of which is: ln Y ij ¼ b0 þ a0 EDUCij þ a1 PISAj EDUCij þ m1j EDUCij þ nij The random parameters model can be estimated using maximum likelihood methods. 5 The PISA also collects information on a wide range of factors thought to have a bearing on student performance, namely: (i) characteristics of individual students (e.g., their home background and learning approach); (ii) characteristics of schools (e.g., school/classroom atmosphere and school resources); and (iii) characteristics of school systems (e.g., the degree to which individual schools are given autonomy within the education system).


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conclusions in order to understand and help make decisions about the natural world and human interactions with it. Table 1 lists information on the mean reading, mathematics, and science literacy scores by country from the 2000 PISA. This table also includes an average score for the OECD. This score is computed using a simple average of the scores for all OECD countries. These scores have been normalized so that the OECD average is 500. The mean reading score for the United States, at 504, is only slightly above the 500-benchmark average across the OECD countries in the survey. There is considerable variation in the reading scores, with the standard deviation of the scores in Table 1 being 54. The reading literacy scores range from below 400 (Peru has a score of 327, Albania 349, Indonesia 371, Macedonia 373, and Brazil 396) to values over 525 (Finland has a score of 546, Canada 534, New Zealand 529, Australia 528, and Ireland 527). The reading score for Mexico, which is the largest source region for immigrants in the United States, is a relatively low 422. The mathematics literacy score for the United States is 493, below the OECD average, while the score for Mexico is 387, which represents a relatively weaker position in mathematics than that reported for reading literacy. The mathematics scores listed in Table 1 are characterized by greater variation than is the case for the reading score: The lowest mathematics score is the 292 for Peru and the highest is Hong Kong’s 560. The range in the scores is thus 268 points, compared to the range of 219 points for reading literacy. Brazil also has a relatively low mathematics score (334), as does Indonesia (367). Countries other than Hong Kong with relatively high mathematics scores are Japan (557) and Korea (547). The standard deviation of the PISA mathematics scores across countries is 65, which is somewhat higher than the standard deviation of the PISA reading scores across countries of 54. There is, however, a very high correlation between the reading and mathematics scores, with the Pearson correlation coefficient between the values in Table 1 being 0.95. The science literacy scores range from Peru’s value of 333 through to the 552 for Korea. Other countries with relatively low scores are Brazil (375), Albania (376), Indonesia (393), and Argentina (396). Other countries with relatively high scores are Japan (550), Hong Kong (541), Finland (538), the UK (532), Canada (529), Australia (528), and New Zealand (528). Thus, the range for the science literacy scores is 219, which is the same as for the reading literacy scores. The standard deviation of the science literacy scores in Table 1, at 53, is also similar to that for the reading scores. The science literacy scores are highly correlated with each of the other measures, with pair-wise correlation coefficients of 0.97 in each instance. The science literacy score for the United States is 499, close to the OECD average. The science literacy score for Mexico is 422, the same distance from the OECD average as for the reading literacy score.

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Table 1.

Mean PISA scores, 2000

Country

Reading

Mathematics

Science

Albania Argentina Australia Austria Belgium Brazil Bulgaria Canada Chile Czech Republic Denmark Finland France Germany Greece Hong Kong Hungary Iceland Indonesia Ireland Israel Italy Japan Korea Latvia Liechtenstein Luxembourg FYR Macedonia Mexico New Zealand Norway Peru Poland Portugal Russian Federation Spain Sweden Switzerland Thailand United Kingdom United States OECD average

349 418 528 507 507 396 430 534 410 492 497 546 505 484 474 525 480 507 371 527 452 487 522 525 458 483 441 373 422 529 505 327 479 470 462 493 516 494 431 523 504 500

381 388 533 515 520 334 430 533 384 498 514 536 517 490 447 560 488 514 367 503 433 457 557 547 463 514 446 381 387 537 499 292 470 454 478 476 510 529 432 529 493 500

376 396 528 519 496 375 448 529 415 511 481 538 500 487 461 541 496 496 393 513 434 478 550 552 460 476 443 401 422 528 500 333 483 459 460 491 512 496 436 532 499 500

Source: Literacy skills for the world of tomorrow – further results from PISA 2000 (OECD and UNESCO Institute for Statistics, 2003).


75

These country data on student performance in reading, mathematics, and science are positively correlated with typical indicators of economic progress or educational status. For example, the correlation of the country test scores with GDP per capita is between 0.61 (science) and 0.68 (reading). The correlation of the country test scores with educational expenditure per student is between 0.70 and 0.79, for the subgroup of 29 countries for which the educational expenditure data are available. Note, however, that while these correlation coefficients are quite high, the correlations are far from perfect, suggesting that the average test scores may have information content on the school-quality differences across countries that varies from the information in the input variables used in previous studies.6 Hanushek and Kimko (2000) base their measure of human capital quality on six international tests of student achievement in mathematics and science undertaken between 1965 and 1991.7 A total of 26 performance series were collected, and converted to a common scale. Country averages were then obtained for the scores available for each country. Scores for 39 countries were compiled this way. Then these scores were related to a number of input variables, including the primary school enrollment rate, pupil–teacher ratio in primary school, and expenditure on education, and the estimates of this educational quality production function used to infer quality scores for a further 51 countries.8 The data for Hanushek and Kimko’s (2000) preferred human capital quality series are presented in Table 2. This table also contains information on whether the data for a particular country were imputed using the procedure described above. The mean score on the Hanushek and Kimko (2000) quality index is 45.18. There is considerable variation across countries in the scores. There are scores below 25 and scores above 65, and the standard deviation is 13.25. Countries with scores below 25 are Iran (18.26), Kuwait (22.50), Papua New Guinea (22.58), Bahrain (23.19), Chile (24.74), and Central Africa (24.77). Countries with scores above 65 are Singapore (72.13), Hong Kong (71.85), New Zealand (67.06), and Japan (65.50). Thus the score for the United States, at 46.77, is slightly above the overall mean.

6

Random measurement error could also result in the correlation coefficients being less than 1. Four of these tests were administered by the International Association for the Evaluation of Educational Achievement and two by the International Assessment of Educational Progress. 8 Hanushek and Kimko (2000) use the human capital quality variable in cross-country growth regressions. Estimation of models based only on countries with observed human capital quality indicators, and with the broader sample that includes countries where the variable is imputed, led Hanushek and Kimko (2000, p.1196) to conclude ‘‘The estimates using this augmented sample confirm the appropriateness of projection to the expanded set of countries.’’ They also compare a number of their imputed scores with evidence from independent tests, and again confirm the appropriateness of the imputation procedure. 7

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Table 2.

Hanushek and Kimko’s (2000) human capital quality index

Country

Imputed score

Score

Country

Imputed score

Score

Algeria Argentina Australia Austria Bahrain Barbados Belgium Bolivia Botswana Brazil Cameroon Canada Republic of Central Africa Chile China Colombia Congo Costa Rica Cyprus Denmark Dominican Republic Ecuador Egypt El Salvador Fiji Finland France West Germany Ghana Greece Guyana Honduras Hong Kong Hungary

| |

28.06 48.50 59.04 56.61 23.19 59.80 57.08 27.47 31.71 36.60 42.36 54.58 24.77

Kenya Republic of Korea Kuwait Lesotho Luxembourg Malaysia Malta Mauritius Mexico Mozambique Netherlands New Zealand Nicaragua

|

29.73 58.55 22.50 51.95 44.49 54.29 57.14 54.95 37.24 27.94 54.52 67.06 27.30

24.74 64.42 37.87 50.90 46.15 46.24 61.76 39.34 38.99 26.43 26.21 58.10 59.55 56.00 48.68 25.58 50.88 51.49 28.59 71.85 61.23

Nigeria Norway Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Singapore South Africa Spain Sri Lanka Swaziland Sweden Switzerland Syria Taiwan Thailand Togo Trinidad and Tobago Tunisia Turkey Uruguay UK USA USSR Venezuela Yugoslavia Zaire Zambia Zimbabwe

Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan

| | | | | | |

| | | | | | | | | |

| | | |

| | |

|

51.20 20.80 42.99 18.26 27.50 50.20 54.46 49.41 48.62 65.50 42.28

Source: Hanushek and Kimko (2000); Table C.1.

| | | | | |

|

| | | |

| |

|

| | | | |

| | | | |

38.90 64.56 46.78 22.58 39.96 41.18 33.54 64.37 44.22 72.13 51.30 51.92 42.57 40.26 57.43 61.37 30.23 56.31 46.26 32.69 46.43 40.50 39.72 52.27 62.52 46.77 54.65 39.08 53.97 33.53 36.61 39.64

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Table 3.

Correlation coefficients between 2000 PISA scores and PISA scores for 2003 and 2006

Score for 2000

Score for 2003

Score for 2006

Reading Mathematics Science

0.955 0.979 0.948

0.927 0.970 0.943

Note: Correlations based on 29 observations for both the 2000–2003 and 2000–2006 comparisons.

The score for Mexico, at 37.24, is about one-half of a standard deviation below the mean. The Hanushek and Kimko (2000) quality index, being based on standardized tests undertaken between 1965 and 1991, appear to have an advantage over the PISA scores for 2000 in that they relate to a period when many of the immigrants in the US labor market in 2000 would have been enrolled in school in their country of origin. The extent of this advantage will depend on the magnitude of the across-country variation in the intertemporal changes in school quality. Where such variation is modest, the PISA data might be preferred, as these data relate to single tests for a specific age group, whereas the Hanushek and Kimko (2000) data are averages for a number of age groups, test types, and years of test assessment. There are two pieces of evidence that may be advanced on this. First, PISA scores are also available for 2003 and 2006, and one can therefore look at the relatedness of the scores for 2000 and those for these later years, although this is a short time span. Correlation coefficients between the PISA scores for 2000 and 2003/2006 (listed in Table 3) indicate that there is a very high degree of stability in the PISA scores across time, at least for the six years covered in this presentation. Second, Hanushek and Kimko (2000, Fig. 1) present a visual display of test scores for various countries across time, ranging from 1965 to 1991 (a time span of 26 years). This also conveys the clear impression of stability in the relative standing of various countries with respect to student achievement. As Hanushek and Kimko (2000, p. 1186) state in relation to their Fig. 1: The test performance in Fig. 1 provides some evidence about the stability (over time) of scores. The United States and United Kingdom participate in all six testing programs. Throughout the period, the United Kingdom consistently performs a little better than the United States. Further, with a few exceptions, countries that outperform either the United States or United Kingdom on one test also tend to do so when they participate in other tests and vice versa.

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There is no a priori way of evaluating the relative merits of the two data series, and hence both are used in the analyses below. There are 32 countries for which there are both PISA scores and a value for the Hanushek and Kimko (2000) human capital quality index.9 The simple correlation coefficients between the Hanushek and Kimko (2000) index (covering 1965 to 1991) and the PISA reading, mathematics, and science scores (for 2000) for this group of countries are 0.774, 0.765, and 0.777, respectively. This, like the correlations for the PISA scores for 2000, 2003, and 2006, suggests only modest across-country variation in intertemporal changes in school quality. In other words, the standardized tests of 15 year olds in 2000 should provide an extremely useful measure of across-country differences in student achievement as far back as 1965. To minimize any unintended consequence associated with the use of the contemporary school-quality data, they are entered into the second step of the model along with per capita GDP data for each country. These per capita GDP information are defined with respect to 1980. The use of a 20-year lag in this analysis follows Bratsberg and Terrell (2002. p. 182) who argue ‘‘We lag the educational quality data by 20 years to better capture differences in school quality at the time immigrants undertook their schooling y.’’10 The changes in the estimated effects of the PISA variables as the per capita GDP data are included in the model will inform on whether the contemporary PISA scores are a proxy for origin-country characteristics linked to school quality 20 years ago. Finally, as a further way of ascertaining the nature of the effects captured by the PISA data for 2000, the sample used in the statistical analysis can be restricted to the one-quarter (or other fraction) of immigrants with the most recent exposure to the origin-country school system.11 Results from this extension are discussed in the following section. 4. Empirical assessment The estimating equation used in the first step of the assessment of the reasons behind the differences by country of origin in the payoff to schooling in the United States is a standard human capital earnings equation ((1) above). In particular, using data from the 2000 US Census, the natural logarithm of annual earnings in 1999 for males aged 25–64 who had nonzero earnings in that year is related to educational attainment, 9

Only nine of these countries have imputed values in the Hanushek and Kimko (2000) index. Betts and Lofstrom (2000), who use a single-equation approach in which origin-country information is interacted with the immigrant’s preimmigration level of education, reference their variables to the time when the immigrant would have been 10 years old. 11 This sample selection is based on the gap between the immigrant’s age and an assumed school-leaving age associated with their highest grade of secondary or primary schooling. 10


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potential labor market experience (computed using the proxy Age Years of Schooling 6), the natural logarithm of weeks worked, dummy variables for married (spouse present), race, US armed forces veteran status, resident of a metropolitan area, resident of a southern state, and English language skills, and, among the foreign born, variables for duration of residence in the United States and citizenship. The data are described in detail in Chiswick and Miller (2010). For the foreign born, the main set of analyses are based on immigrants aged 18 or more at the time of arrival in the United States. This is to ensure that the individuals will typically have completed secondary school in their country of origin, as this is the level that the school-quality data refer to. Definitions of the variables are presented in Appendix A. The Card and Krueger (1992) two-step approach was applied using both the PISA scores in Table 1 and the larger number of countries (73) with information on the Hanushek and Kimko (2000) index (Table 2). These separate estimates suggested that the PISA scores had far greater information content for understanding the variation in the payoff to schooling that immigrants receive in the United States. For example, the R2 in the second step of the Card and Krueger (1992) two-step approach in aggregate-level models based on the Hanushek and Kimko (2000) data were very low: they were even lower than the values reported by Sweetman (2004), and only one-eighth of the R2 in some of the models based on the PISA scores. However, when the analyses were based on the smaller group of 32 countries for which there are both PISA and Hanushek and Kimko scores, the results from the alternative measures are comparable12: in models where the PISA scores are statistically insignificant, the Hanushek and Kimko (2000) index is also statistically insignificant. Where the alternative origin school-quality measures are both statistically significant, the coefficients are of the same sign. Moreover, the relative magnitudes of the estimated effects on the various payoffs (to actual years of schooling, years of required schooling, years of undereducation, and years of overeducation) are similar, regardless of whether the analysis is based on the Hanushek and Kimko (2000) index or the PISA reading, mathematics, or science literacy scores. This similarity in findings presumably follows from the high simple correlation (above 0.76) between the alternative measures noted in Section 3. Given the similarity in statistical findings, any preference between the measures can be made on other grounds. As the standardized PISA scores 12

The difference in the results from analyses for this smaller group of countries and from analyses for all countries with Hanushek and Kimko scores may be associated with either the greater prevalence of imputed values of the Hanushek and Kimko index when using the larger sample (see footnote 9), or simply different roles for origin-country school quality for the purged countries.

80


are for specific tests for 15 year olds in 2000, whereas the Hanushek and Kimko index is based on results from different tests, conducted on various age groups, and in various years, and the majority of which were imputed, ease of interpretation suggests a preference for the PISA scores. The remainder of this chapter, therefore, is based on the PISA scores. Selected findings from the analysis using the Hanushek and Kimko (2000) data are reported in Appendix B.

4.1. Aggregate-level analyses There is information in Table 1 on the PISA scores for 40 countries other than the United States. However, the sample of males aged 25–64 years who worked in the United States during 1999 does not contain any immigrants from Iceland, Liechtenstein, or Luxembourg. Hence the analyses below are based on the remaining 37 countries. Only findings from the second step of the model (i.e., estimation of (3b)) are presented here. There are two sets of results in Table 4 for each PISA score (Reading, Mathematics, Science). The first, in column (i), is based on the payoff to schooling across birthplace groups without country fixed effects in the first-step regression (i.e., the intercept is simply b0). The second, in column (ii), is for the analogous set of analyses where the first-step model takes P account of birthplace fixed effects (i.e., the intercept is generalized to Jj¼1 ½b0j I j ). Table 4. Variable

Estimates from second step of two-step model, aggregate-level analyses Reading literacy Mathematics literacy Science literacy (i)

Constant

(ii)

(i)

0.100 0.173 0.079 (6.28) (4.73) (8.53) PISA/100 0.018 0.029 0.014 (4.42) (3.07) (5.45) 1980 GDP per capita/10,000 0.018 0.031 0.018 (3.88) (2.88) (4.42) Country fixed effects in first step No Yes No R2 0.744 0.598 0.785 Sample size 37 37 37

(ii)

(i)

(ii)

0.145 (6.81) 0.024 (4.07) 0.029 (3.04) Yes 0.655 37

0.091 0.162 (6.44) (5.05) 0.016 0.026 (4.36) (3.16) 0.020 0.034 (4.68) (3.43) No Yes 0.741 0.604 37 37

Notes: Model (i) has a single intercept, b0, in the first-step regression. Model (ii) is based on the PJ flexible specification in the first-step regression, where the intercepts are given by j¼1 ½b0j I j . The dependent variable for each model is the estimated partial effects of education for the countries for which there are PISA scores. Absolute values of t-statistics in parentheses.


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The precision of the estimates of the payoff to schooling will vary across countries. Therefore, weighted least squares is used to compute the secondstep equations, where the weights are the number of workers for each country of origin in the first-step regressions. Hence, important immigrant source countries such as Mexico, Canada, and Korea are assigned relatively more weight than minor source countries such as Denmark and Latvia. An alternative set of weights that was investigated involved the inverse of the variances of the estimates of the birthplace interaction terms in the first step. This alternative gives more weight to birthplace effects that are precisely estimated (e.g., for Mexico, Korea, and Russia) and less weight to birthplace effects that are estimated less precisely (e.g., for Belgium, Denmark, and New Zealand). The two sets of weights are highly correlated (correlation coefficient of 0.983 for the column (i) specification) and so similar results emerge. For simplicity, only those using the country sample sizes are reported here. For the first-step regression for specification (i), the payoffs to schooling for the 37 countries that are the focus of this analysis range from 2.7 percent (for Mexico) to 7.9 percent (for Japan), a range of 5.2 percentage points. The standard deviation of the differentials in the payoff to schooling across the 37 countries is 1.4 percent. According to the Table 4 column (i) results, the birthplace differences in the payoff to schooling are positively associated with both the country-level average PISA scores and with 1980 GDP per capita. Up to 79 percent of the variation in the payoffs to schooling is accounted for by the two regressors, with the level of explanation being highest for mathematical literacy and lowest for science literacy. In alternative estimations (not shown here), the 1980 per capita GDP variable was omitted from the model: this change to the model was associated with an increase in the partial effects of the PISA variables by between 50 and 56 percent. This suggests that the effects of the PISA variables in Table 4 are net of the effects of the level of economic development in the country of origin when many immigrants would have been attending school. Each 100-point increase in the PISA scores is associated with between 1.4 and 1.8 percentage points increase in the payoff to schooling in the United States. Hence a 200-point change in the PISA, which is about the range in the data, is associated with around 3.5 percentage points increase in the payoff to schooling. These relationships are described in Fig. 1 in the case of the PISA reading literacy scores.13 In the column (ii) results in Table 4, the first-step regression has been augmented with 37 country fixed effects. This less-restrictive specification is associated with a greater spread in the estimated payoffs to schooling. 13 Given the broad similarity of the findings for reading, mathematics, and science, Fig. 1 contains information only on the relationship across countries between the payoff to schooling and reading literacy.

82

Barry R. Chiswick and Paul W. Miller (a) Without country fixed effects in first-step regression 0.09

Returns to Schooling

0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 300

350

400

450

500

550

600

Reading Literacy (b) With country fixed effects in first-step regression 0.14

Returns to Schooling

0.12 0.1 0.08 0.06 0.04 0.02 0 300

350

400

450

500

550

600

Reading Literacy

Fig. 1. Relationship between the payoff to schooling and PISA reading literacy. (a) Without country fixed effects in first-step regression. (b) With country fixed effects in first-step regression. For example, the payoff for Mexico is now estimated to be 1.6 percent (compared with 2.7 percent with the common intercept) and that for Japan 8.8 percent (compared with 7.9 percent with the common intercept). The standard deviation of the estimates of the payoff to schooling is 3.2, over two times that when it is assumed that there is a common intercept, as in column (i). While this greater variation in the dependent variable in the second-step regression is associated with a smaller explanatory power of


83

the model, the PISA scores and 1980 per capita GDP variable both remain highly significant, with partial effects that are – following the greater range in the dependent variable – appreciably greater than under the first specification. Specifically, the effect of changes in the PISA scores range from 0.024 to 0.029, with the smallest and largest impacts again being associated with mathematics and reading literacy, respectively. The first-step regression results were also derived in an alternative way to examine the robustness of the findings. Thus, the models were estimated without the approximately 51 percent of the data where there are no PISA scores. This change in the sample was also associated with a widening of the range in the estimated payoffs to schooling. It was also associated with a reduction in the explanatory power of the second step of the model compared to the results in Table 4, of around 15 percentage points for specification (i) and by 2 to 4 percentage points for specification (ii). The partial effects of the PISA variables (not reported here) following this change to the sample, however, were larger than in the benchmark models of Table 4. The analyses were also conducted on subsamples formed using the years since the immigrant would have attended school in the country of origin. Two subsamples were formed: the 25 percent of the original sample with the most recent exposure to the origin-country school system, and the remaining 75 percent. Some of the findings from this disaggregated analysis (particularly those based on the column (i) specification in Table 4) showed that the models had greater explanatory power for immigrants with the most recent exposure to the origin-country school system, whereas other results from the disaggregated analysis (those based on the column (ii) specification in Table 4) were contrary to this. This ambiguity presumably follows from the PISA scores offering a very useful measure of the across-country differences in school quality up to four decades ago. One issue that needs to be addressed in this preliminary set of aggregatelevel analyses relates to the role of Mexico. Mexico is the dominant source of immigrants in the United States. In the sample of adult males used above, 29.2 percent are from Mexico. Among immigrants from countries where there are PISA scores, 60.7 percent of the sample is from Mexico. Accordingly, the analyses can be dominated by this group, particularly where the second-step results are weighted by the size of the birthplace groups.14 There are various ways this issue can be assessed, for example, through conducting the analyses of Table 4 for the 36 countries other than Mexico, or undertaking the analyses without weights (so that Mexico counts the same as any other country). The latter approach is adopted here, as this will also provide the opportunity to illustrate the impact that

14

Antecol et al. (2003) have previously drawn attention to the important role that immigrants from Mexico can have in aggregate-level analyses for the foreign born.

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Table 5. Variable


Estimates from second step of two-step model, aggregate-level analyses, without weights Reading literacy Mathematics literacy Science literacy (i)

(ii)

(i)

Constant

0.047 0.110 0.036 (3.55) (2.31) (3.48) PISA/100 0.010 0.023 0.007 (3.01) (1.97) (2.82) 1980 GDP per capita/10,000 0.012 0.008 0.013 (3.92) (0.73) (4.41) Country fixed effects in first step No Yes No R2 Sample size

0.699 37

0.268 37

0.691 37

(ii)

(i)

0.087 (2.32) 0.018 (1.90) 0.010 (0.96) Yes

0.040 0.106 (3.15) (2.32) 0.008 0.021 (2.59) (1.97) 0.014 0.011 (4.84) (1.11) No Yes

0.262 37

0.681 37

(ii)

0.267 37


weighting has on the analyses. Table 5 replicates Table 4 for this set of analyses. The results in Table 5 are broadly the same as those reported in Table 4. The PISA scores remain as a statistically significant determinant of the across-country variation in the payoff to schooling among immigrants in the United States. The 1980 GDP per capita variable, however, while having a positive impact in each equation, is significant only for the first-step equation without country fixed effects; that is, the equation has a common intercept for all countries. In the model where the across-country variation in the payoff to schooling is obtained from the first-step P equation with country fixed effects (i.e., the intercept is generalized to Jj¼1 ½b0j I j ), these fixed effects apparently capture all of the influence of the different stages of economic development of the origin on the earnings of immigrants in the United States (that is, this effect applies to immigrants of all levels of schooling). The analyses were also undertaken with the estimating equation for the second step augmented with a dummy variable for Mexico. This enables the distance of the data for Mexico from the regression line to be assessed. In these analyses, whether conducted using the PISA scores or the Hanushek and Kimko (2000) index, the variable for Mexico was associated with a significant negative coefficient, of around 2 percentage points. In other words, given the quality of the schooling in Mexico (as measured in this study), and the relative level of economic development of Mexico, immigrants from Mexico would need to gain an extra 2 percentage points payoff to their education in the US labor market (that is, it should be around five percent rather than three percent) in order to conform to the


85

estimated pattern for other countries. The 2-percentage-point shortfall in the payoff to schooling for immigrants from Mexico may be associated with the illegal status in the United States of many from that country. These preliminary results provide strong support for the hypothesis that origin-country school quality, as proxied by the PISA scores, affects the payoff to schooling for immigrants in the United States. The evidence derived using the Hanushek and Kimko (2000) index, reported in Appendix B, reinforced this conclusion. This suggests that the lower payoff to schooling for immigrants in the United States reflects, in part, a lower quality of education acquired in the country of origin.

4.2. The role of age at migration Sweetman (2004) conducts analyses of the links between indicators of origin-country school quality and the payoff to immigrants’ schooling in Canada on subsamples defined using age at migration. Sweetman (2004, p. 30) argues ‘‘If it is the quality of the education system that is driving these results, and not other factors, such as discrimination, then immigrants educated primarily in the Canadian system should not be affected by the source country school quality index.’’ He shows that the payoffs to schooling are greatest for those educated primarily in Canada, and smallest for the foreign-born educated abroad. The payoffs to schooling for those with a mix of preimmigration and postimmigration schooling were of intermediate size. Origin-country school quality had no impact on the payoffs to schooling in Canada among immigrants educated primarily in Canada, whereas the payoff to schooling in Canada for immigrants mostly educated abroad was positively related to origin-country school quality.15 In the current study the analyses were repeated for several child immigrant groups. Selected results by age at immigration are presented in Table 6. The first set of results presented in this table is the benchmark set of findings for adult immigrants, from Tables 4 and 5. The other sets of results are for the two samples of child immigrants, namely those who arrived before their tenth birthday, and the more restrictive definition of those who arrived before their sixth birthday. Two sets of analyses are presented in this table: the first (on the left-hand side) is based on the second-step regression models that are weighted according to the number of workers in each country of origin, and the second (on the right-hand side) is from unweighted regressions. The weighted regressions (where considerable weight is given to Mexico) indicate that the school-quality effects are at least as strong 15

Bratsberg and Terrell (2002) focus only on those who were likely to have obtained their education abroad.

86

Table 6.


Estimate of PISA effect from second step of two-step model, by age at migration, weighted and unweighted regressions

Variable

With weights Reading Mathematics (i) (ii)

Without weights Science (iii)

Age at migration 18 or more (from Tables 4 and 5) PISA/100 0.029 0.024 0.026 (3.07) (4.07) (3.16) R2 Age at migrationr10 PISA/100 R2 Age at migrationr5 PISA/100 R2 Sample size

Reading (iv)

Mathematics (v)

Science (vi)

0.023 (1.97)

0.018 (1.90)

0.021 (1.97)

0.598

0.655

0.604

0.268

0.262

0.267

0.037 (3.25)

0.031 (4.48)

0.034 (3.68)

0.010 (0.81)

0.013 (1.23)

0.016 (1.36)

0.653

0.714

0.675

0.064

0.087

0.095

0.024 (1.95)

0.023 (2.90)

0.023 (2.25)

0.004 (0.27)

0.005 (0.39)

0.009 (0.59)

0.594 37

0.638 37

0.607 37

0.055 37

0.058 37

0.063 37

Notes: The first-step regression is the flexible specification where the intercepts are given PJ by j¼1 ½b0j I j . Second-step regression also includes 1980 GDP per capita variable. The dependent variable for each model is the estimated partial effects of education for the countries for which there are PISA scores. Absolute values of t-statistics in parentheses.

among child immigrants as they are among adult immigrants (Table 6, columns i, ii, iii). This suggests that factors other than pure school-quality effects must also be playing a role. Below, we consider one of these, selectivity in migration among less-well educated immigrants (many of whom will be from Mexico). In the unweighted regressions, however, the PISA variables are statistically insignificant (Table 6, columns iv to vi). The PISA variable is also insignificant for these two ‘‘child immigrant’’ samples if weighted regressions are estimated on the 36 countries other than Mexico. That is, when Mexico is excluded from the sample, there is evidence that school-quality effects on the payoff to schooling dissipate as younger age-at-migration cohorts are considered. Thus, school quality in the origin is not relevant for the payoff to schooling in the United States for those who migrate as young children and therefore have little or no exposure to school quality in the origin. 4.3. Reference education, overeducation and undereducation, and PISA scores It has been shown here that immigrants from countries that perform poorly on standardized tests are associated with lower payoffs to schooling


87

in the United States. Chiswick and Miller (2008) link the low payoff to schooling among the foreign born in the United States to a lower payoff to immigrants’ schooling that is surplus to the standard in their occupations, and to a lower penalty to years of undereducation among immigrants compared to the native born. This section examines the links between the returns to immigrants’ overeducation and undereducation and school quality, as measured by the PISA scores. Chiswick and Miller (2008) show that the payoff to schooling in the conventional earnings equation can be linked to the estimated effects on earnings of the education variables in the ORU model. In particular, greater estimated partial effects of the reference education and overeducation variables are shown to be associated with a higher payoff to education in the conventional earnings equation. A more negative earnings effect of undereducation is also associated with a higher payoff to schooling in the conventional human capital earnings model. To quantify the link between the ORU and conventional earnings equations in the current study of origin-country school-quality effects, it is first necessary to estimate the ORU model (i.e., estimate (2) as the first step in the two-step approach). Then the analyses reported above need to be repeated, replacing in the second step the payoff to schooling from the conventional (first step) earnings function with the payoffs to ORU from the ORU specification of the earnings function. Table 7 presents results from the second step of the model, where the variations across birthplaces in the payoffs to years of overeducation are related to the PISA scores. The structure of this table is the same as Table 4. These results show that the payoffs to overeducation are not affected by the quality of the origin-country schooling, as measured by the PISA scores.16 The insignificance of this relationship implies that years of surplus schooling among immigrants are relatively poorly rewarded in the US labor market, irrespective of the quality of the origin-country schooling system. Perhaps this arises because most of the years of surplus schooling were done at an age older than the age at which the PISA scores are measured. Years of surplus schooling among the native born are also poorly rewarded in the US labor market (see Chiswick and Miller, 2008).17 Table 8 presents information on the links between the payoff to the reference levels of schooling and the quality of immigrants’ origin-country schooling, as indexed by the PISA variables. In this instance the estimated

16 As shown in Appendix B, the payoffs to years of overeducation are also not related to the Hanushek and Kimko (2000) index, or to the PISA scores in an alternative sample considered in Appendix B. 17 In Chiswick and Miller’s (2008) aggregate-level analysis, the payoff to years of surplus schooling was 5.6 percent for the native born and 4.4 percent for the foreign born. For each birthplace group the payoff to years of schooling that were usual in the occupation of employment was around 15.5 percent.

88

Table 7. Variable


Estimates from second step of two-step model, aggregate-level analyses, focus on overeducation Reading literacy Mathematics literacy Science literacy (i)

(ii)

(i)

Constant


0.415 37

0.370 37

0.416 37

(ii)

(i)

0.012 (0.71) 0.005 (0.96) 0.031 (4.06) Yes

0.028 0.000 (1.32) (0.02) 0.003 0.007 (0.46) (1.21) 0.024 0.031 (3.68) (4.29) No Yes

0.370 37

0.412 37

(ii)

0.380 37

Notes: Model (i) has a single intercept, b0, in the first-step regression. Model (ii) is based on the flexible specification in the first-step regression, where the intercepts are given by PJ j¼1 ½b0j I j . The dependent variable for each model is the estimated partial effects of education for the countries for which there are PISA scores. Absolute values of t-statistics in parentheses.

Table 8. Variable

Estimates from second step of two-step model, aggregate-level analyses, focus on required education Reading literacy Mathematics literacy Science literacy (i)

(ii)

(i)

Constant

0.063 0.137 0.048 (4.88) (3.62) (6.24) PISA/100 0.012 0.014 0.009 (3.50) (1.42) (4.07) 1980 GDP per capita/10,000 0.013 0.033 0.013 (3.34) (2.98) (3.69) Country fixed effects in first step No Yes No R2 Sample Size

0.664 37

0.456 37

0.693 37

(ii)

(i)

0.127 (5.48) 0.013 (1.96) 0.031 (3.01) Yes

0.057 0.129 (4.93) (3.88) 0.010 0.012 (3.39) (1.39) 0.014 0.034 (4.01) (3.38) No Yes

0.483 37

0.659 37

(ii)

0.455 37


89


Table 9. Variable

Estimates from second step of two-step model, aggregate-level analyses, focus on undereducation Reading literacy Mathematics literacy Science literacy (i)

(ii)

(i)

Constant


0.238 37

0.265 37

0.292 37

(ii)

(i)

0.067 (3.52) 0.014 (2.68) 0.006 (0.76) Yes

0.078 0.079 (2.75) (2.87) 0.014 0.016 (2.00) (2.24) 0.009 0.009 (1.09) (1.07) No Yes

0.323 37

0.255 37

(ii)

0.285 37

Notes: Model (i) has a single intercept, b0, in the first-step regression. Model (ii) is based on the P flexible specification in the first-step regression, where the intercepts are given by Jj¼1 ½b0j I j . The dependent variable for each model is the estimated partial effects of education for the countries for which there are PISA scores. Absolute values of t-statistics in parentheses.

partial effect of the PISA scores on the differentials in the payoffs to schooling is significant in the majority of the models.18 Hence, a 200-point increase in a specific PISA score is associated with an increase in the payoff to the reference years of schooling of up to 2.6 percentage points. The partial effects in Table 8 are, however, smaller than the partial effects in Table 4 for actual years of schooling. Recall that the payoff to a year of reference schooling is a payoff to the acquisition of that year of schooling and to moving to an occupation where the extra year of schooling is the usual or reference level. The relatively smaller partial effects in Table 8 suggest that the effect on earnings of the occupational mobility is hardly enhanced by the quality of schooling acquired abroad. Table 9 presents the estimated relationships between the wage effects of years of undereducation across birthplaces and the quality of the schooling acquired abroad. When interpreting these effects it is useful to bear in mind what the negative estimated coefficient on the undereducation variable means. It indicates that a worker who obtains a job in an occupation that has a usual or reference level of education greater than the worker’s actual level of schooling receives a lower wage than the workers in the same occupation who have the usual or reference level of education.

18

The Hanushek and Kimko (2000) index is a statistically significant determinant of the variation across countries in the payoff to required years of education (see Appendix B). The PISA scores are also statistically significant in each of the models of the determination of the variation in the payoff to years of required education in the alternative sample considered in Appendix B.

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Table 9 results indicate that the wage disadvantage to these undereducated workers rises with the PISA score.19 That is, a foreign-born worker who obtained his schooling abroad in a lower quality school system has a smaller earnings disadvantage than a foreign-born worker who obtained his schooling abroad in a higher quality school system. Undereducated native-born workers are shown by Chiswick and Miller (2008) to have a greater earnings disadvantage than the comparable foreign born.20 Hence, Table 9 results indicate that undereducated foreignborn workers educated abroad in a higher quality school system are more like undereducated native-born workers than are undereducated foreignborn workers educated abroad in a lower quality school system. Chiswick and Miller (2008) link the differential between the native born and foreign born in the earnings effects of undereducation to self-selection in immigration. This argument drew upon Chiswick (1978, p. 912), who suggested that ‘‘Suppose that among those with little schooling only the most able and most highly motivated migrate, while among those with high levels of schooling the immigrants are drawn more widely from the ability distribution.’’ The findings here in relation to the quality of schooling suggest a generalization of Chiswick’s (1978) argument, to ‘‘Suppose that among those from countries with a poorer quality of school system only the most able and most highly motivated migrate, while among those from countries with a higher quality school system the immigrants are drawn more widely from the ability distribution.’’ The variations in the earnings effects of each of the ORU variables are related to the PISA scores in ways that will lead to the payoff to actual years of schooling being positively related to the PISA scores. The relative importance of the relationships summarized in Tables 7 to 9 in this regard can be assessed using a method based on Chiswick and Miller (2008). This involves using the estimates from the ORU model to predict earnings for workers, and then relating the means of these predictions at each level of actual education to the years of actual education in a linear regression model, weighted by the number of workers at each level of education. The coefficient on the years of actual education variable in this later regression is an estimate of the conventional payoff to schooling. The estimated earnings effects of the ORU variables in Tables 7 to 9 are first evaluated at values of the PISA scores that generate an implied payoff to schooling that is the same as the actual payoff for the foreign born who migrated at age 18 or over (4.9 percent).21 The estimated effect of the ORU 19 Similar findings arise when the Hanushek and Kimko (2000) index is used, or the PISA scores are applied in alternative samples – see Appendix B. 20 The estimated partial effects of the undereducation variable in the aggregate-level analysis in Chiswick and Miller (2008) were 0.067 for the native born and 0.021 for the foreign born. 21 The payoff to schooling for all the foreign born (i.e., including those who immigrated before age 18) is 5.2 percent (see Chiswick and Miller, 2008).

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Table 10.

Implied payoffs to schooling, adjusting for effects of ORU variables at various PISA reading scores

i. Native born Foreign born: ii. No adjustment iii. Adjustment only to the earnings effects of reference education for the foreign born iv. Adjustment only to the earnings effects of overeducation for the foreign born v. Adjustment only to the earnings effects of undereducation for the foreign born vi. Adjustment to all three ORU variables

100 PISA points

Benchmark

þ100 PISA points

10.5

10.5

10.5

– 4.6

4.9 4.9

– 5.2

5.0

4.9

4.8

3.9

4.9

5.9

3.7

4.9

6.1

variables can then be evaluated at other values of the PISA scores (e.g., benchmark 7100 points, which will yield a 200-points range, similar to that in the PISA scores) and the simulation exercise described above repeated to assess how the PISA scores impact the payoff to schooling in the conventional earnings equation through each of the ORU variables. Table 10 presents findings from this analysis based on the PISA reading scores. The first row of Table 10 contains the implied payoff to schooling for the native born. This does not vary with the PISA score, and so is recorded at 10.5 percent in each column. The second row presents the implied payoff to schooling for the foreign born. This has been computed from the predictions of the ORU model, calibrated to produce the actual payoff to schooling for this birthplace group of 4.9 percent. The payoff to schooling for the foreign born who immigrated at age 18 or older is thus less than one-half that for the native born. The third row of Table 10 explores the impact of variation in the PISA scores through the estimated effects of the reference years of education in the ORU model. A change up (down) in the PISA reading score of 100 points is associated with an increase (decrease) of around 0.3 percentage point in the payoff to schooling. As shown in the fourth row of the table, adjustment for the estimated effects of the overeducation variable has minimal effect on the payoff to schooling (the effect is just 0.1 percentage point). However, with the adjustment for undereducation, as seen from the fifth row of the table, a change up (down) in the PISA reading score of 100 points is associated with an increase (decrease) in the payoff to schooling of about 1 full percentage point. The far greater effect of the PISA scores via the undereducation variables is consistent with Chiswick and Miller’s (2008) inference that the earnings effects of undereducation are

92


the more important contributor to the lower payoff to schooling for immigrants in the US labor market. This effect is linked in their analysis to more intense selection in migration among those with lower levels of schooling. In the final row of Table 10 the roles of changes in the PISA scores via all the ORU variables are considered simultaneously. These show that at 100 higher PISA scores the implied payoff to schooling is 6.1 percent compared to 4.9 percent at the immigrant benchmark, but still less than the 10.5 percent for the native born. Thus, these findings show that the quality of schooling acquired abroad matters to the payoff to the schooling that immigrants receive in the United States. However, while some of the effects appear to operate in the expected way – by increasing the payoff to correctly matched schooling – the most important effect appears to operate by altering the selectivity of immigrants at low levels of schooling where undereducation is relatively more important. Hence, immigrants from countries with higher quality school systems, as proxied by the PISA scores, have a more negative earnings effect associated with undereducation. This leads them to be more like the native born in terms of earnings determination. The interpretation of this offered above is that these relatively less well-educated immigrants from countries with high-quality school systems are less intensely selfselected for migration to the United States. Analyses of the effects that the PISA mathematics and science scores have on immigrants’ payoffs to schooling via the earnings effects in the ORU model were also undertaken. Similar findings emerge, which demonstrates the robustness of the results. Relevant findings are presented in Appendix C.

5. Conclusion The payoff to schooling for immigrants in the US labor market is only around one-half of that for the native born. This chapter examines whether this difference is linked to the quality of the schooling acquired abroad by immigrants, and if so, how the school-quality effects are transmitted to earnings in the United States. The analyses offer a comparative assessment of the relative strengths of two measures of the quality of immigrants’ origin-country schooling, the PISA scores, and the Hanushek and Kimko (2000) human capital quality index. As argued above, the Hanushek and Kimko data relate to a period when many of the immigrants in the US labor market in 2000 would have been enrolled in school in their country of origin, whereas the PISA scores relate to testing undertaken in the origin countries in 2000. However, the PISA data relate to single tests for a specific age group, whereas the Hanushek and Kimko (2000) data are averages for a number of age groups, test types,


93

and years of test assessment. Yet the two test scores are highly correlated across countries. The results suggest that from the perspective of predicting the payoff to preimmigration schooling among adult male immigrants in the United States, the PISA scores are relevant indicators of origin-country school quality.22 There is a strong, positive relationship between the payoff to schooling for immigrants in the US labor market and the quality of the schooling they acquired prior to immigration, as measured by the PISA reading, mathematics, and science literacy scores. Moreover, the results suggest that a higher quality of schooling acquired abroad is associated with a higher payoff to correctly matched schooling in the United States, a slightly higher payoff to schooling that appears to be surplus of the usual standards in the jobs held by immigrants, and a greater (in absolute value) penalty associated with years of undereducation. The predictions presented suggest that the latter phenomenon is of greater importance to understanding the lower payoff to schooling among the foreign born in the United States. Chiswick and Miller (2008) associate the differential in the earnings penalty for undereducation between the native born and the foreign born with positive selection in immigration among the foreign born. The results in this chapter suggest that immigrants from countries with a poorer quality of school system are associated with more intense selection in immigration, and it is this selection process, rather than the quality of immigrants’ schooling per se, that is the major driver of the lower payoff to schooling among immigrants in the United States. Acknowledgments We thank Derby Voon for research assistance, and Charles Beach for comments on an earlier paper that led to the research reported here. Miller acknowledges financial assistance from the Australian Research Council. Appendix A. Definitions of variables The variables used in the statistical analyses are defined below. Data source: 2000 Census of Population, Public Use Microdata Sample, 5 percent sample of the foreign born, and 0.15 percent random sample of the native born. The foreign-born sample is restricted to those who were at least 18 years of age at the time of immigration.

22

Similar results emerge using the Hanushek and Kimko (2000) index.

94


Definition of Population: Native-born and foreign-born employed men aged 25–64 years who had nonzero earnings in 1999. Dependent variables Earnings in 1999 Explanatory variables PISA

Natural logarithm of earnings in 1999 (where earnings are defined as gross earnings from all sources).

The mean score for the immigrant’s country of origin from the OECD Programme for International Student Assessment. Separate scores for reading, mathematics, and science literacy are used. GDP per capita in 1980 Data on real GDP per capita for 1980 were obtained from Version 6.2 of the Penn World Tables (Alan Heston, Robert Summers, and Bettina Aten, Penn World Table, Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006). These data are denominated in a common currency so that realquantity comparisons can be made across countries. Years of education This variable records the total years of full-time equivalent education. It has been constructed from the Census data on educational attainment by assigning the following values to the Census categories: completed less than fifth grade (2 years); completed fifth or sixth grade (5.5); completed seventh or eighth grade (7.5); completed ninth grade (9); completed tenth grade (10); completed 11th grade (11); completed 12th grade, no diploma (11.5); completed high school (12); attended college for less than one year (12.5); attended college for more than one year or completed college (14); bachelor’s degree (16); master’s degree (17.5); professional degree (18.5); doctorate (20). As with other Census data, the values for educational attainment are self-reported responses. While academic degrees may have required different years of schooling for immigrants educated in some countries of origin, US values are used in the analysis. Usual level of education This variable records the reference years of education. It is constructed using the modal level of education of the native-born workers in the respondent’s occupation of employment based on the Realized Matches procedure. Years of overeducation The overeducation variable equals the difference between the person’s actual years of education and the years of education required for the person’s job where this computation is positive. Otherwise, it is set equal to zero.


Years of Undereducation

Weeks worked in 1999 Experience Location

Marital status

Veteran

Race

English language proficiency

Years since migration Citizenship

95

The overeducation variable equals the difference between the reference years of education in the person’s job and their actual years of education where this computation is positive. Otherwise, it is set equal to zero. This is a continuous variable for the numbers of weeks the individual worked in 1999. Age Years of Education 6 years. The two location variables record residence in a metropolitan area or in the southern states. The states included in the latter are Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia. This is a binary variable that distinguishes individuals who are married, spouse present (equal to 1) from all other marital states. This is a binary variable set equal to one for someone who had served in the US armed forces, and set equal to zero otherwise. This is a dichotomous variable that distinguishes between individuals who are Black ( ¼ 1) and all other races ( ¼ 0). Three dichotomous variables (speaks English very well; well; not well, or not at all) are used to record the English language proficiency of the respondents who speak a language other than English at home. The benchmark group is those who speak only English at home. This is computed from the year the foreign-born person came to the United States to stay. This is a dichotomous variable set equal to one for foreign born who hold a US citizenship.

Appendix B. Analyses using the Hanushek and Kimko data B.1. Analyses of Hanushek and Kimko using full sample of 73 countries There are 73 countries for which there is information on the Hanushek and Kimko (2000) human capital quality index and data on workers in paid employment in the 2000 US Census. Table B1 lists results obtained from the second-step regression of the across-country variation in the payoff to schooling against the Hanushek and Kimko (2000) index. Tables B2–B4 report findings from the second-step regression based on the ORU specification of the earnings equation. While the imputed values of the Hanushek and Kimko (2000) index are based, among other variables, on GDP per

96


Table B.1. Estimates from second step of two-step model, aggregate-level analyses, based on the Hanushek and Kimko (2000) index Variable

(i)

(ii)

Constant

0.019 (3.69) 0.034 (2.50) 0.008 (2.32) No

0.033 (2.33) 0.068 (1.85) 0.007 (0.72) Yes

0.224 73

0.081 73

HCAP/100 1980 GDP per capita/10,000 Country fixed effects in first step R2 Sample size

Notes: Model (i) has a single intercept, b0, in the first-step regression. Model (ii) is based on the PJ flexible specification in the first-step regression, where the intercepts are given by j¼1 ½b0j I j . Absolute values of t-statistics in parentheses.

Table B.2. Estimates from second step of two-step model, aggregatelevel analyses. Focus on overeducation, based on the Hanushek and Kimko (2000) index Variable

(i)

(ii)

Constant

0.025 (3.53) 0.084 (4.65) 0.001 (0.10) No

0.018 (2.20) 0.065 (3.08) 0.002 (0.44) Yes

0.270 73

0.130 73



capita (in 1960), the GDP per capita variable is retained in the estimating equation for comparison with the models based on the PISA scores.

B.2. Analyses of Hanushek and Kimko indices using subset of countries with both PISA and Hanushek and Kimko measures There are 32 countries for which there is information on the Hanushek and Kimko (2000) human capital quality index, PISA scores, and data on

97


Table B.3. Estimates from second step of two-step model, aggregate-level analyses. Focus on required education, based on the Hanushek and Kimko (2000) index Variable

(i)

(ii)

Constant

0.008 (2.20) 0.006 (0.65) 0.010 (3.74) No

0.037 (2.59) 0.031 (0.87) 0.001 (0.15) Yes

0.223 73

0.015 73



Table B.4. Estimates from second step of two-step model, aggregate-level analyses. Focus on undereducation, based on the Hanushek and Kimko (2000) index Variable

(i)

(ii)

Constant

Country fixed effects in first step

0.005 (0.57) 0.010 (0.46) 0.003 (0.58) No

0.007 (0.61) 0.012 (0.40) 0.005 (0.60) Yes

R2 Sample size

0.013 73

0.012 73

HCAP/100 1980 GDP per capita/10,000

Notes: Model (i) has a single intercept, b0, in the first-step regression. Model (ii) is based on the flexible specification in the first-step regression, where the intercepts are given by PJ j¼1 ½b0j I j . Absolute values of t-statistics in parentheses.

workers in paid employment in the 2000 US Census. Table B5 lists results obtained from the second-step regression of the across-country variation in the payoff to schooling against the Hanushek and Kimko (2000) index for this subset of countries. Tables B6–B8 report findings from the secondstep regression based on the ORU specification of the earnings equation for the same set of countries.

98


Table B.5. Estimates from second step of two-step model, aggregate-level analyses. Based on the Hanushek and Kimko (2000) index, 32 countries analyses Variable

(i)

(ii)

Constant

0.056 (13.95) 0.082 (7.41) 0.017 (6.25) No

0.128 (10.66) 0.165 (4.99) 0.040 (5.00) Yes

0.887 32

0.808 32



Table B.6. Estimates from second step of two-step model, aggregate-level analyses. Focus on overeducation, based on the Hanushek and Kimko (2000) index, 32 countries analyses Variable

(i)

(ii)

Constant


0.008 (0.84) 0.016 (0.62) 0.021 (3.28) No

0.015 (1.45) 0.038 (1.36) 0.030 (4.38) Yes

R2 Sample size

0.317 32

0.425 32



B.3. Analyses of PISA scores using subset of countries with both PISA and Hanushek and Kimko measures There are 32 countries for which there is information on the Hanushek and Kimko (2000) human capital quality index, PISA scores, and data on workers in paid employment in the 2000 US Census. Table B9 lists results obtained from the second-step regression of the across-country variation

99


Table B.7. Estimates from second step of two-step model, aggregate-level analyses. Focus on required education, based on the Hanushek and Kimko (2000) index, 32 countries analyses Variable

(i)

(ii)

Constant


0.036 (4.38) 0.056 (2.47) 0.012 (2.19) No

0.126 (10.89) 0.110 (3.45) 0.038 (4.85) Yes

R2 Sample size

0.477 32

0.745 32



Table B.8. Estimates from second step of two-step model, aggregate-level analyses. Focus on undereducation, based on the Hanushek and Kimko (2000) index, 32 countries analyses Variable

(i)

(ii)

Constant


0.054 (4.81) 0.078 (2.50) 0.015 (2.01) No

0.048 (4.44) 0.074 (2.50) 0.014 (1.97) Yes

R2 Sample size

0.463 32

0.458 32



in the payoff to schooling against the three PISA scores for this subset of countries. Tables B10–B12 report findings from the second-step regression based on the ORU specification of the earnings equation for the same set of countries.

100


Table B.9. Estimates from second step of two-step model, aggregate-level analyses. Based on PISA scores, 32 countries analyses Variable

Reading literacy Mathematics literacy Science literacy (i)

(ii)

(i)

Constant


0.597 32

0.618 32

0.649 32

(ii)

(i)

0.188 (7.91) 0.036 (5.86) 0.019 (2.01) Yes

0.121 0.229 (5.90) (6.40) 0.024 0.042 (4.94) (4.96) 0.011 0.025 (1.95) (2.51) No Yes

0.683 32

0.595 32

(ii)

0.625 32


Table B.10. Estimates from second step of two-step model, aggregate-level analyses. Focus on overeducation, based on PISA scores, 32 countries analyses Variable


(ii)

(i)

Constant


0.298 32

0.304 32

0.292 32

(ii)

(i)

0.030 (1.79) 0.002 (0.49) 0.019 (2.90) Yes

0.029 0.030 (1.45) (1.30) 0.005 0.002 (0.95) (0.36) 0.015 0.020 (2.65) (3.08) No Yes

0.303 32

0.289 32

(ii)

0.301 32


101


Table B.11. Estimates from second step of two-step model, aggregate-level analyses. Focus on required education, based on PISA scores, 32 countries analyses Variable


(ii)

(i)

Constant


0.611 32

0.513 32

0.613 32

(ii)

(i)

0.169 (7.25) 0.026 (4.28) 0.018 (1.99) Yes

0.063 0.196 (5.50) (5.74) 0.013 0.030 (4.66) (3.65) 0.006 0.023 (1.95) (2.42) No Yes

0.568 32

0.573 32

(ii)

0.518 32


Table B.12. Estimates from second step of two-step model, aggregate-level analyses. Focus on undereducation, based on PISA scores, 32 countries analyses Variable


(ii)

(i)

Constant


0.311 32

0.321 32

0.385 32

(ii)

(i)

0.076 (4.19) 0.017 (3.56) 0.004 (0.57) Yes

0.101 0.095 (3.66) (3.65) 0.020 0.020 (3.02) (3.15) 0.008 0.007 (1.03) (0.95) No Yes

0.394 32

0.341 32

(ii)

0.352 32


102


Appendix C. Supplementary results

Table C.1. Implied payoffs to schooling, adjusting for effects of ORU variables at various PISA mathematics scores

i. Native born Foreign born ii. No adjustment iii. Adjustment only to the earnings effects of reference education for the foreign born iv. Adjustment only to the earnings effects of overeducation for the foreign born v. Adjustment only to the earnings effects of undereducation for the foreign born vi. Adjustment to all three ORU variables

100 PISA points

Benchmark

þ100 PISA points

10.5

10.5

10.5

– 4.7

4.9 4.9

– 5.2

5.0

4.9

4.8

4.0

4.9

5.8

3.8

4.9

6.0

Table C.2. Implied payoffs to schooling, adjusting for effects of ORU variables at various PISA science scores

i. Native born Foreign born ii. No adjustment iii. Adjustment only to the earnings effects of reference education for the foreign born iv. Adjustment only to the earnings effects of overeducation for the foreign born v. Adjustment only to the earnings effects of undereducation for the foreign born vi. Adjustment to all three ORU variables

100 PISA points

Benchmark

þ100 PISA points

10.5

10.5

10.5

– 4.7

4.9 4.9

– 5.1

5.0

4.9

4.8

3.8

4.9

5.9

3.7

4.9

6.0

References Antecol, H., Cobb-Clark, D.A., Trejo, S.J. (2003), Immigrant policy and the skills of immigrants to Australia, Canada and the United States. Journal of Human Resources 28 (1), 192–218. Betts, J.R., Lofstrom, M. (2000), The educational attainment of immigrants: trends and implications. In: George, Borjas (Ed.), Issues in the Economics of Immigration. University of Chicago Press for National Bureau of Economic Research, Chicago, pp. 51–115.


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Bratsberg, T., Terrell, D. (2002), School quality and returns to education of U.S. Immigrants. Economic Inquiry 40 (2), 177–198. Card, D., Krueger, A.B. (1992), Does school quality matter? Returns to education and the characteristics of public schools in the United States. Journal of Political Economy 100 (1), 1–40. Chiswick, B.R. (1978), The effect of Americanization on the earnings of foreign-born men. Journal of Political Economy 86 (5), 897–921. Chiswick, B.R., Miller, P.W. (2008), Why is the payoff to schooling smaller for immigrants?. Labour Economics 15 (6), 1317–1340. Chiswick, B.R., Miller, P.W. (2009), Educational mismatch: Are highskilled immigrants really working at high-skilled jobs and the price they pay if they aren’t? In: Barry, R. Chiswick (Ed.), High Skilled Immigration in a Globalized Labor Market, American Enterprise Institute, Washington DC. Hanushek, E.A., Kimko, D.D. (2000), Schooling, labor-force quality, and the growth of nations. American Economic Review 90 (5), 1184–1208. Hartog, J. (2000), Over-education and earnings: where are we, where should we go? Economics of Education Review 19 (2), 131–147. Organisation for Economic Co-operation and Development (OECD) and United Nations Educational, Scientific and Cultural Organization (UNESCO). (2003), Literacy Skills for the World of Tomorrow – Further Results From PISA 2000, OECD and UNESCO Institute for Statistics, Paris. Sweetman, A. (2004), Immigrant Source Country Educational Quality and Canadian Labour Market Outcomes, Ottawa: Statistics Canada, Analytical Studies Branch Research Paper No. 234.

CHAPTER 5

Development and Migration: Lessons from Southern Europe Riccardo Fainia,b,c and Alessandra Venturinic,d,e a

Faculty of Economics, Centre for International and Economic Studies, University of Roma Tor Vergata, Rome, Italy b CEPR-Center for Economic and Policy Research, London, UK c IZA-Institute for the Study of Labor, Bonn, Germany d Department of Economics, University of Torino, Via Po 53, Torino, Italy e European university Institute, Florence 50014 Fiesole, Italy E-mail address: [email protected]; [email protected]

Abstract Policy-makers in OECD countries appear to be increasingly concerned about growing migration pressure from developing countries. At the same, at least within Europe, they typically complain about the low level of internal labor mobility. In this chapter, we try to cast some light on the issues of both internal and external labor mobility. We investigate the link between development and migration and argue, on both theoretical and empirical grounds, that it is likely be nonlinear. More precisely, we find that, in a relatively poor sending country, an increase in income will have a positive impact on the propensity to migrate, even if we control for the income differential with the receiving country, because the financial constraint of the poorest become less binding. Conversely, if the home country is relatively better off, an increase in income may be associated with a fall in the propensity to migrate even for an unchanged income differential. Econometric estimation for Southern Europe over the period 1962–1988 provides substantial support to this approach. We estimate first the level of income for which the financial constraint is no more binding, around $950, and then the level of income for which the propensity to migrate declines, which is around $4,300 in 1985 prices. We therefore predict a steady decline in the propensity to migrate from Southern European countries. Similarly, our results highlight the possibility that the pressure to migrate from Northern African countries and other developing countries may increase with further growth. Keywords: Migration, growth JEL classification: O15



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Riccardo Faini and Alessandra Venturini

1. Introduction International migrants typically leave their home country in search of better living and working conditions abroad. More rapid development in the sending countries should therefore be associated with falling migration. This proposition is based on simple but compelling economic reasoning. Yet, it is not universally accepted. The US Senate Commission on migration argued that, particularly in the short run, income growth at home may foster rather than discourage migration, to the extent that it uproots traditional modes of production and disseminates information about working opportunities abroad. The recent literature (Lopez and Schiff, 1998; Hatton and Williamson, 2006) has highlighted how faster development at home may relax financial constraints on would-be migrants and lead to more rather than less migration (see also Ferenczi and Willcox, 1934; Clark et al., 2002; Hanson and Spilimbergo, 1999a; ILOUNCHR, 1992; Layard et al., 1992; Molle, 1990; Vogler and Rotte, 2000). The empirical evidence is mixed. Schiff (1994) provides convincing evidence that migration costs are indeed quite high and may therefore constrain the mobility choice of financially constrained migrants. Hatton and Williamson (2006) argue that the combination of high mobility costs and binding financial constraints goes a long way in explaining the historical pattern of international migration skewed toward the relatively well-off countries. However, Lucas (2005) fails to find any evidence of a positive relationship between income at home and out-migration rates, even for low-income sending countries. This chapter contributes to this debate in a number of ways. We develop a simple model that highlights the role of income levels in determining the propensity to migrate, while controlling for the income differentials between receiving and sending countries. Much of the literature has failed to control for this latter factor. Second, when assessing the role of the home country income on the propensity to migrate, we introduce a key distinction between the financial constraint and the home bias effects. The former simply states that higher income of the poor at home may help relax financial constraints and lead to more migration, even with an unchanged income differential. The latter, the ‘‘home bias’’ effect, however, leads to the opposite conclusion, namely that, under the plausible assumption that would-be migrants have an intrinsic preference for their home countries’ social amenities, an increase in home income will lead to a fall in the propensity to migrate, even after controlling for the income differential with the host country. The interaction between the financial and the home bias effects generates a complex non-linear relationship between the growth of the sending countries’ income and the propensity to migrate. Our (plausible) conjecture is that, for relatively poor countries, the financial constraint effect will dominate. Accordingly, higher income at home will be associated with a rise in the income of the poor and an increase in

Development and Migration: Lessons from Southern Europe

107

migration pressure. For relatively better-off countries, however, the home bias effect is likely to be relatively stronger. Hence, a boost to home income should lead to a fall in the supply of migrants. In the second part of the chapter, we focus on the case of the Southern European countries, which have now completed their migration transition1 from emigration countries to immigration ones and whose experience therefore has some interesting implications for other areas. For the pooled sample, we estimated two-income turning points: the first when the financial constraint is no longer binding, and the second when the home bias effects start dominating the emigration decision, so that further income growth is associated with a fall in migration. The remainder of this chapter is organized as follows. In the next section, we review the home bias literature in the financial and trade fields and then present a simple model of migration. We then look at the main migration trends from Southern Europe. Countries in Southern Europe have undergone a full migration transition and have now become the destinations of substantial labor flows from Northern Africa, Eastern Europe and many other relatively poor countries. An analysis of their experience can therefore shed some light on the factors that affect the long-run trend in migration from developing countries. Econometric results are presented in Section 4. Concluding comments follow in the last section. 2. The pervasiveness of home bias The inability of economic theory to explain the empirical observation that investors over-invest in domestic equity and consumers over-purchase domestically produced goods and services is known as the ‘‘home bias puzzle.’’ Even in the financial market where equity products are more similar, the preference for buying national equity – the home bias – is very strong. Probably, information about foreign assets is less widespread and brokers propose only the better-known products, which are easy to push with clients. In fact, Ahearne et al. (2004) show that the registration of the financial product, that is, in the US financial market, reduces discrimination in buying foreign equities, but even if the home bias is smaller it still remains. The home bias in trade seems even larger. In his seminal paper McCallun, in 1995, found that in Canada inter-provincial trade was 22 times larger than province-trade. In Europe in the 1980s Nitsch (2000) found that intra-national trade in Europe was 10 times higher 1

The term migration transition is used by demographers to indicate the end of the emigration phase and the beginning of a period of no emigration or of immigration.

108


than intra-communitarian trade, which clearly shows that frontiers in Europe matter. There are many reasons why natives prefer national products. Official and informal barriers probably exist, but costs are mixed with tastes. Different measures of distance have been implemented in order to consider the average size of the goods market, but in the end an unexplained part still remains. Even among the US states in the absence of trade frictions, the home bias is high. Hillberry and Hummels (2003) found a value that was one-third smaller than that reported by Wolf (2000) (four times larger in state trade that among state trade) but still positive (see also Delgado, 2006; Lewis, 1999; Portes and Rey, 2005). Guiso et al. (2004) go more deeply into the causes of the home bias puzzle and use a measure of cultural biases – namely trust2 in people of different nationality – which is significant in explaining trade, portfolio investments and foreign direct investment, even after taking into account different country characteristics, different information sets, historical and cultural variables. What clearly emerges from the survey that they use is that individuals tend to over-trust their fellow citizens and this conditions their economic decisions. Home bias also exists in the labor market. Intra-European migration is very limited, even if the presence of income differentials and of unemployment differentials should encourage more citizens of the European countries to move where wages are higher and where the likelihood of finding a job is greater. Of course, human beings are not goods: they have mobility costs, which are not only monetary costs. International mobility is reduced by the different languages spoken in the destination and sending countries, a factor that, even when it cannot affect professional life, may impact upon social life. Mobility, however, is also very limited internally to countries, when the linguistic barrier is not an issue but personal costs of the location change remain. People have highly idiosyncratic preferences that have been formed while living in their area of origin and are thus conditioned by the way of life prevailing in the area where they have grown up: for instance, they will be very attached to the food produced and eaten in their region.3 People are attached to their reference groups – friends or relatives – and being without them reduces their utility. As culture bias has effects on economic exchange, it biases labor mobility decisions even more so.

2

The measure of trust is derived from a set of surveys conducted by the Eurobarometer. If you sample people according to their schedule for lunchtime, you can likely guess their location around Europe.

3


109

To gain evidence on this issue, we may use the data provided by the European panel4 in 1994 for seven countries: France, Belgium, Denmark, Ireland, Italy, Greece, Portugal, and Spain available. There is a question in the survey that asks respondents if they have ever moved to another region in the same country or abroad. And 76.5% of the households surveyed declared themselves to be ‘‘absolute stayers,’’ that is, they had not changed region of residence.5 If we select only the very rich (with incomes in the last 25th% quartile) the ‘‘absolute stayers’’ increase to 77.6%. Fundamentally what emerges is that the residents of the European countries are so well off that they prefer not to move and even remain in their regions of birth. If we analyze with a logit the probability of being an ‘‘absolute stayer,’’ namely the probability of not leaving the region of origin, it increases with income, with age, it is higher for males, lower for singles and different in different countries, with Portugal, Italy, and Belgium showing the highest level of preferences for the home area and Denmark the lowest. This last finding is easy to explain given the large mobility and similarities among the Northern countries. In addition if we replace the income effect with the level of education – which captures the potential income that could be endogenous to the migration decision – the results presented in Table 1 demonstrate that the higher the level of education, the higher the probability of staying. The European Labour Force Survey is less suited to study long-term mobility because it has only one question on the change of residential position relative to the previous year. However, the results are very similar. In 1994 in the same countries, namely France, Belgium, Denmark, Ireland, Spain, Portugal, Italy, and Greece, 99% of the population had not changed region of residence since the year before. The probability of staying increased with age, was lower for singles and men, and positive to educational variables.6 This is just minor evidence of scant European internal mobility and its relationship with educational level; yet it seems to indicate a strong

4

The ECHP is a large household survey conducted in a number of European countries that yields internationally comparable information on both natives and migrants based on a standardized questionnaire that has a section on the ‘‘Migration trajectory.’’ The survey involves annual interviews of a representative sample of households and individuals in a number of European countries. The total duration of the ECHP is 8 years, running from 1994 to 2001. In the first wave (1994), a sample of almost 130,000 people aged 16 years and more was interviewed in the then 12 Member States of the European Union (EU). The little refreshment of the sample makes the first year, 1994, the most representative of the entire population. Austria, Finland, and Sweden were added later. For more detailed information see for instance Locatelli et al. (2001). 5 The total amount of people interviewed does not include persons that moved abroad and remained aboard. 6 The European Labour Force Survey has also a question on the mobility abroad but the number of units is just too small to make any consideration.

110


Table 1.

Probability of remaining in the same region of birth 1994

Regional bias

Coefficient

s.e.

Men Age-base Age-base 2 Education 1 Education 2 Education 3 Single Belgium Denmark France Ireland Spain Portugal Italy Obs.89354

0.07 0.04 0.00068 0.467 0.967 1.31 0.149 0.885 0.85 0.084 0.22 0.246 1.127 0.96 Wald 21609

0.016 4.5 0.00 0.13 0.0013 30 0.00 0.007 0.0003 22 0.00 0.001 0.029 16 0.00 0.075 0.028 33 0.00 0.15 0.027 47 0.00 0.24 0.0199 7 0.00 0.26 0.04 21 0.00 0.12 0.034 24 0.00 0.17 0.028 3 0.003 0.14 0.03 7 0.00 0.038 0.026 9 0.00 0.042 0.035 31 0.00 0.157 0.03 32 0.00 0.144 Log pseudolikelihood 46658

z

PW|z|

Marginal effect

Standard error 0.0028 0.00023 0.00001 0.0043 0.0038 0.005 0.0035 0.0043 0.0081 0.0049 0.0049 0.0043 0.0036 0.0036

Source: Euro-panel dataset.

preference for living in the area of origin which increases with income and education.

3. A simple migration model The determinants of migration decisions have been the object of much research in the literature. Traditionally, it was assumed that the decision to migrate depended on a comparison between income at home and income in the potential host country. The Harris–Todaro model refined this approach by showing that risk-neutral migrants would weigh the wage in the destination country by the probability of finding a job. The Harris– Todaro model was then extended to allow for nonneutral behavior toward the risk (Banerjee and Kanbur, 1981; Hatton, 1993). The ‘‘new’’ migration literature has focused on several factors that, in addition to wage differentials, may prompt people to migrate, such as the desire to diversify risk, to escape relative deprivation and the presence of imperfect information (Stark, 1991). In the spirit of the new migration literature, our model also emphasizes the role of nonincome factors, in particular the home-bias in locational preferences, in affecting the migration decision. One stylized fact in the empirical literature on migration (Hatton and Williamson, 1993; Wyplosz, 1993) is that very few people migrate, sometimes despite the existence of very large wage differentials. This is particularly true in the case of Europe (Faini and Venturini (1993a, 1993b). In the absence of overwhelming barriers to labor mobility, the puzzle of low migration rates has been attributed to large monetary costs


111

of migration (Easterlin, 1961), to cost of living differentials, to optimistic expectations (Wyplosz, 1993) as well as to widespread uncertainty about the home country’s prospects (Burda, 1993; Faini, 1995).7 Most of these factors, however, are unable to account for the steady fall in migrations from Southern Europe. Indeed, transportation costs and costs of living differentials between Northern and Southern Europe have if anything fallen in the period under consideration. 3.1. The ‘‘home bias’’ model In the model below, we take a different route. Our starting point is the assumption that people prefer to live in their home countries and that, ceteris paribus, they would rather not migrate so that they can avoid the social, cultural, and psychological costs associated with a move to a different location. More formally, it is assumed that individuals derive utility also from the amenities that they can consume at a given location and that such amenities are more conspicuous in their home country.8 Moving abroad involves a loss of utility because of the need to settle into a new and unfamiliar environment and the loss of social relationships. A home market bias in the locational preference is certainly easier to justify than the corresponding bias in consumption patterns (Venables and Smith, 1986) or in financial portfolio allocation (French and Poterba, 1991). As we shall see, one testable implication of this framework is that the wage level in the home country becomes a crucial determinant of the migration decision, even after controlling for the wage differential. Therefore, as in the ‘‘new’’ migration literature, (expected) wage differentials are not all that matters in determining the migratory choice. Formally, we assume that the utility of a potential migrant can be represented as follows: Uðwi ; f i Þ

(1)

where wi and fi denote respectively the wage and the amenities in region i. There are two possible locations, the South (S) and the North (N). The potential migrant initially lives in region S. Following the previous discussion, it is assumed that amenities are larger in the origin country of 7

It may be thought that uncertainty about the home country’s prospects should encourage a risk-averse person to migrate. This is no longer the case, however, once fixed moving costs introduce some irreversibility in the migration choice. 8 A similar hypothesis is made by Djajic and Milbourne (1988) who assume that the marginal utility of consumption at home is always higher than that associated with the same rate of consumption in the host country. This assumption plays a crucial role in their analysis of return migrations. In Courant and Deardorff (1993), each country is endowed with a given level of amenities, but agents have identical preferences. As a result, there is no home market bias in the location choice.

112


the potential migrant, that is, that fsWfn. For migration to occur, evidently the wage differential, wnws, must be large enough to offset the loss of amenities consequent on moving abroad. Given Equation (1), migration will occur if U(wn, fn)ZU(ws, fs). After taking a simple first-order expansion of U(wn, fn) around U(ws, fs), the migration condition becomes: Uðws ; f s Þ þ U w ðwn ws Þ þ U f ðf n f s Þ Uðws ; f s Þ

(2)

or: Uw f fn s Uf wn ws

(3)

where the derivatives of the utility function, Uw and Uf, are evaluated at ws and fs. One crucial consideration is that the right-hand side of Equation (3), that is, the marginal rate of substitution between the real wage (or, more precisely, the goods that such a wage can buy) and the amenities at a given location, will not be generally constant. For instance, if we assume that U(w, f) can be described by a CES function, the migration condition becomes: 1 d f s f n ws 1þr g¼ ¼z (4) d w n ws f s where 1/(1þr) is the elasticity of substitution between w and f,9 while d is the distributional parameter associated with f in the CES function. What Equation (4) suggests is that migration is more likely to occur the larger the wage differential and the smaller the gap in amenities. More crucially, Equation (4) also shows that an increase in the wage in the home country, that is, in ws, will be associated with lower migrations, even with an unchanged wage differential.10 The intuition is simple. In this model, both the wage and the amenities associated with a given location are normal goods. A proportional increase in ws and wn, therefore, has a positive income effect that will prompt consumers to try to consume more of the home country’s amenities. Accordingly, the propensity to migrate will decline. The implications of this result are noteworthy. Increases in the home country income will have a twofold effect on migration, first by reducing the wage differential with the host country, secondly by inducing a decline in the propensity to migrate. Clearly, this would enhance the 9 r different from 1, otherwise consumption and amenities are perfect substitutes and the income effect disappears. 10 Notice that this result cannot be derived simply from the concavity of the utility function. To see this one needs only to take a second order expansion of U(w) or, more simply, to consider the case where U(w) ¼ ln w. Clearly, in the latter case, the incentive to migrate depends only on the (relative) wage differential; the wage level in the home country plays no independent role.


113

effectiveness of those policies that aim at reducing migration by promoting growth in the sending countries. The model developed so far does not allow for heterogeneity among agents. If Equation (4) holds, therefore, all agents would be predicted to migrate. In a general equilibrium framework, wages would adjust so that all agents would be equally indifferent between moving abroad or staying at home. In what follows, we abstract from general equilibrium considerations and allow instead for nonhomogeneous behavior. To this end, we assume that g ¼ (1d)/d is distributed within the home country population according to a Pareto distribution function: y x0 yþ1 (5) x0 g where x0 and y are parameters of the distribution function.11 According to Equation (4), migration will occur if g ¼ (1d)/d is larger than z (the righthand side of the equation). Hence, the migrants’ share in the home country population is equal to: Z1 Probðg zÞ ¼ z

y x0 yþ1 dg ¼ xy0 zy x0 g

(6)

where z ¼ (fsfn)/(wnws) (ws/fs)1þr. 3.2. The role of financial constraints Equation (6) defines the population share of those willing to migrate. However, not all would-be migrants, that is, those for which Equation (4) holds are actually able to move abroad. The presence of minimum educational and wealth requirements may indeed act as a binding constraint for many would-be migrants (Banerjee and Kanbur, 1981). Furthermore, capital markets imperfections may prevent a potential migrant from contracting a loan to pay for the monetary cost of migration.12 Similarly, minimum educational and ability requirements may represent an insurmountable obstacle for many would-be migrants.13 We therefore assume that for someone to be able to migrate, a given characteristic A (say, financial wealth) must be greater than a given critical value (‘‘c’’) and therefore satisfy the condition AZc. The number of actual migrants is 11

The Pareto distribution function is defined over the interval (x0, y). Migration costs may be a substantial constraint on the decision to move. See Schiff (1994) for some direct evidence. 13 It may be the case, however, that agents with a higher propensity to migrate strive to acquire the educational achievements necessary to be able to move abroad (see Stark, 1993, for such an approach). 12

114


then determined by the intersection of the two relevant sets of agents, that is, those for which Equation (4) holds (and are therefore willing to migrate) and those for which the constraint is not binding (and are, as a result, able to move abroad): Z1 Z1 Probðg z; A cÞ ¼

f ðg; AÞdAdg z

(7)

c

where f(g, A) is the joint density function of g and A. In what follows, we assume that A and g are independently distributed and that the characteristic A is distributed among the population according to a Pareto distribution function. It can then be shown that the actual number of migrants (M) as a share of the home country’s population (P) will be equal to: M ¼ xy0 zy xe1 ce P

(8)

In Equation (4), an increase in the home wage was found to discourage desired migration. Plausibly, though, a rise in ws should also relax the financial constraint. The distribution of the characteristic A would then shift to the right. We then assume that x1, the lower limit of the support of the distribution of A, is a function of the ‘‘wage rate of the poor’’ (i.e., first quintile of the income distribution) in the South: x1 ¼ waqls ðwqls oTÞ

(9)

We expect aW0, the implication being that increase in the home wage will relax the constraint (given aW0), but above a threshold level (T) the budget constraint is no longer binding. Substituting Equation (9) and the expression for z in Equation (8) and taking logs yields after some manipulations the following expression: M ln ¼ y ln x0 þ y lnðwn ws Þ yð1 þ rÞ ln ws þ y lnðf s f n Þ P þ yð1 þ rÞ ln f s þ ea ln wqIs ðwqIs oTÞ e ln c

ð10Þ

Relative amenities and relative wages have the expected impact on the migration rate. We see, however, that even after controlling for the wage differential, the impact of ws is a priori ambiguous. This is because of the contrasting effect of the financial constraint and the home bias effects. If y (1þr) is large, the latter dominates, and an increase in ws is associated with a fall in migration. Conversely, if ea is relatively large, the impact of a higher income among the poor (wqIs) may prevail by relaxing the constraint and allowing more would-be migrants to move abroad, and the rate of migration may increase.


115

4. Trends in Southern European migrations Historically, the Southern European countries have represented a constant source of migrant labor for Northern economies. The Great Depression took a heavy toll on the movement of workers between Southern and Northern Europe. However, the Southern European countries resumed their role as a source of migrant workers for the North after the Second World War. During the second half of the 1950s, inter-continental migration ceased and intra-European migration underwent a massive surge. The main destination countries remained France, Germany, Switzerland, Belgium, The Netherlands, and some Northern European countries. The trend continued unabated until the first oil shock, when declining economic opportunities in the receiving countries forced many migrants to return home and discouraged new migrants from trying their luck in Northern Europe. Figure 1 shows how, after a steady increase during the 1960s, migration flows from Southern Europe fell dramatically in the wake of the first oil shock. Analysts typically attribute the fall in migration rates after 1973 to the decline in labor demand in the main receiving countries (Salt, 1991). Interestingly enough, however, when in the 1980s economic conditions in Northern Europe showed a clear improvement, migrations from Southern Europe did not resume. There are several possible explanations for this apparent puzzle. First, it could be argued that wage and income differentials between Northern and Southern Europe during the 1980s were no longer providing an adequate incentive for labor to move. But the evidence is simply not there. Figure 2 shows that there was some income convergence between the main sending countries in Southern Europe (Portugal, Spain, and Greece14) and the main destination countries, but the gap remained substantial, with the sole exception of Spain. Moreover, if anything the income gap during the eighties widened, and this should have augmented the incentive to migrate. Neither do we find a significant improvement in the relative labor market conditions between sending and receiving countries. Figure 3 shows that the increase in unemployment after 1973 did not spare countries in Southern Europe. Second, it is possible that a structural shift in the composition of labor demand toward higher skills meant that employment growth (EG) in the receiving countries no longer had a substantial pull effect on (mainly unskilled) migrations. Nevertheless there was no point system in Europe15

14

Italy is an exception, to the extent that the income gap with Northern Europe declined substantially between 1974 and 1990. For the reasons explained later, however, we do not include Italy in our econometric sample. 15 A points system was not adopted in continental Europe and immigrants in continental Europe were mainly unskilled blue-collar workers. Only migration to the United Kingdom was characterized by skilled migrants, who came from former colonies or were educated in the United Kingdom. Very recently also Ireland has been able to attract skilled immigrants from Eastern Europe.

Fig. 1.

Migration rates from Southern Europe.

116 Riccardo Faini and Alessandra Venturini

Fig. 2.

Income differentials (ratio between host and home country income).

Development and Migration: Lessons from Southern Europe 117

Fig. 3.

Unemployment rate in Southern Europe.

118 Riccardo Faini and Alessandra Venturini


119

that could help in selecting the migrants by skill. In addition no evidence exists that the destination labor markets changed their labor demand; instead, the available evidence suggests that the demand for unskilled immigrants continued after 1973. When migrations from Southern Europe declined or remained flat, immigration to the traditional destination countries continued, but from other sending countries, in particular ones in Northern Africa and Turkey and Yugoslavia,16 which kept on covering unskilled positions with their workers. Other factors, besides the structural shift in labor demand, must therefore have been at work. One plausible conjecture is that the fall in migration rates from Southern Europe reflects supply rather than demand factors. The model developed in Section 2 implies that, even with constant wage differentials, the propensity to emigrate will decline if economic conditions improve in the home country. 5. Econometric analysis 5.1. The estimating equation According to Equation (10), the main determinants of migration are the wage and amenities differentials, the average wage level and the ‘‘wage of the poor’’ in the sending country. For the purpose of estimation, we shall assume the relative level of amenities to be a decreasing function of the number of migrants in the previous three years17 or of the migrant stock in the destination areas, as the new migrants’ social and psychological costs of moving to an unfamiliar location are arguably inversely related to past migrations. In other terms, we expect migration chains to affect not only costs and information but also amenities; the variable capturing this effect will be denoted by MC. Considering that the expected utility of migrant is a weighted function of the utility enjoyed in the state of employment and unemployment, the model can be generalized further to include unemployment rates in both the sending and the receiving countries.18 Even with 16

For an extended description of the migration pattern from Southern Europe by destination see pp. 16–23 and by skill pp. 32–35 in Venturini (2004). 17 In the literature there is a large amount of evidence that a large number of migrants was ‘‘rotating’’ on short-term contracts lasting on average for about three years: see for instance King and Rybaczuk (1993, p. 175). 18 From a formal point of view, unemployment can be introduced into our set-up in a relatively simple manner. Let pi be the probability of being unemployed in region i (with i ¼ N, S) and wi (w i ) the wage rate when employed (unemployed). The migration condition becomes: pn Uðw n ; f n Þ þ ð1 pn ÞUðwn ; f n Þ ps Uðw s ; f s Þ þ ð1 ps ÞUðW s ; f s Þ We only need to take a linear approximation of Uðw n ; f n Þ, U(wn, fn), and Uðw s Þ around U(ws, fs) to find an expression analogous to Equation (3), with the relevant wage variable being now ð1 pi Þwi þ pi w i , that is, the expected wage. In the empirical implementation, we assume that the probability of being unemployed in region i is a function of the unemployment rate there.

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these modifications, Equation (10) only reflects supply determinants of migration. However, demand (i.e., host country’s) considerations will also play a crucial role in defining immigration policies and determining the evolution of migrations. We follow Hanson and Spilimbergo (1999b) in assuming that policy-makers’ efforts to control immigration are a function of labor market conditions in the host country. This is particularly true for Europe, where the main change in migration policy took place with the introduction by the destination countries of restrictive immigration policies in 1973 just after the first oil shock.19 Migration controls, however, can hardly be expected to be fully effective. They rather act like a wire-mesh screen by hindering and slowing down migrations, but also permitting some inflows, particularly if the supply is very strong.20 Unfortunately, we have no information on the amount of money spent on, and the extent of migration controls in the destinations of Southern European migrants. Accordingly, we simply assume that the tightness of migration policy is negatively related to the EG rate in receiving countries21 and add this variable to the list of regressors. If home amenities are a normal good, we can expect wages in the sending country to affect migrations in a complex way. More precisely, there will be two thresholds, capturing respectively the willingness and the ability to migrate. Thanks to the amenities effect, residents in the sending country will be willing to migrate only if their income w is smaller than the desired minimum income, say W1; However, only people with income greater than some level W2 will be able to finance their migration. It follows that if W2WW1 people that can afford to migrate do not wish to do so, and no migration takes place. If instead W1WW2 there may be people that both can afford and are willing to move, namely those whose incomes w are such that W2WwWW1.22

19

For a broader discussion of migration policies in Europe in the period considered see Venturini (2004). 20 We have borrowed this analogy from William Cline’s assessment of protection in the textile and clothing sector (Cline, 1987). 21 Immigration policies may also respond to the unemployment rate in the host country. The coefficient on Un may therefore reflect both supply and demand factors. 22 Assuming that home amenities is a normal good, we want to find two threshold levels of income: the first is the level at which the financial constraint is no longer binding (W2), so that all those who want to migrate are able to do so; the second is the level of income which discourages emigration because potential migrants, even if they able, are no longer willing to migrate (W1) because they feel affluent enough to prefer the normal good, that is, home amenities. On the opposite, if the home bias is an inferior good, migration takes place only if woW1, and the ability to migrate arises only if W1WwWW2; thus if woW2oW1, only the former constraint is binding; if it is the reverse, woW1oW2, only the financial constraint is binding. If the home amenities are first a normal than an inferior good, that is, for a low level of income, both the financial constraint (positive) and the willingness to move (negative) matter, while for a high level of income the financial constraint is no longer binding and the willingness to move increases with income. If the home amenities are first an inferior than a normal good, that is, for a low level of income, the financial constraint has a positive effect on migration and a negative


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To capture this mechanism, we include in our regression not only the wage (log) differentials, but also average income per capita in the sending country (Ws) and its square (because of the home bias, the willingness to migrate, can be assumed to fall rapidly as income grows) and the income of the ‘‘poorest group of the population’’ in all the countries, measured by average income per capita of the first quintile of the population (wqIT). Since the financial constraint is no longer binding when income reaches a threshold wqIT*, this variable is not necessarily always included in the regressions and we will discuss in detail the choice of the truncation point below. lnðM=PÞ ¼ a0 þ a1 lnðwn =ws Þ þ a2 ln wqI T þ a3 ln ws þ a4 ðln ws Þ2 þ a5 ln U s þ a6 ln U n þ a7 ln EGn þ a8 lnðMCÞ

(11Þ

5.2. The data We estimated Equation (11) on a sample of Southern European countries that included Greece, Portugal, Spain, and Turkey. Italy was excluded because of a lack of homogeneous conditions in the country, epitomized by the persistent backwardness of the Mezzogiorno area. Whereas the northern part of Italy stopped being a net emigration area many decades ago, the Mezzogiorno was a steady source of migrant workers until at least the early 1980s. The existence of persistent and substantial regional differences within Italy implies that any aggregate analysis of the migration behavior of the country is most likely to be meaningless or even misleading.23 Furthermore, an analysis of migration behavior in the Mezzogiorno has already been conducted by Faini (1989). The migration variable used refers to the gross inflows of population from a country of origin to a country of destination. Destination data were used because they are more accurate and do not underestimate the emigration as the data of the country of origin do (the sources are indicated in the Appendix). We used gross flows and not net flows for two reasons: first because the decision to emigrate is captured better by the gross flow variable, while the net flow variable is a better proxy for the success of the project, and second because the return flows are not registered correctly in many one on the willingness to move, while for a high level of income, the financial constraint is no longer binding and the willingness to move decreases with an increase in income. 23 Admittedly, regional differences in migration behavior and standard of living are also important for other countries in our sample, such as Spain. We believe, however, that the degree of regional inequality is much more pronounced in Italy than in Spain. For instance, in 1988 the ratio between income in the more and in the less developed regions was equal to 1.41 in Spain and 1.78 in Italy.

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countries and are frequently better reported in the origin country than in the destination one. We also used population data and not worker data because they are better able to capture the migration decision, which is frequently determined by a search for work. We used official data from host countries, thus capturing only legal migrants. However, specific surveys show that the number of illegal migrants is strongly correlated with the number of legal ones. In addition, we did not want to study the distribution of migrants among destinations but rather the aggregate effect of income growth on total emigration. Consequently, we summed all the outflows in a single measure of total gross emigration rate. As a proxy of wages, we used PPP corrected indicators of income per capita for both the sending and the destination countries. There is considerable discussion on whether income or wage indicators should be included in a migration equation (Hatton and Williamson, 1993). However, for medium and long-run migrations, income data may provide a better indication of the earning potentials of prospective migrants. Empirically, the use of either indicators does not seem to make much difference (Gould, 1979). Hence, given also that wage data, especially for the early years, are of dubious quality, we relied on income data. The income of the ‘‘poor’’ was approximated to the income of first quintile of the population, which was derived from the WIID dataset provided by the WIDER on income distribution. Employment and unemployment data were derived from the labor force survey according to the OECD definition. Finally, the destination variables were weighted averages of relevant variables in each of the destination considered. For instance the income per capita in the destination area was obtained as the geometrical average of income per capita in PP parity of each country of destination weighted by the share of immigrants to that destination as a share of the total outflow. The same procedure was adopted for unemployment and employment in destination areas. The weight varies each year if the migrants change the composition of their destinations. This choice was entailed by the type of data available. On deciding whether to move and where to go, the migrant compares the returns on all the possible destinations, so that when emigration from a country is spread across many destinations, the appropriate approach is to include all the destination wages in the emigration equation. This procedure is not feasible with aggregate data, and the correct solution is to combine all the destination variables into a weighted one, which becomes a unique composite destination, while bilateral analyses resents of the changes in the explanatory variables of the other destinations as well.


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We used two proxies for the Migratory chain; the sum of the gross migration of the last three years, which according to the sociological and historical literature seems to be the appropriate average length of stay, and the stock of migrants abroad as the share of the native population the year before migration. Whilst on the one hand this second proxy for the migratory chain seems better able to capture the size of the community abroad, on the other hand it is built with data less suited to a time series analysis because it is revised for each censuses, which may therefore under-report the size of the community excluding naturalized foreigners or over-report it with not registered return.

5.3. Estimation methods and the results 5.3.1. Properties of the data We first tested for the time series properties of the data. We relied on the Im et al. (2003) test for unit roots in panel data. Table 2 shows that for some of the series (migration, income and its square, foreign unemployment, and especially home unemployment and cumulated flows of emigration) the null hypothesis of a unit root was not strongly rejected. However, further testing showed that for these very same series the

Table 2.

Time series properties: the Im–Pesaran–Shin testa Levels

L(M/P) LW LW2 LDIF Ui Un EGn LWqIb MC L(STOCK/P)

**

1.65 1.67** 1.41*** 2.81* 1.07 1.46*** 3.66* 1.98*** 0.79 1.5***

First differences 3.30* 4.31* 4.32* – 3.26* 5.44* – 3.78* 3.14* 1.4**

M: migrations, P: population, LW: log of income in the home country, LDIF: log of the (relative) income differential, Ui: home country’s unemployment, Un: host country’s unemployment, EGn: employment growth in the host country, LWqI: income of the poor, MC: migratory chain as cumulated last three year flows, STOCK: stock of migrants abroad. * The null hypothesis of a unit root is rejected at a 5% confidence level. ** The null hypothesis of a unit root is rejected at a 10% confidence level. *** The null hypothesis of a unit root is rejected at a 1% confidence level. a The Im–Pesaran–Shin W procedure is a test for unit roots in panel data. It does not impose equal roots in the series. It is distributed as an asymptotically normal variable. b The truncated variable is stationary in first differences.

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hypothesis of panel cointegration could not be rejected.24 In the worst case, therefore, we were estimating a regression with a mix of stationary and non stationary (but cointegrated) series (Sims et al., 1990). To err on the conservative side, in what follows we present estimates in both levels and in first differences. To gain efficiency, we pooled the four sample countries together. However, careful testing of the pooling restrictions was indispensable. Both theoretical and Montecarlo evidence (Roberson and Symons, 1992) indicated that forcing the constraint of equal slope coefficients on an heterogeneous panel may result in very large biases. Fortunately, in our case, the pooling restrictions were basically not rejected by the data.25 We therefore relied on a fixed effect framework where the intercept was allowed to differ across countries, but the slope coefficients were assumed to be the same. 5.3.2. Estimation methods of the threshold Because of the presence of an unknown income threshold wqIT*26, above which the financial constraint is no longer binding, we could not use OLS. Moreover, nor could we use nonlinear least square, given that wqIT* (simplified hereafter as T*) entered the regression in a nonlinear and nondifferentiable manner. Fortunately we could follow Khan and Senhadji (2001), who examined a formally similar problem of the relationship between inflation and growth, and use the conditional least square. For any T*, we estimated the model by OLS, obtaining the sum of squared residuals as a function of T*. The least squares estimates of T* was found by selecting the value of T*, which minimized the sum of squared residuals and maximized the Student’s t of the variable LWqIT. M ¼ XbT þ eLW qI T ¼ T ¼ T min ; . . . ; T max ln P 24

(12Þ

We relied on the statistics developed by Pedroni (1999). We used his procedure no. 7, which allows for endogenous regressors and heterogeneous dynamics of the error term and is distributed as a standard normal variable. The test value was equal to 20.5. The null hypothesis of no cointegration was clearly rejected (see also Kiviet, 1986). 25 Standard tests (first on a pairwise basis and then by adding one country at a time) indicated that pooling was appropriate for Greece, Spain, and Turkey. For instance, the F7,39 test for pooling Spain and Turkey was equal to 2.14. Adding Greece yielded an F7,64 equal to 1.70. The pooling restrictions were (marginally) rejected for Portugal. We therefore estimated the equations in Table 3 also without Portugal. The results, however, did not change in any substantial manner, with the sole exception of the coefficient on the income differential, which lost statistical significance. 26 Testing for the actual existence of a threshold effects is a nonstandard problem requiring the use of simulation methods, such as the bootstrap proposed by Hansen (2000). Hence here we shall simply assume the existence of such effects, leaving the issue for further research.


Table 3.

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Test results of thresholds

Threshold

WqI

SSR

t student

log Likelihood

Tmax T* Tmin

1812 930 641

17.8 7.8 13.0

1.1 3.4 1

56 14 41

where bT is a vector of parameters (indexed by T to show its dependence on the threshold which ranges from Tmin to Tmax) and X is the corresponding matrix of observations on the explanatory variables. We defined S(LWQIT) and t(LWQIT) respectively as the residual sum of squares with the threshold level of income fixed at T and the t statistic for the low-income variable. The threshold level T* was chosen as T* ¼ arg minT{S(LWQIT)}, with a grid search on T in the range from 641 (minimum for Turkey) to 1812 (maximum for Greece). As shown by Table 3 (where for reasons of space only extreme values are shown; details available on request,) T* is the one with the highest t test and lowest S residuals, and the coefficient has the expected positive sign. Hence, we could move to estimation of model of Equation (12) with the threshold fixed at T* ¼ 930. 5.3.3. Results The econometric results for the pooled sample are reported in Table 4 (columns 1 and 3). First income differentials affect the evolution of migrations: as the wage differential increases, migration grows. Second, labor market conditions in the receiving countries matter considerably. Indeed, both the unemployment rate and the EG rate in the host country play a highly significant role in affecting migrations. Third, the level of income in the sending country is a significant determinant of migration behavior. As the income of the poorest group of the population increases, the ability to emigrate increases, and the total outflows are positively affected by income growth. In our case, the budget constraint stops being binding when the income per capita reaches $950. Instead, if the average income increases after about $3,500 per capita, its effect starts to become negative and we interpret it as the effect of the home bias. This seems to indicate that, for a given wage dispersion, in the early stages of development, increases in the sending country’s economic wellbeing give rise to more rather than less migrations to the extent that they help relax the financial and educational constraints which prevented many would-be migrants from moving abroad. A similar pattern, but

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Table 4.

The determinants of migration (pooled data)

Dependant variable: ln (M/P)

Constant LDIF U ib Un EGn MC D67 D82 LWqIT LW LW2 R2 SER Sargan w2(25)

OLS

GMM-DIFa

118 (7.3) 2.99 (2.6) 0.01 (0.7) 0.17 (3) 10.7 (4.41) 0.028 (6) 0.70 (6) 0.95 (8) 0.07 (5) 28 (6.8) 1.6 (6) 0.91 0.30 –

– 1.8 (1.6) 0.02 (1.9) 0.14 (3.7) 12.14 (2.9) 0.02 (1.75) 1.2 (2.9) 1.7 (1.7) 0.07 (4.7) 25.2 (3.9) 1.5 (4.0) – 0.31 39

Constant LDIF Uib Un EGn LSTOCK D67 D82 LWQ1T LW LWSQ R2 SER Sargan w2(25)

OLS

GMM-DIFa

194 (4) 3.39 (2.8) 0.01 (0.5) 0.27 (6) 8 (3.6) 0.055 (0.3) .5 (3.6) 0.7 (5.8) 0.09 (2.27) 46 (4.6) 2.7 (4.4) 0.87 0.35 –

– 1.2 (1.3) 0.03 (2.5) 0.14 (4.3) 10.6 (2.4) 0.24 (1.9) 0.9 (10) 0.6 (4.6) 0.047 (1.9) 48.0 (8) 2.9 (9) – 0.26 40

M: migrations, P: population, LW: log of income in the home country, LWSQ, LDIF: log of the (relative) income differential, LwqIT: log income in the first quintile in the home country, truncated at the threshold level $950, Ui: home country’s unemployment, Un: host country’s unemployment, EGn: employment growth in the host country, D67: dummy variable (1967 migration stop in Greece), D82: 1982 French regularization for Portugal, LSTOCKP stock of emigrants abroad on native population, MC sum of the three previous gross emigration outflows. Notes: Country intercepts have been omitted. T-statistics in parenthesis. The Sargan procedure is a test for the overidentifying restrictions in an instrumental variable context. See Arellano and Bond (1991). First-order serial correlation test has been introduced by taking first differences in the original equation. a Dynamic panel data estimation. b Ui in the seventies for Portugal.

in a different context, was identified by Banerjee and Kanbur (1981).27 For relatively higher levels of income, however, further income growth is associated with lower migrations, even after controlling for the income differential. We used two proxies for the migratory chain: the cumulated flows of the last three years of migration, and the share in the native population of the stock of previous migrants abroad. Not surprisingly, the former performed 27

The main difference between our model and the one of Kanbur and Banerjee is that, in the latter, the downward-sloping portion of the income-migration schedule is simply because an increase in the home country’s income entails a reduction in the income differential with the destination country. By contrast, in our model, the income differential is kept constant and the reduction in the migration rate is due to the effect that greater economic well-being exerts on the propensity to migrate.


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better than the latter.28 In principle, the stock variable should be more appropriate because it describes the size of the migration community in the host country; however, the quality of stock data is very poor in many countries (see Appendix for details). Finally, in columns 2 and 4, we follow Arellano and Bond (1991) in allowing for the fact that a fixed effect specification may not be appropriate.29 To deal with this problem, we estimated the equation in a first-difference form30 and relied on an instrumental variable procedure to allow for the resulting correlation between the new error term and the dependent variable.31 The results again provide strong support for our approach. All coefficients, including domestic unemployment, are quite well determined and bear the right sign. Once again, we find that, even after controlling for the wage differential, the level of income in the home country plays a crucial role in influencing migrations, with a positive effect for relatively poor people and a negative effect after a high level of income. As a further check on the robustness of our results, we introduced both a linear and a quadratic trend term into the equation. Neither of these two variables was statistically significant. Furthermore, the size and the statistical significance of all the other coefficients were basically unaffected by this modification. Cumulated emigration performs better than the stock variable, which is now significant but with a negative sign. The negative sign of the stock variable is not unusual, because the change in the stock resents of the return to the home country, which takes place in another phase of the lifecourse and which is negatively correlated with the inflows. We also tested for the conjecture that the structural shift in the composition of labor demand away from low-skilled workers meant that EG in the destination countries had a less significant impact on migrations after 1980.32 We found little evidence in support of this claim.

28

The stock variable is a very painful one. Even if the amount of people abroad come from the same country, they may have originated from different areas of that country and be very different. In addition new migrants have different backgrounds from previous ones, so that they frequently have nothing in common. For a survey see A Venturini (2004, Chapter 2.6.3, pp. 82). It is not the aggregation of the data which causes a poor performance of the stock variable, because also in the analysis of bilateral flows it performs very poorly. 29 This is because when taking the difference from each country’s mean to calculate the country’s fixed P effect, the error term becomes: eit ð1=TÞ Tt¼1 eit and, for relatively small T, is therefore correlated with the lagged dependent variable. 30 Estimation in first difference is also advisable because of the evidence that some series are not stationary, hetheroschedastic s.e. are computed. 31 See Arellano (1987) and Arellano and Bond (1991) for further details on the estimation procedure. 32 Zimmermann (1995) shows that pull factors were much less significant in affecting migrations from Southern Europe after 1973.

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Table 5. Population share of young adult cohorts (14- to 29-year-aged people)

1960 1965 1970 1975 1980 1985 1988

Portugal

Spain

Greece

Turkey

0.236 0.229 0.206 0.238 0.253 0.253 0.251

0.231 0.223 0.218 0.228 0.230 0.241 0.245

0.251 0.234 0.204 0.216 0.215 0.221 0.219

0.250 0.253 0.249 0.270 0.276 0.282 0.283

The statistical properties of the estimated equations appear to be satisfactory. We tested all equations for residual autocorrelation, stability, and predictive power. In no case did we find any indications of significant misspecification. The finding that economic growth in the sending country will have a positive impact on migration for relatively poor countries (to the extent that it relaxes existing constraints on migration), but will exert an opposite effect on middle-income countries (given that potential migrants will then be more willing to consume their home countries’ amenities) offers encouraging support to our model. However, demographic considerations may provide an alternative explanation for this finding. Indeed, demographic transition theories suggest that income growth is initially accompanied by an acceleration in population growth (to the extent that the fall in the death rate predates the decline in the birth rate) and therefore gives rise to an increasing weight of young age cohorts in the population. Given that migration is a (negative) function of age, the larger share of young cohorts will tend to increase migrations. In a second phase, though, the belated decline in the birth rate will induce a decline in the weight of young adult cohorts and a fall in the propensity to migrate. Overall, therefore, demographic factors could fully account for the inverse-U pattern of migrations that we found in our data. We controlled for this factor by introducing into our regressions the share of people aged 14–29 (or 20–29) in the population. Table 5 shows the evolution of the first of these two indicators for our sample countries. The share of young adults at first declines and then rises after 1970.33 Clearly, it is difficult to reconcile this pattern with the supposedly positive effect of young adult cohorts on migrations. This was indeed confirmed by our regression analysis (not reported here). In no case did the share of young adult cohorts (be it measured by the number of people aged 14–29 or 33

Note, however, that data on the size of population cohorts are available only at five-year intervals. In the regression analysis, we were therefore forced to rely on a linear interpolation. For a wider debate on the population issue in migration see for instance Coleman (1991).


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20–29) in the population prove to be an even nearly significant factor in determining migrations. We therefore conclude that, at least for the Southern European countries, demographic factors do not provide a convincing explanation for the hump-shaped pattern of migrations. Overall, our results suggest that the impact of income levels on emigration is rather complex. For relatively richer countries, it will reduce the income differential with the destination countries and also encourage people not to incur the social and psychological costs of migration. Emigration will therefore unambiguously decline. By contrast, for poor countries, the migration impact of higher income should be ambiguous. On the one hand, the income differential with the receiving countries will fall but on the other, the financial constraint which prevented many would-be migrants from going abroad will become less binding. The net effect may plausibly be positive, particularly if the sending country is relatively poor to begin with. Our estimates suggest that after 950$ per capita, the financial constraint is no longer binding and that the turning point in the migration-income relationship falls within a relatively narrow range, about 4000$ per capita. To sum up, our approach moves some steps forward in explaining two apparent paradoxes in the empirics of migrations. First, it is often found that migrants do not come from the relatively poor areas of countries. It is for instance an established fact among economic historians that in the nineteenth century the flow of intercontinental migrations originated mostly from relatively well-off countries in Europe, namely England first and Germany later (Davis, 1984; Razin and Sadka, 1992). Poorer countries in Southern Europe by contrast were relative latecomers as sources of migrant workers. The second puzzle is the fact that often, even in the presence of large and persistent wage differentials, the rate of migration may be very low. The former puzzle is explained by the role of financial constraints for would-be migrants. To account for the second puzzle, we rely on the existence of non-monetary costs of migration and the desire by potential migrants to consume more of their home country’s amenities, when their income increases. The empirical relevance of this approach is likely to be more significant for international migrations, where cultural, geographical and linguistic barriers matter relatively more.

6. Conclusions and policy implications This chapter has sought to shed light on the issues of internal and external labor mobility. Regarding the former, it has shown that the outlook for internal labor mobility in Europe is rather bleak. Despite sometimes persistent wage and income differentials, there is little evidence that even the full abolition of barriers to internal migrations within Europe may lead

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to a resumption of labor flows.34 Our results indicate that the propensity to emigrate from Southern European countries, which used to be the dominant sources of worker migrants within the Community, has fallen dramatically and is not likely to increase again. Indeed, most countries in Southern Europe are well to the right of the migration turning point, meaning that further income growth will further enhance the decline in the propensity to migrate. We have offered a new explanation for this phenomenon, focusing on the impact of income growth, for given wage differentials, on the propensity to migrate. Regarding external migrations, this chapter adds causes of both optimism and pessimism to the traditional view that growth in the sending countries will stem migration pressures. It adds optimism to the extent that it shows that, after a certain point, further growth in the origin countries will lead to lower migration propensity, even with constant wage differentials. Put differently, higher income in the sending countries will lower migrations both through their impact on the income differential and because it will lower the propensity to move abroad. The chapter’s findings, however, are also a cause for pessimism to the extent that they show that such effects will not work for relatively poor countries, where income growth may be associated with more rather than less migrations. Most sending countries in Northern Africa have still a long way to grow before reaching the migration turning point. In these circumstances, aid and development policies, particularly if geared to egalitarian objectives, may not help much in stemming migration. This is not to say, of course, that aid and development policies should not be encouraged. It is meant instead to emphasize that such policies should not be loaded with ancillary objectives such as the discouragement of migration. Acknowledgments We thank Alberto Bisin, Antonio Spilimbergo, Oded Stark, Stephen Yeo, Stefano Fachin for stimulating discussions, and participants at seminars at the University of Munich, Brescia, Cagliari, MIT, Yale, Tufts, Pompeu Fabra for their comments. We are also grateful to Juan Dolado, Nicholas Glytsos, Louka Katseli, and M. Tribalat for supplying some of the data and to Domenico De Palo for superb research assistance. The responsibility for any remaining errors remains ours alone.

34

See Attanasio and Padoa-Schioppa (1991), Eichengreen (1992), and Decressin and Fatas (1995) for further evidence on labor mobility in Europe.


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Appendix. Data and variables appendix A1. Methodology A1.1. Data source The data were collected as part of a CNR project. The use of the dataset was restricted to the members of the project. A1.2. Migration data All data derived from national sources have been cross-checked with the OECD publication now published with the title Migration Trends, which was previously not publicly available and obtained as a restricted internal publication of the SOPEMI group composed of national correspondents. Stock and flows in Germany were derived from the Federal Statistical Office, Wiesbaden. The data refer to the foreign legal population in Western Germany. Auslaendische Wohnbevoelkerung in der Bundesrepublik. Immigration of foreigners into Western Germany. Zuzuege von Auslandern in die Bundesrepublik. Stock and flows in Sweden were derived from SOS Befolken ngsfo¨ra¨ndnagel obtained from the Centre for Research in International Migration and Ethnic Relations, Stockholm University. Ministry of Labour, based on permits issued or renewed by the host countries also not for work purposes. Stock and flows in Switzerland: Statistics Office of the Bundesamt fu¨r Ausla¨nderfragen, Berne. Stock and flows in France: Data received from the INED ‘‘personne entrees’’ (annual source) and foreign population at the Census (interpolated for the missing years). Stock and flows in The Netherlands: CBS Mndstat Bevolking. CBS Jearwerk Buiteniandse Migratie 1977–1985; 1986–1996. CBS, Maandstatistiek Bevolking and Jaarwerk van de Buitenlandse Migrate various years. Stock and flows in Belgium: National Statistical Office. Also used have been: Demographic Statistics, 1990, EUROSTAT Statistiques sur la migration 3C, 1994,EUROSTAT Migration Statistics, 3A, 1995 EUROSTAT A1.3. Other data Total population (P): OECD data. Income per capita in Purchasing Power Parity (Y): Summer and Heston data base.

132


Unemployment rate (U): OECD data. Employment growth (EG): OECD data. Income of the first quintile of the population (LYqI), the share of income of the first quintile was derived from the surveys reported in the WIID of WIDER, which includes the dataset of Deninger and Squire. Unfortunately, annual surveys are not available, so that high-quality surveys were selected and the missing years interpolated. A2. More information on European migration A2.1. Policy issue The only major policy change that took place during this time span, 1962– 1988, is the restriction imposed after the oil shock in 1973, which however was effective after the increase of the unemployment rate and the slowdown of GNP growth of destination countries. Consequently, the dummy is frequently not significant because the policy has been already anticipated by economic changes. Mean

Greece 1960–1988 Log gross migration rate Log income per capita in PP in origin country Log income per capita in PP in destination area Log income differential Log income per capita in PP in origin country I quintile Origin unemployment rate Destination unemployment rate Destination employment growth Log stock of migrants on population Spain 1960–1988 Log gross migration rate Log income per capita in PP in origin Log income per capita in PP in destination area Log INCOME differential Log Income per capita in PP in origin country I quintile Origin unemployment rate

Standard deviation

Max

Min

1.21 8.314

0.81 0.33

2.42 8.66

0.03 7.65

8.982

0.22

9.28

8.58

0.8 7.28

1.36 0.23

1.11 7.50

0.67 6.83

4.46 3.22 0.002

2.12 2.86 0.015

8.143 8.27 0.05

1.68 0.057 0.03

3.35

0.5

3.89

1.9

0.16 8.61

0.89 0.24

1.38 8.91

0.9 8.029

8.97

0.16

9.18

8.65

0.077322 0.086945 7.64 0.33

0.32914 8.16

0.04876 7

8.233436 7.460659

22.01476

1.3466

133


(Continued ) Mean Destination unemployment rate Destination employment growth Log stock of migrants on population Portugal 1960–1988 Log gross migration rate Log income per capita in PP Log income per capita in PP in destination area Log income differential Log income per capita in PP in origin country I quintile Origin unemployment rate Destination unemployment rate Destination employment growth Log stock of migrants on population Turkey 1960–1988 Log gross migration rate Log income per capita in PP in origin country Log income per capita in PP in destination area Log income differential Log income per capita in PP in origin country I quintile Origin unemployment rate Destination unemployment rate Destination employment growth Log stock of migrants on population

Standard deviation

Max

Min

2.580993 2.00187 0.006 0.015 3.17 0.31

5.94694 0.03 3.4

0.32715 0.03 1.9

1.21 8.11 8.97

2.86 8.57 9.22

0.32 7.45 8.51

0.922 0.35 0.23

1.060453 0.098974 6.88 0.37

1.23239 7.39

0.888 6.2

4.989552 2.643175 4.19888 3.006526 0.04 0.007

8.66922 9.26638 0.015

1.78144 0.84681 0.016

3.9

4.55

0.88

1.7

0.908323 0.676404 7.888957 0.224642

2.00182 8.19919

1.17527 7.46737

9.005283 0.221087

9.32451

8.60204

1.245705 1.245705 6.42 0.28

1.33892 6.85

1.12054 5.9

10.76118 1.337179 3.664498 3.013951 0.02 0.0149 2.6

1.28

12.91423 7.54886 3.013951 0.62247 0.05 0.028 3.67

0.3

References Ahearne, G.A., Griever, L.W., Warnock, E.F. (2004), Information costs and home bias: an analysis of US holdings of foreign equities. Journal of International Economics 62, 313–336. Arellano, M. (1987), Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics 4, 431–434.

134


Arellano, M., Bond, S. (1991), Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297. Attanasio, O., Padoa-Schioppa, F. (1991), Regional inequalities, migration and mismatch in Italy, 1960–86. In: Padoa-Schioppa, F. (Ed.), Mismatch and labor mobility. Cambridge University Press, Cambridge. Banerjee, B., Kanbur, S.M. (1981). On the specification and estimation of macro rural-urban migration functions: with an application to Indian data. Oxford Bulletin of Economics and Statistics 43 (3), 7–29. Burda, M. (1993), The determinants of East-West German migration. Some first results. European Economic Review 37, 452–461. Clark, X., Hatton, T., Williamson, J. (2002), Where do U.S. Immigrants come from and Why? NBER Working Paper No. 8998. Cline, W. (1987), The Future of World Trade in Textiles and Apparels. Institute for International Economics, Washington, DC. Coleman, D. (1991), Demographic Projections: Is There a Need for Immigration? Giovanni Agnelli Foundation, Turin. Courant, P., Deardorff, A. (1993), Amenities, non-traded goods, and the trade of lumpy countries. Journal of Urban Economics 34, 299–317. Davis, K. (1984), The migration of human populations. Scientific American 231 (3), 89–105. Decressin, J., Fatas, A. (1995), Regional labor market dynamics in Europe and implications for EMU. European Economic Review 39 (9), 1627–1655. Delgado, J. (2006), Single market trails home bias. Bruegel Policy Brief, n. 5. Djajic, S., Milbourne, R. (1988), A general equilibrium model of guestworker migration. Journal of International Economics 25, 335–351. Easterlin, R. (1961), Influences on European overseas emigration before World War I. Economic Development and Cultural Change 9, 331–351. Eichengreen, B. (1992), Should the Maastricht treaty be saved? Princeton Studies in International Finance, n. 74, Princeton. Faini, R. (1989), Regional development and economic integration. In: da Silva Lopes, J., Beleza, L. (Eds.), Portugal and the Internal Market of the EEC. Banco de Portugal, Lisbon. Faini, R. (1995), Migrations in the integrated EC. In: Baldwin, R., Haaparanta, P., Kiander, J. (Eds.), Expanding European Regionalism: the EC’s New Members. Cambridge University Press, Cambridge. Faini, R., Venturini, A. (1993a), Trade, aid and migrations. Some basic policy issues. European Economic Review 37, 435–442. Faini, R., Venturini, A. (1993b), Italian migrations: the pre-war period. In: Hatton, T., Williamson, J. (Eds.), International Migration and World Development. Routledge, London, pp. 1850–1939. Ferenczi, I., Willcox, W. (1934), International Migrations. United Nations, Geneva.


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French, K., Poterba, J. (1991), Investor diversification and international equity markets. American Economic Review 81, 221–226. Gould, J.D. (1979), European inter-continental emigration 1815–1914. Patterns and causes. Journal of European Economic History 8, 593–679. Guiso, L., Sapienza, P., Zingales, L. (2004), Cultural biases in economic exchange. NBER Working Paper No. 11005. Hansen, B.E. (2000), Non-parametric data dependent bootstrap for conditional moment model. Econometric Society World Congress 2000 Contributed Papers 1556. Hanson, G., Spilimbergo, A. (1999a), Illegal immigration, border enforcement, and relative wages: evidence from apprehensions at the US-Mexico Border. American Economic Review 89 (5), 1337–1357. Hanson, G., Spilimbergo, A. (1999b). Political economy, sectoral shocks, and border enforcement, NBER Working Paper No. 7315, Cambridge. Hatton, T. (1993), A model of U.K. emigration, 1870–1913. CEPR Discussion Paper No. 771, London. Hatton, T., Williamson, J. (1993), Latecomers to mass migration: the Latin experience. In: Hatton, T., Williamson, J. (Eds.), The age of Mass migration. Oxford University Press, New York. op. cit. Hatton, T., Williamson, J. (2006), Global Migration and the World Economy Two Centuries of Policy and Performance. MIT Press, Cambridge, MA. Hillberry R., Hummels, D. (2003), International bias: some explanations. Review of Economics and Statistics, 85 (4), 1089–1092. ILO-UNCHR. (1992), Informal Summary Record: Joint ILO-UNCHR Meeting on International Aid as a Mean to Reduce the Need for Emigration. ILO, Geneva. Im, K., Pesaran, H., Shin, Y. (2003), Testing for unit roots in heterogeneous panels. Journal of Econometrics 122 (11), 81–126. Khan, S.M., Senhadji, S.A. (2001), Threshold effects in the relationship between inflation and growth. IMF Staff papers, vol. 48, no. 1. King, R., Rybaczuk, K. (1993), Southern Europe and the international division of labour: from emigration to immigration. In: King, R. (Ed.), New Geography of European Migrations. Belhaven Press, London, New York: Haltest Press, Wiley. Kiviet, J. (1986), On the rigour of some misspecification tests for modelling dynamic relationships. Review of Economic Studies 53, 241–261. Layard, R., Blanchard, O., Dornbusch, R., Krugman, P. (1992), EastWest migration. The alternatives. MIT Press, Cambridge. Lewis, K. (1999), Trying to explain home bias in equities and consumption. Journal of Economic Literature 37, 571–608. Locatelli, M., Moscato, V., Pasqua, S. (2001), The European community household panel (ECHP): Elements for users with special focus on labour and household economics. CHILD Working Paper No. 24. Lopez, R., Schiff, M. (1998), Migration and the skill composition of the labor force: the impact of trade liberalization in LDCs. Canadian Journal of Economics 31 (2), 318–336.

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Lucas, R.E.B. (2005), International migration and its economic impact. In: Bourguignon, F., Pleskovic, B., Sapir, A. (Eds.), Annual Bank Conference in Development Economics Europe, Are We on Track to Achieve the Millennium Development Goals. The World Bank, Washington. Molle, W. (1990), The Economics of European Integration. Darmouth, Aldershot. Nitsch, V. (2000), National borders and international trade: evidence from the European Union. Canadian Journal of Economics, Canadian Economics Association 33 (4), 1091–1105. Pedroni, P. (1999), Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61 (November Special Issue), 653–669. Portes, R., Rey, H. (2005), The determinants of cross-border equity flows. Journal of International Economics 65, 269–623. Razin, A., Sadka, E. (1992), International migration and international trade. NBER Working Paper No. 4230, Cambridge. Roberson, J., Symons, R. (1992), Some strange properties of panel data estimators. Journal of Applied Econometrics 7, 175–189. Salt, J. (1991), Current and future international migration trends affecting Europe, 4th Conference of European Ministers responsible for migration affairs, Luxembourg. Schiff, M. (1994), Trade Policy, Aid and Remittances: The Impact of International Migrations. International Economics Department. The World Bank, Washington, DC. Sims, C.A., Stock, J.H., Watson, M. (1990), Inference in linear time series models with some unit roots. Econometrica 58 (1). Stark, O. (1993), Some New Thoughts on Migration and Development. Harvard University, mimeo. Stark, O. (1991), The Migration of Labor. Blackwell, Oxford. Venables, A., Smith, A. (1986), Trade and industrial policy under imperfect competition. Economic Policy 3, 621–672. Venturini, A. (2004), Post-War Migration in Southern Europe. An Economic Approach. Cambridge University Press, Cambridge. Vogler, M., Rotte, R. (2000), The effects of development on migration: theoretical issues and new empirical evidence. Journal of Population Economics 13, 485–508. Wolf, H.C. (2000), Intranational home bias in trade. Review of Economics and Statistics 82 (4), 55–563. Wyplosz, C. (1993), Migration from the East: the role of reform and capital mobility. In: Siebert, H. (Ed.), Migration: A Challenge for Europe. J.C.B. Mohr, Tubingen. Zimmermann, K. (1995), European migration: push and pull, Annual Bank Conference on Development Economics.

CHAPTER 6

Geographic Dispersion and Internal Migration of Immigrants Neeraj Kaushala and Robert Kaestnerb a

School of Social Work, Columbia University. 1255 Amsterdam Avenue, New York, NY 10027, USA E-mail address: [email protected] b Institute of Government and Public Affairs, University of Illinois 815 West Van Buren Street, Suite 525, Chicago, IL 60607, USA E-mail address: [email protected]

Abstract We study the correlates of immigrant location and migration choices to address the following questions: What location-specific, economic, and demographic factors are associated with these choices? Does the influence of these factors differ by immigrant characteristics? What are the factors associated with the observed increase in immigrant geographic dispersion during the 1990s? Our analysis suggests that: (1) There is significant heterogeneity in the correlates of immigrant location and migration choices; associations vary by immigrant birthplace, age, gender, education, and duration of residence in the United States. (2) Economic factors are, for the most part, weakly associated with immigrant location decisions. (3) Immigrants appear to be more attracted to states with large (growing) populations; less attracted to states with a high proportion of other foreign-born persons; more attracted to states with high unionization, and less attracted to states with high crime. (4) The association between location-specific characteristics and immigrant location choices changed between 1990 and 2000 for some immigrant groups and this explains most of the increase in geographic dispersion during the 1990s. In contrast, changes in location attributes and changes in immigrant composition explain relatively little of the increase in dispersion. Keywords: Immigrants, Geographic Mobility, residential choices JEL classifications: J11



138

Neeraj Kaushal and Robert Kaestner

1. Introduction Recent years have seen unprecedented geographic dispersion of immigrants within the United States. Although it is true that approximately 70 percent of the foreign-born continue to live in just 6 states, new immigrant communities are growing in regions that did not have any or much foreign-born population even a decade ago. According to the 2000 Census, during the 1990s, the proportion of immigrants more than doubled in 19 states that traditionally did not have many immigrants. Notably, geographic dispersion is not limited to the newly arrived, as immigrants who have been in the country for several years are also spreading out to areas with traditionally few foreign-born persons (Hernańdez-Leoń and Zuń˜iga, 2003; Durand et al., 2000). Geographic dispersion presents challenges for newly emerging immigrant communities. Arrival of immigrant families may create fiscal problems if immigrants use local services, for example public education, but pay less than their share of taxes (Smith and Edmonston, 1998; McCarthy and Vernez, 1998). Alternatively, growing immigrant populations may stimulate economic development and revitalize communities (Moore, 1998; Martin and Widgren, 2002). At the same time, dispersion of immigrants may create ethnic and racial tensions in new immigrant communities (Williams 1994). Indeed, public perception about immigrants ‘‘taking jobs’’ from natives and lowering wages is a source of tension between native and immigrant groups even if empirical evidence supporting this perception is inconclusive (Borjas, 2003; Card, 2001). In short, migration trends have important economic and social implications for both immigrants and natives; and fiscal implications for local communities. An understanding of the factors that determine the location choices of immigrants expands our knowledge of immigrant behavior and can inform debates on the effects of immigration on natives. It is also useful in making forecasts on immigration trends that can in turn be used to assess future regional and local demands for economic and social services. A number of scholars have examined the location choices of newly arrived immigrants (Bartel, 1989; Zavodny, 1999; Borjas, 1999; Bauer et al., 2002; Kaushal, 2005). In comparison, there is limited research on the factors that influence the location choices and migration patterns of immigrants who have been living in the United States for several years, or what we refer to as resident immigrants.1 The purpose of this chapter is to investigate this issue. Specifically, we provide descriptive information 1

Most research on the internal migration of foreign-born population in the US predates the current immigration wave (Bartel, 1989; Bartel and Koch, 1991; Kritz and Nogle, 1994). For research on the location choices of resident immigrants in Europe see Edin et al. (2003), Aslund (2001). For research on the suburbanization of immigrants see Alba et al. (1999) and Logan et al. (2002).

Geographic Dispersion and Internal Migration of Immigrants

139

relating to the following questions: What location-specific characteristics are important correlates of immigrant location and migration choices? For example, how important are economic factors such as wage and employment levels, and how important is the concentration of foreignborn persons or ethnic density? Do the factors that are associated with immigrant location and migration decisions differ by immigrant characteristics such as age, gender, educational attainment, and length of stay in the United States? Is the influence of these factors constant over time? And finally, what ‘‘explains’’ the dispersion of immigrants during the 1990s – was it related to changes in location characteristics, changes in immigrants’ response to these characteristics, or was it due to changing composition of immigrants? 2. Theoretical considerations To study the location decisions of resident immigrants, we begin with a simple behavioral model. Consider a host country with k distinct geographic regions, indexed by j ¼ 1,y, k. Resident immigrants’ choices depend on the net benefits, or utility, of living in a region (Sjaastad, 1962; Greenwood, 1997). Immigrant i’s lifetime utility of living in region j (Uij) is unobserved, but depends on location-specific attributes (Lj) and individual characteristics (Xi) that determine the benefits and costs of residing in that region. Algebraically, we write utility as follows: U ij ¼ gðLj ; X i Þ Immigrant i chooses to locate in region j if U ij 4U ik

8jak

Location-specific attributes that influence the costs and benefits of residing in a region consist of, among other things, economic opportunities and general economic conditions in that region; presence of social and economic networks (e.g., ethnic enclaves and family relationships); provision of public goods and government provided private goods (e.g., social welfare benefits and unemployment benefits); local taxes; psychic costs of living away from the place of birth; and natural (environment and weather) and cultural amenities (Greenwood, 1997; Tienda and Wilson, 1992; Chiswick and Miller, 2005; Bauer et al., 2005; Card and Lewis, 2005). An important location-specific attribute is the presence of other foreignborn persons. Previous research has shown that immigrants locate where other immigrants with the same nativity live (Massey, 1985; Bartel, 1989; Zavodny, 1999; Alba et al., 1999). Underlying this strong empirical regularity is the fact that ethnic enclaves or communities provide access to and information about the local labor, housing, and credit markets (Zhou and Logan, 1989; Tienda and Wilson, 1992; Bauer et al., 2005).

140


Ethnic communities also provide cultural and linguistic affinities that help ease immigrants’ relocation process. On the contrary, locating in ethnic communities may have certain disadvantages. For instance, Chiswick and Miller (2005) find that ethnic enclaves slow down acquisition of destination-specific skills such as English speaking proficiency. Previous studies have reached different conclusions about the effect of certain other location-specific attributes. For example, consider the evidence related to employment. Some studies found that the unemployment rate was positively associated with the probability of living in a state whereas other studies found no or negative link between the unemployment rate and location choices (Greenwood, 1969, 1997; Kritz and Nogle, 1994; Gurak and Kritz, 2000; Aslund, 2001; Jaeger, 2000). Greenwood (1997), in a comprehensive survey of literature on migration, notes, ‘‘one of the most perplexing problems in migration research, at least from the economist’s perspective, was the consistency with which such conflicting results were uncovered in connection with the relationship between unemployment rates and migration’’ (p. 682). Similarly, while Borjas (1999) concluded that immigrants were attracted to states with more generous social welfare benefits, other research rejects the welfare magnet hypothesis (Zavodny, 1999; Kaushal, 2005). Costs and benefits of locating in a state also depend on the personal characteristics of immigrants such as age, marital status, education, employment skills, country of birth, and length of stay in the host country. Mincer (1978) argues that family ties have an important bearing on an individual’s location choice. In the case of natives, he argues, family ties deter migration. For immigrants, family and ethnic ties in the host country ease the process of immigration and significantly influence initial location choice; but as in the case of natives, may deter migration within the host country. Kritz and Nogle (1994) find that foreign-born men were more likely to migrate within the United States than foreign-born women and that residential mobility declined with age. Several researchers have found that highly educated immigrants are more likely to migrate within the United States as compared to the less educated (Bartel, 1989; Kritz and Nogle, 1994; Gurak and Kritz, 2000). In general, immigrants equipped with hostcountry specific skills have better labor market opportunities and are likely to feel less alienated as they possess greater human and cultural capital and this may influence location choices independently of location characteristics (Gurak and Kritz, 2000; Bauer et al., 2005; South et al., 2005). More educated immigrants may also attach less value to living in ethnic enclaves than those without these skills since job-related skills are a substitute for ethnic employment networks (Chiswick and Miller, 2005; Bauer et al., 2005; Kritz and Nogle, 1994). Geographic proximity to the country of birth seems to have an important bearing on where immigrants locate (Schwartz, 1973; Jaeger, 2000; Kaushal, 2005). For example, Kaushal (2005) finds that in 1995–1996 over 90 percent of the newly arrived legal immigrants to the United States


141

from the Caribbean lived on the east coast and an equally large number of newly arrived legal immigrants from Mexico located on the west coast. Length of stay in host country is another crucial determinant of immigrants’ location and migration decisions. Theoretically, length of stay in the United States may encourage or discourage geographic dispersion. Length of residency in host country is a proxy for information (Gurak and Kritz, 2000). The initial choice of locating in ethnic communities may be based on imperfect knowledge, or it may be due to a herd mentality with new immigrants discounting the private information they have in favor of the decisions of recent waves of immigrants without finding out the underlying factors that led to those decisions (Bauer et al., 2002). In either case, immigrants may move to new locations as they acquire more information. Moreover, length of stay may influence immigrant preferences and characteristics. With time, immigrants may acquire labor market skills suitable to the host country, which may open up new economic opportunities outside of ethnic enclaves or communities. Over time immigrants’ visa status may change; temporary residents may receive permanent resident status; permanent residents may become naturalized citizens. Change in visa status may change location preferences or make immigrants eligible for jobs they could not obtain as undocumented or temporary residents, in turn creating opportunities to change location. Alternatively, as immigrants develop bonds within the community, the significance of ethnic communities may grow (Kritz and Nogle, 1994; Gurak and Kritz, 2000). Dependence on ethnic communities may also make it less profitable to invest in skills that may be helpful in acquiring jobs outside the enclave, in turn, limiting immigrants’ location options. Kritz and Nogle (1994) find that immigrants who arrived after 1964 are more likely than earlier arrivals to change locations. In sum, processes explaining the location and migration choices of resident immigrants are complex. These choices may differ depending on immigrants’ age, gender, education, country of birth, and length of stay in the United States. Location-specific attributes may also influence these choices, and the influence of these attributes may differ depending on the characteristics of immigrants. We apply this basic theoretical framework to study the location and migration choices of resident immigrants, and to explain the recent geographic dispersion of some immigrant groups. Mechanically, the recent spatial dispersion of immigrants may be due to changing location characteristics, changing immigrant characteristics, or changes in immigrant responses (associations) to location characteristics. For example, the 1990s may have created better economic opportunities for immigrants outside ethnic enclaves, or immigrants in recent years may have become more responsive to these opportunities. In this chapter, we attempt to identify the important determinants of immigrant location decisions and the contribution of each of these three factors on immigrant dispersion.

142


3. Empirical models 3.1. Current location choice Our first objective is to study how the location choices of resident immigrants of different nativity, age, sex, educational attainment, and length of stay in the United States depend on location-specific attributes such as economic conditions and concentration of foreign-born persons (e.g. share of population from the immigrants’ country of birth, share of ‘‘other’’ foreign-born population). As outlined in the theoretical framework, location choices are outcomes of cost–benefit analyses, and these costs and benefits are rooted in location and individual characteristics. We begin with a model based on the assumption that resident immigrants’ current location represents their long-run equilibrium choice, and that individuals adjust to any change in location-specific attributes instantaneously. The dependent variable for this analysis is the proportion of immigrants from a country or region (pist), who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Membership in a group is defined by age (20–39, 40–59), sex, education (less than high school, high school and some college, and BA plus), and number of years lived in the United States (o6, 6–10, and W10 years). There are 36 ( ¼ 2 2 3 3) groups in each year. We do separate analysis for immigrants from five countries/regions of origin that represent 45% of immigrants living in the United States and a large portion of newly arriving immigrants: Mexico, China, Philippines, and India, and for one grouping of countries, Jamaica, and Dominican Republic (Caribbean).2 We assume that the proportion of resident immigrants belonging to group i that live in state s in year t (pist) depends on the following state characteristics: real wage, employment to population ratio, state population (quadratic), proportion of other foreign-born, proportion born in same country (lagged by 10 years), crime rate, per capita income, poverty rate, and proportion of unionized workers in the state. Many of these factors may potentially be affected by immigrant location decisions. To address the issue of reverse causality, albeit in a limited way, we lag most of the location attributes; details are provided in the data section. Let Lstt be a vector representing the location characteristics of a state.3

2

Country-specific analyses with state-fixed effects control for time-invariant country-state specific factors (such as geographic proximity, weather, psychic costs of relocation, etc.) in an unconstrained manner. 3 Given the potential for reverse causality, our analysis is descriptive in nature, but still valuable given the relative lack of systematic descriptive information on immigrant location and migration choices.


143

The regression model for this analysis is specified by Equation (1): Pist ¼ ait þ gs þ LsðttÞ P þ ist i ¼ 1; . . . ; 36 ðgroupÞ s ¼ 1; . . . ; 51 ðstateÞ

(1Þ

t ¼ 1990; 2000 ðyearÞ The parameters ait and gs are group-year and state fixed effects, respectively. State fixed effects control for unmeasured, time-invariant location-specific attributes. Group-year effects are necessary given that we estimate the model by ordinary least squares. For each group ‘‘i,’’ there are 51 observations (one per state) per year, and these observations sum to one. Thus, within each group-year, the dependent variable is a multinomial probability. Inclusion of group-year effects controls for the mechanical correlation between multinomial probabilities within a group in each year. In addition, calculation of standard errors should account for multinomial correlation (i.e., robust standard errors with clustering at group-year level).4 Equation (1) provides estimates of the effect of location-specific characteristics on immigrant location choices. However, it is likely that the effect of various location-specific characteristics may differ by immigrant characteristics. Further, these effects may differ over time. Thus, we specify a model that allows for such possibility: Pist ¼ ait þ gs þ

2000 X 36 X

LsðttÞ Pit þ ist

t¼1990 i¼1

i ¼ 1; . . . ; 36 ðgroupÞ s ¼ 1; . . . ; 51 ðstateÞ

(2Þ

t ¼ 1990; 2000 ðyearÞ Note that in Equation (2), we allow the effects of location-specific characteristics to differ by 72 group-year categories (36 groups * 2 years). To examine the factors associated with the geographic dispersion of immigrants during the 1990s, we decompose the change in geographic location between 1990 and 2000 into three components: dispersion (between 1990 and 2000) due to changes in group (year) effects, dispersion 4

An alternative way to estimate this model is by using the weighted least squares (WLS) with a block diagonal weighting matrix, with each block representing a year (Zellner and Lee, 1965). However, since many proportions in our analysis are zero, the WLS approach is not feasible. Researchers have also employed conditional logit models on individual level data to study current location choices. This model poses two challenges: first, this model is cumbersome and difficult to interpret as we have 51 choices and second, marginal effects are generally computed around the mean probability of living in a state and such computation ignores the actual distribution of immigrants across states.

144


due to changes in the association (response) between location choice and location characteristics, and dispersion due to changes in location characteristics. To summarize immigrant geographic location and dispersion, we divide states into traditional and nontraditional, where a traditional state is one in which at least 5 percent of the immigrant population from a certain country lived in 1980. We then examine changes in the probability of living in a traditional state between 1990 and 2000. Specifically, let P90 be the proportion of group i that lived in traditional immigrant states in 1990 and P00 be the proportion of group i that lived in traditional immigrant states in 2000. Total change in geographic dispersion for group i is given by DDi ¼ Ps90 Ps00 ¼ ða90 a00 Þ þ Ls90 P90 Ls00 P00

(3)

The total change in dispersion has three components: changes in group response between 1990 and 2000, denoted by Dai; changes in the association between location choice and location characteristics for group i, denoted by Dd1i (keeping location attributes at the 1990 level), and changes in location attributes, denoted by Dd2i (keeping associations at the 1990 level). These components are shown below for one group: DDi Dai þ Dd 1i þ Dd 2i Dai ¼ a90 a00 X Dd 1i ¼ d 1iL ¼ Ls90 ðP90 P00 Þ

(4Þ

L

Dd 2i ¼

X

d 2iL ¼ P90 ðLs90 Ls00 Þ

L

The three components of the change in total dispersion are partial effects and do not add to total dispersion DDi. We prefer to focus on the partial effects rather than using an Oaxaca-type decomposition because the partial effects reflect the fact that the change in dispersion occurs from 1990 to 2000, and therefore the logically appropriate base year to calculate changes between 1990 and 2000 is 1990. The Oaxaca decomposition would use both 1990 and 2000 as the base year. To simplify the presentation of our results, we show the effects computed using Equation (4) for 10 demographic groups defined by age (2 groups), sex (2 groups), education (3 groups), and years in the United States (3 groups). Note that these 10 groups are not mutually exclusive, and that the effects for each of the 10 groups are weighted averages of the effects for the 36 mutually exclusive demographic groups defined by age, sex, education, and number of years lived in the United States. Weights are the share of immigrants from a country in each of the 36 groups.


145

3.2. Internal migration One limitation of the current location choice analysis is the assumption of a long-run equilibrium with instantaneous adjustment. To assess the importance of this assumption, we also examine the migration of resident immigrants. We focus on inter-state migration. Like current location choice decisions, migration decisions too are based on cost–benefit analyses. Here, however, we can suspend the assumption of a long-run equilibrium and examine changes in location directly. We consider immigrant i to have migrated if in year t he lived in a different state than in year t5. According to our theoretical model, the probability that immigrant i living in state j migrates depends on the costs and benefits of migration. The costs and benefits of migration can be summarized by differences in lifetime utilities between locations, which are determined by individual characteristics (X) and location-specific (origin) attributes (L) in year t5. As in the current location analysis, we do not observe all location-specific attributes that may be affecting location choice decisions. Let the unmeasured differences in lifetime utility between locations j and k (k ¼ 1, y, K) be denoted by mjk. Let M* denote a latent variable indexing the gains from migration and let M be an observed indicator of whether or not a move has occurred between period t 5 and t. The regression specification for this model can be written as: M ijt ¼ jj þ jt þ X it jx þ jL Ljt5 þ lf ðmj1t ; . . . ; mjKt Þ þ eijt M ijt ¼ 1

if

M ijt 40

M ijt ¼ 0

if

M ijt 0

(5Þ

i ¼ 1; . . . ; N j ¼ 1; . . . ; 51 t ¼ 1990; 2000 Equation (5) refers to a binomial outcome (Mijt, inter-state move/no inter-state move) and the model includes state-of-origin fixed effects (fj), year-fixed effects (ft), characteristics of the immigrant (Xit), attributes of the origin state in year t5 (Ljt5), and an unspecified function ( f ) of the unmeasured differences in lifetime utility between location j and other locations (mjkt), which may vary by year. Data limitations prevent us from measuring mjkt and therefore we have an omitted variable problem (Kaestner et al., 2003). One way to address the omitted variable problem is to control for the migration rate among natives in the origin state j.5 This specification allows us to adjust for unobserved factors that affect 5

Inclusion of population in the current location choice analysis serves a similar purpose.

146


migration from that state.6 The regression equation can be written as: M ijt ¼ jj þ jt þ X it jx þ jL Ljt5 þ ZN jt5 þ eijt

(6)

In Equation (6), Njt5 denotes the inter-state migration rate of natives who lived in state j in year t5. Time-varying location-specific characteristics (Ljt5) consist of real wage, employment to-population ratio, state population (quadratic), proportion of other foreign-born and proportion of foreign-born with the same ethnicity (measured in year t10); crime rate, per capita income, poverty rate, and proportion of unionized workers. Individual characteristics (Xit) consist of age, education, sex, marital status, other family income, number of children, and number of young children, English speaking ability, number of years lived in the United States, and citizenship status. To estimate the parameters in Equation (6), we use logistic regression with robust standard errors that are clustered on origin states. For this model, we do not obtain separate estimates of the effect of location attributes by immigrant characteristics. The main purpose of this analysis is to assess the restrictiveness of the long-run equilibrium assumption underlying Equation (1) and to address the omitted variable problem present in the current location choice analysis. Here we mainly want to investigate whether estimates from the migration model, which examines changes in location, are consistent with estimates from the current location choice model.

4. Data We use the five percent samples of the Integrated Public Use Microdata series (IPUMS) of the 1990 and 2000 US Census.7 The analysis is conducted on immigrants from five of the largest source countries/ country-groups, viz. Mexico, the Philippines, China, India, and the Dominican Republic and Jamaica. In 2000, approximately 45 percent of all foreign-born population living in the United States was from our selected group of countries that are also among the fastest growing groups of immigrants in the United States. A person is defined as an immigrant if he is born abroad, and is a naturalized US citizen or non-citizen. We restrict our analysis to foreignborn population in age group 20–59. State characteristics such as 6

We compute migration rate among natives, by sex, education, marital status, and age. Arguably, immigration, or arrival of new immigrants to a state, may induce outflow of natives. Empirical research, however, provides conflicting evidence in this regard (Card, 2001; Borjas, 2005). 7 In the inter-state migration analysis, for Mexicans we select a 20 percent random sample and for immigrants from other countries we use the 100 percent sample.


147

employment to population ratio, real wage (deflated by the consumer price index), and proportion of unionized workforce are computed from the Current Population Survey-Outgoing Rotation Groups files using both foreign- and native-born persons. Employment to population ratio and wage are computed by age and gender; thus, within each state and year, there are four values for each of these variables. State crime rate is taken from the Federal Bureau of Investigation crime reports, per capita income is from the Bureau of Economic Analysis and population and poverty rate from the Statistical Abstracts. To minimize measurement error, all location-specific attributes (other than state demographic characteristics) are 3 year averages. In the current location choice analysis, state characteristics are averaged for t2, t1, and t; and in the internal migration analysis, origin state characteristics are averaged over t5, t6, and t7. We use three demographic variables: state population, proportion of state population from immigrant’s country of birth to capture ethnic density, and proportion born in other (other than those with the same nativity) foreign countries to measure immigrant density. To avoid regressing the dependent variable on itself, ethnic density is lagged by 10 years in the current location choice analysis; whereas state population and proportion other foreign-born are measured as of time t. In the internal migration analysis, like other state characteristics, population for the origin state is measured as of t–5; both ethnic and immigrant density are lagged by 10 years since the Census does not have data on the immigrant population in year t5. Census data provide information on demographic characteristics of individuals such as their age, gender, marital status, education, birthplace, when arrived in the United States, which are used to define immigrant groups in the current location choice analysis and as controls in the internal migration analysis. In the internal migration analysis, we also control for citizenship status, English-speaking proficiency, number of children in the family, unearned family income, and state of residence 5 years ago. The variable relating to state of residence 5 years ago is used to define whether an individual moved inter-state; and to construct inter-state migration rate for natives. For this reason, migration analysis is limited to immigrants who have been in the United States for at least 5 years.

5. Results 5.1. Current location choice: Descriptive analysis Table 1 presents the proportion of immigrants living in the six states with the largest share of immigrants from a country/region in 1980, and shows that the degree of clustering varies by immigrants’ country of origin.

India

0.85 0.78 0.69

0.85 0.81 0.68

0.71 0.82 0.79

0.40 0.22 0.03 0.03 0.04 0.04 0.77

0.82 0.77 0.65

0.65 0.71 0.77

0.33 0.23 0.03 0.04 0.04 0.05 0.72

CA HI IL NY NJ VA

0.80 0.80 0.75

0.78 0.78 0.75

0.46 0.10 0.07 0.06 0.04 0.03 0.77

0.78 0.80 0.79

0.78 0.83 0.78

0.53 0.08 0.05 0.05 0.05 0.03 0.78

0.74 0.75 0.76

0.70 0.73 0.76

0.49 0.07 0.04 0.05 0.05 0.03 0.74

NY CA IL NJ TX PA

0.73 0.65 0.60

0.65 0.64 0.56

0.15 0.14 0.12 0.10 0.07 0.04 0.62

0.79 0.73 0.63

0.65 0.72 0.66

0.15 0.20 0.08 0.13 0.07 0.03 0.66

0.73 0.69 0.61

0.60 0.65 0.66

0.11 0.21 0.07 0.13 0.07 0.03 0.63

NY FL NJ CT MA CA

0.93 0.86 0.80

0.88 0.89 0.89

0.65 0.09 0.07 0.03 0.03 0.02 0.89

Note: Traditional immigrant states are the six states with the largest proportion of immigrants from a country (region) in 1980.

0.79 0.80 0.79

schooling 0.94 0.91 0.91 0.91 0.84 0.86

0.39 0.24 0.04 0.03 0.03 0.03 0.77

Years of o12 12–15 Z16

CA NY IL TX MA NJ

of years in the United States 0.93 0.89 0.69 0.77 0.94 0.90 0.76 0.76 0.93 0.93 0.86 0.77

0.45 0.19 0.06 0.05 0.01 0.02 0.78

Number o6 6–10 W10

0.58 0.21 0.06 0.03 0.01 0.01 0.91

1980

0.94 0.87 0.83

0.90 0.89 0.87

0.52 0.17 0.10 0.03 0.05 0.03 0.88

1990

0.92 0.91 0.80

0.85 0.89 0.86

0.48 0.17 0.11 0.03 0.06 0.02 0.86

2000

Dominican Republic and Jamaica

0.58 0.22 0.08 0.03 0.01 0.01 0.93

Philippines

CA TX IL AZ NM WA Total

China

1980 1990 2000 State 1980 1990 2000 State 1980 1990 2000 State 1980 1990 2000 State

Mexico

Proportion of immigrants living in traditional immigrant states, by country of birth, years in the United States and education (Census data on foreign-born individuals aged 20–59)

State

Table 1.

148 Neeraj Kaushal and Robert Kaestner


149

For instance, geographic concentration among immigrants from India, China, and the Philippines is much lower than that among immigrants from Mexico and the Caribbean (Dominican Republic and Jamaica). Between 1990 and 2000, there was a general increase in geographic dispersion for all immigrant groups, especially Mexicans. In 1980 and 1990, a little over 90 percent of Mexicans lived in 6 states; this proportion fell to 78 percent in 2000. The figures in Table 1 also reveal a systematic change in the relationship between time in the United States and geographic dispersion. In 2000, the longer a person had been in the United States, the less likely it was for him to live outside one of the traditional immigrant states. In 1980 and 1990, this was generally not the case, as there was either little difference in geographic dispersion by time in the United States or older immigrants were more dispersed than the newly arrived. The exceptions are newly arrived immigrants from Mexico and China who increased their dispersion between 1980 and 1990. The relationship between immigrant education and geographic location also changed between 1980 and 2000. In 1980 and 1990, more educated immigrants exhibited greater dispersion than the less educated. During the 1990s, however, Mexicans and Filipinos without a college degree dispersed at the fastest pace. As a result, in 2000, geographic concentration of Mexican and Philippine immigrants did not differ by their years of schooling. In sum, geographic assimilation of immigrants during the 1990s was accompanied by changes in relative dispersion of newly arrived and older immigrants. Although in the earlier decades older immigrants exhibited a greater tendency to disperse, in the 1990s, recently arrived immigrants appeared to be spreading out the most. Among the groups studied, newly arrived, low-educated Mexican immigrants and newly arrived, Chinese immigrants dispersed at the fastest pace. Location choices in 2000 also suggest that among the two largest groups of immigrants – Mexicans and Filipinos – less educated persons were as dispersed as the highly educated.

6. Current location choice: Multivariate analysis Table 2 presents estimates of current location choice model based on Equation (1) with state fixed effects. Estimates from a similar analysis without state fixed effects are in Appendix A. Each column in this table is from a separate regression. The dependent variable is the proportion of immigrants from a country (listed in the column heading) who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Immigrants from a country are divided into 36 groups defined by age, sex, education, and years lived in the United States. Each regression also controls for 72 group-year effects.

150

Table 2.


Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth Mexico

Employment/ 0.020** population (0.009) Real wage 0.032 (in 100 dollars) (0.047) Real per capita income 0.003 (in 10,000 dollars) (0.003) Poverty rate (poverty 0.002*** (0.001) rate/10) Unionization 0.026* (0.013) Population (in millions) 0.033*** (0.005) Population squared 0.001*** (0.000) (in millions) Proportion other 0.231*** (0.075) foreign-born Proportion with same 0.958*** nativity (in t10) (0.200) Crime rate (per 100 0.004*** persons) (0.001)

China

Philippines

India


0.004 (0.012) 0.109** (0.049) 0.001 (0.003) 0.002** (0.001) 0.017 (0.016) 0.017*** (0.004) 0.001*** (0.000) 0.193*** (0.069) 1.404 (4.189) 0.003*** (0.001)

0.013 (0.026) 0.005 (0.043) 0.002 (0.007) 0.000 (0.002) 0.009 (0.026) 0.014** (0.005) 0.0004*** (0.0001) 0.054 (0.123) 0.982 (1.794) 0.003** (0.001)

0.013 (0.009) 0.130*** (0.037) 0.010*** (0.003) 0.001 (0.001) 0.001 (0.015) 0.011*** (0.003) 0.0002** (0.0001) 0.097* (0.056) 1.114 (1.132) 0.001 (0.001)

0.032** (0.013) 0.006 (0.048) 0.010* (0.005) 0.004* (0.002) 0.054** (0.026) 0.004 (0.004) 0.0001 (0.000) 0.055 (0.081) 3.279** (1.405) 0.0002 (0.001)

Notes: Estimated coefficients in each column are from a single regression. The dependent variable is the proportion of immigrants from a country (listed in the column heading) who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Membership in a group is defined by age, sex, education, and number of years lived in the United States. Heteroscedasticity adjusted standard errors clustered on group-year are in parenthesis. Each regression controls for state fixed effects, 36 group effects and 36 group-year interactions. * 0.05opr0.10. ** 0.01opr0.05. *** pr0.01.

Robust (Huber–White sandwich estimates) standard errors clustered on group-year are in parentheses. Estimates in Table 2 suggest that the effect of location characteristics varies depending on immigrants’ country of origin. Economic attributes have modest and sometimes statistically significant effects on immigrant location choices. For instance, a one percentage point increase in the employment rate (employment /population) raised the proportion of immigrants from China, India, and the Philippines living in a state by a statistically insignificant 0.4–1.3 percentage points and significantly lowered the proportion of immigrants from Mexico, Jamaica, and Dominican Republic by 2.0–3.2 percentage points. Note that our inconsistent findings with respect to the effect of employment


151

opportunities on location choices, and the counterintuitive finding that higher employment leads to a lower probability of locating in a state in the case of immigrants from Latin America is in line with previous research that failed to find a consistent effect of the employment rate on immigrant migration choices (Greenwood, 1997; Kritz and Nogle, 1994). Estimates in Table 2 also suggests that a one dollar increase in real wage: lowered the probability of living in a state by 0.005–0.1 percentage points for immigrants from Mexico, China, India, and the Caribbean, and raised the probability of living in a state for Chinese immigrants by 0.1 percentage points. The real wage effects are larger and statistically significant for immigrants from China and India. A $10,000 increase in state per capita income had no statistically significant effect on the location choice of Mexican, Chinese, and Filipino immigrants, but was associated with an increase in the proportion of immigrants from India, Jamaica, and the Dominican Republic by a statistically significant one percentage points. State poverty rate too had a modest effect on the location choices of immigrants; a 10 percentage point increase in the poverty rate raised the proportion of immigrants by 0–0.4 percentage points, with the effects being statistically significant for Chinese, Mexican, and Caribbean immigrants. In general, immigrants appear to be more likely to live in states with more unionized labor force. A one percentage point increase in the proportion of unionized workforce raised the probability of living in a state by 0.001–0.05 percentage points; estimates of the effect of unionization are relatively large and statistically significant for Mexican and Caribbean immigrants. Unlike the effect of economic factors, the impact of state demographic attributes is large and more uniform across various immigrant groups. For instance, a one million increase in population raised the proportion of immigrants 0.5–2.8 percentage points (computed around the mean state population of 5 million) with the effects being statistically significant for all groups except immigrants from Jamaica and the Dominican Republic. A one percentage point increase in the proportion of other foreignborn (other than own country of birth) in a state’s population reduced the proportion of immigrants 0.05–0.2 percentage points and 3 of the 5 estimates – the larger estimates – are statistically significant. A one percentage point increase in the proportion of state population from the same country of birth (lagged by 10 years) reduced the probability that an immigrant from the same country will locate in a state for all immigrant groups except for the Chinese, who were more likely to live in states with high density of Chinese immigrants. All estimates of the effect of samenativity population are large, although not always statistically significant, and imply one-to-one or greater changes in same-nativity population; every one percentage point change in own-nativity population is associated with a one, or greater than one, percentage point change in the probability

152


of living in a state.8 The absence of statistical significance for associations of such magnitude is not simply a result of the statistical imprecision due to a lack of within-state variation in own ethnic density. In two of the three cases in which the estimate of the effect of own-nativity population is not statistically significant, the estimate would remain insignificant even if we use the standard errors from a model that excluded state fixed effects (see Appendix A). Finally, high crime rates reduced the probability that Mexican, Chinese, and Philippine immigrants would live in a state; the location choices of Caribbean and Indian immigrants, however, were unaffected by crime. Effect sizes are relatively small – a percent increase in crime (a unit increase in crime per 100 people) changed the proportion of immigrants 0.4–0.1 percentage points. To sum up, our analysis so far suggests that the correlates of location choices differ significantly across immigrants from different origin countries. However, some uniformity was found. Immigrants appear to be attracted to states with large (growing) populations, and less attracted to states with high proportion of other foreign-born persons. Surprisingly, a higher share of own-nativity population is associated with a lower probability of living in a state for all immigrant groups in our analysis except for the Chinese. This finding is inconsistent with much past research and is explained by the inclusion of state fixed effects in our analysis. Estimates in Appendix A reveal that in models without state fixed effects, a larger share of own-nativity population is positively associated with the probability of locating in a state. Moreover, the magnitudes of the estimates are extremely large. However, when state fixed effects are included, the large positive association is eliminated and in fact, the sign is reversed in four of the five cases. It should also be added that most of the previous research was based on immigration trends before the 1990s, and our finding may partly reflect new immigration patterns. In general, the association between economic factors and location choices is quite heterogeneous usually small in magnitude, and often statistically insignificant. Better economic opportunities such as higher wages and greater employment levels are often (60% of the time) negatively associated with the probability of living in a state, and higher poverty rates are always positively associated with the probability of living in a state. These are counterintuitive findings if we believe that immigrants are attracted to places with greater economic opportunity and stronger economies. Obviously, there may be an omitted variables problem, but less so in this analysis than in other analyses that fail to include state fixed effects, and these measures may not reflect the true economic opportunities

8

The mean value of the share of state population with same nativity varies from 0.1 to 0.6 percent for the 5 ethnic groups.


153

for immigrants. However, these results may be accurate and if so, merit further theoretical and empirical investigation. Finally, immigrants tend not to locate in states with high crime and are generally more likely to live in states with high rates of unionization. To examine whether the associations between location characteristics and immigrant location choice changed between 1990 and 2000, we repeat the above analysis with one modification – now we allow the effect of location characteristics to differ in 1990 and 2000. The results from this analysis are presented in Table 3 (Appendix B shows models without state fixed effects). Estimates in Table 3 suggest that in 2000, the association between immigrant location choices and several location characteristics were statistically different from similar associations in 1990. These differences are indicated using the symbol (þ). Among Mexican immigrants, significant differences were observed in the association between location choices and the following characteristics: employment rate, poverty rate, unionization, population, and crime rate. Some of these differences do not appear to be qualitatively important, for example, differences in the associations related to population but others such as those pertaining to employment, poverty rate, and unionization are more substantial. For example, in 1990 employment rate had a negative and statistically significant effect on the location choices of Mexican immigrants; in 2000, the size of the effect is greatly reduced and statistically insignificant. The other group for which associations between location choices and several state attributes changed from 1990 to 2000 is immigrants from the Dominican Republic and Jamaica; differences in associations are observed for the following attributes: poverty rate, unionization, population, and proportion with same nativity. Most of these differences, while statistically significant, are not qualitatively different in that the sign and general magnitudes of the associations do not change; one exception is the poverty rate, which was negatively associated with the probability of living in a state in 1990 and positively associated with this probability in 2000. For the remaining immigrant groups, there were few major differences over time in the associations between location choices and location characteristics. Overall, the associations between economic factors and location choices continue to be small, and often statistically insignificant, in both the years. Associations between immigrant location choices and demographic attributes of locations become relatively modest when the effect of location attributes is allowed to differ by year (in comparison with the estimates in Table 2). For instance, we find that population does not have a statistically significant effect on the location choices of immigrants from China, the Philippines, and India in both 1990 and 2000 and the estimated coefficients are much smaller in Table 3 than in Table 2. Population continues to have a positive effect on Mexican immigrants, although the size of the effect is

***

0.014þ (0.013) 0.066 (0.074) 0.012*** (0.004) 0.014***þ (0.003) 0.035*þ (0.018) 0.018***þ (0.004) 0.000***þ (0.000) 0.231* (0.130) 0.722* (0.320) 0.007***þ (0.002)

2000 0.002 (0.016) 0.152** (0.063) 0.020*** (0.006) 0.001 (0.002) 0.002 (0.021) 0.001 (0.010) 0.000 (0.000) 0.197*** (0.057) 7.776 (5.328) 0.003 (0.002)

1990 0.004 (0.012) 0.053 (0.066) 0.013***þ (0.004) 0.009**þ (0.004) 0.019 (0.028) 0.003 (0.009) 0.000 (0.000) 0.123** (0.059) 4.887 (4.320) 0.004þ (0.002)

2000

China

0.006 (0.035) 0.054 (0.047) 0.011* (0.006) 0.002 (0.003) 0.034 (0.026) 0.002 (0.004) 0.000** (0.000) 0.089 (0.083) 1.111 (0.983) 0.002 (0.001)

1990 0.024 (0.031) 0.040 (0.044) 0.010* (0.005) 0.004* (0.002) 0.013 (0.026) 0.000 (0.003) 0.000 (0.000) 0.123 (0.079) 0.879 (0.757) 0.003*þ (0.001)

2000

Philippines

0.015 (0.013) 0.147*** (0.043) 0.012** (0.005) 0.001 (0.001) 0.031** (0.013) 0.002 (0.004) 0.000 (0.000) 0.014 (0.054) 0.901 (5.880) 0.000 (0.001)

1990 0.011 (0.008) 0.095* (0.048) 0.008*þ (0.004) 0.003 (0.003) 0.016þ (0.015) 0.001 (0.004) 0.000* (0.000) 0.012 (0.042) 0.638 (2.603) 0.001 (0.002)

2000

India

*

0.032 (0.018) 0.087 (0.067) 0.004 (0.005) 0.004*** (0.001) 0.051*** (0.014) 0.012*** (0.004) 0.000*** (0.000) 0.192* (0.102) 13.715*** (2.949) 0.000 (0.001)

1990 0.026** (0.010) 0.016 (0.035) 0.010*** (0.003) 0.005*þ (0.003) 0.064***þ (0.018) 0.010***þ (0.003) 0.000***þ (0.000) 0.187** (0.092) 5.900***þ (1.279) 0.001 (0.001)

2000


Notes: Estimated coefficients for each country are from a single regression. The dependent variable is the proportion of immigrants from a country (listed in the column heading) who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Membership in a group is defined by age, sex, education, and number of years lived in the United States. Heteroscedasticity adjusted standard errors clustered on group-year are in parenthesis. Each regression controls for 36 group effects, 36 group-year interactions and state fixed effects. All location characteristics are interacted with the two year dummy variables and reported figures are estimates based on these interactions. The symbol ‘‘þ’’ indicates that the coefficient for 2000 is statistically different from the coefficient for 1990. * 0.05opr0.10. ** 0.01opr0.05. *** pr0.01.

0.044 (0.009) Real wage (in 100 dollars) 0.014 (0.037) Real per capita income (in 10,000 dollars) 0.011** (0.004) Poverty rate (poverty rate/10) 0.003** (0.001) Unionization 0.006 (0.014) Population (in millions) 0.016*** (0.004) Population squared (in millions) 0.000 (0.000) Proportion other foreign-born 0.326* (0.173) Proportion with same nativity (in t10) 0.882 (0.550) Crime rate (per 100 persons) 0.006*** (0.002)

1990

Mexico

Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth and year

Employment/population

Table 3. 154 Neeraj Kaushal and Robert Kaestner


155

smaller. For immigrants from the Dominican Republic and Jamaica, population is negatively associated with location choices, which is opposite of the finding in Table 2. The variable measuring proportion with the same nativity (own-ethnic density) is associated with a decline in the proportion of Mexicans living in a state in both 1990 and 2000. For all other groups, increased own ethnic density is associated with an increase in the proportion of immigrants living in a state. All estimates are relatively large, but imprecisely estimated. In all cases (except for immigrants from Mexico), the estimated coefficients are larger in 1990 as compared to 2000 suggesting that the appeal of ethnic communities diminished during the 1990s. Finally, the association between location choice and state crime rate changed for some groups during the 1990s; Mexican, Chinese, and Filipino immigrants were less likely to live in states with high crime, but the location choices of Indian, Jamaican, and Dominican immigrants were indifferent to state crime rate in both years. Overall, estimates in Table 3 demonstrate that there were changes in response to location characteristics between 1990 and 2000 for some groups. We return to this point below when we examine and summarize the correlates of the greater geographic dispersion in 2000. At this point, it is clear that changes in immigrant responses to location characteristics are an important element in explaining changes in immigrant location choices between 1990 and 2000.

6.1. Internal migration: Descriptive analysis There are two notable limitations of the current location choice analysis. One, it is based on the assumption that the current location choices of individuals are their long-term equilibrium decisions. Two, there may be unobserved time-varying location attributes correlated with observed attributes that may confound the estimated coefficients. We can address these problems by studying the internal migration of immigrants. Specifically, we drop the assumption that individuals’ current location choices are their long-term equilibrium and analyze changes in location, and we address the unobserved variable problem by including native migration propensities. Table 4 presents descriptive information of the internal migration of immigrants, by country of birth, years in the US and years of schooling. Due to data limitations, the sample of analysis is restricted to persons who have been in the United States for at least 5 years. We define a person to have made an inter-state move if his/her current residence is in a different state than 5 years ago. Several aspects of the figures in Table 4 merit comment. One, inter-state mobility of immigrants differs depending on their country of origin.

0.10 0.10 0.10 52,181

0.12 0.10 0.08

0.10

1990

0.09 0.09 0.09 3,129,743

0.13 0.09 0.07

0.09

2000

Philippines

Note: Move is defined as an interstate change in residence between year t–5 and t.

0.06 0.08 0.24 37,758

0.05 0.06 0.14 67,475

Years of schooling o12 years 12–15 years Z16 years Sample size

0.07 0.07 0.09 34,226

0.22 0.15 0.06

0.12 0.09 0.05

0.05 0.05 0.09 80,306

0.14

0.09

All 0.05 0.07 Number of years in the United States 5–10 years 0.08 0.09 10–15 years 0.04 0.07 W15 years 0.04 0.05

2000

1990

China

2000

1990

Mexico

0.07 0.12 0.18

0.23 0.13 0.12

0.16

1990

0.11 0.12 0.21

0.27 0.18 0.11

0.18

2000

India

0.07 0.13 0.17

0.11 0.12 0.11

0.11

1990

0.07 0.10 0.13

0.10 0.10 0.09

0.09

2000


0.09 0.12 0.20

– – –

0.14

1990

0.09 0.11 0.19

– – –

0.13

2000

US

Proportion of inter-state moves, by country of birth, education, and years lived in the United States (Census data for individuals aged 20–59 living in the United States for at least 5 years)

Country/region of birth

Table 4.

156 Neeraj Kaushal and Robert Kaestner


157

Immigrants from India are the most mobile, followed by immigrants from China, with Mexican immigrants being the least mobile group. Two, in 2000, the inter-state migration of Indian and Chinese immigrants was higher than that of natives; whereas the inter-state migration of the other three groups was lower. Three, immigrants who had been in the United States for less than 10 years were more mobile than those who had been in the country for 10 or more years, with the sole exception of immigrants from the Dominican Republic and Jamaica. Finally, in general, more educated persons exhibited a higher tendency to make inter-state residential moves, with the exception of immigrants from the Philippines.

6.2. Internal migration: Multivariate analysis Table 5 contains estimates from the regression analysis of migration based on Equation (5). Figures in each cell are changes in the probability or marginal effects derived from a logisitic regression model and robust standard errors clustered within origin states are in parenthesis. Location attributes are for the origin state. Each column represents a separate regression for immigrants from a specific country (region). Estimates of the effects of personal characteristics on migration vary depending on an individual’s country of birth. Gender appears to have no statistically significant effect on the inter-state mobility of immigrants with the exception of Filipino men who are 20 percent (evaluated relative to mean) less likely to migrate, as compared to women. Married Mexican families are less likely to move inter-state, although the effect size is small at approximately 10 percent (of mean); married persons from India, the Dominican Republic and Jamaica are more likely to move inter-state (effect size is 10–16 percent of mean), and marital status has no statistically significant effect on the inter-state mobility of Chinese and Filipino immigrants. Estimates in Table 5 also suggest that English speaking ability has a positive and statistically significant effect on the migration propensity of persons from China and the Philippines (effect size 12–30 percent of mean), and no statistically significant effect on the inter-state mobility of immigrants from Mexico, India, and the Caribbean. In general, more educated immigrants are more mobile with the exception of Filipino immigrants. Mexican immigrants with 12–15 years of schooling are slightly less likely to migrate than those with less than 12 years of schooling; but those with a BA degree are more mobile (than those with less than 12 years of education). Among Chinese and Indian immigrants, higher education has a particularly large effect on migration – persons with 16 or more years of schooling have a 25–50 percent higher migration propensity as compared to those with less than 12 years of schooling. Research pertaining to internal migration of all foreign-born persons

Table 5. Logitistic estimates of the effect of individual characteristics and location attributes on the inter-state migration of foreign-born persons, by country of birth Mexico

Personal characteristics Male

China

Philippines

India


0.010 0.002 0.019*** (0.009) (0.007) (0.004) 0.001 0.002 0.007* (0.004) (0.006) (0.004) 0.002 0.014*** 0.033*** (0.001) (0.003) (0.013) 0.004** 0.012*** 0.006 (0.002) (0.004) (0.011) 0.063*** 0.015 0.013* (0.007) (0.008) (0.017) 0.015** 0.036*** 0.009*** (0.007) (0.004) (0.003) 0.008** 0.033** 0.001 (0.004) (0.004) (0.003) 0.009*** 0.034*** 0.016*** (0.002) (0.004) (0.002)

0.003 0.011 (0.010) (0.012) 0.016* 0.016*** (0.010) (0.006) 0.002 0.007 (0.010) (0.007) 0.003 0.024*** (0.008) (0.004) 0.037*** 0.033*** (0.008) (0.005) 0.047*** 0.001 (0.006) (0.005) 0.013*** 0.003 (0.004) (0.003) 0.029*** 0.007*** (0.005) (0.003)

0.015 0.021 0.094*** (0.040) (0.045) (0.021) Real wage (in 100 dollars) 0.028 0.079 0.060 (0.089) (0.102) (0.102) Real per capita income (in 10,000 0.026 0.020 0.008 (0.062) (0.048) (0.023) dollars) Poverty rate (poverty rate/10) 0.011 0.015 0.011 (0.029) (0.028) (0.020) Unionization 0.211 0.241 0.071 (0.282) (0.422) (0.215) Population (t5) (in millions) 0.005 0.021* 0.013* (0.008) (0.012) (0.007) Population squared (t5) 0.0001 0.001*** 0.0003* (0.000) (0.000) (0.00001) 0.102 Proportion other foreign-born 0.857 1.355* (0.656) (0.781) (0.347) (t10) Proportion with same nativity 0.982 2.712 0.439 (t10) (0.637) (4.993) (1.430) Crime rate (per 100 persons) 0.005 0.003 0.003 (0.005) (0.009) (0.005) Inter-state migration of natives 0.094*** 0.089*** 0.187*** (0.031) (0.033) (0.058)

0.067 0.059 (0.064) (0.076) 0.088 0.021 (0.212) (0.081) 0.042 0.094* (0.049) (0.038) 0.061 0.015 (0.039) (0.045) 0.282 0.457 (0.407) (0.485) 0.021 0.018* (0.014) (0.009) 0.0003 0.0001 (0.0003) (0.0003) 0.043 0.149 (0.600) (0.571) 3.341 0.449 (3.564) (2.025) 0.003 0.003 (0.006) (0.007) 0.391*** 0.233*** (0.058) (0.030)

Married Speaks English well Education ¼ 12–15 years Education Z16 years In United States for 5–10 years In United States for 10–15 years Citizen Location attributes Employment/population

Notes: Figures in each cell are marginal effects of logitistic regressions; heteroscedasticity adjusted standard errors clustered on origin states are in parenthesis. Location attributes are for the origin state. Employment/population and real wage are defined by age and gender; employment/population, real wage, crime rate, per capita income, poverty rate, and unionization rate are origin state averages for t5, t6, and t7. Each column is from a separate regression; each regression also controls for age, number of children, number of young children, other family income, state and year fixed effects. * 0.05opr0.10. ** 0.01opr0.05. *** pr0.01.


159

during the 1970s and 1980s had a similar finding (Bartel, 1989; Kritz and Nogle, 1994). Recent arrivals are more likely to move inter-state than those who have been in the United States for more than 15 years, with the exception of immigrants from Dominican Republic and Jamaica, whose probability to migrate is unaffected by years of stay in the United States. Time in the United States has large effects on migration of Chinese and Indian immigrants; for example, those in the United States for 5–10 years have a migration propensity that is 26–31 percent larger than those who have been in the country for more than 15 years. Comparing this finding with the results presented in Table 1 suggests that the geographic dispersion of more recently arrived immigrants (who have been in the United States for more than 5 but less than 10 years) is a combination of two factors: their initial location choices are more diverse, and they have a greater tendency to migrate as compared to older immigrants. Finally, being a citizen reduces the probability of making an inter-state move by 7–30 percent (of mean), which is perhaps a reflection that citizens develop a higher level of social capital in the communities they live, and are less likely to make inter-state moves. Estimates in the bottom panel of Table 5 show the effect of location characteristics on inter-state moves. It suggests that location-specific economic attributes are not related to the migration decisions of immigrants in a substantial way. The estimates are modest and statistically insignificant in most cases. These results are qualitatively similar to those for the current location choice model. State population has a negative effect on the inter-state mobility of all immigrants except for those from Jamaica and the Dominican Republic. A one million increase in state population lowered the probability of outmigration by 7–13 percent in the case of immigrants from Mexico, China, the Philippines, and India; and raised the probability of out-migration of immigrants from the Caribbean by 18 percent. These results are generally consistent with the analogous estimates in Table 2 with the exception of the finding for immigrants from the Caribbean countries. Here population is negatively associated with living in a state whereas in Table 2 population had a small positive association. Proportion of other foreign-born persons in the origin state had no statistically significant effect on the out-migration of all immigrant groups except for those from China who were less likely to move out of states with high foreign-born population. These findings are inconsistent with those in Table 2, which indicated that the proportion of foreign-born persons was negatively associated with the probability of living in a state. Here, the only statistically significant association suggests the opposite. The proportion of state population from immigrant’s country of origin also had no statistically significant effect on the internal migration of immigrants, although most estimates are positive (except for Indian immigrants) and relatively large; a one percentage point increase in the

160


proportion of the state population with the same nativity raises the probability of migrating by 0.4–2.7 percentage points. These effects are consistent with those in Table 2 except in the case of Indians, and suggest that at least part of the dispersion observed in recent years is on account of internal migration to states with low same-ethnic density. The last row of Table 5 presents the effect of inter-state migration of natives on the interstate mobility of immigrants. Inter-state migration of natives is computed by sex, education, marital status, and age and it is intended to control for unmeasured state-specific factors. As expected, inter-state migration of natives has a positive association with the inter-state migration of immigrants – a one percentage point increase in the inter-state outmigration of the US-born raised the probability that the immigrant population will out-migrate by 0.1–0.4 percentage points. To recapitulate, our analysis in Table 5 suggests that internal migration of immigrants depends on their personal characteristics. Recently arrived immigrants are more mobile than older immigrants. English speaking ability encourages and being a citizen discourages the inter-state migration of immigrants. With the exception of Filipino immigrants, more educated persons have a higher probability to migrate. Several location-specific attributes such as population and percent of population with same nativity appear to have a similar effect on the internal migration and current location choices of immigrants. The one significant exception pertains to proportion of state population that is foreign-born, but not from the same country of origin.

6.3. Decomposition analysis Our next objective is to identify the factors that ‘‘explain’’ the geographic dispersion observed between 1990 and 2000. Here we focus on immigrants from Mexico and China for whom dispersion was substantial. These groups are also two of the largest and fastest growing immigrant groups in the United States. Since the degree of dispersion experienced by the other three groups is relatively modest (Tables 1 and 8), we decide not to present the analysis for those groups. Specifically, we investigate whether dispersion away from traditional immigrant states was due to changes in immigrant responses to location characteristics, changes in location characteristics, or changes in immigrant propensities to locate in traditional immigrant states (see Equation 4). For this analysis, we define traditional immigrant states as those where at least 5 percent of the immigrant population from a certain country lived in 1980.9 9

Using this criterion, the traditional immigrant states for Mexican immigrants are: CA, TX, and IL; for Chinese immigrants: CA and NY; for Filipino immigrants: CA, HI, IL, and NY; for Indian immigrants: NY, CA, IL, NJ, and TX and for immigrants from the Dominican Republic and Jamaica: NY, FL, and NJ.


161

Dispersion is measured as the difference between the proportion of immigrants belonging to group i that lived in traditional immigrant states in 1990 and 2000. Table 6 presents estimates of the change in dispersion explained by changes in responses to location characteristics (location characteristics are set at the 1990 level). Table 7 presents estimates of the change in dispersion explained by changes in location attributes between 1990 and 2000 (holding constant the response to location attributes at the 1990 level). Note also that while the regression models allow the effect of each location-specific attribute to differ for the 36 demographic groups in 1990 and 2000, we present the results for 10 groups, which are not mutually exclusive. Tables 6 and 7 have the same format – columns (1) and (2) give the proportion of immigrants living in the traditional immigrant states in 1990 and 2000, respectively; and the column labeled (1)–(2) computes the level of dispersion, or the difference between the proportion living in traditional immigrant states in 1990 and 2000. Column (3) in Table 6 gives the total dispersion due to changes in immigrant responses (Db); columns (4)–(13) show the change in dispersion due to changes in group response (column 4) and due to changes in response to specific location attributes (columns 5–13). The effect of change in group response measures variation in dispersion due to change in a group’s propensity to locate in a state; for instance, change in the propensity between 1990 and 2000 that young Mexican males with less than a high school degree would locate in traditional immigrant states. The figures in Table 6 suggest that changes in response to location attributes explain a significant amount of the change in geographic dispersion. Estimates in column (3) indicate that changes in responses led to increased dispersion; the modal effect is between 10 and 12 percentage points suggesting a 10–12 percentage point decrease in the probability of living in traditional immigrant states between 1990 and 2000. However, there is significant variation within an immigrant group depending on individual characteristics, and between Mexican and Chinese immigrants in the amount of dispersion explained by changes in responses. Estimates in columns (5)–(13) suggest that immigrants even from the same country have varied responses to location characteristics. For instance, real wage, state population, and ethnic density appear to have much more modest effects on the dispersion of older (in terms of years in the United States) Mexican immigrants as compared to the newly arrived. Similarly, real wage, employment/population ratio and per capita income have a much larger effect on the dispersion of Chinese immigrants with a BA degree than of those without it. Overall, changes in the response to real wages, population and density of ‘‘other’’ foreign-born have the largest effects on changes in geographic dispersion for both Mexican and Chinese immigrants, and in most cases these effects are positive indicating that

0.62 0.63 0.59 0.65 0.73 0.69 0.49 0.57 0.70 0.64

China Male Female Age 20–39 Age 40–59 o 12 years education 12–15 years education Z16 years education In United States o6 years In United States 6–10 years In United States W 10 years

0.55 0.56 0.50 0.62 0.72 0.65 0.43 0.47 0.52 0.62

0.68 0.74 0.68 0.77 0.70 0.71 0.68 0.55 0.66 0.78

0.07 0.07 0.09 0.03 0.01 0.04 0.06 0.10 0.18 0.02

0.16 0.13 0.18 0.09 0.15 0.14 0.11 0.27 0.18 0.09

(1)–(2)

Difference

0.07 0.06 0.05 0.09 0.11 0.12 0.02 0.01 0.12 0.08

0.10 0.09 0.11 0.07 0.10 0.10 0.06 0.18 0.12 0.07

0.01 0.02 0.06 0.04 0.11 0.06 0.10 0.00 0.03 0.01

0.02 0.02 0.03 0.03 0.01 0.06 0.04 0.00 0.04 0.03

0.00 0.01 0.04 0.03 0.05 0.04 0.06 0.01 0.02 0.01

0.01 0.01 0.01 0.03 0.02 0.04 0.04 0.01 0.04 0.02

0.06 0.05 0.09 0.02 0.03 0.04 0.08 0.08 0.08 0.04

0.08 0.05 0.07 0.03 0.06 0.05 0.07 0.15 0.06 0.02

0.05 0.06 0.08 0.02 0.04 0.03 0.07 0.06 0.06 0.04

0.02 0.02 0.02 0.01 0.03 0.00 0.04 0.07 0.02 0.01

Total Demographic Employment/ Real Real per Group population wage capita income (3) (4) (5) (6) (7)

0.01 0.01 0.00 0.00 0.09 0.02 0.05 0.00 0.00 0.00

0.03 0.02 0.03 0.03 0.03 0.02 0.04 0.05 0.03 0.01

(8)

0.00 0.01 0.00 0.01 0.01 0.02 0.01 0.00 0.01 0.01

0.01 0.02 0.01 0.02 0.02 0.02 0.01 0.00 0.03 0.03

(9)

Poverty Unionization rate

0.03 0.02 0.00 0.06 0.03 0.08 0.01 0.03 0.06 0.04

0.11 0.08 0.12 0.04 0.09 0.11 0.05 0.22 0.09 0.05

0.02 0.03 0.03 0.01 0.04 0.03 0.01 0.00 0.02 0.03

0.02 0.03 0.02 0.03 0.03 0.03 0.03 0.01 0.02 0.03

Population Proportion other foreign born (10) (11)

0.03 0.03 0.03 0.02 0.01 0.02 0.04 0.04 0.04 0.02

0.03 0.01 0.03 0.01 0.01 0.03 0.02 0.11 0.01 0.01

0.00 0.00 0.00 0.00 0.08 0.01 0.04 0.01 0.00 0.00

0.01 0.01 0.01 0.02 0.02 0.00 0.02 0.04 0.02 0.00

Crime Proportion born in same rate country (12) (13)

Change in predicted probability of living in traditional immigrant states 1990 location characteristics dispersion due to change in responses (Db)

Probability of living in traditional immigrant states

Note: Traditional immigrant states are defined as states where at least 5 percent of the persons with the same nativity lived in 1980.

0.84 0.87 0.86 0.86 0.85 0.85 0.79 0.82 0.84 0.87

(2)

(1)

Mexico Male Female Age 20–39 Age 40–59 o 12 years education 12–15 years education Z16 years education In United Stateso6 years In United States 6–10 years In United States W 10 years

2000

1990


Table 6.

0.62 0.63 0.59 0.65 0.73 0.69 0.49 0.57 0.70 0.64

China Male Female Age 20–39 Age 40–59 o12 years education 12–15 years education Z16 years education In United States o6 years In United States 6–10 years In United States W10 years

0.55 0.56 0.50 0.62 0.72 0.65 0.43 0.47 0.52 0.62

0.68 0.74 0.68 0.77 0.70 0.71 0.68 0.55 0.66 0.78

0.07 0.07 0.09 0.03 0.01 0.04 0.06 0.10 0.18 0.02

0.16 0.13 0.18 0.09 0.15 0.14 0.11 0.27 0.18 0.09

(1)–(2)

Difference

0.01 0.00 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00

(4)

0.04 0.00 0.06 0.00 0.02 0.00 0.08 0.00 0.09 0.00 0.09 0.00 0.00 0.01 0.00 0.00 0.07 0.00 0.07 0.00

0.02 0.00 0.01 0.01 0.01 0.00 0.06 0.04 0.03 0.00

(3)

0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02

0.01 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.01

(5)

Total Employment/ Real population wage

0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.01 0.01 0.02

0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.02

Real per capita income (6)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.01 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 0.01 0.00 0.00 0.00 0.00 0.01

0.01 0.03 0.00 0.04 0.01 0.05 0.00 0.02 0.03 0.04

0.04 0.03 0.05 0.00 0.03 0.04 0.02 0.08 0.01 0.02

0.01 0.01 0.02 0.02 0.02 0.04 0.01 0.04 0.01 0.02

0.03 0.01 0.04 0.01 0.02 0.03 0.03 0.06 0.01 0.01

Poverty Unionization Population Proportion rate other foreign born (7) (8) (9) (10)

0.05 0.06 0.03 0.08 0.07 0.09 0.01 0.01 0.07 0.07

0.04 0.02 0.03 0.03 0.03 0.03 0.08 0.04 0.04 0.03

0.01 0.01 0.01 0.01 0.02 0.01 0.00 0.00 0.01 0.02

0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05

Crime Proportion born in same rate country (11) (12)

Change in predicted probability of living in traditional immigrant states 1990 responses to locations dispersion due to change in location characteristics (DX)



0.84 0.87 0.86 0.86 0.85 0.85 0.79 0.82 0.84 0.87

(2)

(1)

Mexico Male Female Age 20–39 Age 40–59 o12 years education 12–15 years education Z16 years education In United States o6 years In United States 6–10 years In United States W10 years

2000

1990

Probability of living in traditional immigrant States

Table 7.

Geographic Dispersion and Internal Migration of Immigrants 163

164


changes in responses to these three attributes between 1990 and 2000 resulted in increased dispersion. Interestingly, changes in group responses do not dominate; i.e. in most cases changes in geographic dispersion was not driven by changes in propensity (conditional on measured characteristics) to locate by some groups. Estimates in Table 7 show change in dispersion due to changes in location characteristics. There has been relatively little change in state characteristics, so it is unlikely that changes in characteristics will explain a large share of the change in dispersion. Results in column (3) of the table suggest that changes in location characteristics (keeping immigrant responses at the 1990 level) explain between 0–6 percentage points (or 0–55 percent) of the actual dispersion experienced by Mexican immigrants, depending on their personal attributes. In the case of immigrants from China, changes in location attributes do not explain any dispersion. Indeed, the analysis suggests that if responses in 2000 were the same as in 1990, and only location characteristics had changed proportion of immigrants living in traditional immigrant states would have increased. In general, no single location characteristic has any substantial effect on dispersion of Mexican and Chinese immigrants. In the case of Mexican immigrants, changes in proportion born in the same country appear to have increased dispersion, and changes in crime rate lowered dispersion. In the case of Chinese immigrants, changes in proportion born in the same country lowered dispersion while most other factors had negligible impact.

6.4. Dispersion due to changes in immigrant composition One possibility that we have not considered yet is that changes in geographic dispersion between 1990 and 2000 may have been due to changes in immigrant composition – for example, a relative increase in the proportion of immigrants who had low propensity to locate in traditional immigrant states. To investigate whether that was the case, we examine the effect of changing demographic composition of immigrants on geographic dispersion. Specifically, we compute the probability that immigrants from a country/region would live in the traditional immigrant states if only composition were changed (the probability of living in traditional immigrant states for group ‘‘i’’ were kept at the 1990 level). Table 8 has the results of this analysis. Figures in Table 8 show that demographic composition of immigrants varies depending on their country of origin. For instance, in 2000, 70 percent of Mexican immigrants were young (aged 20–39 years) as compared to only 54 percent of the Caribbean. Similarly, 70 percent of Indian immigrants had at least a BA degree in comparison with only 4 percent of the Mexicans; and a third of Indians had been in the country

Philippines

India

0.56 0.44 0.70 0.30 0.61 0.35 0.04 0.23 0.19 0.58 0.71

0.85

0.56 0.44 0.73 0.27 0.67 0.30 0.03 0.24 0.21 0.55 0.85

0.85

0.00

0.00 0.00 0.03 0.03 0.06 0.05 0.01 0.01 0.02 0.03 0.14 0.63

0.50 0.50 0.43 0.57 0.25 0.40 0.35 0.33 0.22 0.45 0.63 0.59

0.04

0.48 0.02 0.52 0.02 0.52 0.09 0.48 0.09 0.19 0.06 0.35 0.05 0.47 0.12 0.27 0.06 0.24 0.02 0.49 0.04 0.56 0.07 0.70

0.41 0.59 0.56 0.44 0.08 0.47 0.44 0.23 0.22 0.54 0.70 0.70

0.00

0.41 0.00 0.59 0.00 0.46 0.10 0.54 0.10 0.06 0.02 0.49 0.02 0.45 0.01 0.13 0.10 0.19 0.03 0.67 0.13 0.65 0.05 0.61

0.55 0.45 0.57 0.43 0.07 0.28 0.64 0.28 0.24 0.48 0.61 0.60

0.01

0.54 0.01 0.46 0.01 0.61 0.04 0.39 0.04 0.06 0.01 0.24 0.04 0.70 0.06 0.34 0.06 0.19 0.05 0.47 0.01 0.58 0.03


0.78

0.45 0.55 0.63 0.37 0.31 0.57 0.12 0.22 0.23 0.54 0.78


Demographic composition Male Female Age 20–39 Age 40–59 o12 years education 12–15 years education Z16 years education In United States o6 years In United States 6–10 years In United States W10 years Proportion in traditional immigrant states Proportion in traditional immigrant states if only composition changed

China

Dispersion due to changes in composition of immigrants

0.78

0.45 0.55 0.54 0.46 0.27 0.59 0.13 0.14 0.20 0.67 0.76

0.00

0.00 0.00 0.09 0.09 0.04 0.02 0.01 0.09 0.03 0.13 0.02

1990 2000 Difference 1990 2000 Difference 1990 2000 Difference 1990 2000 Difference 1990 2000 Difference

Mexico

Table 8.


166


for less than 5 years in comparison with only 14 percent of the Caribbean immigrants. As in the earlier analysis, changes in demographic characteristics of immigrants are not uniform across immigrants from different countries. Overall, composition of Mexican immigrants registered a modest change during the nineties, and these changes fail to explain any dispersion. Demographic characteristics of Chinese immigrants however changed significantly during the 1990s – the proportion of young immigrants (age 20–39 years) rose by 9 percentage points (21 percent); the proportion with a BA degree increased by 12 percentage points (34 percent) and the proportion of recently arrived immigrants increased by 6 percentage points (17 percent). These changes in composition of Chinese immigrants explain 57 percent of their total dispersion. The demographic characteristics of immigrants from the other countries also changed during the 1990s, but these changes fail to explain any dispersion in the case of immigrants from the Philippines and the Caribbean, and explain a third of the total dispersion among Indian immigrants.

7. Conclusion In this chapter, we study the correlates of location and migration choices of immigrants from five countries (regions), and find that immigrant response to location-specific attributes varies depending on their country of birth. Even among immigrants from the same country, associations between location choices and location-specific attributes vary depending on immigrant characteristics, such as age, gender, education, and years lived in the United States. Other studies based on immigrant location choices during the 1990s have reached similar conclusions (Massey, 2008). We also find that the association between several location characteristics and location choices has changed between 1990 and 2000. Most research on immigrants is done with the assumption that foreign-born persons are a homogenous population. Our analysis, however, suggests this not to be the case, and that generalizations based on the assumption of homogeneity could lead to erroneous conclusions. We find that economic attributes are associated with a relatively modest and often statistically insignificant effect on the location choices of immigrants. This finding is similar to earlier research that failed to find a uniform effect of economic factors on migration decisions of natives (Greenwood, 1997) and immigrants (Kritz and Nogle, 1994). We also find that immigrants are less likely to live in states with high crime and more likely to live in states with high unionization. Immigrants appear to be more attracted to states with large (growing) populations, and less attracted to states with high proportion of other foreign-born persons.


167

Our analysis also suggests that internal migration of immigrants differed depending on individual characteristics with newly arrived, more educated, English speaking, noncitizens exhibiting a higher propensity to migrate. Like the current location choice analysis, the internal migration analysis also suggests that economic factors are associated with modest changes in inter-state moves. We find that the geographic assimilation of immigrants during the 1990s was accompanied by changes in relative dispersion of newly arrived and older immigrants. Although in the earlier decades older immigrants exhibited a greater tendency to disperse, during the 1990s, recently arrived immigrants appeared to be spreading out the most. Among the groups studied, the newly arrived, low-educated Mexican immigrants and newly arrived Chinese immigrants exhibited the fastest dispersion. The current location choices in 2000 also suggest that for the two largest groups of immigrants – Mexicans and Filipinos – less educated persons were as dispersed as the highly educated. However, like previous research based on internal migration during the 1970s and 1980s, our research also showed that more educated immigrants are more mobile (Bartel, 1989; Kritz and Nogle, 1994). For Mexican and Chinese immigrants, the two groups that dispersed the fastest during the 1990s, we examine whether dispersion was due to changes in response to location characteristics, changes in group response, or changes in location characteristics; and find that in most cases, change in response to location characteristics appears to explain most of the dispersion; changes in group response do not appear to explain much dispersion nor do changes in location characteristics. Our analysis suggests that Chinese immigrants would have been more clustered in traditional immigrant states if their response to location characteristics had not changed during the 1990s. Our study also suggests that changing demographic composition of immigrants does not explain the geographic dispersion of immigrants from Mexico, the Philippines and the Caribbean; it explained 57 percent of the dispersion among Chinese immigrants and a third of the dispersion experienced by Indian immigrants.

168


Appendix A. Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth Mexico

China

Philippines

Employment/ 0.008 0.024* 0.033** population (0.015) (0.013) (0.015) Real wage (in 100 0.200*** 0.325*** 0.358*** dollars) (0.040) (0.049) (0.035) Real per capita 0.000 0.004** 0.010*** (0.002) (0.002) (0.002) income (in 10,000 dollars) Poverty rate (poverty 0.011*** 0.017*** 0.012*** rate/10) (0.001) (0.002) (0.002) Unionization 0.063*** 0.039*** 0.002 (0.005) (0.006) (0.005) Population (in 0.003*** 0.001** 0.007*** (0.000) (0.000) (0.000) millions) Population squared 0.0004*** 0.0002*** 0.001*** (in millions) (0.000) (0.000) (0.000) Proportion other 0.193*** 0.036*** 0.040*** (0.010) (0.010) (0.013) foreign-born Proportion with same 1.566*** 11.047*** 1.593*** nativity (in t10) (0.060) (0.917) (0.168) Crime rate (per 100 0.004*** 0.005*** 0.002*** persons) (0.000) (0.000) (0.000)

India


0.027*** (0.009) 0.070** (0.034) 0.008*** (0.001)

0.004 (0.012) 0.155** (0.061) 0.066*** (0.003)

0.005*** 0.030*** (0.001) (0.001) 0.055*** 0.077*** (0.006) (0.009) 0.002*** 0.005*** (0.000) (0.000) 0.0001*** 0.0001*** (0.000) (0.000) 0.107*** 0.026*** (0.012) (0.009) 15.405*** 25.075*** (0.542) (0.912) 0.002*** 0.001*** (0.000) (0.000)

Note: Estimated coefficients in each column are from a single regression. The dependent variable is the proportion of immigrants from a country (listed in the column heading) who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Membership in a group is defined by age, sex, education, and number of years lived in the United States. Heteroscedasticity adjusted standard errors clustered on group-year are in parenthesis. Each regression controls for 36 group effects and 36 group-year interactions. *0.05opr0.10, **0.01opr0.05, and *** pr0.01.

1990

2000

0.029***þ 0.022 0.010 (0.010) (0.018) (0.015) 0.066 0.275*** 0.351*** (0.047) (0.040) (0.048) 0.008***þ 0.009*** 0.008*** (0.001) (0.002) (0.002) 0.015*** 0.042***þ 0.012***þ (0.001) (0.002) (0.005) 0.083***þ 0.001 0.025***þ (0.007) (0.007) (0.007) 0.002***þ 0.001** 0.002***þ (0.001) (0.000) (0.001) 0.000***þ 0.000*** 0.000***þ (0.000) (0.000) (0.000) 0.140***þ 0.051*** 0.029***þ (0.009) (0.015) (0.008) 1.561***þ 12.106*** 10.947*** (0.043) (0.920) (1.086) 0.001***þ 0.006*** 0.011***þ (0.000) (0.000) (0.001)

2000

China

0.024 (0.023) 0.240*** (0.042) 0.002 (0.002) 0.005*** (0.002) 0.043*** (0.006) 0.009*** (0.000) 0.001*** (0.000) 0.109*** (0.026) 1.846*** (0.281) 0.000 (0.000)

1990

1990

2000

India

1990

0.033**þ (0.014) 0.385***þ (0.057) 0.051***þ (0.002) 0.018***þ (0.002) 0.070***þ (0.010) 0.005***þ (0.000) 0.000***þ (0.000) 0.068***þ (0.007) 20.358***þ (0.484) 0.004*** (0.001)

2000


0.065*** 0.033*** 0.023*** 0.051*** (0.018) (0.010) (0.006) (0.016) 0.315*** 0.129*** 0.059þ 0.306*** (0.036) (0.031) (0.033) (0.077) 0.011***þ 0.021*** 0.007***þ 0.098*** (0.001) (0.002) (0.001) (0.004) 0.022***þ 0.008*** 0.012*** 0.029*** (0.002) (0.001) (0.002) (0.002) 0.013***þ 0.028*** 0.042*** 0.030*** (0.005) (0.007) (0.008) (0.010) 0.007***þ 0.000 0.001***þ 0.006*** (0.000) (0.000) (0.000) (0.000) 0.001***þ 0.000*** 0.000***þ 0.000*** (0.000) (0.000) (0.000) (0.000) 0.015þ 0.239*** 0.081***þ 0.262*** (0.013) (0.019) (0.010) (0.016) 1.411*** 37.212*** 15.950***þ 38.785*** (0.192) (1.075) (0.562) (1.031) 0.002***þ 0.004*** 0.004*** 0.005*** (0.000) (0.000) (0.000) (0.001)

2000

Philippines

Note: Estimated coefficients for each country are from a single regression. The dependent variable is the proportion of immigrants from a country (listed in the column heading) who belong to group ‘‘i’’ and live in state ‘‘s’’ in year ‘‘t.’’ Membership in a group is defined by age, sex, education, and number of years lived in the United States. Heteroscedasticity adjusted standard errors clustered on group-year are in parenthesis. Each regression controls for 36 group effects and 36 group-year interactions. All location characteristics are interacted with the two year dummy variables and reported figures are estimates based on these interactions. *0.05opr0.10, **0.01opr0.05, and ***pr0.01. The symbol ‘‘þ’’ indicates that the coefficient for 2000 is statistically different from the coefficient for 1990.

Employment/population

0.017** (0.008) Real wage (in 100 dollars) 0.018 (0.022) Real per capita income 0.017*** (0.001) (in 10,000 dollars) Poverty rate (poverty rate/10) 0.001 (0.001) Unionization 0.031*** (0.004) Population (in millions) 0.006*** (0.000) Population squared (in millions) 0.001*** (0.000) Proportion other foreign-born 0.223*** (0.009) Proportion with same 3.108*** nativity (in t10) (0.110) Crime rate (per 100 persons) 0.001*** (0.000)

1990

Mexico

Appendix B. Estimates of the effect of location attributes on the current location choices of immigrants, by country of birth and year


Appendix C. Logitistic estimates of the effect of individual characteristics and location attributes on the inter-state migration of foreign-born persons, by country of birth Mexico

Personal characteristics Male Married Speaks English well Education ¼ 12–15 years EducationZ16 years In United States for 5–10 years In United States for 10–15 years Citizen Location attributes Employment/population

China

Philippines India


0.007 0.008 (0.011) (0.008) 0.002 0.007* (0.004) (0.006) 0.002 0.015*** (0.001) (0.003) 0.004*** 0.012** (0.002) (0.005) 0.013** 0.059*** (0.006) (0.009) 0.015** 0.037*** (0.007) (0.005) 0.035*** 0.009** (0.004) (0.004) 0.008*** 0.035*** (0.002) (0.004)

0.019*** (0.006) 0.002 (0.004) 0.033*** (0.013) 0.007 (0.011) 0.017 (0.016) 0.009*** (0.003) 0.0001 (0.003) 0.016*** (0.002)

0.017 0.006 (0.013) (0.012) 0.016* 0.017*** (0.009) (0.006) 0.003 0.007 (0.010) (0.007) 0.001 0.023*** (0.009) (0.004) 0.035*** 0.025*** (0.008) (0.006) 0.045*** 0.002 (0.006) (0.005) 0.012*** 0.003 (0.004) (0.003) 0.031*** 0.006*** (0.005) (0.003)

0.147*** (0.055) 0.438*** (0.140) 0.003 (0.019) 0.071*** (0.012) 0.149** (0.064) 0.003*** (0.001) 0.00001 (0.00004) 0.243 (0.168) 1.985 (2.020) 0.007*** (0.003) 0.140*** (0.033)

0.119*** (0.031) 0.239* (0.131) 0.006 (0.018) 0.079*** (0.010) 0.018 (0.058) 0.000 (0.001) 0.0001** (0.00004) 0.034 (0.156) 0.635*** (0.190) 0.011*** (0.003) 0.218*** (0.059)

0.166** 0.007 (0.083) (0.070) 0.014 0.201 (0.216) (0.148) 0.012 0.026 (0.026) (0.027) 0.088*** 0.038** (0.018) (0.020) 0.152 0.198* (0.097) (0.114) 0.002 0.006*** (0.002) (0.002) 0.0001** 0.0003*** (0.0001) (0.00001) 0.080 0.782*** (0.204) (0.231) 18.62*** 0.987 (3.366) (1.052) 0.018*** 0.009** (0.005) (0.004) 0.412*** 0.327*** (0.082) (0.047)

0.046 (0.060) Real wage (in 100 dollars) 0.054 (0.104) Real per capita income (in 0.025 10,000 dollars) (0.025) Poverty rate (poverty rate/ 0.032** (0.015) 10) Unionization 0.108 (0.071) Population, (t5) (in 0.003*** (0.001) millions) Population squared (t5) 0.0001* (0.000) Proportion other foreign0.324 (0.211) born (t10) Proportion with same 0.943*** nativity (t10) (0.331) Crime rate (per 100 persons) 0.009** (0.004) Inter-state migration of 0.113*** (0.032) natives

Notes: Figures in each cell are marginal effects of logitistic regressions; heteroscedasticity adjusted standard errors clustered on origin states are in parenthesis. Location attributes are for the origin state. Employment/population and real wage are defined by age and gender; employment/population, real wage, crime rate, per capita income, poverty rate, and unionization rate are origin state averages for t5, t6, and t7. Each column is from a separate regression; each regression also controls for age, number of children, number of young children, other family income, and year fixed effects. *0.05opr0.10, **0.01opr0.05, and ***pr0.01.


171

References Alba, R., Logan, J.R., Stults, B.J., Marzan, G., Zhang, W. (1999), Immigrants groups in the suburbs: a re-examination of suburbanization and spatial assimilation. American Sociological Review 64 (3), 446–460. Aslund, O. (2001), Now and forever? Initial and subsequent location choices of immigrants. IFAU-Office of Labour Market Policy Evaluation. Working Paper 2001:11. Bartel, A.P. (1989), Where do the new US immigrants live? Journal of Labor Economics 7 (4), 371–390. Bartel, A.P, Koch, M.J. (1991). Internal migration of U.S. immigrants. In: Abowd, J.M., Freeman, R.B. (Eds.), Immigration, Trade and the Labor Market. A National Bureau of Economic Research Project Report. The University of Chicago Press, Chicago, pp. 121–134. Bauer, T., Epstein, G., Gang, I.N. (2002), Herd effects or migration networks? The location choice of Mexican immigrants in the US. IZA Discussion Paper No. 551. Bauer, T., Epstein, G., Gang, I.N. (2005). Enclaves, language and the location choice of migrants. Journal of Population Economics 18 (4), 649–662. Borjas, G. (1999), Immigration and welfare magnets. Journal of Labor Economics 17 (4), 604–637. Borjas, G. (2003), The labor demand curve is downward slopping: re-examining the impact of immigration on the labor market. Quarterly Journal of Economics 118, 1335–1374. Borjas, G. (2005), Native internal migration and the labor market impact of immigration. NBER Working Paper No. 11610. Card, D. (2001), Immigrant inflows, native outflows, and the local market impacts of higher immigration. Journal of Labor Economics 19 (1), 22–64. Card, D., Lewis, E. (2005), The diffusion of Mexican immigrants during the 1990s: explanations and impacts. NBER Working Paper No 11552, August. Chiswick, B.R., Miller, P.W. (2005), Do enclaves matter in immigrant adjustment?. City and Community 4 (1), 5–35. Edin, P.-A., Fredriksson, P., Aslund, O. (2003), Ethnic enclaves and the economic success of immigrants: evidence from a natural experiment. Quarterly Journal of Economics 118 (1), 329–357. Durand, J., Massey, D.S., Charvet, F. (2000), The changing geography of Mexican immigration to the United States. Social Science Quarterly 81 (1), 1–15. Greenwood, M.J. (1969), An analysis of the determinants of geographic mobility in the United States. Review of Economics and Statistics 51 (2), 189–194.

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Greenwood, M.J. (1997), Internal migration in developed countries. In: Rosenzweig, M.R., Stark, O. (Eds.), Handbook of Population and Family Economics (Vol. 1B). Elsevier, New York. Gurak, T.D., Kritz, M.M. (2000), The Inter-state migration of US immigrants: individual and contextual determinants. Social Forces 78 (3), 1017–1039. Jaeger, D. (2000), Local Labor Markets, Admission Categories, and Immigrant Location Choice. Hunter College, New York, Unpublished document. Hernańdez-Leoń, R., Zuń˜iga, V. (2003), Mexican Communities in the South and Social Capital: the Case of Dalton, Georgia. Southern Rural Sociology 19 (1), 20–45. Kaestner, R., Kaushal, N., Ryzin, G.V. (2003), Migration consequences of welfare reform. Journal of Urban Economics 53 (3), 357–376. Kaushal, N. (2005), New immigrants’ location choices: magnets without welfare. Journal of Labor Economics 23 (1), 59–80. Kritz, M.M., Nogle, J.M. (1994), Nativity concentration and internal migration among the foreign-born. Demography 31 (3), 509–524. Logan, J.R., Alba, R.D., Zhang, W. (2002), Immigrant enclaves and ethnic communities in New York and Los Angeles. American Sociological Review 67 (2), 299–322. Martin, P., Widgren, J. (2002), International migration: facing the challenge. The Population Bulletin, Vol. 57, No. 1, Population Reference Bureau, Washington, DC. Massey, D. (1985), Ethnic residential segregation: a theoretical synthesis and empirical review. Sociology and Social Research 69, 315–350. Massey, D. (Ed.) (2008). New Faces in New Places: The Changing Geography of American Immigration. Russell Sage Foundation, New York. McCarthy, K.F, Vernez, G. (1998). Immigration in a changing economy: California’s experience-questions and answers. MR-854/1-OSD/CBR/ FF/WFHF/IF/AMF. Mincer, J. (1978), Family migration decisions. Journal of Political Economy 86 (5), 749–773. Moore, S. (1998), Immigration and the rise and decline of American cities. Essays in Public Policy series: Hoover Institution. Sjaastad, L.A. (1962), The costs and returns of human migration. Journal of Political Economy 70 (Suppl.), 80–93. Schwartz, A. (1973), Interpreting the Effects of Distance on Migration, Journal of Political Economy. Smith, J.P., Edmonston, B. (1998). The immigration debate: studies on the economic, demographic, and fiscal effects of immigration. Panel on the Demographic and Economic Impacts of Immigration, National Research Council.


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South, S.J., Crowder, K., Chavez, R. (2005), Migration and spatial assimilation among US Latinos: classical versus segmented trajectories. Demography 42 (3), 497–521. Tienda, M., Wilson, F.D. (1992), Migration and the earnings of Hispanic men. American Sociological Review 57, 661–678. Williams, R., Jr. (1994), The sociology of ethnic conflicts: comparative international perspectives. Annual Review of Sociology 20, 49–79. Zavodny, M. (1999), Determinants of recent immigrants’ locational choices. International Migration Review 33, 1014–1030. Zellner, A., Lee, T.H. (1965), Joint estimation of relationships involving discrete random variables. Econometrica 33 (2), 382–394. Zhou, M., Logan, J.R. (1989), Returns on human capital in ethnic enclaves: New York City’s Chinatown. American Sociological Review 54, 809–820.

PART II

Production, Earnings and Competition

CHAPTER 7

Understanding the Wage Dynamics of Immigrant Labor: A Contractual Alternative Christoph M. Schmidta,b a

Rheinisch-Westfa¨lisches Institut fu¨r Wirtschaftsforschung, Hohenzollernstr. 1-3, D-45128 Essen, Germany b Ruhr-Universita¨t Bochum E-mail address: [email protected]

Abstract Empirical evidence on the labor market performance of immigrants shows that migrant workers suffer from an initial disadvantage compared to observationally equivalent native workers, but that their wages subsequently tend to increase faster than native earnings. Economists usually explain these phenomena by spot markets for labor and investments into human capital. By contrast, this chapter proposes a contractual model. This alternative has important implications for integration policy, because it suggests investing into the transparency of foreign educational credentials. Also contrasting human capital theory, the model suggests that permanent migrants never earn higher wages than equally skilled temporary migrants. Keywords: Migration, wage dynamics, human capital, implicit contracts Jel classifications: J31, J41, D83

1. Introduction Economic globalization and the collapse of the former Socialist economies in Eastern Europe are just two important factors that have contributed to the remarkable surge in international migration flows during the past decades (OECD, 2009). Meanwhile, the typical immigration countries, Australia, Canada, and the United States, have been joined by other wealthy economies, foremost by those of Western Europe, as major receiving countries for immigrants. In all cases, the degree of their economic and social integration is an important aspect both of immigrants’ own prosperity and of the economic future of their destination countries. Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008013


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The extent of integration will determine whether the receiving society is open for further immigration or rather prefers closing its borders (Bauer et al., 2000; Fertig and Schmidt, forthcoming). This is all the more relevant for – old and new – European immigration countries, for which the age and skill distributions have been changing rapidly, as have the countries of origin of incoming migrant cohorts. Economists commonly assess the speed of economic assimilation by comparing the observed wage or earnings histories of previous immigrant cohorts to that of natives. Existing empirical evidence, most of which has been derived for the United States, suggests that immigrants typically suffer from an initial disadvantage relative to observationally equivalent native workers, but that their wages subsequently tend to rise faster. Early work used cross-sectional data and found this difference in growth rates to be remarkably high. As a stylized fact, immigrants seemed to earn even more than comparable (in terms of age and skill) natives, on average, after spending less than two decades in the destination country (Chiswick, 1978). Whereas the general tendency has been confirmed in numerous studies conducted in a broad range of destination countries (see Borjas, 1994; Bauer et al., 2005, for overviews), its genuine extent is heavily debated in the literature. The seminal contribution by Borjas (1985) emphasized large variations in unobservable traits across cohorts of immigrants who entered at different times. As apparently this unobservable ‘‘quality’’ has declined steadily in US post–World War II history, the more recent literature has debated whether this apparent assimilation is genuine or simply reflects the inability of cross-sectional data to uncover declining cohort ‘‘quality’’. Yet, the literature never seriously questions the economic interpretation of the empirical evidence regarding assimilation dynamics, whatever their magnitude. Without exception, wage dynamics of immigrant labor are explained by productivity gains that are remunerated one-to-one in perfectly competitive spot markets for labor. The faster wage growth of migrant workers is viewed as a result of their incentives for investing into country-specific human capital and, correspondingly, their fast and substantial gains in average productivity relative to natives. This perspective supports clear policy implications. Most importantly, low initial earnings might not be a matter of concern, because most migrants enter as young adults, and the bulk of human capital investment takes place in the initial periods of a worker’s life cycle. Secondly, to improve upon the speed of integration, one should mainly support the acquisition of country-specific human capital. Contractual models of the labor market (Gibbons and Murphy, 1992; Malcomson, 1997) have demonstrated in a more general context, however, that rising age-earnings profiles do not necessarily reflect productivity growth. Rather, the observation of low initial earnings and comparatively steep immigrant wage profiles could be the result of increasing information

Understanding the Wage Dynamics of Immigrant Labor

179

on actual (time-constant) abilities. This chapter formalizes this idea and explores its implications for economic policy. The discussion suggests that an important element of rational migration policy is investing into the transparency of foreign educational credentials. It also contrasts human capital theory (Dustmann, 1993) that argues that observing low initial wages among recent immigrants might be indicating substantial human capital investments. According to the contractual view, this could also signal low average abilities, and thus be a matter of concern. The proposed model is an adaptation of the model by Harris and Holmstrom (1982) to the case in which the true abilities of migrant workers are uncertain. By contrast to the insightful asymmetric-information models of Stark (1991), here the imperfect information is symmetric. Increasing information on true abilities is derived from sequential observations on the workers’ output. In equilibrium, the wages of workers who generate surprisingly high output are bid up by competitive firms, whereas the wages of all other workers remain constant. On the contrary, risk-aversion leads migrants to accept initial wage disadvantages as an insurance against the detection of low ability. As a consequence, the average wages of migrant cohorts are rising over time. This mechanism complements any increases in productivity. The chapter is organized as follows. Section 2 briefly reviews human capital arguments, Sections 3 and 4 present the contractual model and its equilibrium, respectively, and Section 5 discusses its policy implications.

2. The orthodoxy: Country-specific human capital Upon entry into their host country, migrants are different from natives in many respects. As their time of residence in their destination country increases, they typically grow closer to the native population in social, cultural, and economic terms. Economists are mainly concerned with the question, whether the labor market outcomes of immigrants tend to improve considerably over time, as they pick up the language and other destination country-specific skills that make them more productive. Yet, observing their performance over time also reveals, to both the immigrants themselves and their employers, the migrants’ inherent (timeconstant) abilities. The current orthodoxy in the economic migration literature neglects the question of information and emphasizes the acquisition of new skills. Whether these learning processes are sufficient to narrow or even close the initial gap is of crucial importance for the economic future of the respective host country. Will the immigrants invariably belong to an economic underclass and thus become a burden to the welfare system? This question is particularly important for societies with an elaborate system of unemployment insurance and welfare like the Western European

180


countries. In addition, those are exactly the countries whose age structure bends more and more toward the elderly and thus makes it more and more unlikely to sustain the welfare state. Incoming migrants could in principle rejuvenate the society and secure the necessary economic growth, but only if their own economic performance is satisfactory. International research on the labor market performance of immigrants has tended to focus on the earnings dynamics of first-generation immigrants. Most of the studies have been conducted for the United States (see Borjas, 1994; Bauer et al., 2005, for overviews). The influential study by Chiswick (1978) found that immigrants suffer from an initial earnings disadvantage of about 15%, but the earnings profiles of immigrants cross those of natives from below after about 14 years of stay and are higher for immigrants thereafter. Due to problems of identification, the true magnitude of the growth differential between native and immigrant earnings remains hotly debated until today (Borjas, 1985, 1987, 1995; LaLonde and Topel, 1992). In particular, no clear pattern arises from the scarce evidence on immigrants to Germany (see Dustmann, 1993, and Schmidt, 1997, for early, and Fertig and Schurer, 2007, for recent contributions). Predominantly, economists interpret these wage dynamics as a reflection of productivity gains. Human capital theory (Mincer, 1974; Becker, 1975) formalizes the idea that, over time, a worker might acquire general knowledge about the functioning of the labor market or specific to the job. In the context of immigration, the focus is on country-specific human capital (Chiswick, 1978; Duleep and Regets, 1999) that is enhancing worker productivity with all potential employers in the destination country. For native workers, this is part of their endowment at the time of labor market entry, immigrant workers have to acquire it through active investment. It is generally argued that migrants face lower opportunity cost of foregone earnings and have higher returns on human capital investment and therefore have an incentive to invest more. Such an investment will mainly take place in the initial periods of stay in the destination country. In a spot market for labor in which workers are paid according to their value marginal product and where information about worker productivities is common knowledge, these productivity increases translate directly into lower initial earnings and a steeper earnings profile. According to human capital theory, the longer the horizon over which the resulting profits arise, the higher is the rate of return and, thus, the higher is the incentive to invest. The planning horizon of an immigrant at the time of entry into the labor market of the host country is determined by two aspects. First, when an older worker enters the country with the same deficit in country-specific human capital as a young worker, his horizon is shorter. Consequently, he will invest less and his wage profile will be relatively flat. Second, the horizon depends on the intended duration of stay. A worker with a shorter expected duration of residence


181

has relatively small incentives to invest into country-specific human capital. His wage profile would therefore also tend to have a larger intercept and a small slope. As it is likely that most of the investment will happen in the initial periods, the earnings profile of a worker with a long planning horizon should be steeper than that of a comparable worker with a short planning horizon but of equal innate ability. Unfortunately, the planning horizon of immigrants is an unobservable aspect of migrant behavior – and it might even vary over time which could complicate matters considerably. To operationalize the distinction between temporary and permanent migration, Borjas (1987) identifies propensities to return to the home country by the political conditions in the source country. By contrast, Dustmann (1993) and Bauer and Sinning (forthcoming) use interview information on the expected duration of stay at survey time to infer on the expected duration at the time of immigration. Yet, it is inherently difficult to disentangle the wage effects of duration of residence in the destination country from the reverse effects of match quality on duration. This identification problem is reminiscent of the empirical literature on the wage effects of job tenure (Abraham and Farber, 1987; Altonji and Shakotko, 1987; Topel, 1991). It seems fair to conclude that the evidence regarding the precise nature of the duration of residence-wage profile and of its variability with (intended) duration of residence is suggestive at best. The next section develops an alternative justification for immigrants’ age-earnings profiles being steeper than those of native workers, which is completely unrelated to productivity increases. In this contractual model, the revelation of inherent abilities as duration of residence in the destination country is extended is the major force of immigrant wage growth. It will also be demonstrated that this model implies the effects of a variation in return propensities to differ from the human capital view.

3. The contractual model It is one of the undisputed results of labor economics that substantial heterogeneity exists among the members of the labor force. Typically, there are many components of heterogeneity that are revealed to the economic agents themselves only imperfectly and about which information can only be collected gradually over time. In particular, a worker’s inherent ability to perform in the labor market is such a component. Suppose that this ability can be measured by a unidimensional ability index. For native workers, the assessment of their ability is relatively easy. First, in a typical industrialized society, they went through an elaborate system of education and tests of their capabilities starting from early childhood. By the time they enter the labor market, a lot of information about them has already been revealed. Second, because native workers are

182


close to other native workers from previous generations in cultural and social terms, it is easier for all native economic agents to evaluate their abilities. In particular, employers have routinely assessed the abilities of native workers for a long time and this experience is reflected in the quality of their evaluation. Gauging migrant abilities is more complicated. Entering migrants themselves cannot use previous information at school or at work the same way native workers would do this. In addition, they will generally find fewer cases similar to their own to compare themselves with. This also holds for potential employers who might not be able to assess workers’ ability with the same ease as that of native workers. Only if, over time, a relevant history of migration has been established between a particular sending country and a particular receiving country, these problems are reduced. The situation of uncertainty thus seems specifically important to Western Europe, as it increasingly receives migrants from a multitude of new countries of origin. During their labor market career in the host country, the output of migrant workers can be observed, and, over this time, increasing information about their true abilities – as compared to the written credentials they bring with them – will be revealed. This gain in information will have effects on migrants’ wages. In this section, a theory of migrant earnings growth is developed based on this uncertainty idea. The model used is an adaptation of Harris and Holmstrom (1982), a seminal contribution that has been the intellectual foundation for a large range of other important contributions (Narayanan, 1985; Thomas and Worrall, 1988; Beaudry and DiNardo, 1991; Farber and Gibbons, 1996; Chiappori et al., 1999; Holmstro¨m, 1999; Grant, 2003). In this model, the employment relation is not taken as a sequential spot exchange, but as a long-term relationship. In particular, risk-averse workers are insured by risk-neutral firms against random, publicly observed fluctuations in their marginal revenue product, an application of the ideas of implicit contract theory (Parsons, 1986; Azariadis, 1989). As a consequence of this insurance arrangement, average earnings of immigrants are lower initially, but grow faster over time than those of natives, because individual abilities are more uncertain for immigrants than for native workers at the time of labor market entry. In the modeled economy, a large number of infinitely lived identical firms produce a single good. The only factor of production is labor. There are overlapping and equally sized generations of migrants and natives with finite life span T. In the following, observational differences and individual subscripts are suppressed. Workers supply their labor inelastically to the firm making the most favorable offer at each period. They derive no disutility from effort and they can switch employers costlessly. The output yt of a particular worker, either immigrant or native, depends both on this worker’s ability Z and on a disturbance term et that for tractability is


183

assumed to follow a standard normal distribution: yt ¼ Z þ t .

(1)

Two independent stochastic processes are modeled to determine the dynamics of individual earnings development for migrants. The first process is taken directly from Harris and Holmstrom (1982) and concerns the way that workers and employers learn about individual productivities. At the beginning of the first employment relationship after labor market entry, at t ¼ 0, the immigrant worker, the employer, and all other employers in the market share the identical prior belief that the individual’s ability is distributed normally with mean m0 and precision h0. This prior distribution coincides with the ability distribution of the native workers in the market. In each period t, all parties observe the workers output yt and all update their beliefs according to the Bayesian rule: fðytþ1 ZÞ xt ðZÞ xtþ1 ðZytþ1 Þ ¼ R 1 , 0 0 0 1 fðytþ1 Z Þ xt ðZ Þ dZ

(2)

where f(.) denotes the standard normal density function, and xt(.) denotes the normal density function attributed to the unknown ability at the beginning of period tþ1. Consequently, the posterior beliefs of all agents are again identical. After observing the worker’s output for t periods, this belief can be written as follows: P h0 m0 þ tt¼1 yt (3) Zy1 ; . . . ; yt N ; ðh0 þ tÞ1 . h0 þ t In particular, the mean of the individual’s perceived ability follows a martingale, and the precision increases deterministically. Whereas initial perceived mean abilities are the same for every migrant in an incoming immigrant cohort, over time the beliefs become more and more accurate for each individual. As a consequence of revealed individual information, mean perceived abilities spread out over the range of the distribution, until in the limit the belief for each worker is a point mass about his or her true ability. The limiting frequency distribution of perceived abilities equals the frequency distribution of true abilities in the native population. In a competitive spot market for labor, the consequences for observed wages would be trivial. At each point in time, every worker would be paid his or her expected marginal value product, based on the current belief about this worker’s ability. Although individual productivities do not change over time, perceived abilities and therefore individual earnings would, on average, fluctuate around the population mean. In effect, even if uncertainty on abilities was higher for immigrants than for natives, there would not be any difference in average wage growth between migrants and natives.

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The second stochastic process governs the way in which migrants decide upon return migration to their respective country of origin. At the end of each period, the migrant worker might return to the source country for exogenous reasons. In the theoretical model, the probability of return migration conditional on still residing in the host country in period t is assumed to be constant over time, q(t) ¼ q. The value of q is assumed to be perfectly observable by all parties. Temporary migrants display a high value of q, permanent migrants have a lower value of q. Migrant workers’ utility is normalized to be zero once they have returned to their source country and, while they are staying in the host country, capital markets do not allow them any borrowing or saving. Consequently, migrants are not able to smooth their consumption paths, and wages and consumption coincide in each period they spend in their destination country, ct ¼ wt. Individual preferences are assumed to be time-separable and workers are assumed to be risk-averse, Vðc1 ; . . . ; cT Þ ¼

T X ½b ð1 qÞt1 Uðct Þ,

(4)

t¼1

where UuW0 and Uvo0. Firms discount their profits with the same discount rate bo1 as individuals. However, there is asymmetry in the attitude toward risk. As firms are risk-neutral, their preferences can be written as follows: T X Y ½b ð1 qÞt1 ðyt wt Þ. ðy1 ; . . . ; yT ; w1 ; . . . wT Þ ¼

(5)

t¼1

The probability of return migration is taken as exogenous by migrants in maximizing their expected utility as well as by firms in maximizing their expected profits. In effect, different values of q are reflected in different discount rates, but those are equal for both sides of the market. 4. Optimal contracts One essential ingredient of the model is the existence of long-term labor contracts that bind employers to pay a fixed wage over the worker’s entire career. On the contrary, workers are allowed to quit whenever a better offer appears, an assumption that prevents involuntary servitude. Whether these assumptions are realistic depends entirely on the labor market to be modeled. For the German economy, for example, this institutional asymmetry has indeed some intuitive appeal: Turnover is low compared to the United States (Schmidt, 2000) and the labor law is designed to protect the individual worker (Schmidt, 2008). In the model, firms can commit to output-contingent wage contracts, W ¼ ðw1 ðy1 Þ; w2 ðy1 ; y2 Þ; . . . ; wT ðy1 ; y2 ; . . . yT ÞÞ, but workers cannot commit to staying with the firm at a

185


given wage in the future. Instead, wages can be bid up at any time. The labor market is competitive and firms have to solve the following problem at the time the migrant enters the labor market: T P E ½b ð1 qÞt1 Uðwt Þjm0 ; h0 maxW s:t:

E

ð1:Þ ( ð2:Þ E

T P

t¼1

½b ð1 qÞ

t¼1 T P

t1

ðyt wt Þjm0 ; h0

¼0 )

½b ð1 qÞt1 ðyt wt Þm0 ; h0 ; y1 ; . . . yt

(6) 0

t¼tþ1

where the second condition has to be satisfied for all t ¼ 1; . . . ; T and for all possible output histories ðy1 ; :::; yT Þ 2 > > k > > : 0 if xj 4xi 8i a j It can be verified that there exists a unique symmetric Nash equilibrium as well as a continuum of asymmetric Nash equilibria. In any equilibrium, groups 3 through m invest zero effort in activities with probability one (see Baye et al., 1996) so that only the two groups who make the greatest efforts will participate. We conduct our analysis for two groups, groups 1 and 2. Without loss of generality, assume that n1Wn2; thus, group 1 has greater gain from winning the contest. It is clear, therefore, that group 1 is able to bid more than group 2. However, it is not clear how much each will bid in equilibrium. Based on these findings, we can obtain equilibrium expected expenditures, equilibrium probabilities, and expected payoffs. Since the efforts of the two groups are random variables, it is clear that the probability that xi ¼ xj equals zero (P(xi ¼ xj) ¼ 0) (see Cohen and Sela, 2007). Thus, in the case of only two groups, the probability

A Political Economy of the Immigrant Assimilation

of winning the grant becomes ( 1 if xi 4xj Pri ¼ . 0 if xj 4xi

331

(6)

The expected activity level for each group is (see appendix for calculations) Eðx1 Þ ¼

n2 n2 and Eðx2 Þ ¼ 2 . 2 2n1

(7)

The equilibrium probability of winning the contest for each group equals Pr1 ¼

2n1 n2 n2 and Pr2 ¼ . 2n1 2n1

(8)

The expected equilibrium payoff for each group equals Eðw1 Þ ¼ n1 n2 and Eðw2 Þ ¼ 0.

(9)

In equilibrium, the total amount of activities carried out by the groups equals EðX Þ ¼ Eðxi þ xj Þ ¼

n22 þ n2 n1 n2 ðn2 þ n1 Þ ¼ . 2n1 2n1

(10)

Notice that if both groups can obtain the same benefit, n1 ¼ n2 ¼ n, the expenditure of each group is Eðx1 Þ ¼ n=2 and Eðx2 Þ ¼ n=2; the probability of winning for each equals one half, Pr1 ¼ Pr2 ¼ 1=2; the expected payoff for each group is zero, Eðw1 Þ ¼ Eðw2 Þ ¼ 0; and the total effort invested equals X* ¼ n. 2.2. The relative ranking Here, we consider the case when groups compete with one another in a contest in which there is no single winner. Later we will compare the two extreme cases with one another: the absolute ranking with the relative ranking. Without a winner taking all the grants, each group fights to obtain its maximum possible portion. We assume that the contest is characterized by the relative ranking (Lockard and Tullock, 2001), Pri ¼ xri =ðxrj þ xri Þ for r 2. The return to effort in this lottery function is captured by the parameter r. When r approaches infinity, the relative ranking becomes the absolute ranking under which the group that invests in the highest level of activities wins the contest (see Baye et al., 1993, 1996). The idea behind this is that the group with the higher benefit has a weight of infinity and thus

332


will win with probability one, and the group with the lower stake will lose with probability one. For now we assume that r is known and fixed and rr2. The expected net payoff (surplus) for the risk neutral group is thus given by Eðwi Þ ¼

xri

xri ni xi þ xrj

8i ¼ 1; 2.

(11)

The first-order condition, as stated in Equation (2), which ensures that the group maximizes its expected payoff, is given by r rxr1 @Eðwi Þ i xj ¼ r ni 1 ¼ 0 @xi ðxi þ xrj Þ2

8i; j ¼ 1; 2 i a j.

(12)

Denote by xi 8i; j ¼ 1; 2 i a j the Nash equilibrium outcome of the contest. Solving Equation (12) for both groups using a Nash equilibrium, we obtain that the level of activities each group participates equals2 xi ¼

r rnrþ1 i nj

ðnri þ nrj Þ2

8i; j ¼ 1; 2 i a j.

(13)

We can also think of this term of the proportion of the grants obtained from the contest. Therefore, the Nash equilibrium proportion of the grants obtained in the contest equals Pri ¼

nri nri þ nrj

8i; j ¼ 1; 2 i a j.

(14)

The expected equilibrium payoff for each group equals Eðwi Þ ¼

r r rnrþ1 n2rþ1 ðr 1Þnrþ1 nri i nj i i nj n ¼ i nri þ nrj ðnri þ nrj Þ2 ðnri þ nrj Þ2

(15)

8i; j ¼ 1; 2; i a j; ro2. And finally, we can calculate the total amount of effort invested in the contest by the two groups. In the chapter, this measure is called grant dissipation and usually has a negative connotation, that is, the contest designer tries to decrease the grant dissipation. Here, grant dissipation can be seen in a positive light as it helps the country needing help. 2 We obtain from the first-order conditions (Equation (12)) that 8i; j ¼ 1; 2 i a j; ððrxir1 xrj Þ=ðxri þ xrj Þ2 Þni ¼ 1; therefore, it holds that ððrx1r1 xr2 Þ=ðxr1 þ xr2 Þ2 Þn1 ¼ 1 and ððrx2r1 xr1 Þ=ðxr1 þ xr2 Þ2 Þn2 ¼ 1. Using these two equations, we obtain that ðx2 =x1 Þðn1 =n2 Þ ¼ 1 and thus x2 ¼ x1 ðn2 =n1 Þ. Substituting x2 (x2 ¼ x1 ðn2 =n1 Þ) into ðrx1r1 xr2 Þ=ððxr1 þ xr2 Þ2 Þ n1 ¼ 1, we obtain that x1 ¼ ðr n1rþ1 nr2 Þ=ðnr1 þ nr2 Þ2 . In a similar way, we calculate the optimal level of x2.


333

We denote this total effort in equilibrium by X*: X ¼ xi þ xj ¼

rnri nrj ðni þ nj Þ ðnri þ nrj Þ2

8i; j ¼ 1; 2 i a j.

(16)

In the case where the groups are symmetric, that is, n1 ¼ n2 ¼ n, we would obtain the following: the level of activities of each group equals xi ¼ nðr=4Þ 8i; j ¼ 1; 2 i a j (remember that r is less than or equal to 2 and therefore the total expenditure will be at the maximum when xi ¼ ni =2); the Nash equilibrium proportion of the grants obtained from the contest will be equal to one half, Pri ¼ 1=2; the expected equilibrium payoff to each group equals (2r)n/4 (once again, remember that r is less than or equal to 2),3 and finally the total effort in equilibrium equals X* ¼ rn/2. Let us consider how changes in r affect the expected equilibrium payoff, Eðwi Þ ¼

r r rnrþ1 n2rþ1 ðr 1Þnrþ1 nri i nj i i nj n ¼ i nri þ nrj ðnri þ nrj Þ2 ðnri þ nrj Þ2

8i; j ¼ 1; 2; i a j; ro2, and how total effort is affected in equilibrium, X ¼ xi þ xj ¼

rnri nrj ðni þ nj Þ ðnri þ nrj Þ2

8i; j ¼ 1; 2 i a j.

To simplify our calculations denote by a the relative benefit of the second group receiving the grant in relationship to that of the first group receiving the grant: a ¼ n2/n1. Given a, we recalculate the expected payoff and total effort in equilibrium as Eðwi Þ ¼

ni ð1 ðr 1Þar Þ rnar ð1 þ aÞ aj and X ¼ 8a ¼ ; i ¼ 1; 2; ro2, 2 2 r r ai ð1 þ a Þ ð1 þ a Þ

where ð@Eðwi ÞÞ=ð@rÞ ¼ ðni ð1 þ a2r ð1 þ ðr r2 ÞLnðaÞÞ þ rar ð2 þ ðr2 3Þ LnðaÞÞÞÞ= ðð1 þ ar Þ2 Þ and ð@X Þ=ð@rÞ¼ ðnar ð1 þ aÞð1 þ rLnðaÞ þ ar ð1 rLnðaÞÞÞÞ=ðð1 þ ar Þ3 Þ:

As we can see from the above, the effect of a change in the parameter r has an ambiguous affect on the expected payoff and expenditure of the groups. For example, without pffiffiffi loss of generality, assume that ao1. Since Ln(a)o0 then for 1oro 3, @Eðwi Þ=@r40 and for a ¼ 1, @Eðwi Þ=@r40 and @X =@r40. For ro1, it holds that @X =@r40. 3

For rW2, the equilibrium differs from this one as it is based on mixed and not pure strategies. This is the case in the all-pay auction that we previously described.

334


2.3. Comparing the investment of effort of the groups under both situations The groups do not have a choice between the absolute ranking and relative ranking contests we model above. They face what they face. Over time what they face may change, and we are interested in the outcomes of each of the situations. We now compare these two types of contests from the perspectives of the groups, immigrant society, and the foundation. X* gives the aggregate activity of the groups in equilibrium (for the case of stakes that do not depend on the efforts invested by the contestants, see Epstein and Nitzan, 2006a, 2006b, 2007). Under the relative ranking, Pri ¼ xri =ðxrj þ xri Þ for r 2, from Equation (16), we obtain that the total amount of activities carried out is equal to X L ¼ xi þ xj ¼ ðrnri nrj ðni þ nj ÞÞ=ððnri þ nrj Þ2 Þ 8i; j ¼ 1; 2 i a j. In order to simplify our analysis, let us assume that r ¼ 1 (remember that the values that r can take on in this case are between 2 and 0). Under the absolute ranking, from Equation (10), we obtain that the total investment into activities is equal to EðX p Þ ¼ n2 ðn2 þ n1 Þ=ð2n1 Þ. The total amount of expenditure invested in the contest is higher under the relative ranking than under the absolute ranking regime if X L ¼

n2 n1 n2 ðn2 þ n1 Þ 4 ¼ EðX p Þ. n1 þ n2 2n1

(17)

Equation (17) holds if and only if n21 2n1 n2 n2 40.

(18)

From Equation (18), we may conclude that the total amount of expenditure invested in the contest by the different groups is higher under the relative ranking rather than under the absolute ranking regime if pffiffiffi (19) n1 4n2 ð1 þ 2Þ. Since, by assumption, n1Zn2, the result tells us that in order for the lottery contest to be worse for the receiving, the grant that one of the groups can obtain from such actions must be larger than the other group’s grant (more than twice as large). We summarize this result in the following proposition: If the variance of grants that can be generated pffiffiffi by helping immigrant society is sufficiently large, that is, n1 =n2 41 þ 2, then the foundation – which is interested in maximizing the total effort of the groups – prefers that the absolute ranking contest where the group that invests the most effort wins. If each group has the same stake, that is, n1 ¼ n2, then the foundation prefers the relative ranking. In order to analyze the preferences of the groups, we must compare their expected payoffs under both the relative ranking and the absolute ranking regime. Remember that we assumed, without loss of generality,


335

that group 1 has at least as large a stake as the second group (n1Zn2). The groups prefer the regime that generates for them the maximum expected equilibrium payoff, Eðwi Þ. Under the relative ranking, and again assuming r ¼ 1, the expected equilibrium payoff for group 2 (the weaker player) equals Eðw2 Þ ¼ n32 =ðn1 þ n2 Þ2 ; while the expected equilibrium under the absolute ranking equals zero, Eðw2 Þ ¼ 0. Therefore, it is clear that The weaker group, the group that has less to gain from helping its countrymen, always prefers the relative ranking system. For the stronger group, the expected equilibrium payoff under the relative ranking equals E L ðw1 Þ ¼ n31 =ðn1 þ n2 Þ2 ; while the expected equilibrium under the absolute ranking equals E P ðw1 Þ ¼ n1 n2 . The expected payoff for group 1 under the relative ranking regime is greater than that obtained under the absolute ranking regime and thus this group prefers the relative ranking regime if E L ðw1 Þ ¼

n31 4n1 n2 ¼ E p ðw1 Þ ðn1 þ n2 Þ2

(20)

Equation (20) holds if and only if n21 2n1 n2 n2 o0.

(21)

From Equation (21), we may conclude that the expected payoff in the contest and efforts made are higher under the relative ranking rather than under the absolute ranking regime if pffiffiffi (22) 0on1 on2 ð1 þ 2Þ. In other words, The group with the higher stake, with more to gain from helping their countrymen, prefers the relative ranking to an absolute ranking pffiffiffiif the difference between the groups is not sufficiently large, n1 =n2 o1 þ 2. Note that the interests of foundation and the strongest group always align. 3. Conclusion In our society, there exists a foundation that has resources and that wishes to help migrants. In order to help migrants, the foundation needs grass roots organizations to invest their efforts in helping migrants (e.g., in the United States, the McArthur Foundation and the Ford Foundation that want to help immigrants.) The foundation announces a prize (grant) for which the groups compete. The competition is such that the one that invests more resources in helping the migrants has a higher probability of winning and obtaining more resources. The question is how the foundation elicits the most effort from the different ‘‘grass roots’’ groups.

336


In a highly structured and simple model, we characterize and compare two ex ante regimes: (1) the absolute reward scheme presented by an all-pay auction in which the winner takes all available grants and (2) the relative reward scheme in which the grant allocation rule is a lottery and each group obtains a proportion of the grants. In the former regime, the equilibrium is in mixed strategies, the ‘‘stronger’’ group could actually lose the contest and get nothing. However, the expected payoff for the weaker group is zero. The contests we address are the fractious relationships among groups seeking to increase their expected payoff. We are able to derive a very specific condition allowing us to see when each of the concerned parties wins and when each loses their contests. If the difference between the groups in terms of the rewards they can obtain from helping the country is not sufficiently large, all parties – the two groups and the foundation itself – prefer the lottery regime relative ranking to an absolute ranking. However, if the difference between the groups in terms of the rewards that can be obtained is sufficiently large, then the group with the low benefit, group 2, prefers the relative ranking regime while the other group and the foundation prefer the absolute ranking. The contests we address are the fractious relationships among groups seeking help for their immigrant society. Aside from the insights we are able to provide about the reward-ranking scheme, our work is further distinguished by accounting for (i) the possibility of recipient activities that can change the groups’ ordering of the regimes and (ii) recipients gain based on reward regime.

Acknowledgment Financial support from the Adar Foundation of the Economics Department at Bar-Ilan University is gratefully acknowledged

Appendix It is a standard result that there are no pure strategy equilibria in all-pay auctions (Hillman and Riley, 1989; Ellingsen, 1991; and Baye et al., 1993, 1996). Suppose group 2 bids 0ox2rn2. Then the first group’s optimal response is x1 ¼ x2þeon1 (i.e., marginally higher than x2). But then x2W0 cannot be an optimal response to x1 ¼ x2þe. Also, it is obvious that x1 ¼ x2 ¼ 0 cannot be an equilibrium. Hence, there is no equilibrium in pure strategies. There is a unique equilibrium in mixed strategies given by the following cumulative distribution functions

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(see Hillman and Riley, 1989; Ellingsen, 1991; and Baye et al., 1996): G1 ðx1 Þ ¼

x1 n2

for x1 2 ½0; n2 Þ and G2 ðx2 Þ ¼ 1

n 2 x2 þ n1 n1

for x2 2 ½0; n2 Þ.

The equilibrium c.d.f.’s show that group 1 bids uniformly on [0, n2], while group 2 puts a probability mass equal to (1n2/n1) on x2 ¼ 0. The expected investment expenditures are Z

n2

x1 dG1 ðx1 Þ ¼

Eðx1 Þ ¼ 0

n2 and Eðx2 Þ ¼ 2

Z

n1

x2 dG2 ðx2 Þ ¼ 0

n22 . 2n1

Note, we think of the all-pay auction as probabilistic – that is, the stronger group is more likely to win the contest.

References Alesina, A., Eliana La, F. (2000), Participation in heterogeneous communities. Quarterly Journal of Economics (August), 847–904. Altonji, J.G., Blank, R.M. (1999), Race and gender in the labor market. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics, vol. 3C. Amsterdam, The Netherlands, Elsevier Science B.V., pp. 3143–3259. Anas, A. (2002), Prejudice, exclusion and compensating transfers: the economics of ethnic segregation. Journal of Urban Economics 52 (3), 409–432. Bauer, T., Epstein, G.S., Gang, I.N. (2005), Enclaves, language and the location choice of migrants. Journal of Population Economics 18 (4), 649–662. Baye, M.R., Kovenock, D., de Vries, C.G. (1993), Rigging the lobbying process: an application of the all-pay auction. American Economic Review 83 (1), 289–294. Baye, M.R., Koveneock, D., de Vries, C.G. (1996), The all pay auction with complete information. Economic Theory 8 (2) 203–291. Bhaumik, S.K., Gang, I.N., Yun, M.-S. (2006), Ethnic conflict and economic disparity: Serbians and Albanians in Kosovo. Journal of Comparative Economics 34 (4), 754–773. Bisin, A., Verdier, T. (2000), ‘‘Beyond the melting pot’’: cultural transmission, marriage, and the evolution of ethnic and religious traits. Quarterly Journal of Economics August, 955–988. Blau, F.D., Kahn, L.M. (1997), Swimming upstream: trends in the gender wage differential in the 1980s. Journal of Labor Economics 15 (1), 1–42. Blau, F.D., Kahn, L.M. (2006), The US gender pay gap in the 1990s: slowing convergence. Industrial and Labor Relations Review 60 (1), 45–66.

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Blau, F.D., Kahn, L.M. (2007), The gender pay gap. The Economists’ Voice 4 (4), Article 5. Available at http://www.bepress.com/ev/vol4/ iss4/art5 Chiswick, B.R. (1977), Sons of immigrants: are they at an earnings disadvantage? American Economic Review 67 (1), 376–380, Papers and Proceedings. Chiswick, B.R., Miller, P.W. (1995), The endogeneity between language and earnings: international analyses. Journal of Labor Economics 13, 246–288. Chiswick, B.R., Miller, P.W. (1996), Ethnic networks and language proficiency among immigrants. Journal of Population Economics 9, 19–36. Cohen, C., Sela, A. (2007), Contests with ties. The B.E. Journal of Theoretical Economics 7 (1), (Contributions), Article 43. Constant, A., Gataullina, L., Zimmermann, K.F. (2009), Ethnosizing immigrants. Journal of Economic and Behavioral Organization 69 (3), 274–287. Deutsch, J., Epstein, G.S., Lecker, T. (2006), Multi-generation model of immigrant earnings: theory and application. Research in Labor Economics 217–234. Dustmann, C., Fabbri, F., Preston, I. (2004), Ethnic concentration, prejudice and racial harassment of minorities. CReAM Discussion Paper 05/04. Available at www.econ.ucl.ac.uk/cream/. Ellingsen, T. (1991), Strategic buyers and the social cost of monopoly. American Economic Review 81 (3), 648–657. Epstein, G.S., Gang, I.N. (2009a), Ethnicity, assimilation and harassment in the labor market. Research in Labor Economics 79, 67–90. Epstein, G.S., Gang, I.N. (2009b), Good governance and good aid allocation. Journal of Development Economics 89 (1), 12–18. Epstein, G.S., Nitzan, S. (2006a), Reduced prizes and increased effort in contests. Social Choice and Welfare 26 (3), 447–453. Epstein, G.S., Nitzan, S. (2006b), Effort and performance in public policy contests. Journal of Public Economic Theory 8 (2), 265–282. Epstein, G.S., Nitzan, S. (2007), Endogenous Public Policy and Contests. Springer, Berlin, Heidelberg. Gang, I.N., Rivera-Batiz, F. (1994), Labor market effects of immigration in the United States and Europe: substitution vs complementarity. Journal of Population Economics 7, 157–175. Gang, I.N., Rivera-Batiz, F., Yun, M.-S. (2002), Economic Strain, Ethnic Concentration and Attitudes Towards Foreigners in the European Union, IZA Discussion Paper 578. Available at www.iza.org Gang, I.N., Zimmermann, K.F. (2000), Is child like parent? Educational attainment and ethnic origin. Journal of Human Resources 35, 550–569. Hillman, A.L., Riley, J.G. (1989), Politically contestable rents and transfers. Economics and Politics 1 (1), 17–39.


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Kahanec, M. (2006), Ethnic specialization and earnings inequality: Why being a minority hurts but being a big minority hurts more. IZA Discussion Paper 2050. Available at www.iza.org. Lazear, E.P. (1999), Culture and language. Journal of Political Economy 107 (6, pt. 2), S95–S126. Lockard, A.A., Tullock, G. (Eds.) (2001), Efficient rent-seeking, chronicle of an intellectual quagmire. Kluwer Academic Publishers, Boston, MA. Nitzan, S. (1994), Modelling rent-seeking contests. European Journal of Political Economy 10 (1), 41–60, (also appears in Lockard and Tullock, 2001). Smith, J.P., Welch, F.R. (1989), Black Economic Progress after Myrdal. Journal of Economic Literature 27 (2), 519–564.

CHAPTER 14

Assimilating Under Credit Constraints: Public Support for Private Efforts Sajal Lahiri Department of Economics, Southern Illinois University Carbondale, Carbondale, IL 62901-4515, USA E-mail address: [email protected]

Abstract We examine the effect of borrowing constraint facing new immigrants on the process of their assimilation in the new society. We shall do so in a twoperiod model. In period 1, immigrants invest, with some costs to them, in trying to assimilate. The probability of success in this endeavor depends on the amount invested and also on the level of the provision of a ‘‘public’’ good paid for by lump-sum taxation of ‘‘natives’’. Those who succeed enjoy a higher level of productivity and therefore wages in period 2. The level of investment is endogenously determined. Assimilation also affects remittances by immigrants. Given this framework, we examine the effect of public support on the degree of assimilation and income repatriation. We do so under two scenarios regarding the credit market facing new immigrants. In the first, they can borrow as much as they want in period 1 at an exogenously given interest rate. In the second scenarios, there is a binding borrowing constraint. We compare the equilibrium under the two scenarios. Keywords: Immigration, assimilation, culture, credits JEL classification: I28, J61

1. Introduction Migration – domestic and international – has been going on since time immemorial. It has very significant short- and long-run implications for everyone involved in the process: immigrants, natives, the country of origin of immigrants, and so on. Therefore, all these groups of people respond to waves of migrations. As for the place or country of origin, the early literature expressed concerns for brain drain and suggested ways of compensating the source countries of migration (see, e.g., Bhagwati and Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008020


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Wilson (1989)). More recently, the issues have been reassessed and viewed very differently. Stark (2004), for example, has shown the possibility of human capital development in the source country in the presence of emigration possibilities. The volume of remittances by immigrants and their impacts on the source countries have also led to discussions on the benefits from emigration for source countries (see, e.g., OECD, 2005; Fajnzylber and Lo´pez, 2008; Hanson, 2010).1 As for the host country of migration, in spite of protestations from vested interest groups about immigration, most studies look at immigration favorably (see, e.g., Friedberg and Hunt, 1995). According to Tilghman (2003), 20% of the members of the U.S. National Academy of Sciences are of foreign origin. About one-third of Nobel laureates from the United States are foreign-born. However, it should be acknowledged that the effect of immigration on the employment and wages of natives may well depend on the specific characterisctics of the immigrants as well as the characteristics of the labor market (see, e.g., Gang and Rivera-Batiz, 1994; Gang et al., 2002). As for immigrants themselves, the extent of their well-being depends, inter alia, on the level of their assimilation in the new society (see, e.g., Constant et al., 2008). Perhaps, it is because of the lack of assimilation that immigrants tend to earn a lot less than their comparable natives (see, e.g., Altonji and Blank, 1999; Blau and Kahn, 1997; Bhaumik et al., 2006). Why is there a lack of efforts on the part of many immigrants to assimilate? Epstein and Gang (2009) explain this phenomenon in terms of hostility and harassment from natives in the labor market. Hatton and Leigh (2010) find that immigrants tend to assimilate as communities rather than as individuals, and this makes the issue of assimilation much more complex. Epstein and Gang (2006) analyzes the interlinkages between assimilation and networks, and its impact on the level of assimilation. Fan and Stark (2007) show that when assimilation results in immigrants getting ‘‘closer’’ to their richer natives and more ‘‘distant’’ from their fellow immigrants, the efforts for assimilation get muted. Efforts by immigrants does not always imply that they will succeed in their attempts to assimilate. There could be many factors that would determine the rate of success in assimilation for a given level of effort from immigrants. We have already mentioned about hostility from natives, and this will reduce the probability of success. Chiswick and Miller (1996) and Bauer et al. (2005) examine the effect of high adjustment costs (such inadequate language skills or lack of information on the labor market) on the probability of success in assimilation. Public policies can of course help immigrants in overcoming some of these hindrances. For example, publicly provided language schools, information centers, etc. can go a

1

For some source countries such as Bangladesh total remittance from remittances form a very large part of their total foreign exchange earnings.

Assimilating Under Credit Constraints

343

long way in helping immigrants to succeed in assimilating into their new environment. Many of these schemes exist in many of the countries where the inflow of immigrants is high. For example, in Canada new immigrants are entitled to settlement assistance such as free language training under provincial government administered programs usually called Language Instruction for Newcomers to Canada (LINC), for which the federal government budgeted about $350 million to give to the provinces for the fiscal year 2006–2007.2 The assimilation of immigrants not only has effect on their earnings, it may also affect their preferences in other ways. In particular, assimilation can lead to immigrants caring more about themselves and relatively less about people left behind in the country of their origin. This can, as Fan and Stark (2007) show, lead to less income repatriation by immigrants. From the above discussions it should be clear that private efforts by immigrants to assimilate and remittances by them are interdependent on each other, and public support for assimilation of immigrants can affect both these variables in a significant way. However, one aspect of the host country that hitherto has not been considered in the literature in explaining the lack of private efforts in assimilation is the access, or the lack of it, to credits by immigrants. The manner in which credit ratings are normally calculated in most developed countries are by design stacked against newcomers in those countries. Often low-risk skilled immigrants are denied credits because of a lack of records on their credit history, while, as it is now well known, the same financial institutions have been bending over backwards to offer credits to high-risk natives resulting in one of the worst financial crisis since the Great Depression.3 In fact, because of the lack of credits from the formal credit institutions, many immigrant groups form their own credit institutions. For example, the institution of Rotating Credit and Savings Associations (ROSCA) can be found among many immigrant groups in the United States of America: among Mexican and Cuban immigrants in Southern California (Velez-Ibanez, 1983; Gama et al., 2010), Caribbean immigrants in New York City (Laguerre, 1998), and Korean Immigrants in Los Angeles (Light et al., 1990) to name a few. Although the ROSCAS help the immigrants in many ways particularly in acquiring consumer durables, there are not typically used for investment purposes. Since the costs of private efforts at assimilation are incurred upfront, and benefits in the form of higher wages come in the future, credits have an obvious role to play in the determination of private efforts at assimilation. It is this void in the literature that the present chapter attempts to fill. 2 http://en.wikipedia.org/wiki/Economic_impact_of_immigration_to_Canada; accessed on February 27, 2010. 3 Perhaps, the financial institutions have fears of an immigrant defaulting and returning to the home country.

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Sajal Lahiri

In order to do so, we shall develop a two-period model in which an immigrant spends a certain amount of effort in assimilation, and this has an opportunity cost to the immigrant in terms of time and income. The probability that the immigrant succeeds in assimilation depends on the level of this private effort and the level of public support for assimilation in the host country. If the immigrant succeeds in assimilation, not only that it raises the wage income of the immigrant but it also has an implication for its preference for remittances sent to people back home. The level of private efforts is optimally chosen by the immigrant. We consider two scenarios. In the first scenario, the immigrant can borrow as much as it wants at a given interest rate, and in the second the immigrant is subject to a binding borrowing constraint. In this framework, we shall examine if restrictions on borrowing by the immigrants does indeed affect private efforts at assimilation adversely. We shall also examine if the effect of public support on private efforts at assimilation is lower in the presence of a binding borrowing constraint facing the immigrant than in the absence of it. 2. The theoretical framework We develop a two-period model of a small open economy. A number of identical immigrants arrive in the country at the beginning of period 1. We shall treat this number as exogenous and, without any loss of generally, assume it to be unity. The number of hours available to each immigrant is assumed to be exogenous and once again, without loss of any generality, taken to be unity. In period 1, the immigrant makes some effort to assimilate in the adopted country and this costs him/her e ð 1Þ hours. The immigrant succeeds in assimilation with probability p, which depends on e, and the level of public support for the assimilation program, denoted by g. We shall treat g as a public good. p ¼ pðe; gÞ,

(1)

where we assume:4 ASSUMPTION 1. p1 40; p2 40, and p12 40. The assumption p12 40 implies complementarity between private efforts in, and public support of, assimilation. For simplicity and without any loss of generality, we assume that there is one good in each period, the price of which is normalized to unity. Denoting by ci the consumption of the good in period i, the utility of the immigrant, uI , depends not only on his/her consumption of the good,

4 For any function f ð Þ, we denote by f i as its partial derivative with respect to the ith argument.


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but also on the amount repatriated to its country of origin.5 The levels of repatriations or remittances in the two periods are denoted by T 1 and T 2 , respectively, and the the level of utility from repatriated income (in the absence of assimilation) is f ðT 1 ; T 2 Þ. Implicitly, we assume that the immigrant cares about the family (direct or extended) left behind. We assume that ASSUMPTION 2. f 1 40, f 2 40, f 11 o0, f 22 o0, and f 12 40. The first-order effects f 1 and f 2 can be different for a variety of reasons. For example, if the family at the source country is subject of credit constraints, an extra income in period 1 would reduce the family’s demand for loan and therefore reduce the rate of interest it faces. An extra income in period 2 will have exactly the opposite effect.6 Thus, the same amount of (real) income from repatriation would have different effect on the family’s utility. We assume that assimilation has two effects on the immigrant. First, it increases his/her income in period 2. Second, it reduces the immigrant’s ‘‘link’’ with his/her country of origin. The ‘‘link’’ can be reduced for many reasons. For example, an assimilated immigrant may have more commitments in the adopted country and therefore its relative preference (weight) for own consumptions may go up. Denoting by y ðy 1Þ the weight it puts on utility from remittances after assimilation, the immigrant’s expected utility from repatriated income is ð1 pÞf þ pyf . We assume that the utility from consumption and that from remittances to be additively separable. That is, the expected utility uI of the immigrant is given by uI ¼ vI ðc1 ; c2 Þ þ ½1 ð1 yÞpðe; gÞf ðT 1 ; T 2 Þ,

(2)

where vI(c1,c2) is the direct utility from consumption. The choice variables for the migrant are c1 , c2 , T 1 , T 2 , and e. We shall describe the migrants optimization problem a little later. From (2), the expenditure function of the immigrant is derived as E I ð1; 1=ð1 þ rI Þ; uI ½1 ð1 yÞpð Þf ð ÞÞ where rI is the interest rate the immigrant faces. As is well known, the partial derivative of the expenditure function with respect to a price of a good gives the compensated demand for that good, and the partial derivative with respect to the utility is the reciprocal of the marginal utility of income.7 The expenditure function for the natives are denoted by Eð1; 1=ð1 þ rN Þ; uN Þ where rN is the interest rate facing natives and uN their utility level. 5 Gaytan-Fregoso and Lahiri (2000) provide a microfoundation for this formulation by explicitly modeling the source country. 6 See Jafarey and Lahiri (2005) for an explanation, albeit in a different context. 7 It is also true that E 11 o0, E 22 o0, E 12 40, E 33 40 and if the goods are normal, then E 13 40 and E 23 40. For these and other properties of an expenditure function see, for example, Dixit and Norman (1980).

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Turning to the production side, the revenue functions in the two periods are R1 ð1; 1 e; gÞ and R2 ð1; 1 p þ lpÞ, respectively, where l ðl 1Þ is the amount of ‘‘effective’’ labor of the immigrant if assimilation succeeds. The partial derivative of the revenue functions with respect to an endowment of factor of production gives the rate of return to that factor (see, Dixit and Norman, 1980). The function R1 ð Þ is in fact the ‘‘restricted’’ revenue function in period 1, representing total value of production in the private sector when the level of public good provision is g. Since all other endowments do not vary, they are omitted from the arguments of the revenue functions. It is well known that the unit cost of production of the public good is R3 .8 For simplicity, we assume that the cost of production of the public good is paid for by a lump-sum taxation of natives and that its production only uses factors that belong to natives. With these assumption, the budget-balance equations for the government, the immigrant, and natives are given, respectively, by R13 g ¼ T, E

I

(3)

1 1; ; uI ½1 ð1 yÞpf 1 þ rI

½1 p þ lpR22 T2 1 þ T , ¼ ð1 1 þ rI 1 þ rI 1 R2 1 ; u þ ð1 eÞR12 E 1; ¼ R N 1 þ rN 1 þ rN

(4)

eÞR12

½1 p þ lpR22 R13 g T. 1 þ rN

(5)

Equation (3) states that the total cost of producing the public good (the left-hand side) is equal to the amount of lump-sum tax levied on natives (right-hand side). The left-hand sides of (4) and (5) are the discounted present value of expenditures on consumption by the immigrant and natives, respectively. The first term on the right-hand side of (4) is the wage income of the immigrant in period 1. The second term is the discounted present values of the second-period expected wage income. The third and the fourth terms are the present values of the repatriated amounts sent back to the country of origin. The first four terms on righthand side of (5) together give the income of the natives from private sector (the total factor income in the economy minus the factor income of the immigrant). The fifth terms is natives’ income from the public sector, and the last term is the lump-sum tax that is levied on them. 8

For the derivation and properties of a restricted revenue function, please see, e.g., Hatzipanayotou et al. (2002).


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The borrowing by the immigrant in period 1, B, is given by B ¼ E I1 ð1 eÞR12 þ T 1 ,

(6)

which is the excess of expenditure over income of the immigrant in period 1. We shall make the Heckscher–Ohlin assumption that that factor endowments do not affect factor prices, that is, the factor endowments lie within the cone of diversification and there are no factor intensity reversals (see Dixit and Norman, 1980) for details). That is, ASSUMPTION 3. R122 ¼ R133 ¼ R123 ¼ R222 ¼ 0. It now remains to describe how e; T 1 , and T 2 are determined. For this we differentiate (1)–(4) to obtain. ðl 1ÞR22 I E ð1 yÞfp E I3 duI ¼ R12 þ 1 de 3 1 þ rI 2 R ðl 1Þp2 I þ 2 E ð1 yÞfp 2 dg 3 1 þ rI (7) þ ½1 þ f1 pð1 yÞgE I3 f 1 dT 1 1 B I þ þ f1 pð1 yÞgE 3 f 2 dT 2

drI . 1 þ rI 1 þ rI An increase in the efforts to assimilate has two costs: (i) reduction in wage income in period 1 ðR12 Þ, and (ii) a reduction in utility because of caring less for the family back home (the third term in the coefficient for de). It benefits the immigrant by increasing its wage income in period 2 ððl 1ÞR22 Þ. An increase in g also has the same costs and benefits associated with an increase in e, but it does not reduce wage income in period 1. An increase in either T 1 or T 2 has direct costs (the first terms in the coefficients of dT 1 and dT 2 ); they also benefit the immigrant by increasing the utility from repatriating income (the second terms in the coefficients of dT 1 and dT 2 ). Finally, an increase in the interest rate reduces the utility of immigrant since it is a net borrower (the so-called intertemporal terms-of-trade effect). The immigrant decides on the levels of e, T 1 , and T 2 by maximizing uI for a given value of the interest rates. The first-order conditions for the immigrant’s optimization problem are given by @uI p ðl 1ÞR22 ¼ 0 ) R12 þ E I3 p1 f ð1 yÞ ¼ 1 , @e 1 þ rI

(8)

@uI ¼ 0 ) 1 ¼ E I3 f1 pð1 yÞgf 1 , @T 1

(9)

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@uI 1 ¼0) ¼ E I3 f1 pð1 yÞgf 2 . 1 þ rI @T 2

(10)

The marginal costs and benefits associated with the three choice variables have been explained after (7). The left-hand sides of the above three equations are the marginal costs of the three variables, and the right-hand sides are the marginal benefits. From (9) and (10), we find f 1 ¼ ð1 þ rI Þf 2 .

(11)

Note that, Equation (11) implies that in equilibrium we must have f 1 4f 2 , a property that is consistent with our discussion after the statement of Assumption 2. This completes the description of our theoretical framework. We shall assume rN to be exogenous. However, we start with the assumption that rI is also constant. But, later we shall assume that the immigrant is subject to a binding borrowing constraint so that its demand for loan, given by (6), that is, is equal to an exogenously given supply of the loan B, B ¼ B.

(12)

When (12) holds, the interest rate facing the immigrant, rI , becomes an endogenous variable. 3. Public support and private assimilation In this section We shall examine the effect of an increase in the provision of the public good on the level of assimilation of the immigrant.9 we shall do so under two scenarios: (i) the immigrant can borrow as much it wants at the given interest rate rI , and (ii) it faces a binding borrowing constraint so that rI is endogenous, and then we shall examine how the existence of the borrowing constraint affects the results. Using the optimality conditions (8)–(10), Equation (7) reduces to E I3 duI ¼

B p R1 drI þ 2 2 dg. I p1 1þr

(13)

We also find that duN ¼ 0. That is, an increase in g unambiguously increases the utility of the immigrant when e, T 1 , and T 2 are optimally chosen. The latter three variables do not affect uI directly as these are optimally chosen (the envelope property). The effect of rI on uI is as before. The utility of natives are unaffected as the public good is produced using factors owned by 9

The actual policy is the lump-sum taxation of natives for the public support of assimilation. However, since the unit cost of providing public service is constant in our analysis, there is no analytical difference between the two.

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natives, and factor prices are assumed not to be influenced by factor endowments (Assumption 3). Differentiation of (8), (9), (11), and (12), the and use of (8)–(10) and (13), give us 1 R2 p11 f 2 ð1 yÞ2 ðp1 Þ2 E I33 de p1 1 R p R1 ¼ 2 12 f ð1 yÞp2 E I33 f ð1 yÞp1 þ 2I dg p1 E3 (14) 2 p1 ðl 1ÞR2 p1 f ð1 yÞE 32 f ð1 yÞE I33 p1 B I dr þ E I3 ð1 þ rI Þ ð1 þ rI Þ2 ð1 þ rI Þ2 þ p1 ð1 yÞa½f 1 dT 1 þ f 2 dT 2 , 1 E I13 B I I E 12 I dr ¼ d B R12 þ E I13 f ð1 yÞp1 de 2 I E3 ð1 þ r Þ þ E I13 f1 pð1 yÞg½f 1 dT 1 þ f 2 dT 2 E I13 p2

(15)

R12 þ f ð1 yÞ dg p1 E I3

½E I3 f 11 f1 pð1 yÞgðf 1 Þ2 E I33 dT 1 þ ½E I3 f 12 f1 pð1 yÞgf 1 f 2 E I33 dT 2 (16) I p1 f 1 ð1 yÞa p2 f 1 b f1 E 32 E I33 B I de þ dg þ þ I dr , ¼ 1 pð1 yÞ 1 pð1 yÞ 1 þ rI 1 þ rI E3 I

1

2

I

(17)

I

½ð1 þ r Þf 12 f 11 dT þ ½ð1 þ r Þf 22 f 12 dT ¼ f 2 dr , where a ¼ E I3 fE I33 f1 pð1 yÞg, b ¼ E I3 ð1 yÞ f1 pð1 yÞgE I33

R12 . f ð1 yÞ þ p1 E I3

Note that the coefficient of de on the left-hand side of (14) is negative, and this is consistent with the second-order condition for the immigrants optimization problem. There are two opposite effects of an increase in g on e for given levels of T 1 , T 2 , and rI . First, it increases both the marginal cost and the marginal benefit of increasing e (the second term on the left-hand side, and the term on the right-hand side, of (8)), but, at the equilibrium, the increase in marginal benefit dominates the increase in marginal costs. This effect is given by the first term in the coefficient of dg on the right-hand side of (14). The second effect of an increase in g on e appears in terms of an income effect: an increase in g increases real income of the immigrants (see (13)) and this reduces their marginal utility of income, and this in turn increases the marginal cost of increasing e

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(the second term on the left-hand side of (8)). An increase in either T 1 or T 2 , for a given level of e and rI , increases the marginal cost of increasing e by increasing the value of f ð Þ and this effect will tend to reduce the value of e. However, an increase in either T 1 or T 2 also increases the marginal utility of income and reduces the marginal cost of increasing e. This effect will tend to increase the value of e. The net effect will be positive if the value of a is negative. An increase in rI has two positive and one negative effect on e for given levels of T 1 , and T 2 . The negative effect is due to the fact that an increase in rI reduces the marginal benefit of increasing e (the term on the right-hand side of (14)). The positive effects come via income effects: (i) an increase in rI reduces the utility of the immigrant via the intertemporal terms-of-trade effect (see (13)) and this reduces the marginal cost of increasing e, and (ii) an increase in rI reduces the present value of the second-period price and this increases the marginal utility of income and thus reduces the marginal cost of increasing e. An increase in the borrowing limit reduces the interest rate facing the immigrant by increasing its supply, for given levels of e, T 1 , and T 2 . An increase in e, for given levels of T 1 and T 2 , reduces income in the first period and increases that in the second period. Both these effects increases the demand for loan in the first period, increasing the interest rate. An increase in g raises the probability of success in attempts to assimilate and thus the expected income in the second period. This will increase the demand for loan in the first period, increasing the interest rate. An increase in either T 1 or T 2 , for a given level of e, will reduce the direct utility from consumption (given by the third argument in E I ð Þ) and thus reducing the demand for consumption and loan in the first period. This will reduce the interest rate. As for the effects of e, rI , and g on the equilibrium levels of T 1 and T 2 , from (16) and (17) we find @T 1 p1 f 1 ð1 yÞa ¼ ½f ð1 þ rI Þ f 12 ; @e 1 pð1 yÞ 22 @T 2 p f ð1 yÞa ¼ 1 1 ½f ð1 þ rI Þ f 11 , D @e 1 pð1 yÞ 12 I @T 1 f1 E 32 E I33 B þ I ½ð1 þ rI Þf 22 f 12 D I ¼ @r 1 þ rI 1 þ rI E3 D

þ f 2 ½E I3 f 12 f1 pð1 yÞgðf 1 Þ2 E I33 , I @T 2 f1 E 32 E I33 B þ I ½f 11 ð1 þ rI Þf 12 D I ¼ @r 1 þ rI 1 þ rI E3 f 2 ½E I3 f 11 f1 pð1 yÞgf 1 f 2 E I33 , @T 1 p2 f 1 b ½ð1 þ rI Þf 22 f 12 ; ¼ 1 pð1 yÞ @g @T 2 p2 f 1 b ¼ D ½f ð1 þ rI Þf 12 , @g 1 pð1 yÞ 11 D

(18)


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where D ¼ ½E I3 f 11 f1 pð1 yÞgðf 1 Þ2 E I33 ½ð1 þ rI Þf 22 f 12 ½ð1 þ rI Þf 12 f 11 ½E I3 f 12 f1 pð1 yÞgf 1 f 2 E I33 . Note that D must be positive to satisfy the second-order conditions for the immigrant’s optimization problem. An increase in e increases the probability of success in assimilation p and thus reduces the marginal benefit of increasing either T 1 or T 2 . However, an increase in e increases the direct utility of consumption and thus reduces the marginal utility of income. This will raise the marginal benefit of increasing either T 1 or T 2 . The net effect is positive if and only if a is positive. The same argument goes for the effect of an increase in g on either T 1 or T 2 . The net effect this time is positive if and only if b is positive. The effect of an increase in rI on T 1 is a little different than that on T 2 . This is because an increase in rI reduces the marginal cost of increasing T 2 , but not that of increasing T 1 . An increase in rI reduces the marginal benefit of increasing either T 1 or T 2 by increasing marginal utility of income because of a reduction in utility and in the present value of the second-period income. There are other effects that occur indirectly because of the interdependence in T 1 and T 2 . Having discussed the partial effects, we now look at the total effects. For this, we simultaneously solve (14)–(17) to get Aee de ¼ Aeg dg þ Aer drI ,

(19)

Lr drI ¼ d B þ Lg dg,

(20)

where Aee ¼ Aeg ¼ Aer ¼ Lr ¼

Lg ¼

R12 p11 @T 1 @T 2 , þf2 f 2 ð1 yÞ2 ðp1 Þ2 E I33 p1 ð1 yÞaÞ f 1 p1 @e @e R1 p R1 @T 1 @T 2 þf2 , 2 12 þ f ð1 yÞp2 E I33 f ð1 yÞp1 þ 2I þ p1 ð1 yÞa f 1 p1 @g @g E3 p1 ðl 1ÞR22 p1 f ð1 yÞE 32 f ð1 yÞE I33 p1 B @T 1 @T 2 ð1 yÞa f þ f , þ p 1 1 2 @rI @rI E I3 ð1 þ rI Þ ð1 þ rI Þ2 ð1 þ rI Þ2 1 E I B @T 1 @T 2 @T 1 E I12 13I E I13 f1 pð1 yÞg f 1 I þ f 2 I þ I 2 @r @r @r E3 ð1 þ rI Þ 1 2 @T @T Aer þf2 þ R12 þ E I13 f ð1 yÞp1 E I13 f1 pð1 yÞg f 1 , @e @e Aee 1 R2 @T 1 @T 2 @T 1 E I13 p2 þ f ð1 yÞ þ E I13 f1 pð1 yÞg f 1 þf2 I @g @g @g p1 E 3 1 2 @T @T Aeg . R12 þ E I13 f ð1 yÞp1 E I13 f1 pð1 yÞg f 1 þf2 @e @e Aee

Note that Aee has to be negative for the second-order condition of the immigrant’s optimization problem to be satisfied. Also Lr is the slope

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of the excess demand for loan function and this has to be negative as well for the system to be Walrasian stable. We shall now examine the effect of a change in g on the equilibrium value of e under two scenarios: (i) the immigrant faces no credit constraint, that is, it can borrow as much as it wants at the interest rate at the exogenously given rI , and (ii) the immigrant faces a binding borrowing constraint (12) and the rate of interest rI is endogenous. These scenarios will now be considered in turn. Case 1: No credit constraint: In this case drI ¼ 0 in Equations (19) and (20) is not applicable. Therefore, de ‘0 () Aeg x0. dgdrI ¼0 First of all note that when y ¼ 1, that is, when assimilation does not reduce the immigrant’s degree of altruism toward its family back home, de=dg40. The complementarity between private effort and public support for assimilation implies that an increase in public support for assimilation increases both the marginal cost and marginal benefit of increasing private efforts (see (8)). However, the increase in marginal benefit dominates that in marginal costs and the net effect is positive. When yo1, the effect is generally ambiguous. However, if the marginal utility of income of the immigrant is more or less constant, that is, E I33 ’ 0, then once again de=dg40. If the marginal utility of income is constant, then the marginal benefit of increasing T 1 or T 2 decreases with g and therefore the optimal values of both T 1 and T 2 decrease as g is increased. These reductions in T 1 and T 2 reduces the marginal costs of increasing e and thus reinforcing the positive effect on e of an increase in g because of the complementarity between private effort and public support for assimilation. These results are formally stated in the following proposition. PROPOSITION 1. In the absence of any credit constraint facing immigrants, an increase in the level of public support for assimilation increases the level of private efforts in assimilation if either y ’ 1 or E I33 ’ 0. Turning to the effect of an increase in the interest rate on the equilibrium level of e, it is to be noted that the effect works through many channels: some positive, some negative. However, when either y ’ 1 or E I3 is constant, it is easy to verify that de=d B40. When y ’ 1, a reduction in rI increases the marginal benefit of increasing e (the right-hand side of (8)) and thus raise the equilibrium level of e. When yo1, there is an additional effect via induced changes in T 1 and T 2 . When E I3 is constant, a reduction in rI increases the marginal cost of increasing T 2 (the left-hand side of (10)) and thus reduces the optimal value of T 2 and thus that of T 1 . This reduction in T 1 and T 2 reduces the marginal cost of increasing e


353

(the left-hand side of (8)), reinforcing the increase in marginal benefits. Formally, PROPOSITION 2. A reduction in the interest rate rI increases the equilibrium level of private efforts in assimilation if either y ’ 1 or E I33 ’ 0 and E I32 ’ 0. Case 2: Binding credit constraint: Having identified sufficient conditions under which an increase in g increases the equilibrium value of e in the absence of any credit constraint facing the immigrant, we shall now examine what the existence of a binding borrowing constraint does to this comparative static result: is it more likely or less likely that an increase in g will increase the equilibrium value of e under a borrowing constraint than in the absence of it? We shall also examine the effect of a relaxation of the borrowing constraint on the equilibrium value of e. Turning to the second issue first, a relaxation of the borrowing con straint ðd B40Þ unambiguously reduces the equilibrium level of the interest I rate r . The intuition is very straightforward. An increase in B shifts the supply function to the right and does not affect the demand function, resulting in a reduction of the equilibrium price (interest rate). A reduction in rI , however, affects the equilibrium level of e via many channels: some positive and some negative, as shown before. Therefore, in general, the sign of de=d B is ambiguous. However, using Proposition 2, we find PROPOSITION 3. A relaxation of the borrowing constraint facing immigrants increases the level of private efforts in assimilation if either y ’ 1 or E I33 ’ 0 and E I32 ’ 0. Finally, turning to the effect of a change in g on the equilibrium level of e under a borrowing constraint, it is to be noted that g affects the demand for loan in many ways. It affects consumption in the first period via an increase in utility, via an induced changes in remittances, and via an induced change in private efforts in assimilation, and via a change in the probability of success in assimilation because of an increase in g. It also affects first-period incomes because of induced changes in e and T 1 . The net effect is ambiguous even when either y ’ 1 or E I3 is constant. We need an additional condition to sign drI =dg. Suppose that public support for assimilation, on its own, does not have a significant effect on the probability of success in assimilation, but has a significant effect on the marginal probability of private efforts, that is, p2 ’ 0, but p12 40. In this case, g will have no effects on either first-period consumption directly or via induced changes in T 1 or T 2 , for given level of e. It will only affect first-period income via an induced change in e. Under the conditions in Proposition 1, that is, when either y ’ 1 or E I33 ’ 0, we know from that proposition that an increase in g will increase the equilibrium value of e. This increase in turn will reduce the first-period income of the immigrant and therefore increase the demand for loan and thus the equilibrium value of rI . Using Proposition 2, we can then conclude that, under the conditions

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stipulated, an increase in g will have a lower impact on the equilibrium level of private efforts in assimilation under a borrowing constraint than in the absence of it. Formally, PROPOSITION 4. Suppose that p2 ’ 0. Furthermore suppose that either y ’ 1 or E I33 ’ 0. Then, an increase in g will have a lower impact on the equilibrium level of private efforts in assimilation under a borrowing constraint than in the absence of it. We conclude this section by drawing a number of policy implications of our results. In the preceding analysis we have found that while an increase in public support for immigrant assimilation is likely to increase private efforts by immigrants to assimilate, a binding borrowing constraint reduces this effect. Furthermore, a relaxation of the borrowing constraint is likely to increase private efforts. Therefore, it seems that public support for immigrant assimilation should be strengthened and restrictions for credits for immigrants should be reduced in order for public support to have the necessary impact. 4. Conclusion The assimilation of immigrants, or the lack of it, in their new adopted country has been receiving a lot of attention of late in the literature on international migration. Since with assimilation immigrants are very likely to improve their well-being, the lack of efforts on their part to assimilate is somewhat puzzling. Many factors which stops immigrants from making adequate efforts at assimilation, have been identified. Hostility of natives because of perceived adverse effect of immigration on their wages and employment is one such factor. A lack of public support in the assimilation process is another factor. The existence of networks among the immigrants is yet another factor. However, one factor that has not been analyzed in the literature is the lack of credits facing newly arrived immigrants. Since the costs of private efforts at assimilation are incurred upfront, and benefits in the form of higher wages come in the future, credits have an obvious role to play in the determination of private efforts at assimilation. It is surprising, therefore, that this issue remains unexamined in the literature. In this chapter, we have tried to fill this void in the literature. We have developed a two-period model in which immigrants make an effort at assimilation in the first period, and, if they succeed, enjoy a higher wage rate in the second period. The probability of success at assimilation not only depends on the level of efforts that immigrants make, but also on the level of public support for it. Successful assimilation also affect their preference for remittances to people at their country of origin. In this framework, we examined the effect of public support on the level private efforts at assimilation. We also analyzed if the above effect is smaller when


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immigrants face a binding a borrowing constraint. We have found that the presence of a binding borrowing constraint can indeed reduce the beneficial effect of public support for private assimilation. The broad policy prescription of this research is that restrictions for credits for immigrants should be reduced in order for the public supports to have the necessary impact.

Acknowledgment I am grateful to an anonymous referee for helpful comments and suggestions.

References Altonji, J.G., Blank, R.M. (1999), Race and gender in the labor market. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics, vol. 3C. Elsevier Science B.V., Amsterdam, pp. 3143–3259. Bauer, T., Epstein, G.S., Gang, I.N. (2005), Enclaves, language and the location choice of migrants. Journal of Population Economics 18 (4), 649–662. Bhagwati, J.N., Wilson, J.D. (Eds.), 1989. Income taxation and international mobility. M.I.T. Press, Cambridge, MA. Bhaumik, S.K., Gang, I.N., Yun, M.-S. (2006), Ethnic conflict and economic disparity: Serbians and Albanians in Kosovo. Journal of Comparative Economics 34 (4), 754–773. Blau, F.D., Kahn, L.M. (1997), Swimming upstream: trends in the gender wage differential in the 1980s. Journal of Labor Economics 15 (1), 1–42. Chiswick, B.R., Miller, P.W. (1996), Ethnic networks and language proficiency among immigrants. Journal of Population Economics 9, 19–36. Constant, A., Gataullina, L., Zimmermann, K.F. (2008), Ethnosizing Immigrants. Journal of Economic and Behavioral Organization 69 (33), 274–287. Epstein, G.S., Gang, I. (2006), Ethnic networks and international trade. In: Foders, F., Langhammer, R.J. (Eds.), Labor Mobility and the World Economy. Springer, Berlin, Heidelberg, pp. 85–103. Epstein, G.S., Gang, I. (2009), Ethnicity, assimilation and harassment in the labor market. In: Polachek, S., Tatsiramos, K. (Eds.), Ethnicity and Labor Market Outcomes: Research in Labor Economics, Vol. 29. Emerald Group Publishing Limited, Bingley, UK, pp. 67–88. Fajnzylber, P., Lo´pez, J.H. (Eds.) (2008), Remittances and development lessons from Latin America, Latin American Development Forum Series, The World Bank, Washington, DC.

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Fan, C.S., Stark, O. (2007), A social explanation of the reluctance to assimilate. Kyklos 60 (1), 55–63. Friedberg, R.M., Hunt, J. (1995), The impact of immigrants on host country wages, employment and growth. The Journal of Economic Perspectives 9 (2), 23–44. Gama, R., D. Medrano, L. Medrano, 2010, Tandas and Cundinas: Mexican-American and Latino-American Rotating Credit Associations in Southern California, in Anthropology of Money in Southern California (http://www.anthro.uci.edu/html/Programs/Anthro_Money/ Tandas.htm). Gang, I.N., Rivera-Batiz, F. (1994), Labor market effects of immigration in the United States and Europe: substitution vs. complementarity. Journal of Population Economics 7, 157–175. Gang, I.N., Rivera-Batiz, F., Yun, M.-S. (2002), Economic strain, ethnic concentration and attitudes towards foreigners in the European Union. IZA Discussion Paper 578 (www.iza.org). Institute for the Study of Labor, Bonn, Germany. Gaytan-Fregoso, H., Lahiri, S. (2000), Foreign aid and illegal immigration. Journal of Development Economics 63, 515–527. Hanson, G. (2010), International migration and the developing world. In: Rodrik, D., Rosenzweig, M. (Eds.), Handbook of Development Economics, vol. 5. Elsevier, B.V., North-Holland, The Netherlands, pp. 4363–4414. Hatton, T.J., Leigh, A.L. (2010), Immigrants assimilate as communities, not just as individuals. Journal of Population Economics. Forthcoming. Hatzipanayotou, P., Lahiri, S., Michael, M.S. (2002), Can cross-border pollution reduce pollution? Canadian Journal of Economics 35, 805–818. Jafarey, S.S., Lahiri, S. (2005), Food for education and funds for education quality: policy options to reduce child labor. Canadian Journal of Economics 38, 394–419. Laguerre, M.S. (1998), Rotating credit association and the diasporic economy. Journal of Development Entrepreneurship 3, 23–34. Light, I., Kwuon, I.J., Zhong, D. (1990), Korean Rotating Credit Associations in Los Angeles. Amerasia Journal 16, 35–54. OECD (2005), Migration, Remittances and Development. OECD Publishing, Paris. Stark, O. (2004), Rethinking the brain drain. World Development 32 (1), 15–22. Tilghman, S.M. (2003), Dealing with foreign students and scholars in the age of terrorism: visa backlogs and tracking systems. Testimony presented to the U.S. House of Representatives Committee on Science, March 26, 2003. Velez-Ibanez, C.G. (1983), Bonds of Mutual Trust: The Culture System of Rotating Credit Associations Among Urban Mexicans and Chicanos. Rutgers University Press, New Brunswick, Canada.

CHAPTER 15

Immigrant Networks and the U.S. Bilateral Trade: The Role of Immigrant Income Kusum Mundra* Department of Economics, Rutgers University, Newark, NJ 07901, USA E-mail address: [email protected]

Abstract This chapter examines the role of immigrant networks on trade, particulalry through the demand effect. First, we examine the effect of immigration on trade when the immigrants consume more of the good that is abundant in their home country than the natives in a standard Heckscher–Ohlin model and find that the effect of immigration on trade is a priori indeterminate. Our econometric gravity model consisting of 63 major trading and immigrant-sending country for the United States over 1991–2000. We find that the immigrants income, mostly through demand effect has a significant negative effect on U.S. imports. However, if we include the effect of the immigrant income interacted with the size of the immigrant network, measured by the immigrant stock, we find that higher immigrants income lowers the immigrant network effect for both U.S. exports and imports. This we find in addition to the immigrants stock elasticity of 0.27% for U.S. exports and 0.48% for U.S. imports. Capturing the immigrant assimilation with the level of immigrant income and the size of the immigrant enclave this chapter finds that the immigrant network effect on trade flows is weakened by the increasing level of immigrant assimilation. Keywords: Immigrant networks, immigrant income, trade, immigrant demand, immigrant assimilation, Heckscher-Ohlin JEL classifications: F22, F11, J10, J61

* Earlier version of Section 2 Immigrant and the Heckscher-Ohlin Model has benefitted from discussion with Prasanta Pattanaik. Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008021


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1. Introduction Beginning from the work by Gould (1994) there is increasing literature examining the effect of immigrant networks on trade with the immigrants’ home country. There is increasing empirical evidence that the immigrant population, particularly stock of immigrants living in a country, provides the social and coethnic networks that facilitate trade with their home country by removing some informal trade barriers and lowering transactions cost to trade.1 The literature has found that the immigrants (or immigrant based networks) have a positive effect on bilateral trade for the United States (Gould, 1994; Dunlevy and Hutchinson, 1999; Rauch, 1996; Herander and Saavedra, 2005; Bandyopadhyay et al., 2008) and for Canada (Head and Reis, 1998). Immigrants ‘‘home-link’’ increases trade with home countries through the immigrants’ home country information (information effect) and through their demand for goods from their home country (demand effect). In previous literature both the immigrant information and the demand effect is measured by the size of the immigrant stock. In addition to the size of the immigrant stock measuring immigrants’ effect on trade in this chapter we explore the role of immigrants’ income on the bilateral trade, particularly through the immigrants’ demand effect. Immigrants carry home-country information that helps in matching buyers and sellers and enforcement of trading contacts (information effect). Immigrants have information on different traders and the type of goods available both in the United States and their home countries. This knowledge helps in promoting bilateral trade between the host and the home country. In addition, immigrants’ information on the legal set up in their country of origin, familiarity with the home-country language, and knowledge on how business is conducted in their home country helps in enforcing trading contacts with their home country. Immigrants also demand goods from their home country increasing their home country exports to the host country – demand effect.2 Light et al. (2002), while exploring the effect of English speaking immigrants on export claims that immigrant entrepreneurs import familiar goods from their home countries since there is a demand for these goods in their host country. This chapter examines the demand effect of the immigrants, particularly the effect of immigrants’ income on trade. In the literature there is no explicit attempt to distinguish the immigrants’ information effect from the demand

1

In international trade Trefler (1997) have found a strong evidence of coethnic and social networks in explaining the missing trade links and Grief (1993) and Rauch and Casella (1998) have shown that business and social networks help in alleviating informal trade barriers. 2 There is an extensive literature on the role played by immigrants demand for goods from their home country in generating and sustaining immigrant entrepreneurship. For a good discussion on immigrants demand and growth of ethnic business enclaves see Portes and Rumbaut (1996), Light and Bonacich (1988), and Halter (1995) to name a few.

Immigrant Networks and the U.S. Bilateral Trade

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effect and hence there are conflicting and different findings regarding the effect of immigrants’ information and demand on trade (Wagner et al., 2002). Head and Reis (1999) find that the immigrant elasticity for imports is three times of that of the exports and they argue that if the information effect for both exports and imports is assumed to be of equal magnitude, then the demand effect of immigrants has to be twice that of their information effect. However, Girma and Yu (2002) and Gould (1994) find higher immigrant elasticity for exports than for the U.S. imports. In this chapter we include immigrants’ income in the United States as a proxy for immigrants’ level of assimilation and purchasing power and estimate the demand effect of immigrants’ after controlling for the size of the immigrant network. Immigrants’ demand and its effect on the global economy is under studied. In the majority of international trade models goods mobility is analyzed assuming consumers in the two trading partners (or multi trading partners) have identical demand patterns.3 With increasing migration around the world the immigrants demand for different type of goods will be significant and may have important effects on the terms of trade and trade flows. The relationship between trade and immigration, whether they are substitutes or complements, is also an important question for bilateral trade agreements and immigration policy. It is often assumed that the goods and the labor flows are substitutes, as was the case with NAFTA. It was expected that relatively freer trade between Mexico and the United States may raise Mexican wages and eventually lower the immigration from Mexico to the United States (also possibly undocumented migration) – making trade and labor flows substitutes. However, Martin (2005) show that there is an evidence of increased migration post-NAFTA from Mexico to the United States and thus post-NAFTA trade and migration were complements instead of substitutes. Different demand patterns of immigrants from natives may have a significant effect on the trade between the sending and the receiving country of the immigrants. Typically, when labor is mobile across countries, it is assumed that migration changes the labor supply of the host and the home country. While the effect of migration on the labor supply is crucial, there are other important effects of migration, in particular on the demand side that are neglected both in the migration and in the trade literature and deserve further exploration. In this chapter, in addition to the empirical investigation of the effect of immigrant income on trade, we also examine the effect of immigrants’ different demand from natives on the trade between the immigrants’ host and their home country in the widely used two input-two good standard Heckscher–Ohlin (H–O) model. We distinguish between the immigrants and the natives on the basis of their

3

It is generally assumed that both migrants and natives have identical and homothetic demand.

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demand patterns and assume that the immigrants on an average consume more of the goods that are available in abundance in their home countries than the natives. For instance, food is an example where immigrants and natives have different demand patterns. Immigrants demand food from their home countries and there are studies identifying that food choices are determined by individual, cultural, social, economical, and historical factors as in Fischler (1988) and Capella and Arnold (1993). The chapter is organized as follows. In Section 2 we discuss the simple H–O model used in this chapter with different demand for immigrants and natives and Section 3 talks about the effect of immigrant income on trade through their demand effect. Section 4 presents the empirical model and we conclude in Section 5. 2. Immigrant and the Heckscher–Ohlin model In this section we explore the effect of immigration on the terms of trade between the country of origin (H) of the immigrants and the country of settlement (F), if the immigrants and natives have different demand patterns, in the most extensively used H–O trade model. We assume on the lines of the demand effect of immigrants on trade that immigrants on an average demand and consume more goods from their home country than the natives. Suppose because of tariffs and other trade barriers, the relative prices of the final goods and hence the factor prices are different in the two countries. Given the initial terms of trade before immigration, immigrants in the host country will have a different level of income and will be faced with different product prices. Therefore, at the terms of trade that prevailed in the equilibrium before immigration the aggregate world demand for commodities can change. This change on the demand side together with the change on the production side from changes in factor supplies in the two countries due to immigration, can lead to changes in the terms of trade. In our simple H–O model there are two countries, H (the immigrants country of origin or the home country) and F (the immigrants’ host country or the foreign country), i ¼ H and F. There are two goods, A and B, produced in both the countries, j ¼ A and B. There are two factors of production (labor L and capital K). Lij is the amount of labor employed in sector j in country i; K ij is the amount of capital employed in sector j in country i; wi is the wage in country i, aiLj and aiKj are, respectively, the labor–output ratio and the capital–output ratio in sector j in country i; and Dij is the demand for good j in country i. 2.1. Assumptions (A2.1) A is labor intensive and B is capital intensive, that is, for every faced price ratio ðw=rÞ ¼ o, ðaLA =aKA Þ4ðaLB =aKB Þ.


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(A2.2) There is constant returns to scale in both the sectors A and B with positive and diminishing marginal productivity. (A2.3) Country H is labor abundant and country F is capital abundant, ðK=LÞH oðK=LÞF . (A2.4) Individuals and firms are price takers. (A2.5) Country F imposes a small tariff at a rate t on its imports. (A2.6) Capital is owned equally in both the countries and is not mobile across countries. (A2.7) Each individual in country H has a continuous locally nonsatiated, strictly quasiconcave utility function U(.). Similarly the individual utility function in country F is given by V(.). At any given prices and income level people in country H buy more of good A and less of good B than people in country F.4 From assumption (A2.2) it follows that aiLj ¼ aiLj ðoÞ and aiKj ¼ aiKj ðoÞ. The requirement of full employment of labor is, aiLA Ai þ aiLB Bi ¼ Li and for capital is aiKA Ai þ aiKB Bi ¼ K i . Unit cost in each industry is equal to the market price: aiLj wi þ aiKj ri ¼ pij . Assume that country F imports A and country H imports B. Let B be numeraire, so that pB ¼ 1. Let the world equilibrium price ratio be p ¼ pA . From (A2.5) it follows that F H F F H pF ¼ pF ¼ ðpH A ¼ ð1 þ tÞ p, where p A , p A =pB Þ and pB ¼ pB ; this H H F F H F H F makes ðw =r Þoðw =r Þ where w ow and, r 4r . The higher wages in country F is an incentive for people to migrate from country H to F. Assumption (A2.6) would be cleared in the next section. 2.2. Analysis Utility maximization subject to the budget constraint gives the demand H H function for good A and good B in country H as DH A ðp ; y Þ and F F F H H DH B ðp ; y Þ, similarly in country F the demand function is DA ðp ; y Þ and F F F i DB ðp ; y Þ, where y is the individual income in country i. Let us assume mnH proportion of the world population move from country H to country F, where nH ¼ LH =ðLH þ LF Þ.5 At unchanged equilibrium price p migration affects world excess demand for good A through the following channels: (1) Effect on the production of the host country: The increase of labor supply in country F (by dLH ) increases the production of good A at unchanged H F equilibrium price, by dAF ¼ ðaF KB =a ÞdL , say X (see Appendix A). (2) Effect on the production of the home country: The fall in the labor supply of country F (by dLH ) due to migration, lowers the production H H of good A by dAH ¼ ðaH KB =a ÞdL , say Y (see Appendix A).

4 5

We assume that there is no demand reversal. In most of the countries migration and immigration is controlled by the government.

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(3) Effect on the demand of immigrants: (a) Price effect: The immigrants face a higher price in country F at the unchanged equilibrium price and this lowers their demand for good A by mnH DH Ap ðy; pÞdp, say T, where dp ¼ p t is the change in price for good A in terms of good B faced by the immigrants when they move from country H to country F and DH Ap ð:Þ is the partial change in the demand for good A due to the price change. (b) Income effect: The immigrants lose their income out of capital and gain income in the form of higher wages they earn in country F, it can be said that the net effect on the income is positive otherwise the immigrants have no incentive to move to the host country. The immigrants leave their capital ðmnH K H Þ behind and thus the change in the income of the immigrants due to the loss of rental income on the capital is mnH rH ðK H =LH Þ and this lowers the demand for good A by mnH rH ðK H =LH ÞDH Ay . The higher wage earned by the immigrants is given by ðwF wH Þ ¼ dw (see Appendix A). The effect on the demand for good A is given by mnH DH Ay dw, say F. (4) Effect on the demand of the population in country H who do not migrate: The capital left behind by the immigrants is enjoyed by the natives of country H and their rental income goes up by ð1 mÞnH rH ½ðK H =ð1 mÞ LH Þ ðK H =LH Þ, this in turn increases their demand for good A by ð1 mÞnH rH ½ðK H =ð1 mÞ LH Þ ðK H =LH ÞDH Ay , say S. This distribution of income assumes that there is an equal distribution of capital among the population, assumption (A2.6). With the world prices held fixed at the initial equilibrium level the change in the excess demand can be written as ¼X þY þT þF þS H H ¼ dAF dAH þ mnH DH Ap ðy; pÞdp þ mn DAy dw

þ ð1 mÞnH rH ½ðK H =ð1 mÞ LH Þ ðK H =LH ÞDH Ay

(1)

H F F H ¼ mnH ½ðDH aH Ay dw þ DAp dpÞ þ ðaKB =a KB =a Þ

In the present analysis the change in the excess demand given by (1) is a priori ambigous. The effect of immigration on the terms of trade is indeterminate and the indeterminacy in this analysis comes from the demand side combined with the production side. The change in demand owing to a price change and the change in the demand owing to the change in wages work in opposite directions, therefore, the excess demand change for good A at the unchanged world price can go up, remain same or go down after immigration from one country to another. If the excess demand for good A goes up after immigration from country H to country F, then the world prices for good A must go up, moving the terms of trade in favor of country H. But if the excess demand for good A after immigration falls then the terms of trade would move against country H. Thus, this further makes a case for an empirical examination of the effect of immigration on trade.


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2.3. Sufficient condition Given our assumption that stability conditions hold in the international market at the initial equilibrium prices p , if immigration increases the excess demand for good A, then the terms of trade will move in favor of good A. However, we have already shown that when both goods are normal at p , immigration will increase the demand for both goods. Therefore, it is clear that if, at p , immigration reduces the production of A in country H more than it increases the production of A in country F, then the terms of trade will move in favor of A. At fixed p , ðL=KÞFA and ðL=KÞH A are fixed, therefore, a sufficient condition for the terms of trade to move in favor of good A (at the initial or before immigration prices and wages) is that the fall in the production of good A in the country H exceeds the increase in the production of good A in country F. This implies: H dK FA cðL=KÞFA odK H A cðL=KÞA

(2)

where cðlL=KÞ is the average product of capital written as a function of L=K. After substituting for the change in the amount of capital employed in sector A of country F after migration at p , given by H H H dK FA ¼ dLF =fðL=KÞFA ðL=KÞFB g and dK H A ¼ dL =fðL=KÞA ðL=KÞB g in (2) we get ½jdLF j=fðL=KÞFA ðL=KÞFB gcðL=KÞFA o½jdLH j=fðL=KÞH A H ðL=KÞH B gcðL=KÞA

(3)

F H At the initial equilibrium, cðL=KÞFA ocðL=KÞH A and dL ¼ dL . Thus, (3) holds if H ½ðL=KÞFA ðL=KÞFB ½ðL=KÞH A ðL=KÞB

(4)

After some manipulation (4) becomes H H F H H F H E B ðL=KÞH B =ðo ðo o ÞÞ E A ðL=KÞA =ðo ðo o ÞÞ

(5)

where E A and E B are the elasticities of factor substitution in sectors A and B. The inequality in (5) holds iff H E A =E B ðL=KÞH B =ðL=KÞA

(6)

However, the RHS of (6) is always less than 1 because good A is more labor intensive than good B. Hence, if E A E B , then (2) will necessarily hold and the terms of trade move in favor of good A. Similarly it can be shown that when E B 4E A , then the terms of trade move in the favor of good B.

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3. Immigrants’ income and demand In the previous studies the findings on the effect of immigrants on trade are puzzling, particularly because the two channels of immigrant links, immigrant information effect and the immigrants demand effect, are not distinguished and immigrant stock is a proxy for both the effects. In this chapter we attempt to distinguish between the immigrant ‘‘information effect’’ and the ‘‘demand or preference effect’’ by including immigrant stock (measuring the size of the immigrant network) as well as the immigrant income levels from various U.S. trading partners. Immigrants demand goods from their home country and this increases the U.S. imports from their home country. For example, Indian immigrants demand spices from India and gradually there are Indian immigrants in the United States as well as traders of non-Indian origin involved in spice trade with India. It is recognized that this will have a positive effect on the U.S. imports and will not affect U.S. exports. Immigrants’ income will significantly affect their demand for goods from their home country, in turn affecting more U.S. imports than exports. If the home country goods are more costly in the United States than some local cheaper substitutes, the demand for home country goods will increase as immigrants’ income rises. However, if the goods from immigrants home country are inferior, higher is the immigrants’ income lower will be their demand for these goods. Immigrants demand for goods from their home country via their income will also depend on the immigrant’s enclave and assimilation in the United States. Immigrant income levels are strongly correlated with the levels of education and past studies have shown that education levels are important in determining the degree of immigrant assimilation in the United States (Borjas, 1995; Greenwood and McDowell, 1986). The literature on the immigrants assimilation in the United States have found evidence that immigrants assimilation not only depends on their education levels, but also on the number of immigrants from their home country living in the United States (Borjas, 1995; Chiswick, 1984). Chiswick and Miller (1996, 2002) measuring immigrants’ social networks by the extent of linguistic concentration in the area where the migrant resides find that higher the immigrant network lower is immigrants’ incentive to learn English and hence lower is their assimilation into the host society. Immigrants with a large immigrant enclave will maintain their strong demand for home country goods, but will also have all the resources required to invest in import substitution activities. Dunlevy and Hutchinson (1999) find that immigrants lower imports from New Europe, and the reason being that the new immigrants have not been in the U.S. long enough to be able to use their home-country information. But they also argue that the falling pro-trade effect of immigrants over


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time is explained by the argument that the immigrants are becoming Americanized and their ‘‘demand effect’’ is falling. Again food is an excellent example here. We do find that the extensive varieties of salsa and Mexican hot sauce production in the United States is due to the large Mexican immigrants. With increasing immigrants from Indian subcontinent in the United States one finds more and more Indian snack that were previously imported from India are now produced by local businesses owned by Indian immigrants. All these are examples where immigrants with higher income levels and larger immigrant enclaves are substituting the imports from their home country with the U.S.-produced substitutes for ethnic home imports. In the literature on the effect of immigrant networks on trade, it is argued that the immigrant income and demand will have a more significant effect on import, however, immigrants’ income might have an indirect effect on the strength of immigrant home link and potentially affecting exports. Larger immigrant stock have a more stronger ‘‘home-link’’ effect. With higher income and more economic assimilation the information effect often captured by immigrant stock might also be getting weaker and thus lowering the immigrant effect on exports. However, there is evidence that there might be a reverse effect with a possibility that over time and with higher upward income mobility in the United States immigrants might specialize in the production and exports of goods from the United States to their home countries. As immigrants rise up in their economic status in the United States they are in a better position and have more well developed social networks in the United States to engage in entrepreneurial activities and opening trade in new channels with their home countries. 4. Empirical model The empirical model is based on the ‘‘gravity framework’’ – where the trade between the United States and its trading partners, who are also immigrant-sending countries, is explained by different economic factors in the United States and the home countries. It is very well known in empirical trade literature that gravity model works well in overall explanation of the trade between countries and is consistent with many trade theories.6 We begin our specification with Frankel (1997) basic constant elasticity gravity model where the trade is proportional to the product of GNP or GDP of the two countries and is inverse to the

6

Helpman (1987) showed that the bilateral trade between countries is proportional to their GDP levels in the differentiated products and increasing returns framework, whereas Deardorff (1998) has tried to reconcile the gravity models with traditional H–O frameworks.

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distance, Dij , between the two countries7 F ij ¼

Y iY j Dij X ij

(7)

To this multiplicative gravity model we add product of per capita GNP, which takes into account the diverse stage of development of different countries (Frankel, 1995; Rauch, 1996). The vector Xij includes factors that assist or hinder trade by influencing the transaction or transportation cost. In addition to the total income capturing the size of the economy and relative income accounting for the similarity between the United States and other countries, we include on the lines of Frankel whether the United States and its trading partners are both English speaking countries. The gravity model in (7) extends to F USj ¼ ðGNPUS GNPj Þa ðPGNPUS PGNPj Þb ðDISTANCEÞg eX USj

(8)

where F USj is U.S. imports from the home country j and exports to the home country; GNPUS GNPj is the product of the U.S. and the home country’s GNP; PGNPUS PGNPj is the product of the per capita GNP of the home country and the United States; DISTANCE is the bilateral distance between the home country and the United States and X usj ¼ ðENGLISH; lnðIMMSTOCKÞjUS ; IncomejUS Þ ENGLISH is a dummy variable measuring whether the immigrant home country is a majority English speaking country, measuring the language similarity with the United States, IMMSTOCK jUS is the stock of immigrants from country j in the United States, and INCOME jus is the average income of the immigrants from country j in the United States. With higher income we might expect that the immigrants might be demanding more of the relatively expensive goods from their home country or with higher income there is a possibility that immigrants are more assimilated within the American society and demand less of the ethnic goods.8 The log gravity model in (7) becomes ln F USj ¼ r þ a lnðGNPUS GNPj Þ þ b lnðPGNPUS PGNPj Þ þ g ln DISTANCE USjt þ dENGLISH

(9)

þ Z1 lnðIMMSTOCKÞjUS þ Z2 INCOME jUS þ USj We will expect that higher the IMMSTOCK, higher will be the positive effect on trade (Z1 40) and if the higher income might have a positive effect on trade (Z2 40) or a negative effect on trade (Z2 o0). To further explore 7 In a recent chapter Disdier and Head (2008) find that after controlling for different sample and methods used to estimate gravity models the negative impact of distance on trade is robust. 8 This might possibly not hold for ethnic restaurant food.

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the role of the immigrants assimilation and income on trade we interact the average immigrant income from country j (INCOME) with the immigrant stock from country. Thus, X usj ¼ ðENGLISH; lnðIMMSTOCKÞjUS ; INCOME jUS ; INCOME jUS

lnðIMMSTOCKÞjUS and the model in (9) becomes ln F USj ¼ r þ a lnðGNPUS GNPj Þ þ b lnðPGNPUS PGNPj Þ þ g ln DISTANCE USjt þ dENGLISH þ Z1 lnðIMMSTOCKÞjUS þ Z2 INCOME jUS

(10)

þ Z3 lnðIMMSTOCKÞ INCOME jUS þ USj There is extensive evidence that larger the size of the immigrant enclave less is the immigrants’ incentive to assimilate with the natives and potentially less is the immigrant integration into the host society. What does this mean for the immigrant effect on bilateral trade flows? Possibly that higher is the immigrant stock from country j, higher is the home effect on trade flows and with rising income and large IMMSTOCK US greater will be the effect of immigrants on U.S. trade with their home country, particularly U.S. imports (Z3 40). However, there is a possibility that with larger share of immigrants from their home country the immigrants might be potentially producing the ethnic goods in the United States and substituting their imports with the goods produced in the United States. In this case we will see that the effect of higher income on the trade flow with the immigrants’ home country will be mitigated by the immigrant stock (Z3 o0). For U.S. exports with rising immigrant income, signifying a higher economic assimilation of the immigrants, makes the immigrant home-link weaker (Z3 o0). To further examine the level of income assimilation of immigrants relative to the natives we include the ratio of average immigrant income from country j in the United States relative to the average native income (PINCOMEUS j ). We estimate the model given by (9) and (10) for U.S. exports and imports. 4.1. Data Our sample consists of 63 countries over 1991–2000.9 The list of the countries is given in Appendix A. The U.S. import data is obtained from the extension of the World Trade Database of Statistics Canada, which is a part of the NBER World Trade Database by Feenstra et al. (2005) and the 9

We add El Salvador and Nicaragua and remove Yugoslavia from the sample of countries used in Frankel (1997).

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Table 1.

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Log of Export and Import, Immigrant Network and Income. U.S. Exports U.S. Imports U.S. Exports

lnðGNPUS GNPj Þ lnðPGNPUS PGNPj Þ lnðDISTANCEÞ ENGLISH lnðIMMSTOCKÞ INCOME

0.482*** (0.065) 0.597*** (0.084) 0.311* (0.158) 0.682*** (0.172) 0.266*** (0.070) 0.0001 (8.16e-06)

0.588*** (0.089) 0.490*** (0.114) 0.506** (0.219) 0.839*** (0.237) 0.4847*** (0.0941) 0.00003** (0.00001)

325 50.56 0.000

331 27.93 0.000

INCOME ðln IMMSTOCKÞ Number of observations F-statistic p-value

U.S. Imports

0.579*** 0.688*** (0.062) (0.089) 0.600*** 0.484*** (0.079) (0.110) 0.201 0.397* (0.149) (0.213) 0.979*** 1.142*** (0.166) (0.239) 1.068*** 0.915*** (0.132) (0.189) 0.0004*** 0.0004*** (0.00007) (0.00009) 0.00004*** 0.00004*** (5.84e-06) (8.31e-06) 325 331 56.73 28.70 0.000 0.000

***Significant at 1%; **significant at 5%; *significant at 10%.

nominal GNP and population is from the Penn World tables.10 Annual data on immigrants across occupation is from the Immigration Statistical Yearbook by the Immigration and Naturalization Services (INS), now called Department of Homeland Security. The data on distance and English language is obtained from the Frankel.11 The annual data on average personal income for foreign born from different trading countries is derived from the March Current Population Survey for the years 1994–2000.12

5. Results Table 1 gives the results from estimating (9) and (10) for the aggregate U.S. exports and imports. From columns (1) and (2) we find that immigrant stock has a significant and positive effect on the U.S. bilateral trade flows. A 1% increase in the immigrant stock increases U.S. exports by 0.27% and U.S. imports by 0.48%.13 However, we find that a 1% increase in 10

The trade data is downloaded from the Center for International Data at the UC Davis (http://cid.econ.ucdavis.edu.) and the website for the Penn World Tables is http:// pwt.econ.upenn.edu. 11 Distance is from ‘‘Direct-Line Distances’’, International Edition, Gary L. Fitzpatrick and Marilyn J. Modlin, Scarecrow Press, Inc. Metuchen NJ and London 1986. 12 Foreign born income is missing for 28 countries in 1994 CPS. 13 This is in line with the previous findings in the literature.


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the average immigrant income level lowers U.S. imports by 0.003%. Thus, higher income levels of the immigrants in the U.S., signifying more assimilation of the immigrants in the U.S., lowers U.S. imports. However, we find a similar significant negative effect of income on both exports and imports when we interact the income level with the size of the immigrant enclave. From columns (3) and (4) we find that a 1% increase in the income level lowers the U.S. exports and imports by 0.005%. This indicates that higher income coupled with a larger size of the immigrant enclave weakens the effect of immigrant networks on trade flows, both for exports and imports. In Table 2 we give the results from estimating the effect of average income of immigrants from country j relative to natives, a better measure of immigrant assimilation than simply the average level of immigrant income from country j. From col columns and (2) in Table 2 we find that higher is the PINCOMEUS lower is the effect on U.S. imports. This clearly shows that as the immigrants income levels are closer to that of the natives or rising above the natives, higher is the immigrant assimilation in the United States and lower is their demand for the home country goods. When we interact the level of PINCOMEUS with the level of the immigrant stock, we find that for both the U.S. exports and imports higher PINCOMEUS lowers the trade flows. The fall is higher for U.S. exports (around 0.10%) than the imports (around 0.9%). Table 2.

Log of Export and Import, Immigrant Network and Relative Income.

lnðGNPUS GNPj Þ lnðPGNPUS PGNPj Þ lnðDISTANCEÞ ENGLISH lnðIMMSTOCKÞ PINCOMEUS

U.S. Exports

U.S. Imports

U.S. Exports

U.S. Imports

0.490*** (0.064) 0.602*** (0.084) 0.308* (0.158) 0.720*** (0.172) 0.256*** (0.070) 0.004 (0.002)

0.589*** (0.089) 0.494*** (0.114) 0.503** (0.219) 0.839*** (0.237) 0.141 (0.097) 0.005** (0.002)

325 51.59 0.000

331 27.99 0.000

0.615*** (0.061) 0.589*** (0.077) 0.173 (0.145) 0.979*** (0.166) 1.095*** (0.164) 0.095*** (0.012) 0.009*** (0.001) 325 61.97 0.000

0.702*** (0.090) 0.478*** (0.111) 0.386* (0.213) 1.142*** (0.239) 0.984*** (0.203) 0.080*** (0.018) 0.008*** (0.002) 331 28.64 0.000

PINCOMEUS ðln IMMSTOCKÞ Number of observations F-statistic p-value

***Significant at 1%; **significant at 5%; *significant at 10%.

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Other variables are what we expected. GNP and PGNP are all positive and significant. English language dummy have a significant positive effect on both U.S. exports an imports. Distance has a negative significant effect on trade flows.

6. Concluding remarks The effect of immigrants’ demand in their host country has been neglected when analyzing the effect of immigration. In the literature exploring the effect of immigrants on trade, immigrant stock is a proxy for both the immigrant information effect and the demand effect. In this chapter we propose to include the effect of income in the host country United States over and above the size of the immigrant stock while examining the effect of immigrant networks on trade. Immigrants relative income to the natives will give us some information on the extent of assimilation of the immigrants in the United States and this assimilation will have an important effect on trade flows, a priori more so for imports than exports. In this chapter we emphasize that immigrants are more than laborers and they have different demand for goods from the natives. We assume that immigrants on an average consume more of the goods that are abundant in their home country in a simple H–O model and find that at the terms of trade that prevailed in the equilibrium before immigration, the aggregate world demand for commodities can change. Such a change on the demand side, together with the change on the production side that results from immigration across two countries can lead to changes in the terms of trade. Our econometric model consisting of 63 major trading and immigrant sending country for the United States over 1991–2000 show that the immigrants income, mostly through demand effect, has a significant negative effect on U.S. imports only. However, if we include the effect of the immigrant income interacted with the size of the immigrant network, measured by the immigrant stock, we find that the income has a negative effect on both the U.S. exports and imports. Higher income of the immigrants coupled with the large size of the immigrants stock weakens the immigrants network effect with their home country, lowering the immigrant network effect for both U.S. exports and imports. This we find in addition to the immigrants stock elasticity of 0.27% for U.S. exports and 0.48% for U.S. imports. In this chapter we argue that the immigrant network effect on trade flows is weakened by the level of immigrant assimilation. We capture immigrant assimilation by their level of income in the U.S. We find a stronger effect of income assimilation on U.S. imports than exports. This chapter is an attempt to raise the question that simply looking at the


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size of the immigrant stock to capture the effect of the immigrant networks on trade might only be a part of the picture, the effect of immigrant assimilation in the host country also needs to be examine in detail while examining the effect of the immigrant networks on trade. Appendix A Preimmigration trade production in both the countries is as follows: i i i i i i i i Ai ¼ ð1=ai ÞðLi ai KB K aLB Þ ¼ ½L ðkB k Þ=½aLA ðkB kA Þ, i i i i i i i i Bi ¼ ð1=ai ÞðK i aH LA L aKA Þ ¼ ½L ðkB k Þ=½aLA ðkB kA Þ i i i i i i H H H where ai ¼ aH LA aKB aLB aKA , kA ¼ ðK=LÞA , kB ¼ ðK=LÞB , k ¼ ðK=LÞ , and i ¼ H; F. The wage–rental ratio in both the countries are: H H H H H H H oH ¼ ðpaH KB aKA Þ=ðaLA aLB pÞ ¼ kB ðp ðaKA =aKB ÞÞ=ððaLB =aLA Þ pÞ,

oF ¼ ðpaFKB aFKA Þ=ðaFLA aFLB pÞ ¼ kB ðpð1 þ tÞ ðaFKA =aFKB ÞÞ=ððaFLB =aFLA Þ pÞ The higher wage income earned by the immigrants is given by: H H F F F H dw ¼ ½aF ðpaH KB aKA Þ a ðpð1 þ tÞaKB aKA Þ=a a

Appendix B The 63 trading partners are Algeria, Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, China, Colombia, Denmark, Ecuador, Egypt, El Salvador, Ethiopia, Finland, France, Ghana, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Iran, Ireland, Israel, Italy, Japan, Kenya, Kuwait, Libya, Malaysia, Mexico, Morocco, Netherlands, New Zealand, Nicaragua, Nigeria, Norway, Pakistan, Paraguay, Peru, Philippines, Poland, Portugal, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sudan, Sweden, Switzerland, Taiwan, Thailand, Tunisia, Turkey, United Kingdom, Uruguay, Venezuela, Germany. References Bandyopadhyay, S., Coughlin, C., Wall, H. (2008), Ethnic networks and U.D. exports. Review of International Economics 16 (1), 199–213. Borjas, G.J. (1995), The economic benefits from immigration. Journal of Economic Perspectives Spring 3–22.

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Capella, L.M., Arnold, D.R. (1993), Acculturation, ethnic consumers, and food consumption patterns. Journal of Food Products Marketing 1 (4), 61–79. Chiswick, B.R. (1984), Illegal aliens in the United States labor market: an analysis of occupational attainment and earnings. International Migration Review 18, 714–732. Chiswick, B.R., Miller, P.W. (1996), Ethnic networks and language proficiency among immigrants. Journal of Population Economics 9 (1), 19–35. Chiswick, B.R., Miller, P.W. (2002), Immigrant earnings: language skills, linguistic concentrations and the business cycle. Journal of Population Economics 15. Deardorff, A. (1998), Determinants of bilateral trade: does gravity work in a neoclassical world? In: Frankel, J.A. (Ed.), The Regionalization of the World Economy. University of Chicago Press, Chicago, pp. 7–28. Disdier, A., Head, K. (2008), The puzzling persistence of the distance effect on bilateral trade. Review of Economics and Statistics 90 (1), 37–48. Dunlevy, J.A., Hutchinson, W.K. (1999), The impact of immigration on American import trade in the late nineteenth and early twentieth century. The Journal of Economic History 59 (4), 1043–1062. Feenstra, R.C., Lipsey, R.E., Haiyan, D., Ma, A.C., Mo, H. (2005), World trade flows: 1962–2000. NBER Working Paper no. 11040. Massachusetts, USA. Fischler, C. (1988), Food, self and identity. Social Science Information 27, 275–292. Frankel, J.A. (1997), Regional Trading Blocs in the World Economic System. Institute for International Economics, Washington, DC, USA. Gould, D.M. (1994), Immigrant links to the home country: empirical implications for U.S. bilateral trade flows. The Review of Economics and Statistics 76, 302–316. Girma, S., Yu, Z. (2002), The link between immigration and trade: an evidence from the United Kingdom. Review of World Economics 138 (1), 115–130. Greenwood, M.J., McDowell, J.M. (1986), The Factor Market Consequences of U.S. Immigration. Journal of Economic Literature 24 (4), 1738–1772. Grief, A. (1993), Contract enforceability and economic institutions in early trade: the Maghribi Traders’ coalition. American Economic Review 83 (3), 525–548. Head, K., Reis, J. (1998), Immigration and trade creation: econometric evidence from Canada. Canadian Journal of Economics 31 (1), 47–62. Helpman, E. (1987), Imperfect competition and international trade: evidence from fourteen industrial countries. Journal of the Japanese and International Economics 1 (1), 62–81.


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Herander, M.G., Saavedra, L.A. (2005), Exports and the structure of immigrant-based networks: the role of geographical proximity. The Review of Economics and Statistics 87 (2), 323–335. Halter, M. (1995), New Migrants in the Marketplace: Boston’s Ethnic Entrepreneurs. University of Massacusetts Press, Amherst, MA. Light, I., Bonacich, E. (1988), Immigrant entrepreneurs: Koreans in Los Angeles. University of California, CA. Light, I., Zhou, M., Kim, R. (2002), Transnationalism and American exports in an english-speaking world. International Migration Review 36, 702–725. Martin, P. (2005), NAFTA and Mexico-US migration. In: Hufbauer, G.C., Schott, J.J. (Eds.), NAFTA Revisited. Institute for International Economics, Washington, DC. Portes, A., Rumbaut, R.G. (1996), Immigrant America: A portrait, second ed. University of California Press, Berkeley, CA. Rauch, J.E., Casella, A. (1998), Overcoming informational barriers to international resource allocation: prices and group ties. NBER Working Paper no. 6628. Rauch, J.E. (1996), Networks versus markets in international trade. Journal of International Economics 48, 7–35. Trefler, D. (1997), Immigrants and natives in general equilibrium models. NBER Working Paper no. 6209. Massachusetts, USA. Available at http://www.nber.org/papers/ Wagner, D., Head, K., Reis, J. (2002), Immigration and the trade of provinces. Scottish Journal of Political Economy 49 (5), 507–525.

CHAPTER 16

The Societal Integration of Immigrants in Germany Michael Fertiga,b,c a

ISG-Institut fu¨r Sozialforschung und Gesellschaftspolitik, Ko¨ln, Germany RWI-Rheinisch-Westfa¨lisches Institut fu¨r Wirtschaftsforschung, Essen, Germany c Institut zur Zukunft der Arbeit (IZA), Bonn, Germany E-mail address: [email protected] b

Abstract This chapter investigates whether and to what extent immigrants in Germany are integrated into German society by utilizing a variety of qualitative information and subjective data collected in the 1999 wave of the German Socio-Economic Panel (GSOEP). To this end, leisure-time activities and attitudes of native Germans, ethnic Germans and foreign immigrants of different generations are compared. The empirical results suggest that conditional on observable characteristics the activities and attitudes of foreign immigrants from both generations differ much more from those of native Germans than the activities/attitudes of ethnic Germans. Furthermore, the attitudes of second-generation immigrants tend to be characterized by a larger degree of fatalism, pessimism and self-doubt than those of all other groups, although their activities and participation in societal life resemble more those of native Germans than those of their parents generation. Keywords: Subjective data, first- and second-generation immigrants, ethnic Germans Jel classifications: J15, J61

1. Introduction Together with the enlargement of the European Union and the consequences of demographic change, the integration of immigrant minorities is Europe’s most important challenge over the next decade. These three challenges are intimately related. The enlargement of the European Union to incorporate countries of Central and Eastern Europe Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008022


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will in all likelihood be associated with additional – though probably moderate (see Bauer and Zimmermann, 1999; Fertig, 2001; or Fertig and Schmidt, 2001a) – migration flows toward the current member states. These flows in turn will have effects on overall population growth, and potentially on the relative status of the immigrant communities in each country. At this stage, however, we do not sufficiently understand the mechanisms governing the integration of immigrant minorities into society, and the available policies to smooth this process. An illustrative example in this context is Germany. In the period up to the 1970s migrants to Germany were mainly labor migrants from Southern Europe, driven by labor market opportunities in Germany and depressed conditions in the sending regions. Over the past three decades, the ethnic composition of immigration to Germany has changed (see Fertig and Schmidt, 2002), and the geographic and cultural gaps between Germany and the sending countries have widened. Furthermore, due to its citizenship law Germany has a sizeable community of second generation foreigners whose social and economic characteristics and outcomes are a matter of growing concern (see, e.g., the symposium on second-generation immigrants in the Journal of Population Economics, 2003). Figure 1 illustrates the share of foreigners living in Germany for selected years. Before 2000,1 citizenship law in Germany was dominated exclusively by the jus sanguinis principle, that is, citizenship was acquired by descent. Only children born to either a German mother or a German father received German citizenship upon birth. This regulation that dated back to a law from 1913 was reformed in 2000 to some extent. Children born after January 1, 2000, will be German nationals by birth if at least one parent is German or if at least one parent legally lives in Germany for a minimum of eight years. Moreover, naturalization law that used to be rather restrictive was reformed in 2000 as well. Thus, since 2000 it is considerably easier for children born to foreigners in Germany to acquire German citizenship. These reforms are associated with a decline in the share of foreigners in born in Germany that reached its maximum in 2000 (22.1%) and has declined to around 19% in 2009 (Figure 1). Many observers of the situation of immigrants in Germany fear that as migrant integration opportunities remain limited, the risk of increasing economic and cultural isolation rises, setting the stage for the creation of permanent second class citizens. For instance, participants of the European Economic and Social Committee (EESC) conference on the integration of immigrants emphasized the need for increased political rights for migrants, in addition to equal access to welfare, health, and education (see EESC press release No. 64/2002, September 2002). In Germany,

1

The year 2000 is especially relevant in the context of this chapter, since our data refer to the 1999 (see also below).

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The Societal Integration of Immigrants in Germany 25

Percent

20

15

10

5

0 1995

2000

2005

2009

Fig. 1. Share of foreigners born in Germany (in % of all foreigners). the Federal Office for Migration and Refugees encourages the social and societal integration of immigrants by supporting integration projects in cooperation with associations, foundations, initiatives, and other authorities with the explicit aim to communicate values and norms, to establish contacts between immigrants and natives and to promote societal acceptance of immigrants. Despite the growing recognition of this situation, relatively little research has targeted the question of migrants’ integration into society, nor are the potential consequences of different policies regarding the participation of migrants and other minorities in the society and the political process fully understood. Even less is known about the integration of the descendants of the migrants, the so-called secondgeneration immigrants. This chapter aims at contributing to a better understanding of these processes by investigating whether and to what extent immigrants in Germany are integrated into the German society. To this end, we utilize a variety of qualitative information and subjective data collected in the 19992 wave of the German Socio-Economic Panel (GSOEP) and compare native Germans, ethnic Germans and foreign immigrants of different generations along various dimensions. Specifically, we investigate whether there are differences between these groups regarding their leisure-time activities and their attitudes toward specific areas of life. Among the latter are areas which are perceived as important for individual well-being and satisfaction and different views on various aspects of life. Finally, we analyze a range of indicators of the 2

This is the only wave of data containing all relevant items, especially the questions on fundamental attitudes.

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societal integration of immigrant groups which are collected for these groups only, like their German language ability or their contact with natives. In this endeavor, we control for a large set of observable characteristics of individual respondents to account for heterogeneity in individual activities and attitudes. The empirical results suggest that conditional on observable characteristics the activities and attitudes of foreign immigrants from both generations differ much more from those of native Germans than the activities/attitudes of ethnic Germans. Since ethnic Germans are (first generation) immigrants who receive German citizenship directly upon entry, these results suggest that citizenship status seems be an important constituent of societal integration. This conclusion is also supported by the finding that the attitudes of second-generation immigrants tend to be characterized by a larger degree of fatalism, pessimism and self-doubt than those of all other groups, although their activities and participation in societal life resemble more those of native Germans than those of their parents generation. The remainder of this chapter is organized as follows. Section 2 provides an overview on the existing literature regarding the economic and social integration of immigrants. In Section 3 the utilized data and the empirical strategy are explained. Estimation results are presented in Section 4 and Section 5 offers some conclusions.

2. Economic and societal integration Economic research concerning migration issues can be conceptualized into three broad fields: (i) the decision to migrate, (ii) the performance of migrants in the destination country, and (iii) the impact of immigration on the population indigenous to the destination country. All these research areas are intimately related and carry important implications for immigration policy. The integration of immigrants into destination countries’ societies is a central part of the research done under the heading of (ii). Typically, analyses conducted within this field investigate whether wages or employment prospects of immigrants converge or diverge as the duration of residence unfolds compared to that of natives and which reasons can be found for these developments. Another aspect of this line of research concerns the degree of discrimination against immigrants as well as the degree and the consequences of geographical and/or occupational segregation, that is, the clustering of immigrants or specific immigrant groups in certain geographical areas or occupational groups. The received literature for the US-American experience demonstrates that skills play a dominant role for immigrant performance. These do not only comprise human capital acquired formally as secondary or

The Societal Integration of Immigrants in Germany

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post-secondary schooling and vocational training, but also informally like labor market experience, or cognitive ability and motivation (see, e.g., the seminal papers by Chiswick, 1978, and Borjas, 1985, 1987). Furthermore, these contributions provide evidence that only part of the human capital acquired by immigrants in their origin country can be transferred to the labor market at the destination. Consequently, upon arrival these immigrants possess a lower earnings capacity, and – since their labor supply is typically inelastic – relatively low earnings. Over their time of residence, they tend to acquire the lacking human capital, for example, the language spoken at the destination. Their low initial earnings capacity implies that the opportunity cost of their investment are relatively low, which makes substantial human capital acquisition likely. After some years of residence in the destination country the earnings of immigrants typically catch up to those of natives (Chiswick, 1978). For the case of Germany, several empirical analyses address the issue of wage performance of the so-called guest workers in the German labor market of the 1980s and early 1990s (see, e.g., Dustmann, 1993; Kurthen et al., 1998; Schmidt, 1997). On balance, these papers demonstrate that in the German labor market formal skills play a decisive role for immigrant wage earnings. For instance, Schmidt (1997) concludes that those immigrants who received their schooling and post-secondary education in Germany achieve earnings parity with native workers, while the typical first-generation migrant from the ‘‘guest worker’’ countries lags some 20% behind the average native worker in terms of wages. Dustmann (1993) demonstrates that the distinction of permanent and temporary migrants might be important for the question of earnings dynamics. Furthermore, Schmidt (1997) compares migrants from the ‘‘guest worker’’ countries with ethnic German immigrants – concluding that the latter group of immigrants is typically better educated and economically well integrated. Finally, Dustmann and Schmidt (2000) address the wage performance of female immigrants. To date, almost the complete migration literature and certainly all studies of the German case have concentrated on the analysis of the economic performance of male immigrants. In their paper, Dustmann and Schmidt (2000) emphasize the treatment of labor supply issues that plague all analyses of female wage earnings. They conclude that for the relative wages of female immigrants not only their own formal education, but also their family circumstances – most notably the return plans of their family – play an important role. In general, the majority of the received literature in this field concentrates on relative economic success. The focus is almost exclusively on measurable differences in economic outcomes (e.g., wages or employment opportunities) that cannot be traced back to observable differences in the determinants of these outcome measures. One exception is Dietz (2003). The author investigates group formation, values, and attitudes of a

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sample of young ethnic German immigrants who entered Germany from the former Soviet Union between 1990 and 1994. Her results indicate that the circle of friends of the majority of these youngsters consists primarily of members of their own group, which they suffer from language problems and reside in rather segregated areas. Furthermore, the values and attitudes of this immigrant group are characterized by a high acceptance of parental authority, rather traditional gender roles and strong orientation toward collective values rather than an individualistic life style. Another exception is Dustmann (1996). The author investigates the determinants of the feeling of national identity for migrants living in Germany. His results suggest that individual demographic characteristics, nationality and indicators for the family context of respondents affect migrant’s social integration. By contrast, indicators for the labor market status do not exhibit significant effects. Moreover, almost all studies for the case of Germany concentrate on first-generation migrants, whereas the offspring of these immigrants, the so-called second generation, has not attracted a comparable level of attention. There are two notable exceptions. First, Fertig and Schmidt (2001b) provide a detailed characterization of both immigrant generations in Germany by demographic and socioeconomic characteristics. From their analysis it becomes transparent that there exist considerable differences between both immigrants and natives as well as among the different immigrant generations themselves. The paper, furthermore, investigates the welfare dependence of migrants and contrasts the findings on the determining factors of the moderate risk of migrants to depend on public assistance payments with the perception of immigrants by native Germans using two complementary datasets. And second, Riphahn (2003) investigates the educational attainment of second-generation immigrants in Germany by analyzing school attendance and completed schooling degrees. The author finds that after controlling for a variety of individual background characteristics statistically significant negative differences between second-generation migrants and comparable natives remain. The ultimate aim of this chapter is the provision of a comprehensive portrait regarding various aspects of the societal integration of different immigrant groups in Germany by analyzing a large set of individual-level data for the year 1999. The next section explains the utilized dataset and the pursued empirical strategy.

3. Empirical strategy and data Measuring societal integration is anything but trivial. Since there is no objective scale, this phenomenon is by its very nature relative. That is, a specific group of individuals might resemble the behavior or the attitudes/values of a chosen reference group relatively more than another


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group and might therefore be labeled more integrated. However, the reference group is obviously a choice variable and the extent to which the members of the chosen reference group perform an adequate benchmark might be controversial. Furthermore, preferences, tastes, and values clearly vary from one individual to another, inducing the necessity to control for observable heterogeneity between different respondent groups. But even if significant differences between certain population groups remain after controlling for socio-demographic characteristics, it is difficult to establish precisely if these differences are large or frequent enough to label them societal disintegration. In the case at hand, German citizens who were born in Germany form the comparison group for all immigrant groups. Furthermore, we pursue a careful examination and interpretation of estimation results to ward off fallacious conclusions given the aforementioned difficulties. In our empirical analyses, we utilize individual-level data from the 1999 wave of the GSOEP. The GSOEP is a representative longitudinal study of private households in Germany. It collects information on all household members, consisting of Germans living in the old and new German states, foreigners, who have entered the country in the 1960s and early 1970s, and recent immigrants to Germany. Information collected includes household socioeconomic composition, occupational biographies, employment, earnings, as well as health and life satisfaction indicators. Furthermore, there are different waves with special questionnaires on, for example, social security, education, and training. The 1999 wave contains a special set of questions related to respondents’ views on life and on the importance of different areas of life for satisfaction and well-being. We explicitly consider the following mutually exclusive immigrant groups in Germany: (i) Ethnic German immigrants, (ii) first-generation (foreign) immigrants, and (iii) second-generation (foreign) immigrants. These groups are defined as follows. Ethnic German Immigrants: This group of migrants that entered Germany from Eastern Europe during the 1990s and that receives citizenship status immediately upon arrival is not directly observable in the data. However, the data provide information on German citizenship, place of birth, and immigration year. Therefore, all respondents possessing the German citizenship, which were not born in Germany and which did not live in Germany before 1990 were accounted as ethnic German immigrants. Clearly, this definition is not completely accurate, since it is possible that German citizens who were born outside Germany and entered the country after 1990 are accounted as ethnic German immigrants as well. However, the vast majority of these people should be ethnic Germans who immigrated from Eastern Europe during the 1990s. First-generation (foreign) immigrants: This group contains respondents without German citizenship who were not born in Germany. The majority

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of individuals in this group comprises the so-called guest workers of the 1960s and early 1970s. Second-generation (foreign) immigrants: This group contains respondents without German citizenship which were born in Germany. The majority of individuals in this group comprises the offspring of the so-called guest worker immigrants of the 1960s and early 1970s. The distinction between these three immigrant groups, and especially the differences in treatment with respect to citizenship status, allows us the shed some light on the nexus of societal integration and citizenship status. In our empirical analyses all three groups are compared to respondents possessing the German citizenship who were born in Germany. In these comparisons, we control for various individual characteristics of the respondents. Besides the immigrant group indicators, respondents’ education, marital status, gender, age, employment status, years of residence in Germany, and other characteristics are taken into account.3 Table A1 in the appendix provides a detailed description of all explanatory variables. To analyze the societal integration of different immigrant groups living in Germany, we utilize three large sets of questions: (a) Questions on leisure-time activities, (b) questions on attitudes, and (c) foreigners/ immigrants specific questions. For the first two sets of questions information is collected for native Germans as well as for all immigrant groups. The last set is specific to the situation of foreigners/immigrants in Germany. Hence, for this set a comparison to Germans is not possible. Tables A2–A4 in the appendix provide detailed descriptions of the various questions and the answer possibilities. The first set comprises questions on leisure-time activities that are supposed to measure the degree of immigrant participation in cultural and leisure activities. This does not only entail the extent to which respondents participate in, for example, cultural, religious, or sport events but also how much they engage in social intercourse with friends or neighbors and the degree they are involved in public initiatives or political parties. The second set of questions comprises the attitudes of respondents toward areas that are important for their well-being and satisfaction. These areas encompass the personal sphere – for example, the importance of family, friends, and career success – as well as general areas like environmental protection and the maintenance of peace. Furthermore, this set also comprises the degree of agreement on several statements regarding attitudes toward life and the future. For instance, respondents are asked for their (dis-)agreement to the statements ‘‘I decide the way my 3

Because a substantial share of respondents – especially within the group of second generation immigrants – is rather young and, hence, still in education, we abstain from using income/wages or other job-related characteristics (e.g., occupation or hours worked) as control variables.


383

life is run,’’ ‘‘In comparison to others, I haven’t achieved what I deserve,’’ and ‘‘If I ever hit upon difficulties in my life, I doubt my capabilities.’’ Therefore, the extent to which respondents agree to these statements can be interpreted as indicators for the degree of fatalism, self-doubt, and discontent with which they perceive their own life. Finally, this set also contains a question on respondents’ general optimism toward the future, the extent to which they feel connected with the place they live and their willingness to move away from this place. The third set of questions that is confined to immigrants/foreigners only contains data on the proximity between immigrants and natives (existence of contacts and visits) as well as on the language ability of respondents (regarding German and the language of the origin country). Furthermore, respondents are asked which language they typically use in everyday life and how they perceive their acceptance in German society (experience of disadvantages; wish to stay permanently; feeling as German and connection to origin country). Table A5 in the appendix reports some summary statistics for the utilized sample. From this table, it becomes transparent that for many questions there are large (unconditional) differences in the answer distributions for the different groups. However, the last panel of Table A5 reveals that these groups also differ considerably with respect to observable characteristics. Therefore, a multivariate analysis that controls for observed heterogeneity between respondents is indispensable. The results of our (ordered) probit analyses are reported in the next section. 4. Results In this section the estimation results of our empirical application are reported. Owing to the large number of estimations, it is infeasible to report the full set of results. Rather, the following tables contain a summary of the estimation results that indicate the direction and significance of coefficient estimates only.4 In these tables a ‘‘þ’’ denotes a statistically significant (95% level) positive difference between the estimated group indicators. A ‘‘’’ indicates that this difference is statistically significant negative, and a ‘‘0’’ denotes an insignificant difference between the respective groups. That is, for instance, the information in row 1 of Table 1 has to be interpreted as follows: The ‘‘þ’’ in columns 1 and 2 indicate that Germans (born in Germany) display a higher probability to visit cultural events than foreigners and ethnic Germans. By contrast, the ‘‘’’ in column 5 suggests that the first generation of (foreign) immigrants tend to be less likely than ethnic Germans to visit cultural events. The ‘‘0’’ in the last column 4

Full estimation results are available from the author upon request.

German vs. foreign

German vs. ethnic German

Ethnic German vs. foreign

0

0 0

0

0

0

0

0

0

0

0

0

First vs. second

Notes: A ‘‘þ’’ denotes a statistically significant positive, a ‘‘’’ a statistically significant negative, and ‘‘0’’ an insignificant difference between estimated group indicators. For a description of the utilized control variables see Table A1 in the appendix.

þ

0

0

0

0 0

0 þ

þ

þ

þ

Second vs. ethnic German

þ

Second vs. German

First vs. Ethnic German

First vs. German

Results of ordered probit estimations for leisure-time activities

Which of the following activities do you participate in during your free-time? Visits to cultural events þ þ þ Cinema visits, visits to pop 0 0 þ concerts, discos, etc. Active sport þ 0 þ Social intercourse with friends, 0 relatives, or neighbors Lend help to friends, relatives, or 0 0 0 neighbors Honorary office participation þ þ 0 in clubs, etc. Participation in public 0 0 0 initiatives, etc. Church-going, visits to religious þ events

Question

Table 1.

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indicates that there is no difference between the second and the first generation of migrants. Table 1 reports a summary of estimation results for the different leisuretime activities. From this table it becomes transparent, that even after controlling for observable differences between respondent groups like age, gender, education, marital status, etc., significant differences between natives and foreigners in Germany remain. Estimation results indicate that Germans compared to foreigners display a significantly higher probability to visit cultural events and to do sports actively. Furthermore, they are significantly more likely than foreigners to participate in clubs etc. as a honorary office worker but display a statistically significant lower probability to engage in social intercourse with friends or neighbors and to be involved in religious activities. In general, the differences between the first generation of immigrants and Germans born in Germany are much more pronounced than those between natives and the second generation. Existing differences between both immigrant groups indicate that the second generation is closer to native Germans than their parents. However, in the majority of cases the differences between both immigrant generations are negligible. By contrast, ethnic Germans and Germans born in Germany tend to behave similarly. For the majority of leisure-time activities estimation results indicate no statistically significant difference between these two groups. The only exceptions are, first, that ethnic Germans are less likely than native Germans to visit cultural events and to participate in clubs etc. as honorary worker. And second, ethnic Germans display a statistically significant higher probability to be involved in religious activities. In general, ethnic Germans are the population group with the highest incidence of religious activity in their leisure-time. Furthermore, leisure-time activities of ethnic Germans tend to be significantly different from those of noncitizens. For the most part, these significant differences are driven by the discrepancies between ethnic Germans and the first generation of (foreign) immigrants, whereas the activities of the second generation are more similar to those of ethnic Germans. Overall, all immigrant groups in Germany are participating in various dimensions of societal life where the second generation of (foreign) immigrants seems to be more assimilated to the activities of native Germans than their parents. In Table 2A the results for first part of the attitudes questions are reported. Here respondents are asked which areas of life are important for their well-being and satisfaction. Estimation results indicate that in the majority of cases there are no significant differences between Germans and foreigners. In contrast to the leisure-time activities, this result is, however, mainly driven by the similarity in responses of first-generation immigrants and Germans, whereas the answers of a typical respondent from the second immigrant generation differs more from those of Germans.

German vs. foreign



0

0

0

0 þ

N.A.a 0 0 0 0 0 0


0 0 0 0 0 0 0 0 þ 0 0 0

Second vs. German

þ 0 0 0 0 0 0 0 0 0 0 0 0 0

First vs. second

Notes: A ‘‘þ’’ denotes a statistically significant positive, a ‘‘’’ a statistically significant negative, and ‘‘0’’ an insignificant difference between estimated group indicators. a Since all respondents in the Group of ethnic Germans have chosen the same answer category, this comparison is not possible. For a description of the utilized control variables see Table A1 in the appendix.

N.A.a þ 0 0 0 0 0

First vs. ethnic German

0 0 0 0 0 0 0 0 0 0 þ 0 þ

First vs. German

Results of (ordered) probit estimations for attitudes

Which of the following areas are important for your well-being and satisfaction? Work þ þ þ N.A.a Family 0 N.A.a Friends 0 þ Income 0 0 0 Housing 0 0 þ Influence on political decisions 0 þ Career success þ 0 0 Free-time 0 0 0 Health 0 0 0 Protection of the natural environment 0 þ Faith, religion 0 Residential area 0 þ Mobility to get everywhere quickly þ If you think about the future in general, 0 þ are you optimistic? To what extent do you feel connected with 0 0 þ the place and the area that you live in? Would you consider moving away, e.g., þ þ because of family or job?

Question

Table 2A.

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Estimation results, furthermore, suggest that Germans have a significantly higher probability than foreigners to regard work and career success as important factors for their well-being. On the contrary, religion and mobility tend to be significantly less important for them. The importance of religious activities, however, is especially pronounced in the first generation of immigrants, whereas the second generation tends to perceive this area as important as native Germans. For the most part, ethnic Germans tend to perceive different areas as important for their well-being and satisfaction than (foreign) immigrants of both generations and Germans born in Germany. Compared to the latter group, ethnic Germans display a significantly higher probability to regard influence on political decisions, environmental protection, the residential area they are living, mobility and religion as important factors for their well-being. The latter finding supports the results from Table 1 where ethnic Germans display a higher probability to be involved in religious activities during their free-time. Furthermore, ethnic Germans tend to consider work as less important than native Germans. These results suggest that for ethnic Germans collective values like political influence, environmental protection, residential area, and religion carry more weight than individualistic values like work. Against the background of the poor economic conditions in the countries they emigrated from, this is certainly a surprising result that might be a reflection of their upbringing in a socialistic society and supports the findings of Dietz (2003) for ethnic German youngsters. The lower panel of Table 2A aims at inquiring how optimistic respondents are regarding future. Furthermore, the final two questions address the extent to which respondents feel connected with the place they live in and whether they are willing to move away from there. Estimation results suggest that ethnic Germans exhibit the highest probability to look ahead optimistically, whereas both foreign immigrant generations are more pessimistic. Interestingly, ethnic Germans feel more connected to the place or area they are living than native Germans or noncitizens. They are, however, also the most willing to move away for reasons of family or job. The second generation of immigrants is the population group that is the most similar to ethnic Germans with respect to these issues, whereas their parents generation and Germans born in Germany display the lowest willingness to be mobile. Table 2B contains a summary of the estimation results for the second part of the attitudes questions under investigation. These questions try to establish the degree of respondents’ agreement to different views on life, and therefore, try to extract rather fundamental attitudes of respondents. The first five questions can be interpreted as aiming to extract the degree of fatalism with which respondents view their life and its prospects. Interestingly, foreigners unambiguously tend to display a higher degree of fatalism than native Germans and for the vast majority of cases also compared to ethnic Germans. This phenomenon is especially pronounced for the second generation of (foreign) immigrants and manifests itself in

German vs. foreign



First vs. German


þ þ þ þ þ

0

þ þ þ þ 0

0

0

0

0

0

0

0

0 0

First vs. second

Notes: A ‘‘þ’’ denotes a statistically significant positive, a ‘‘’’ a statistically significant negative, and ‘‘0’’ an insignificant difference between estimated group indicators. For a description of the utilized control variables see Table A1 in the appendix.

þ


þ

Second vs. German

Results of (ordered) probit estimations for attitudes

The following statements express varying attitudes towards life and the future. Do you agree/disagree? I decide the way my life is run þ 0 þ 0 I have little control over the things 0 þ þ that take place in my life One has to work hard to achieve þ 0 þ 0 success What one achieves in life is mainly a 0 þ þ question of luck or fate I often make the discovery that 0 0 þ 0 others influence my life If I ever hit upon difficulties in my þ 0 þ life, I doubt my capabilities In comparison with others, I haven’t 0 þ þ achieved what I deserve The possibilities in my life are 0 0 0 þ determined by the social conditions If one is socially or politically active, 0 0 0 0 0 one can influence the social conditions

Question

Table 2B.

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their significantly higher probability to perceive their life as less selfdetermined and their prospects in life as determined by faith or luck rather than their own endeavors. The differences in agreement between groups are not only statistically significant, but also quantitatively substantial. For instance, the probability to agree to the statement ‘‘I have little control over the things that take place in my life’’ is on average 11.5 percentage points higher for foreigners than for native Germans and 8.5 percentage points higher for foreigners than for ethnic Germans. Furthermore, second-generation immigrants tend to exhibit a 14.5 and 18 percentage points higher agreement propensity than native German and ethnic Germans, respectively. For comparison, being female is associated with a 2.5 percentage point and being unemployed with an 8 percentage point higher agreement probability. The sixth question can be interpreted as an indicator for the extent to which respondents doubt their own abilities. Estimation results indicate that Germans born in Germany as well as ethnic Germans tend to be less afflicted by self-doubts than non-citizens. Again, this result is primarily driven by the difference between citizens and the second generation of immigrants. The next question refers to the degree of respondents’ satisfaction with their life and what they have achieved so far, whereas the last two questions indicate the degree of skepticism or pessimism with which respondents view the level of self-determination of their own life and their influence on the political and social environment they are living. With the exception of the last question, which exhibits no significant differences whatsoever, estimation results for these attitudes confirm the results of the preceding questions. Second-generation immigrants are less satisfied with their life compared to German citizens and display a significantly higher probability to doubt that their life is self-determined than ethnic Germans. Again, the deviations between population groups are quantitatively substantial. For instance, agreement to the statement ‘‘If I ever hit upon difficulties in my life, I doubt my capabilities’’ is 4.5 and 8.5 percentage points higher for foreigners than for Germans born in Germany and ethnic Germans, respectively. The difference between second generation foreigners and native Germans amounts to 8.5 percentage points, whereas agreement is around 16 percentage points higher for the second generation than for ethnic Germans (for comparison: females exhibit a ten percentage point higher and unemployed an 8 percentage point higher agreement propensity). Altogether, estimation results indicate that even after controlling for a large set of socio-demographic characteristics and in stark contrast to their similarity in behavior, the second-generation of immigrants is a deeply unsettled population group that is plagued by self-doubts and a rather fatalistic and pessimistic view on their life and its prospects. Finally, estimation results for the questions to immigrants/foreigners only are summarized in Table 3. These results indicate that typical members of the second generation of (foreign) immigrants tend to have

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Table 3.

(Ordered) probit results for questions to noncitizens only

Variable

First vs. second



Contact to Germans Visits to Germans Visits from Germans Spoken German Written German Write language of origin country Speak language of origin country Mainly use German Mainly use language of origin country Use both equally Disadvantage Wish to stay Feel German Feel connected with origin country

0 0 0 þ 0 0 þ

N.A. N.A. N.A. þ þ þ 0 þ þ

N.A. N.A. N.A. 0 0 þ 0 0 þ

Note: For a description of the utilized control variables see Table A1 in the appendix.

more contact with Germans (including visits from Germans) than their parents generation. Furthermore, self-assessed fluency in (written and spoken) German is higher for this group than for their parents generation. However, ethnic Germans display the highest self-assessed fluency compared to all other immigrant groups. By contrast, first-generation immigrants tend to assess a higher fluency in the language of their origin country compared to ethnic Germans, whereas there is no difference in assessment between both immigrant generations. In line with these findings, ethnic Germans are more likely to use German as the main language at home, whereas first-generation immigrants tend toward the language of their origin country and the second generation is, again, in between. Moreover, members of the first-generation immigrant group reported a significantly higher experience of disadvantage due to their origin than ethnic Germans, whereas estimation results reveal no difference between the second-generation and ethnic Germans. Unsurprisingly, ethnic Germans display the highest willingness to stay permanently in Germany and to feel German, whereas the first generation exhibits the lowest likelihood. Second-generation immigrants are again in between both other groups. By contrast, first-generation immigrants are the group with the highest feeling of connection to their origin country and ethnic Germans display the lowest association with the country they emigrated from. In general, this last set of estimation results reveals no surprising findings. In the context of societal integration of immigrant minorities, language fluency and the feeling of connection to the country of residence as well as the origin country are the most interesting pieces of information.


391

With respect to these indicators, ethnic Germans tend to display the highest degree of integration into the German society since their command of the German language and their connection to Germany as their country of permanent residence are higher compared to both noncitizen immigrant groups. However, this is not to say that the language fluency of ethnic Germans is high in absolute terms. Furthermore, self-assessed measures are always susceptible for misclassification errors.5

5. Conclusions Over the past three decades, the ethnic composition of immigration to Germany has changed, and the geographic and cultural gaps between Germany and the sending countries have widened. Germany now has a sizeable community of second-generation immigrants whose social and economic characteristics are a matter of growing concern. Yet, despite the growing recognition of this situation, relatively little research has targeted the question of migrants’ integration into society. Furthermore, even less is known about the integration of the so-called second-generation immigrants. Hence, this chapter contributes to our understanding of these processes by providing an analysis of the extent to which immigrants in Germany are integrated into the German society. Specifically, we utilize a large set of qualitative information and subjective data collected in the 1999 wave of the GSOEP and compare native Germans, ethnic Germans and foreign immigrants of different generations along various dimensions. We investigate whether there are differences between these groups regarding their leisure-time activities and their attitudes toward specific areas of life. Finally, we analyze various indicators of the societal integration of immigrant groups that are collected for these groups only, like their German language ability or their contacts to natives. In this endeavor, we control for a large set of observable characteristics of individual respondents to account for heterogeneity in individual activities and attitudes. The empirical results suggest that conditional on observable characteristics the activities and attitudes of foreign immigrants from both generations differ much more from those of native Germans than the activities/attitudes of ethnic Germans. These results indicate that citizenship status plays an important role for societal integration of immigrants. Clearly, the cultural background between ethnic Germans on the one and first as well as second generation foreigners on the other hand might differ substantially and can exert considerable influence on their perceptions on life. However, it seems more than plausible that 5 Dustmann and van Soest (2001) demonstrate in the context of language fluency as a determinant of earnings that misclassification of self-assessed language command might be a severe problem.

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individuals holding citizenship of their country of birth or permanent residence feel much more welcome their and, thus, perceive much more control of their life. Finally and most importantly, our estimation results for the questions regarding different views on life indicate that even after controlling for a large set of socio-demographic characteristics the second-generation of immigrants is a deeply unsettled population group which is plagued by self-doubts and a rather fatalistic and pessimistic view on their life and its prospects. This finding stands in stark contrast to the observed similarity in leisure-time activities of this population group compared to native Germans. Since the typical respondent from the second-generation immigrant group is rather young, their pessimistic perception of life and its prospects should be alarming. Whether and to what extent this is the cause or the consequence of their performance on the German labor market is a currently unresolved issue which has to be addressed in future research. In any case, by ignoring the rather gloomy orientation of this immigrant generation, we are running the risk of losing a sizeable fraction of young people as content and productive members of our future society. Acknowledgments The author is grateful to Thomas K. Bauer and Christoph M. Schmidt for helpful comments. Appendix

Table A1. Variable

German First (generation) Second (generation) Foreign

Secondary schooling Intermediary schooling Technical schooling Upper secondary school. Other schooling No schooling degreea Singlea

Description of explanatory variables Description Immigrant group indicators 1 if respondent has German citizenship and is born in Germany; 0 otherwise 1 if respondent does not have German citizenship and is not born in Germany; 0 otherwise 1 if respondent does not have German citizenship but is born in Germany; 0 otherwise 1 if respondent belongs to first generation or second generation of immigrants; 0 otherwise Education category indicators 1 if respondent has secondary schooling degree; 0 otherwise 1 if respondent has intermediary schooling degree; 0 otherwise 1 if respondent has technical schooling degree; 0 otherwise 1 if respondent has upper secondary schooling degree; 0 otherwise 1 if respondent has other schooling degree; 0 otherwise 1 if respondent has no schooling degree; 0 otherwise Marital status indicators 1 if respondent is single; 0 otherwise


393

Table A1. (Continued ) Variable

Description

Married

1 if respondent is married or lives with permanent partner; 0 otherwise 1 if respondent is divorced; 0 otherwise 1 if respondent is widowed; 0 otherwise Other control variables 1 if respondent is female; 0 otherwise (Squared) Age of respondent in years 1 if respondent is registered as unemployed; 0 otherwise 1 if respondent is currently in training (school, university etc.); 0 otherwise 1 if children under 15 live in respondent’s household; 0 otherwise 1 if respondent lives in eastern Germany; 0 otherwise Number of years, the respondent lives in Germany

Divorced Widowed Female (Squared) Age Unemployed In training Children under 15 East Time spent in Germany a

Denotes the reference category within the respective indicator groups.

Table A2.

Description of questions on leisure-time activities

Which of the following activities do you participate in during your free-time? Visits to cultural events, e.g., concerts, theatre, presentations Answer possibilities: Cinema visits, visits to pop concerts, dance events, discos, sporting 1 ¼ never, 2 ¼ rarely, events 3 ¼ every month, Active sport 4 ¼ every week Social intercourse with friends, relatives or neighbors Lend help to friends, relatives, or neighbors when something has to be done Honorary office participation in clubs, associations or social services Participation in public initiatives, in political parties, local government Church-going, visits to religious events

Table A3.

Description of questions on attitudes

Which of the following areas are important for your well-being and satisfaction? Work Original answer possibilities: very Family important, important, not very Friends important, totally unimportant. Income These are summarized into: 1 ¼ very Housing important and important; 0 Influence on political decisions otherwise Career success Free-time Health Protection of the natural environment Faith, religion Residential area Mobility to get everywhere quickly

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Table A3. (Continued ) The following statements express varying attitudes toward life and the future I decide the way my life is run Original answer possibilities: totally In comparison with others, I haven’t achieved what I deserve agree, agree slightly, disagree slightly, What one achieves in life is mainly a question of luck or fate totally disagree. These are If one is socially or politically active, one can influence the summarized into: 1 ¼ totally agree social conditions and agree slightly; 0 otherwise I often make the discovery that others influence my life One has to work hard to achieve success If I ever hit upon difficulties in my life, I doubt my capabilities The possibilities in my life are determined by the social conditions I have little control over the things that take place in my life If you think about the future in general, are you optimistic? Original answer possibilities: optimistic, more optimistic than pessimistic, more pessimistic than optimistic, pessimistic. These are summarized into: 1 ¼ optimistic and more optimistic than pessimistic; 0 otherwise To what extent do you feel connected with the place and the area that you live in? Original answer possibilities: very strong, strong, not very strong, not at all. These are summarized into: 1 ¼ very strong and strong; 0 otherwise Would you consider moving away, e.g., because of family or job? Answer possibilities: 1 ¼ yes; 2 ¼ possibly, cannot exclude the possibility; 3 ¼ no

Table A4. Variable

Contact to Germans Visits to Germans Visits from Germans

Spoken German Written German Write language of origin country Speak language of origin country Mainly use German Mainly use language of origin country Use both equally Disadvantage Wish to stay Feel German Feel connected with origin country

Description of questions to foreigners only Description Contact to Germans Since you have lived in Germany, have you had close contact to Germans? 1 ¼ yes; 0 otherwise In the last 12 months did you visit any Germans in their home? 1 ¼ yes; 0 otherwise In the last 12 months were you visited by any Germans in your home? 1 ¼ yes; 0 otherwise Language ability In your opinion, how well can you speak and write German/the language of your origin country? Answer possibilities: 1 ¼ not at all; 2 ¼ poorly; 3 ¼ fairly; 4 ¼ good; 5 ¼ very well

Language use What language do you speak here in Germany for the most part? Answer possibilities: Mostly German; the language of your origin country; both about equally as much

Perception of acceptance in German society How often have you experienced disadvantages in the last two years because of your origins? 1 ¼ never; 2 ¼ seldom; 3 ¼ often Do you want to stay in Germany forever? 1 ¼ yes; 0 otherwise To what degree do you think of yourself as German? 1 ¼ not at all; 2 ¼ barely; 3 ¼ in some respect; 4 ¼ mostly; 5 ¼ completely To what extent do you feel connected with the country of your or your family’s origin? 1 ¼ not at all; 2 ¼ barely; 3 ¼ in some respect; 4 ¼ mostly; 5 ¼ completely

Mean

Standard deviation

Germans

0.631 0.833 1.085 0.762 0.854 0.601 0.276 1.167 0.327 0.049 0.312 0.154 0.187 0.444 0.463 0.348 0.049 0.306 0.492 0.315 0.332

0.879 0.998 0.891 0.976 0.964 0.268 0.691 0.859 0.998 0.896 0.592 0.889 0.874

Standard deviation

1.583 1.798 1.741 3.315 2.495 1.213 1.060 2.367

Mean

Ethnic Germans

0.785 0.984 0.917 0.958 0.956 0.219 0.611 0.883 0.997 0.839 0.650 0.855 0.838

1.454 1.616 1.588 3.389 2.449 1.242 1.057 2.065

Mean

0.411 0.125 0.276 0.201 0.205 0.414 0.488 0.321 0.058 0.368 0.477 0.353 0.368

0.656 0.798 1.038 0.775 0.935 0.658 0.293 1.069

Standard deviation

First generation

Summary statistics – questions on leisure-time activity and attitudes

Which of the following activities do you participate in during your free-time? Visits to cultural events. 1.845 0.687 Cinema visits, visits to pop concerts, discos, etc. 2.056 0.889 Active sport 2.106 1.240 Social intercourse with friends, relatives, or neighbors 3.116 0.824 Lend help to friends, relatives, or neighbors 2.382 0.803 Honorary office participation in clubs, etc. 1.547 0.950 Participation in public initiatives, etc. 1.143 0.468 Church-going, visits to religious events 1.700 0.922 Which of the following areas are important for your well-being and satisfaction? Work 0.836 0.370 Family 0.981 0.137 Friends 0.892 0.310 Income 0.960 0.196 Housing 0.971 0.168 Influence on political decisions 0.307 0.461 Career success 0.718 0.450 Free-time 0.903 0.296 Health 0.994 0.080 Protection of the natural environment 0.881 0.323 Faith, religion 0.328 0.469 Residential area 0.896 0.306 Mobility to get everywhere quickly 0.878 0.327

Questions

Table A5.

0.844 0.977 0.953 0.941 0.924 0.272 0.785 0.939 0.988 0.836 0.539 0.825 0.904

1.750 2.750 2.548 3.574 2.456 1.304 1.102 1.823

Mean

0.364 0.151 0.211 0.235 0.265 0.446 0.412 0.240 0.107 0.371 0.499 0.380 0.295

0.697 0.927 1.225 0.705 0.924 0.715 0.380 0.926

Standard deviation

Second generation


Mean

If you think about the future in general, are you optimistic? To what extent do you feel connected with the place and the area that you live in? Would you consider moving away, e.g., because of family or job?

0.425 0.411 0.793

0.764 0.784 2.215

2.198

0.870 0.666

0.307 0.954 0.248 0.620 0.849 0.180

0.817

0.336 0.472

0.462 0.210 0.432 0.486 0.359 0.385

2.110

0.719 0.597

0.365 0.938 0.329 0.702 0.785 0.323

Mean

0.427 0.193 0.433 0.476 0.419 0.364

Standard deviation

0.856

0.450 0.491

0.482 0.241 0.470 0.458 0.411 0.468

0.411 0.500 0.481 0.488

Standard deviation

First-generation

0.784 0.476 0.638 0.392

Mean

Ethnic Germans

Do you agree/disagree? 0.308 0.862 0.345 0.456 0.336 0.473 0.480 0.511 0.500 0.489 0.392 0.489

Standard deviation

Germans

The following statements express varying attitudes towards life and the future. I decide the way my life is run 0.894 In comparison with others, I haven’t achieved what I deserve 0.295 What one achieves in life is mainly a question of luck or fate 0.360 If one is socially or politically active, one can influence the social 0.396 conditions I often make the discovery that others influence my life 0.239 One has to work hard to achieve success 0.961 If I ever hit upon difficulties in my life, I doubt my capabilities 0.250 The possibilities in my life are determined by the social conditions 0.652 More important than any endeavors, are your own capabilities 0.772 I have little control over the things that take place in my life 0.158

Question

(Continued)

Summary statistics – questions on attitudes

Table A5.

2.078

0.853 0.715

0.395 0.928 0.380 0.673 0.828 0.291

0.875 0.424 0.557 0.440

Mean

0.857

0.354 0.452

0.490 0.260 0.486 0.470 0.378 0.455

0.331 0.495 0.497 0.497

Standard deviation

Second-generation

396 Michael Fertig

Contact to Germans Visits to Germans Visits from Germans Spoken German Written German Write language of origin country Speak language of origin country Mainly use German Mainly use language of origin country Use both equally Disadvantage Wish to stay Feel German Feel connected with origin country

Question

N.A. N.A. N.A. 4.027 3.718 4.416 4.146 0.583 0.350 0.068 1.499 0.947 4.298 2.430

Mean

N.A. N.A. N.A. 0.742 0.933 0.736 1.011 0.494 0.477 0.252 0.556 0.225 0.917 1.111

Standard deviation

Ethnic Germans

0.895 0.769 0.814 3.546 2.837 4.476 4.115 0.257 0.392 0.351 1.526 0.619 2.441 3.879

Mean

0.307 0.422 0.389 1.012 1.295 0.618 0.969 0.437 0.488 0.478 0.599 0.486 1.108 0.938

Standard deviation

First-generation

Summary statistics – questions to immigrants/foreigners only

0.979 0.938 0.947 4.570 4.386 4.074 3.547 0.507 0.402 0.091 1.469 0.795 3.079 3.291

Mean

0.142 0.241 0.225 0.676 0.868 0.833 1.054 0.501 0.491 0.288 0.586 0.404 1.082 0.990

Standard deviation

Second-generation


a

0.416 0.336 0.034 0.159 0.015 0.018 0.250 0.602 0.081 0.067 0.524 45.329 0.066 0.111 0.329 0.317 45.329

Mean

0.493 0.473 0.182 0.366 0.121 0.131 0.433 0.490 0.273 0.250 0.499 17.023 0.248 0.314 0.470 0.465 17.023

Standard deviation

Germans

0.130 0.110 0.012 0.039 0.571 0.100 0.184 0.742 0.041 0.034 0.487 40.364 0.111 0.135 0.564 0.002 8.438

Mean

0.337 0.314 0.110 0.194 0.496 0.301 0.388 0.438 0.198 0.181 0.500 15.435 0.315 0.342 0.497 0.049 1.889

Standard deviation

Ethnic Germans

Denotes the reference category within the respective indicator groups.

Secondary schooling Intermediary schooling Technical schooling Upper secondary school. Other schooling No schooling degreea Singlea Married Divorced Widowed Female Age Unemployed In training Children under 15 East Time spent in Germany

Variable

(Continued)

0.186 0.026 0.017 0.044 0.445 0.277 0.092 0.816 0.064 0.028 0.484 44.679 0.106 0.028 0.465 0.006 23.291

0.389 0.159 0.131 0.206 0.497 0.448 0.289 0.388 0.246 0.164 0.500 13.801 0.308 0.166 0.499 0.076 9.680

Standard deviation

First-generation Mean

Summary statistics – explanatory variables

Table A5.

0.391 0.183 0.046 0.122 0.058 0.104 0.614 0.330 0.049 0.006 0.487 26.023 0.070 0.348 0.429 0.003 26.023

Mean

0.489 0.388 0.210 0.328 0.234 0.306 0.487 0.471 0.217 0.076 0.501 8.857 0.255 0.477 0.496 0.054 8.857

Standard deviation

Second-generation

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399

References Bauer, T.K., Zimmermann, K.F. (1999), Assessment of possible migration pressure and its labour market impact following EU enlargement to central and Eastern Europe. IZA Research Report No. 3, IZA-Bonn. Borjas, G.J. (1985), Assimilation, changes in cohort quality, and the earnings of immigrants. Journal of Labour Economics 3, 463–489. Borjas, G.J. (1987), Self-selection and the earnings of immigrants. American Economic Review 77, 531–553. Chiswick, B.R. (1978), The effect of Americanization on the earnings of foreign-born men. Journal of Political Economy 86, 897–921. Dietz, B. (2003), Post-soviet youth in Germany: Group formation, values and attitudes of a new immigrant generation. In: Horowitz, T., Kotik-Friedgut, B., Hoffmann, St. (Eds.), From Pacesetters to Dropouts. Post-Soviet Youth in Comparative Perspective. University Press of America, New York/Oxford, pp. 253–271. Dustmann, C. (1993), Earnings adjustments of temporary migrants. Journal of Population Economics 6, 153–168. Dustmann, C. (1996), The social assimilation of migrants. Journal of Population Economics 9, 79–103. Dustmann, C., Schmidt, C.M. (2000), The wage performance of immigrant women: full-time jobs, part-time jobs, and the role of selection. IZA Discussion Paper No. 233, IZA-Bonn. Dustmann, C., van Soest, A. (2001), Language fluency and earnings: estimation with misclassified language indicators. Review of Economics and Statistics 83, 663–674. Fertig, M. (2001), The economic impact of EU-enlargement: assessing the migration potential. Empirical Economics 26, 707–720. Fertig, M., Schmidt, C.M. (2001a), Aggregate level migration studies as a tool for forecasting future migration streams. In: Djajic, S. (Ed.), International Migration: Trends, Policy and Economic Impact. Routledge, London, pp. 110–136. Fertig, M., Schmidt, C.M. (2001b), First- and second-generation immigrants: What do we know and what do people think? In: Rotte, R. (Ed.), Migration Policy and the Economy: International Experiences. Ars & Unitas, Neuried, pp. 179–218. Fertig, M., Schmidt, C.M. (2002), Mobility within Europe – the attitudes of European youngsters. RWI Discussion Paper No. 1, RWI-Essen. Kurthen, H., Fijalkowski, J., Wagner, G.G. (Eds.) (1998), Immigration, Citizenship, and the Welfare State in Germany and the United States: Immigrant Incorporation. Industrial Development and the Social fabric: An International Series of Historical Monographs (Vol. 14, Pt. A). JAI Press, Stamford/London.

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Riphahn, R.T. (2003), Cohort effects in the educational attainment of second generation immigrants in Germany: an analysis of census data. Journal of Population Economics 16, 711–737. Schmidt, C.M. (1997), Immigrant performance in Germany. Labor earnings of ethnic German migrants and foreign guest-workers. Quarterly Review of Economics and Finance 37, 379–397.

CHAPTER 17

Who Matters Most? The Effect of Parent’s Schooling on Children’s Schooling Ira N. Gang Department of Economics, Rutgers University, New Brunswick, NJ 08901-1248, USA Institute for the Study of Labor (IZA), Bonn, Germany CReAM-Center for Research and Analysis of Migration, London, UK E-mail address: [email protected]

Abstract This chapter examines the differential effects of mother’s schooling and father’s schooling on the acquisition of schooling by their offspring. It does this in a ‘‘cross-cultural’’ context by comparing results across three countries: Germany, Hungary, and the Former Soviet Union. It looks within these countries, by gender, at different ethnic subgroups. Evidence is found, generally, that father’s schooling is more important than mother’s, but this does vary by ethnic group. Mother’s schooling plays a relatively larger role for females.

1. Introduction This chapter looks at the effect of parents’ schooling on the schooling attainment of their children. We examine the differential effects of mother’s schooling and father’s schooling on the acquisition of schooling by their offspring. Schooling is examined in a ‘‘cross-cultural’’ context by comparing results across countries, as well as within a country by looking at these effects by ethnic group and gender. The study makes use of three household level data sets: the German Socio-Economic Panel (GSOEP), the Hungarian Household Panel Survey (HHPS), and the Soviet Interview Project (SIP). We analyze each during times of economic change for the participants in each country. Each of these data sets contains information on various subgroups of the population: the GSOEP consists of Germans and immigrants into Germany and their families, the HHPS makes it possible to distinguish Gypsies and Hungarian, non-Gypsies, and the SIP allows us to identify by Republic of the former Soviet Union (FSU) each persons’ place of birth, and for Frontiers of Economics and Globalization Volume 8 ISSN: 1574-8715 DOI: 10.1108/S1574-8715(2010)0000008023


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Russians, whether the person was Jewish or not. We use these data to analyze demographically comparable groups; in particular we examine the schooling attainment of those born after World War II (approximately) and relate it to the schooling attainment of their parents. Of course, the idiosyncrasies of each data set do not allow perfectly comparable samples. Why should parent’s schooling attainment matter in determining children’s schooling attainment? Parental schooling may be a proxy for a host of unobservable determinants, such as parental preferences for education, children’s ability, and assistance given by parents in school work (Gertler and Glewwe, 1992). If parent’s education matters, it is natural to ask which parent’s education matter’s more? The conventional wisdom is that the mother’s education is more important than the father’s education in children’s attainments, including schooling. This arises from a large number of studies in developing economies and in the United States (e.g., Schultz, 1984; Chiswick, 1988; Arai, 1989; Thomas, 1994; Gertler and Glewwe, 1992). Haveman and Wolfe (1995) examined the large literature on the determinants of children’s attainments in the United States, and conclude that the (p. 1855) ‘‘human capital of the mother is usually more closely related to the attainment of the child than is that of the father.’’1 Why is mother’s schooling is more important than father’s? One explanation rests on the time allocation model.2 Time spent in child care and time spent in the labor market both contribute to high quality children, for example, children’s schooling attainment. This raises the question of the role of nonmarket versus market inputs in children’s educational attainments. If we assume that nonmarket inputs are more important, then the parent who engages in relatively greater non-market activity will exert a greater influence on children’s schooling. Alternatively, if the contribution through market work is more important in determining children’s education than is the input through non-market work, it is the spouse who is relatively more engaged in market activity who will have the greater influence. The above argument implies, for example, that if women spend relatively more time than men at home versus in the labor market, their influence will be greater. However, we might expect to see variations in this influence (1) across countries and subgroups that face different relative prices of market versus nonmarket activity and (2) across countries and

1

The evidence is not unambiguous; Haveman and Wolfe are making a judgment based on the preponderance of evidence. This is true in the development literature as well. For example, Tansel (1997) finds for Ghana and the Cote d’Ivoire that father’s literacy was more important than mother’s in children’s schooling attainments. See also, Devereux and Salvanes (2005), Rey and Racionero, (2002), Riphahn (2003), and Deutsch et al. (2006). 2 Alternative, complementary models are the socialization/role model perspective, the lifespan development approach, and stress theory and coping strategies. See Haveman and Wolfe (1995) for a discussion of these models and how they relate to the time allocation model.

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subgroups that have differing elasticities of child-rearing activity with respect to labor force activity. These two elements might lead us to expect a different effect of mother’s versus father’s schooling on children’s schooling by country, by subgroups within a country, and by gender. What is the evidence on country and subgroup effects? Work by Gang and Zimmermann (2000) finds that in comparing the schooling attainments of Germans and comparable second generation immigrants (the children of the guestworkers), both father’s and mother’s education was important to Germans, with father’s being more important, while for most of the second generation immigrant subgroups neither parent’s educational background substantially influenced children’s schooling. These results do, however, vary by origin country. Schultz (1984) finds that in the United States native-born parent’s schooling affects their offspring educational attainments, with the effect of mother’s schooling about twice that of father’s. Among immigrants, the mother’s effect was less and the father’s was more, but generally the relationship was weaker than for the native-born. Here too the results varied by origin country.

2. Data This study uses data from the GSOEP, the HHPS, and the SIP. The GSOEP is described in Wagner et al. (1993). The first wave of the GSOEP was drawn in 1984, from which most of our information is taken. In 1986 a question was asked on parent’s education, and that was matched to the 1984 respondents. Our focus here is on the children of the guestworkers, immigrants from Turkey, Yugoslavia, Greece, Italy and Spain who arrived in Germany from the early 1960s until the program was stopped in the early 1970s. From the sample guestworker children we keep those who were born in Germany or who arrived before the age of 16, and who in 1984 were 17–38 years old. These are considered to be the second generation migrants (Kossoudji, 1989, p. 497). From native German households, we examine the same age cohort. The subsample used here is described in greater detail in Gang and Zimmermann (2000) and Gang (1997). The first wave of the HHPS was drawn in 1992 and is described in Sik (1995). In 1993 a question on parents’ education was asked, and we draw our data from the 1993 wave. The interviewers were asked whether they thought the respondent was a Gipsy or not, and we use this to identify Gypsies versus those Hungarians who are not Gypsies. We restricted our sample to those who were 17 to 47 years old in 1993. This leaves us with a sample of 2031 individuals. The SIP data provide us with a contemporary sample of e´migre´s who moved from the Soviet Union to the United States in the period from January 1, 1979, to April 30, 1982, and provides us with detailed

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background information on those who emigrated.3 The SIP data were collected in 1983 and reports on various aspects of household behavior of respondents during their lifetime in the Soviet Union through the end of their last normal period (LNP), the date on which they declared their intention to emigrate from the Soviet Union.4 Our study is based on a subsample of 919 (the Blue supplement) for whom both basic and extended household characteristics are known, and who were born in the various republics of the FSU.5 Of these, we further restricted the sample to the 519 participants who were between 25 and 50 years old in 1983. Each data set has its own definition of each variable; we tried to make the variables comparable. The critical variables of the study are children’s and parent’s schooling. For children’s schooling, we translated the different degrees into years of schooling for all three data sets. GSOEP provides data for each individual on the type of school attended. To convert these into years of schooling, we followed the procedure outlined in Gang and Zimmermann (2000). Instead of just adding the standard years for the various educational degrees, we use a more conservative measure that adjusts for ‘‘duplicate’’ degrees and discounts alternative post-schooling degrees (vocational training, university, and the like) by one year. A similar procedure was employed in translating the degrees in the HHPS and the SIP into years of schooling. For parent’s schooling, using the HHPS and the SIP, we also directly translated degrees into years of schooling. Although there were significant changes in the structure of schooling in the Soviet Union and Hungary, in balance parents went through the same general type of school system as the children (see Dobson, 1984). However, it is difficult to compare schooling in Germany with the schooling levels acquired by migrants in their home countries. For the non-Germans in the GSOEP, the parent’s human capital dummy variable takes value 1, if its education is at least ‘‘mandatory with degree.’’ In the case of a German, the dummy takes value 1, if the parent has at least a high school degree (Realschule or Abitur). Below we perform separate analyses for each subgroup of the population. For the GSOEP, this means we analyze Germans, Turks, Italians, Spaniards, Greeks and Yugoslavs; for the HHPS, Gypsies and 3

The initial SIP consisted of 2,793 respondents aged 21–70 years at time of emigration. A detailed discussion of the original materials can be found in Millar et al. (1987). The first major studies from the data base including commentary on the data can be found in Millar (ed.) (1987) while recent analysis of the household can be found in Ofer and Vinokur (1992), Linz (1995), and Gang and Stuart (1996, 1997). 4 The concept of LNP is important. It was assumed that once a family declared its intention to emigrate its circumstances would change, possibly dramatically, due to official hostility. 5 How representative is our sample? The ethnic composition of the original SIP database is known such that weights could be derived to make any sample representative of the entire Soviet population. We did not weight our observations to obtain a representative picture of the entire Soviet population. This does not affect the slope coefficients in our regressions.


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Hungarian, non-Gypsies; for the SIP, Russians (by place of birth), Ukrainians, people born in the Baltics, Belarus and Moldava, Caucasus and Central Asia. We further break down the analysis into males and females, and for Russians into Jews and non-Jews. This allows us a complete set of interactions of ethnicity and gender with our explanatory variables, rather than forcing everything into the intercept term. The gain is a better picture of the effect of parent’s schooling on children’s schooling by ethnicity and gender. The cost is that some of analyses are performed on very few observations. This suggests that we should be generous in interpreting our results in terms of statistical significance, looking instead for general patterns. What is perhaps surprising is that even with small samples we get reasonably strong results.

3. Empirical Results The analysis is performed using OLS with children’s schooling on the left hand side, and mother’s and father’s schooling and other control variables on the right hand side. The exact specification varies by data set, and is given in the notes to Tables 1–3. Tables 1–3 have the same structure, with Table 1 from the analysis of the GSOEP, Table 2 from the analysis of the HHPS, and Table 3 from the analysis of the SIP. The tables summarize the results of the analysis, giving the means of children’s, mother’s and father’s schooling and the estimated elasticities of children’s education with respect to mother’s schooling and father’s schooling. The elasticity estimates are shaded if significant at the .10 level for ease of presentation. In addition, the tables present the results of two tests (1) whether parent’s education matters at all in children’s schooling and (2) whether mother’s and father’s effects on the child are significantly different from each other. Recall that for some subgroups the sample size is very small, and this should be taken into account in interpreting the results. Let us examine the results using the GSOEP in Table 1. First notice that, with regard to years of schooling in Germany, German children have more years than do non-German children. For Germans, the mean is 12.1 years, while for non-Germans it ranges from 7.6 for Turks to 9.3 for Spaniards. Recall that these numbers are for the same post-World War II cohort. As for parents schooling, the numbers mean something different for Germans and non-Germans. For Germans, recall that our measure of German’s parents education is whether or not parents have at least a high school education. This level of education was achieved by 12 percent of the parents. For non-Germans they mean having completed the basic mandatory degree in their country of origin; we find this ranges from 9 percent of the Turks to 21 percent of the Yugoslavs. Note that what is ‘‘mandatory’’ varies from country to country.

1920

Female

161

132

73

30

43

Male

Female

Yugoslavs All

Male

Female

293

1920

Male

Turks All

3840

Germans All

Sample size

7.9 (4.8) 8.9 (4.1) 7.2 (5.2)

7.6 (4.2) 8.3 (3.8) 6.7 (4.4)

12.1 (2.5) 12.3 (2.5) 11.9 (2.4)

Children’s education (years of schooling)

Table 1.

.16 (.37) .10 (.31) .21 (.41)

.04 (.19) .04 (.19) .05 (.22)

.08 (.28) .08 (.28) .09 (.28)

Mother’s education

Means

.21 (.41) .13 (.35) .26 (.44)

.09 (.29) .06 (.24) .13 (.33)

.12 (.32) .11 (.32) .13 (.33)

Father’s education

.218* (.034) .121* (.029) .342* (.049)

.017 (.011) .021*** (.012) .020 (.017)

.006* (.001) .005* (.002) .007* (.002)

Children’s education with respect to mother’s education

.173* (.032) .093* (.032) .281* (.042)

.006 (.014) .022* (.009) .008 (.028)

.014* (.001) .015* (.002) .013* (.002)

Yes Yes Yes

Yes Yes

No

Yes

Yes

No

Yes

No

.40

.10

.33

.14

.05

.13

.23

Yes**

Yes

No

.35

.29

Adjusted R-squared

Yes

Yes

Mother’s and father’s education significantly different from one another

Yes

Yes

Mother’s and father’s Children’s education education with jointly respect to father’s significant education

Estimated elasticities

Influence of Parent’s Schooling on Children’s for Those Living in Germany

406 Ira N. Gang

68

113

68

45

Female

Spaniards All

Male

Female

9.3 (3.6) 9.3 (3.6) 9.1 (3.8)

8.1 (4.5) 8.2 (4.6) 8.1 (4.3)

8.9 (4.4) 9.3 (4.3) 8.3 (4.5)

.12 (.33) .18 (.38) .04 (.21)

.10 (.30) .08 (.27) .13 (.34)

.09 (.28) .08 (.27) .09 (.29)

.17 (.38) .21 (.41) .11 (.32)

.13 (.34) .09 (.28) .19 (.39)

.10 (.31) .06 (.25) .15 (.36)

.006 (.019) .024 (.029) .001 (.006)

.003 (.026) .044* (.016) .088** (.051)

.005 (.023) .010 (.025) .016 (.047)

.036 (.022) .087* (.032) .005 (.018)

.002 (.031) .001 (.021) .053 (.070)

.031 (.022) .031** (.018) .007 (.054)

No

Yes

No

Yes

No

No

Yes

Yes

No

No

No

No

No

Yes

No

No

No

No

.25

.11

.17

.15

.31

.18

.05

.26

.13

Notes: The OLS regressions have children’s schooling in years on the left-hand side. The children are between 17 and 38 years old in 1984. On the right-hand side is mother’s schooling, father’s schooling, child’s age and age-squared, a dummy variable taking on the value 1 if the child is still in school, and, where appropriate, a dummy variable taking on the value of 1 if the child is male. Note that the measure of mother’s and father’s schooling differs for Germans and non-Germans in the analyses done with the GSOEP. For Germans, the variables take on the value of 1 if at least Realschule or Abitur has been earned, 0 if not. For the non-Germans, the variables take on the value of 1 if at least the mandatory degree in the home country has been earned, 0 if not. Source: Author’s calculations from German Socio-Economic Panel (Wagner et al., 1993). Yes** indicates a p-value between .05 and .10; otherwise yes indicates a p-value below .05. * Indicates a p-value below .05. ** Indicates a p-value between .05 and .10. For ease of reading, elasticities are shaded if p-value is below .10.

91

Male

54

Female

159

62

Male

Italians All

116

Greeks All

The Effect of Parent’s Schooling on Children’s Schooling 407

52

Female

7.5 (2.4) 7.8 (2.1) 7.1 (2.7)

10.7 (2.3) 10.5 (2.1) 10.9 (2.4)


3.6 (3.3) 3.7 (3.3) 3.6 (3.3)

7.9 (3.0) 8.0 (3.1) 7.9 (3.0)

Mother’s education (years of schooling)

Means

4.6 (3.2) 4.9 (3.2) 4.3 (3.2)

8.6 (3.3) 8.6 (3.3) 8.5 (3.2)

Father’s education (years of schooling)

.023 (.041) .025 (.054) .029 (.063)

.119* (.016) .101* (.023) .138* (.022)


.100** (.055) .183* (.087) .103 (.069)

.120* (.016) .118* (.024) .124* (.020)

Children’s education with respect to father’s education


No

No

Yes No

No

Yes**

No

No

Yes Yes

No

Mother’s and father’s education significantly different from one another

Yes

Mother’s and father’s education jointly significant

Influence of Parent’s Schooling on Children’s for Those Living in Hungary

.20

.18

.16

.25

.25

.25

Adjusted R-squared

Notes: The OLS regressions have children’s schooling in years on the left-hand side. The children are between 17 and 47 years old in 1993. On the right-hand side is: mother’s schooling in years, father’s schooling in years, child’s age and age-squared, a dummy variable taking on the value 1 if the child is still in school, a dummy variable taking on the value 1 if the child is now in an urban environment, a dummy variable taking on the value 1 if the child is now the head of a household, and, where appropriate, a dummy variable taking on the value of 1 if the child is male. Note that Hungarian means the non-Gypsies in the sample. Source: Author’s calculations from the Hungarian Household Panel Survey (1995). Yes** indicates a p-value between .05 and .10; otherwise yes indicates a p-value below .05. * Indicates a p-value below .05. ** Indicates a p-value between .05 and .10. For ease of reading, elasticities are shaded if p-value is below .10.

54

Male

990

Female

106

935

Male

Gypsies All

1925

Hungarians All

Sample size

Table 2.

408 Ira N. Gang

105

143

Male

Female

22

80

Female

Baltics All

101

Male

68

Male, Jewish

181

37

Male, not Jewish

Ukrainians All

80

Female, Jewish

Female, not Jewish 63

248

Russians All

Sample size

Table 3.

11.5 (2.3)

11.9 (2.5) 12.3 (2.5) 11.6 (2.4)

13.1 (2.3) 13.6 (2.1) 12.7 (2.4) 12.8 (2.3) 12.6 (2.5) 13.4 (2.2) 13.8 (2.1)


9.5 (3.5)

8.9 (3.6) 8.9 (3.4) 8.9 (3.8)

10.3 (3.7) 10.7 (3.9) 10.0 (3.6) 9.9 (3.4) 10.1 (3.7) 10.1 (4.1) 11.1 (3.7)


Means

9.5 (3.10)

9.3 (3.8) 9.4 (3.8) 9.2 (3.9)

10.8 (4.0) 11.1 (4.1) 10.6 (3.9) 11.3 (4.0) 10.1 (3.8) 11.3 (4.3) 11.0 (4.0)


.057 (.234)

.122* (.045) .151* (.059) .111 (.073)

.074* (.038) .086 (.053) .083 (.051) .110 (.069) .080 (.074) .126** (.071) .072 (.073)


.019 (.175)

.063 (.042) .069 (.055) .042 (.064)

.126* (.035) .139* (.047) .100* (.050) .147* (.065) .070 (.079) .179* (.072) .101** (.060)



No

Yes**

No

No

Yes

No

No

Yes

No

Yes

No

Yes**

No

No

Yes

Yes

No

No

No

.04

.03

.21

.16

.07

.33

.17

.28

.20

.17

.20

Mother’s and Adjusted father’s education R-squared significantly different from one another

Yes

Yes

Yes


Influence of Parent’s Schooling on Children’s for Those Born in the Former Soviet Union

The Effect of Parent’s Schooling on Children’s Schooling 409

35

28

41

Female

Caucasus All

Central Asia All

11.5 (6.1)

12.9 (2.6)

10.9 (2.7) 10.4 (2.8) 11.4 (2.6)


8.5 (3.6)

10.4 (4.0)

7.5 (3.4) 8.2 (3.7) 6.9 (3.0)


Means

9.2 (4.3)

11.4 (3.9)

7.8 (3.9) 7.6 (4.0) 8.0 (3.8)


.339* (.081)

.151 (.104)

.114** (.062) .088 (.085) .093 (.096)


.022 (.073)

.107 (.120)

.176 (.068) .151 (.092) .221** (.109)



Yes

Yes**

Yes

Yes

Yes


Yes

No

No

No

No

.28

.11

.14

.24

.20

Mother’s and Adjusted father’s education R-squared significantly different from one another

Notes: The OLS regressions have children’s schooling in years on the left hand side. The children are between 25 and 50 years old in 1983 and living in the United States. On the right-hand side is: mother’s schooling in years, father’s schooling in years, child’s age and age-squared, a dummy variable taking on the value 1 if the child is still in school, a dummy variable taking on the value 1 if the child was born in a city, a dummy variable taking on the value 1 if the child migrated within the Former Soviet Union, and, where appropriate, a dummy variable taking on the value of 1 if the child is male, and a dummy variable taking on the value of 1 if the child is Jewish. Source: Author’s calculations from the Soviet Interview Project (Millar et al., 1987). Yes** indicates a p-value between .05 and .10; otherwise yes indicates a p-value below .05. * Indicates a p-value below .05. ** Indicates a p-value between .05 and .10. For ease of reading, elasticities are shaded if p-value is below .10.

36

Male

Belarus and Moldava All 71

Sample size

Table 3. (Continued )

410 Ira N. Gang


411

What about the effects of parent’s schooling on children’s schooling? For Germans, the estimated elasticities are very small, but significantly different from zero. Children’s education is very inelastic with respect to parent’s schooling. Father’s schooling is more important than mother’s, on the order of three times as important for males and twice as important for females. This pattern is not maintained among Germany’s second generation immigrants. For Greeks, parent’s education plays no role in children’s schooling. For Turks, no role is found for women, while for men father’s education plays a significant role. Mother’s schooling plays a role for Italian females; for males mother’s schooling actually seems to lower their educational achievement. Father’s schooling matters to male Spaniards, but not mother’s; for females there is no parental effect. Yugoslavs are very different. Mother’s schooling is three times more important for females over males; and the effect of father’s schooling is to lower children’s schooling for both males and females. Parental schooling has an effect on the schooling attainment of the next generation in varying degrees. For Germans, father’s education is a more important influence on educational attainment than mother’s education. For Yugoslavs the opposite is true. For the other groups parent’s schooling has a weak relationship, certainly a weaker relationship to children’s schooling attainments. Schultz (1984) also found, for the United States, that there is a weaker link between the second generation and their parents than between the children of the native-born and their parents. The shock of immigration weakens the inter-generational transfer of human capital through this mechanism. Let us now turn to the results from using the HHPS. The average non-Gipsy Hungarian born after 1945 had 10.7 years of schooling, while the Gypsy has a much lower 7.5 years. The level of Gipsy education is more compatible to the parental generation of Hungarians. Gipsy mothers averaged 3.6 years, and fathers 4.6 years of schooling. For Hungarians, parent’s schooling matters to children, very slightly more so for females. Mother’s and father’s schooling have the same effects on children. It is a different matter for Gypsies. For females, parent’s schooling has no effect. For males, father’s does and mother’s does not. The effect of father’s is quite strong (though inelastic). Hypothetically, if the Gipsy father had twice their average level of education, the Gipsy son would have approximately 1.5 additional years of schooling. For the cohort we analyze from the FSU, the year of birth may have been as early as 1933. This was necessitated by the age structure of the sample. Still, we find the mean level of schooling quite high across all of the Republics, although clearly it is highest among Russians, and it is higher among males than females. Even parents’ schooling is relatively high, although it is as low as 6.9 years for females from Belarus and

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Moldava. The greatest gains in schooling has occurred outside of Russia and among females. Examining the FSU results, it is quite clear that, except in the Baltic Republics (where we only have 22 observations), parent’s education matters. Furthermore, the effects of mother’s and father’s schooling on children’s are not significantly different from one another. However, examining the estimated elasticities presents a slightly different story, especially considering our small sample sizes. For Russians, father’s education is more important, about 1.5 times than mother’s (except for Jewish females, for whom mother and father exert the same influence). For Ukrainians, mother’s education is about two times more important than father’s. Fathers matter about twice as much for those from Belarus and Moldava; while mothers 1.5 time for those from the Caucasus. For those from Central Asia, mother’s education is extremely important, father’s not at all. Indeed the elasticity estimate for Central Asians of children’s education with respect to mother’s schooling is the largest in all three data sets. Finally, in comparing non-Jewish Russians to Jewish Russians, we find the effect of parental schooling on children’s schooling attainment stronger for non-Jews, particularly with respect to father’s schooling attainment.

4. Conclusions We have examined the effects of mother’s and father’s schooling on children’s schooling. Although the conventional wisdom argues that mother’s schooling is more important than father’s in determining children’s schooling, our findings show a different result. We have found, contrary to the conventional wisdom, that overall the evidence indicates that father’s education is more important than mother’s and that mother’s schooling is relatively more important for females. However, this varies quite a bit for different ethnic groups, and there is a lot of evidence within this study that contradicts this general statement. The effect of parent’s schooling is generally less for second generation immigrants in Germany and for Gypsies in Hungary. This may be because of the low schooling attainments of the parents and the institutionalization of schooling in the children’s generation. Note that all of the results, even though for the most part statistically different from zero, are small, that is, children’s schooling is very inelastic with respect to parent’s schooling. Perhaps this is because of the average parent’s low educational attainment relative to their children, or the institutionalization and increased egalitarianism of the educational systems in all of the countries in the second half of this century. This would tend to place factors other than parent’s schooling attainments as important actors in the determination of children’s schooling attainment.


413

It is, perhaps, most surprising that we find parental influences, and that they vary so widely across countries, subgroups, and gender. We must raise the point that these three data sets encompass countries that loosely can be termed Central Europe. In the context of the discussion of why one might expect mother’s or father’s schooling to matter more, we much raise the question: ‘‘Is there a Central European phenomenon that is different then what we witness in the United States or in the developing economies?’’ This chapter raises many questions. It tells us that we need to further explore the underlying socioeconomic settings that determine the link between children’s schooling attainment and that of their parents. Understanding this link and what it responds to may become especially important in light of changes in developing and in the transition economies, and the commensurate changes in family settings, economic opportunities and politics.

References Arai, K. (1989), A cross-sectional analysis of the determinants of enrollment in higher education in Japan. Hitotsubashi Journal of Economics 30, 101–120. Chiswick, B.R. (1988), Differences in education and earnings across racial and ethnic groups: tastes, discrimination, and investments in child quality. Quarterly Journal of Economics 103 (3), 571–597. Deutsch, J., Epstein, G.S., Lecker, T. (2006), Multi-generation model of immigrant earnings: theory and application. In: Solomon, P., Konstantinos, T. (Eds.), The Economics of Immigration and Social Diversity (Research in Labor Economics, Vol. 24). Emerald Group Publishing Limited, Bingley, UK, pp. 217–234. Devereux, P., Salvanes, K. (2005), Why the apple doesn’t fall far: understanding intergenerational transmission of human capital. American Economic Review 95 (1), 437–449. Dobson, R.B. (1984), Soviet education: problems and policies in the urban context. In: Morton, H.W., Stuart, R.C. (Eds.), The Contemporary Soviet City. M.E. Sharpe, Armonk, NY, pp. 156–177. Gang, I.N. (1997), Schooling, parents and country. DIW-Vierteljahrshefte (Quarterly Journal of Economic Research) 1–97, 180–186. Gang, I.N., Stuart, R.C. (1996), Urban to urban migration: Soviet patterns and post-Soviet implications. Comparative Economic Studies 38 (1), 21–36. Gang, I.N., Stuart, R.C. (1997), What difference does a country make? Earnings of Soviets in the Soviet Union and in the United States. The Quarterly Review of Economics and Finance 37 (Suppl. 1), 345–360.

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Gang, I.N., Zimmermann, K.F. (2000), Is child like parent? educational attainment and ethnic origin. Journal of Human Resources 35 (3), 550–569. Gertler, P., Glewwe, P. (1992), The willingness to pay for education for daughters in contrast to sons: evidence from Rural Peru. The World Bank Economic Review 6 (1), 171–188. Haveman, R., Wolfe, B. (1995), The determinants of children’s attainments: a review of methods and findings. Journal of Economic Literature 33, 1829–1878. Hungarian Household Panel Survey 1992–1994 [Computer File] (1995), Conducted by TARKI (Social Research Informatics Centre), Department of Sociology, Budapest University of Economics, Hungary. Kossoudji, S.A. (1989), Immigrant worker assimilation: is it a labor market phenomenon? Journal of Human Resources 24, 495–527. Linz, S.J. (1995), Russian labor market in transition. Economic Development and Cultural Change 43, 693–716. Millar, J. R. (Ed.) (1987), Politics, Work, and Daily Life in the USSR. Cambridge University Press, New York. Millar, J.R., et al. (1987), Soviet Interview project, 1979–1983. Inter-University Consortium for Political and Social Research, Ann Arbor, MI. Ofer, G., Vinokur, A. (1992), The Soviet Household under the Old Regime. Cambridge University Press, New York. Rey, E., Racionero, M. (2002), Optimal education choice and redistribution when parental education matters. Oxford Economic Papers 54, 435–448. Riphahn, R.T. (2003), Cohort effects in the educational attainment of second generation immigrants in Germany: an analysis of census data. Journal of Population Economics 16 (4), 711–737. Schultz, T.P. (1984), The schooling and health of children of U.S. immigrants and natives. In: Schultz, T.P., Wolpin, K.J. (Eds.), Research in Population Economics, Vol. 5. JAI Press, Greenwich CT, pp. 251–288. Sik, E. (1995), Measuring the unregistered economy in post-communist transformation. Eurosocial Report 52, Vienna, Austria. Tansel, A. (1997), Schooling attainment, parental education, and gender in Cote d’Ivoire and Ghana. Economic Development and Cultural Change 45 (4), 825–856 (University of Chicago Press). Thomas, D. (1994), Duncan ‘‘Like father, like son or like mother, like daughter: Parental education and child health’’. Journal of Human Resources 29 (4), 950–989. Wagner, G.G., Burkhauser, R.V., Behringer, F. (1993), The English language public use file of the German socio-economic panel. Journal of Human Resources 28, 429–433.

CHAPTER 18

Intergenerational Transfer of Human Capital under Post-War Distress: The Displaced and the Roma in the Former Yugoslavia Martin Kahaneca and Mutlu Yukselb a

Department of Economics, Central European University (CEU), Nador u. 9, H-1051 Budapest, Hungary E-mail address: [email protected] b Department of Public Policy, Dalhousie University, Halifax, NS, Canada B3H 3J5 E-mail address: [email protected]

Abstract In this chapter, we investigate the effects of vulnerability on income and employment in Bosnia and Herzegovina, Croatia, Montenegro, and Serbia using a unique 2004 UNDP dataset. Treating the collapse of the former Yugoslavia as a natural experiment, we compare three groups that have been differently affected by the wars and post-war distress: the majority as the benchmark, the ex ante and ex post vulnerable Roma people, and the ex ante equal but ex post vulnerable refugees and internally displaced people (RIDPs). Our findings reveal significant negative effects of vulnerability on income and employment. RIDPs seem to be about as negatively affected as Roma across the four states, which indicate that vulnerability inflicted by relatively recent displacement may have similar effects as vulnerability rooted deep in the past. When we look at education as one of the key determinants of socio-economic outcomes, both groups exhibit similarly substandard educational outcomes of children and significant inertia in intergenerational transfer of human capital. Our findings highlight the need for policies that not only tackle vulnerability as such, but address the spillover effects of current vulnerability on future educational attainment. Keywords: Vulnerability, labor market, education, Roma, refugees, internally displaced people, discrimination integration JEL classifications: I21, I12, J24, N34



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Martin Kahanec and Mutlu Yuksel

1. Introduction It has become customary in the literature to look at the roles that ethnicity and immigrant origin may play for socio-economic outcomes in contexts characterized by a static partition of the studied population by ethnicity or immigrant origin. Vulnerability in terms of inclusion into social and economic relationships and outcomes is then ascribed to these static measures. Constant and Zimmermann (2008) propose a two-dimensional measure of ethnicity whereby the strength of the attachment to the host and own cultures is measured and shown to affect socio-economic outcomes. In some situations, however, ethnicity remains constant but the changing context interacts with ethnicity and engenders vulnerability of some ethnic groups. One such example is the case of former Yugoslavia, where the violent conflicts of the 1990s gave rise to new boundaries and displaced people along ethnic lines. Internally displaced persons (IDPs) – ‘‘Persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or humanmade disasters, and who have not crossed an internationally recognized state border’’ exemplify such a situation (UN High Commissioner for Refugees, 1998). Examples include Serbs displaced from Kosovo to Serbia during or shortly after the NATO bombing of Kosovo in 1999; or Muslim Bosnians displaced from Serb-dominated parts of Bosnia and Herzegovina to Bosnia as a consequence of the Bosnian War (1992–1995). A remarkable peculiarity of displacement in former Yugoslavia is that in most cases displacement entailed a changing status from being a minority in the given settlement (e.g. Muslim Bosnian in a predominantly Serb village in Bosnia and Herzegovina) to being a member of ethnic majority (e.g. Muslim Bosnian in Bosnia). This applies to the displaced Serbs as well, who were dominant in former Yugoslavia, but were displaced from non-Serb settlements to those dominated by Serbs. Another specific feature of the context of former Yugoslavia is that several groups that were a minority before the 1990s have become a majority group in the newly emerged states (e.g. Kosovars in Kosovo). The context of former Yugoslavia thus enables one to study socio-economic outcomes of people that were ex ante fairly integrated and equal, but put in a vulnerable position by the armed conflicts of the 1990s that resulted in their displacement and ex post vulnerability that does not match the traditional ethnic minority–majority dichotomy. In this chapter, we evaluate the effect of vulnerability on income and employment in the context of former Yugoslavia. We study the effects of vulnerability for two groups that shared the social, political, and economic developments and the distress caused by the armed conflicts, yet their

Intergenerational Transfer of Human Capital under Post-War Distress

417

experience differed in their ex ante and ex post vulnerability: The RIDPs who were put in a vulnerable position by exogenous events – the Yugoslav wars and the Roma who were in a vulnerable position regardless of the wars. Those who were neither displaced nor members of the Roma ethnic minority can serve as a natural control group. As educational attainment is one of the key determinants of socio-economic outcomes, we then study the educational attainment of children and intergenerational transfer of human capital across the three studied groups. This approach enables us to elucidate the long-run effects of vulnerability on socio-economic outcomes. The possibility to benchmark RIDPs’ outcomes, besides the usual control group of those who were not affected or vulnerable, to those of the people who were vulnerable ex ante as well as ex post makes the former Yugoslav context particularly interesting for the study of vulnerability. In fact, being tied to the fall of the Berlin War that marked the end of the bipolar world order that weakened the communist federal regime in SFRY and unleashed the separatist factions in turn, these armed conflicts can in fact be interpreted in the present study as a natural experiment. This enables us under certain conditions to interpret the effects of vulnerability on income and employment outcomes as causal. In the next section, we review the literature on the topics studied. We then discuss the context of former Yugoslavia and the fates of IDPs and Roma in particular. The following section introduces and describes the data. We then develop an estimation strategy and present the results. Finally, we conclude and discuss some policy implications.

2. Literature review Extensive literature looks at association between armed conflicts and country’s socio-economic performance from a macroeconomic perspective. This strand of the literature mainly finds that war impacts are limited to the destruction of physical capital, in line with the predictions of the neoclassical economic growth model, which suggests rapid catch-up growth post-war. Among others, using the extensive U.S. bombing campaign in Vietnam as a quasi experiment, Miguel and Roland (2010) show that U.S. bombing did not have had long lasting impacts on poverty rates, consumption levels, infrastructure, literacy and population density 25 years after the war in Vietnam. Studies that focus on U.S. bombing during WWII – including in Japan (Davis and Weinstein, 2002) and Germany (Brakman et al., 2004) – also find few if any persistent impacts of the bombing on local population or economic performance. Along these lines, Organski and Kugler (1977, 1980) provided similar evidence on war devastation mainly for European countries suggesting that for both

418


capitalist and socialist economies, the economic effects of the two world wars tended to dissipate after only 15–20 years. Owing to data constraints however, only a handful of studies has attempted to provide microlevel evidence on the cost of armed conflicts on civilians’ outcomes. Using plausibly exogenous city-by-cohort variation in the intensity of WWII destruction in Germany as a natural experiment, Akbulut-Yuksel (2009) shows that wartime destruction had a substantial negative effect on long-term human capital formation, health, and labor market outcomes of Germans who were at school-age during WWII. Angrist and Kugler (2008) show that an exogenous upsurge in conflict activities arising from increase in coca prices and cultivation in Colombia has a negative effect on teenager boys’ school enrollment. Shemyakina (2006) examines the effects of civil conflict in Tajikistan and finds that girls residing in conflict areas are less likely to complete secondary school education; however, the civil conflict had little, or no, effect on educational attainment of boys. Similarly, using WWII as an instrumental variable to estimate the causal effect of education on earnings, Ichino and Winter-Ebmer (2004) find that individuals who were 10 years old during or immediately after WWII acquire less education and earned significantly less in adulthood compared to other cohorts within Germany and Austria as well as to individuals of the same cohort born in non-war countries (viz., Switzerland and Sweden). This study closely relates to literature looking the labor market impacts of displacement. Using the 15 years of civil conflict in Colombia as a natural experiment, Calderoń and Ibań˜ez (2009) studied the impact of forced migration on the labor market outcomes. To address the endogeneity in the location decision, they use an interaction of the number of massacres at the origin and the distance to the state capital as an instrumental-variable for these immigrants final destination. They find that the labor supply shock induced by the displaced people has negative impacts on wages and employment opportunities of all workers, but these adverse labor market impacts are particularly large for low-skill workers. Kondylis (forthcoming) provides similar evidence from the civil conflict in Bosnia and Herzegovina. Using the level of violence in the pre-war residence as an instrument for individual’s displacement, she finds that there is a positive selection into displacement. However, she shows that displaced Bosnians are less likely to be in work, particularly women, even though they assimilate into the labor market over time. She suggests that the high levels of informality are likely to contribute to the negative effect of displacement. However, the inactivity of Bosnian women after displacement also leaves room for channels such as cultural and sociological factors that play an important role in intra-household allocations. This chapter also contributes to literature looking at Roma and their socio-economic outcomes. Using data from the UNDP/ILO survey conducted in Bulgaria, the Czech Republic, Hungary, Romania and


419

Slovakia in 2001, Milcher and Zigova´ (2005, 2006) analyze the educational attainment of Roma people and to what extent their human capital is rewarded in the labor market. They test whether the insufficient education is a mediator for their weak attachment to the labor market and high poverty among Roma. First, they find that Roma people are more likely to reside in regions with lower economic performance and school enrollment rates. Second, they show that the likelihood of obtaining a regular wage job increases and the probability of being passive beneficial decreases substantially if one household member has higher education. Moreover, they find that the propensity to have occasional wage income is similar across different education categories and education has a marginal, negative impact on the probability of households living on loans. Although the education serves as a way out of poverty trap for Roma people, they concluded that education is more important for Roma residing in less developed economies in Central and Eastern Europe. O’Higgins and Ivanov (2006) revisit the same questions using two surveys compiled in 2002 and 2004. Similar to Milcher and Zigova´ (2005), they find that the lack of formal education explains the considerable part of the high unemployment among Roma. However, they also find that discrimination against Roma in the labor market has an important role for their weaker labor market attachment. Thus, due to the statistical and taste-base discrimination in the labor market, majority of Roma people work in informal sector in low-quality jobs. The authors argue that policy makers should devise labor market programs that are likely to generate opportunities for autonomous income rather than temporary employment programs to improve the labor market outcomes of Roma people. Milcher (2006) provides further evidence on the well-being and vulnerability of Roma people. Using microlevel data on Roma, refugees, IDPs and the majority living in proximity to the Roma, she first shows that income and expenditure are highly correlated with individual’s educational attainment, labor market outcomes, and access to secure housing and health care. However, the difference between poor and nonpoor households is less pronounced in Roma sample relative to refugees and internally displaced sample. In other words, she finds that regardless of education or other individual characteristics, the probability of being poor is substantially higher among Roma people or, to a lesser extent, among refugee or IDP compared to the majority population.

3. Background on internally displaced people and Roma population in Europe One of the venerable groups we analyze in this study is internally displaced people. In the UN report (2006), internally displaced person is defined as ‘‘Persons or groups of persons who have been forced or obliged to flee or

420


to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or humanmade disasters, and who have not crossed an internationally recognized state border.’’ According to the Internal Displacement Monitoring Centre (IDMC) in 2005, there were 24 million internally displaced people in 51 countries worldwide. In contrast to other vulnerable groups across the globe, displaced people are not necessarily vulnerable before their displacement per se (UNDP, 2006). However, the conflicts and the consequent displacements mostly cost them their wealth, homes, jobs, and networks. In many cases, they have limited ability to transfer their human capital to their new destinations and have hardship in entering the local labor market in their new homes. In this study, we mainly focus on individuals and families in the Central and Eastern Europe who were displaced during the past two decades. Owing to the outbreaks of series of civil conflicts and political turmoil in Socialist Federal Republic of Yugoslavia during 1990s, thousands of families were forced to leave their homes and communities without the institutional and organizational infrastructure to accommodate such displacement. In general, inter-ethnic relations in pre-war Socialist Federal Republic of Yugoslavia were cordial, as Tito managed to enforce a strict policy of ‘‘brotherhood and unity’’ by suppressing ethno-nationalism among the various ‘‘nationalities’’ or ‘‘ethnicities.’’ However, shortly after the fall of the Berlin Wall, the communist federal regime in Socialist Federal Republic of Yugoslavia weakened mounting tensions between Federalist (Serbs, Yugoslavs) and Separatist factions (Croats, Slovenes). Subsequently, Croatia, Macedonia and Slovenia declared independence in 1991 and Yugoslavia began to dissolve. Following these events, civil war broke out in Bosnia (1992–1995) between the pro-independence Bosniak–Croat coalition and the Serbs who boycotted the referendum for independence (Swee, 2009). At the same time, the Croatian War of Independence (1991– 1995) broke out between the Croatian army and the Serbia-controlled Yugoslav People’s Army (JNA) and the local ethnic Serbs in Croatia, when the latter announced their secession from Croatia. As a result, the Serb forces in Bosnia and Croatia carried out waves of aggression that marked the earliest events of the Bosnian War and Croatian War of Independence, killing and displacing thousands of Bosnians and Croats (Vulliamy, 1994). In August 1995, the North Atlantic Treaty Organization conducted sustained air strikes against the Serb strongholds, thus internationalizing the conflict in its final stages (Owen, 1997a; Owen, 1997b). Subsequently, Serbs, Bosnian, and Croats signed the Dayton Peace Agreement in December 1995, concluding the Europe’s deadliest conflict since WWII. The agreement partitioned Bosnia by an Inter-Entity Boundary Line (IEBL) into two ethnically divided entities – the Bosniak–Croat Federation of Bosnia and Herzegovina (FBiH) and the Serb Republika Srpska (RS).


421

Overall, the human cost of the armed conflict was tremendous. Reports by the International Criminal Tribunal for the former Yugoslavia (ICTY) estimate that 102,000 people were declared missing or dead. According to 1999 data, the conflict in Bosnia and Herzegovina caused 2.2 million people to be displaced from their homes, which is half of the total population of Bosnia and Herzegovina estimated in the 1991 Census. Between 1996 and 2004, over a million of the displaced return back to their pre-war residences both from locations in Bosnia and Herzegovina, from other Yugoslav successor states, and from further abroad. Even though refugee returns have continued since then, it seems that close to a million Bosnians retain some form of displaced status (UNDP, 2006). The second source of major displacements in the territory of former Yugoslavia was the armed conflict in Kosovo. During the Socialist Federal Republic of Yugoslavia, Kosovo was given an autonomous status within the Republic of Serbia since the majority of the population in Kosovo was Albanian. However, Kosovo declared independence with the dissolution of Socialist Federal Republic of Yugoslavia, which initiated the years of conflict between Yugoslav government and Kosovo Albanian rebel guerillas (and the near conflagration in Macedonia in 2001). The conflict was resolved after NATO attacked Yugoslavia, and Yugoslav troops were withdrawn from Kosovo. Nevertheless, like the aforementioned conflicts in Bosnia and Croatia, war in Kosovo caused a massive displacement of population in Kosovo which is estimated to be close to a million people. Another vulnerable group we examine in this study is Roma people who have been historically subjected to persecution and discrimination (Fraser, 1992). Estimates suggest that there are approximately 5–10 million Roma people worldwide, majority of them residing in the Central and Eastern Europe. Roma is one of the most vulnerable groups in Europe with very low labor force participation, extremely high unemployment (often reaching 50–80%). Even when Roma people are employed, they primarily work in the informal sector in unsecured jobs, especially in Southeastern Europe. A lack of formal education, poor health and discrimination has been put forward as some of the potential reasons for the underrepresentation of Roma in the formal sector. For instance, the UN study in 2006 reports that 2 of the 3 Roma (compared with one in seven in majority communities) do not complete primary school, and 2 of the 5 (compared to 1 in 20 in majority communities) do not attend primary school. The figures are even more striking when we focus only on Roma women. Estimates in the UN report show that three quarters of Roma women do not complete primary education (compared with one in five women from majority communities) and almost a third is illiterate (compared with 1 in 20 women from majority communities). The economic and social vulnerability of Roma population seem to prevail over generations. Like their parents, Roma children have lower educational attainment, spend less time at school and are more likely to be

422


illiterate. For instance, 38% of Roma children do not complete elementary school, compared to only 4% for children from majority households. On the contrary, only a small fraction of Roma children with elementary education stay on at school to complete either primary or secondary education.

4. Data and descriptive statistics In this chapter, our results are based on the UNDP’s Vulnerable Groups Survey, conducted in Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Montenegro, Romania, Serbia, and Kosovo in 2004. UNDP conducted a comprehensive survey on all the households in Roma settlements and areas with large fraction of Roma population, RIDPs, and residents of majority communities living in proximity to these two vulnerable groups. This survey provides a wide range of information on individual and household characteristics as well as detailed information on community environment, labor market, and discrimination. In the empirical analysis, we mainly use information collected in Croatia, Bosnia and Herzegovina, Montenegro, and Serbia from this survey as displaced people in the sample are residing only in these countries. In UNDP data, the areas with Roma enclaves were determined using countries’ census data. UNDP survey was conducted in areas where percentage of Roma was equal to or higher than the nationwide percentage of Roma obtained from census data. Likewise, communities with large share of refugees/displaced people (RIDPs, thereafter) were defined following the similar methodology. However, in order to construct the national averages for RIDPs, official registries and data provided by relevant institutions dealing with displaced populations were used for the sampling design instead of census data. In addition, UNDP data provides detailed information on majority population defined as ‘‘non-Roma, non-displaced’’ that is living in proximity to these two vulnerable groups. The control groups’ samples were constructed using similar approach as for the two vulnerable groups. For Roma sample, majority population interviewed is representative samples of non-Roma communities living in settlements with Roma communities of ‘‘average and above’’ size. Similarly, the control group for displaced sample is nondisplaced populations living in proximity. In this respect, our data is representative within communities with larger share of Roma and RIDPs. Thus, it is worth noting that the status of majority samples could be worse than national averages as these samples are representative of communities living in proximity to the two vulnerable samples. However, these control groups still provide the ‘‘benchmark’’ needed for evaluation of Roma and displaced persons’ poverty and vulnerability since they live in the same community and face with similar hurdles.


423

The strengths of this data are multifold. First of all, it is a unique data that allows us to analyze the majority, the ex ante and ex post vulnerable Roma people, and the ex ante equal but ex post vulnerable IDPs within the same dataset. Most of the previous studies were able to study only one of these aforementioned vulnerable groups in Europe; however analyzing Roma and internally displaced people together may help us understand the hurdles these groups encounter in labor market and human capital formation and devise policies to improve the next generations’ economic status and well-being. Second, it provides similar information on Roma and RIDPs for all ex-Yugoslavian countries, which allows us to compare experiences of Roma and RIDPs across different countries. Table 1 presents the characteristics of household heads for majority population, Roma and RIDPs in ex-Yugoslavian countries, respectively. Hence, mostly household heads provide general information about the household and each person residing in the household in the survey, they entail detailed analysis. Table 1 points to substantial differences in characteristics of household heads between majority population and two vulnerable groups. For example, in Bosnia and Herzegovina, Roma household heads are younger, have larger household size, lower employment probabilities, labor market income, as well as educational attainment relative to both majority and RIDPs. On the contrary, household heads in RIDP households are only slightly different from majority household heads in terms of age, marital status, household size, employment probability, and education. Similar to majority, 50% of RIDP household heads have secondary education and their average years of schooling are approximately 10 years. However, Table 1 also shows that their high levels of educational attainment are not rewarded in the labor market of host communities. It seems that RIDP household heads are earning 70% of majority household heads in the labor market even though latter has only 1 year of additional education. We observe similar patterns between groups in Croatia, Montenegro, and Serbia as well, as summarized in columns (4)–(12). Taken together, Table 1 suggests that Roma household heads are less equipped for the labor market; thus they are more likely to have adverse labor market experience both in terms of participation and earnings. On the contrary, it seems that RIDP household heads suffer in the local labor market despite their high levels of educational attainment. Likely mechanisms behind this penalty might be their lack of local labor market knowledge and ethnic networks or taste-based discrimination against them in the local labor market. Another potential explanation might be their limited ability to transfer the skills they acquired in their previous place of residence. Of course it is not possible to provide definitive proof of any of these stories, and undoubtedly additional mechanisms are at work too, but this seems to be plausible and important mechanisms for RIDP household heads’ negative labor market experience (which we will rigorously elaborate in the empirical analysis).

404

400

398

43.61 48.74 24.50 34.92 4.59 9.76 86.00 37.94 13.75 50.00 0.25 12.06 72.75 64.07 53.00 61.31 4.85 3.47 1.24 0.42 12.25 31.91 132.63 177.56 (116.12) (109.47) 89.87 205.50 (174.61) (200.61) 193.62 281.31 (208.78) (190.50) 258

49.12 41.47 11.35 23.35 54.09 22.57 59.69 30.71 2.81 0.20 54.65 480.00 (406.54) 622.01 (659.82) 789.83 (667.20)

(6)

255

198

37.27 49.94 11.37 26.26 6.32 8.97 81.70 49.74 17.45 46.11 0.85 4.15 87.45 69.19 20.24 17.77 4.97 3.33 1.75 0.47 23.14 33.33 435.15 301.97 (831.65) (258.21) 183.54 249.81 (342.81) (353.88) 435.32 398.05 (377.31) (344.80)

(5)

198

49.71 18.69 12.51 9.64 58.88 31.47 73.74 36.87 3.54 0.26 59.09 285.21 (219.08) 407.57 (317.13) 497.64 (319.30)

(7)

206

Serbia

(9)

204

404

49.20 25.99 12.12 13.12 57.18 29.70 70.05 78.20 3.18 0.21 54.95 217.33 (179.13) 252.91 (243.72) 376.26 (272.27)

(10)

(12)

R&IDPs

400

405

46.40 48.66 16.50 17.78 6.45 10.67 75.19 27.90 22.56 49.88 2.26 22.22 79.75 76.30 79.70 66.50 4.41 3.85 1.02 0.72 24.75 31.36 128.14 140.80 (202.36) (111.94) 103.21 148.54 (169.90) (228.12) 170.28 229.51 (192.19) (268.77)

(11)

R&IDPs Majority Roma

46.97 50.38 22.33 18.63 3.17 11.44 94.50 23.27 5.50 53.96 0.00 22.77 73.79 73.04 23.12 37.25 3.51 3.47 1.06 0.43 17.96 37.75 147.85 195.77 (98.82) (105.48) 117.27 203.14 (102.27) (190.86) 197.02 260.92 (164.55) (229.93)

(8)


Montenegro

Notes: The table includes percentages, means, and standard deviations for household heads between the ages of 23 and 65 using the 2004 UNDP dataset. Income variables are in Euros.

N

49.30 23.02 11.33 18.56 57.92 23.51 71.78 70.30 3.07 0.18 48.02 237.39 (180.24) Household income-wage 292.12 (267.12) Household income-all sources 355.56 (252.06)

Age Female Years of schooling Primary school Secondary school Tertiary Married Urban Family size Number of children Employed Income

(4)

(3)

(1)

(2)


Croatia

Summary statistics for household heads

Majority Roma

Bosnia and Hertzegovina

Table 1.

424 Martin Kahanec and Mutlu Yuksel


425

In the next section, we particularly focus on households with children in analyzing the intergenerational transmission of human capital over generations. Therefore, it is of interest to analyze whether the differences across groups summarized in Table 1 also prevail among household heads with children younger than 22 years of age residing within the same household. Table 2 reports the descriptive statistics for this subsample. Table 2 shows that approximately less than half of the household heads in our sample have young children residing within the same household. As expected, household heads with children are younger, more likely to be married, more likely to be employed, have higher household income and educational attainment relative to household heads without children. However, Table 2 points to even larger discrepancy in returns to education between majority and RIDPs. Table 2 reveals that in all four countries in our sample, RIDP household heads with children earn 40–55% of the majority in the labor market and as a household income even though both groups have comparable educational attainment. This finding suggests that the earning penalty/loss among RIDPs generated by the displacement is even more striking when we focus only on household heads with children. Thus, this disparity in status of RIDP household heads raises concerns not only for household heads themselves but also for next generations’ economic and social well-being. Having shown the characteristics of household heads, we now turn to analysis of entire sample. Table 3 displays the characteristics of all individuals in our sample between the age of 23 and 65. Table 3 mimics patterns presented in Tables 1 and 2. Similar to previous tables, Table 3 also suggests that RIDPs resemble majority population in terms of marital status, educational attainment, and number of children. Similar to majority population, RIDPs are also more likely to be high school graduate and have none or only one child. However, Roma people are more likely to have primary or elementary education and three children or more. For example, in Montenegro 70% of the Roma population has only primary education, where the average year of schooling for Roma is 3.94 years. In Bosnia and Herzegovina, Croatia and Serbia, the majority of Roma has elementary or secondary education, whereas only 1% of them have university degree compared to 25–30% of majority and 5–20% of RIDPs in these countries. Another striking pattern emerges from Table 3 is differences in employment probabilities between majority and two vulnerable groups. Table 3 shows that even though RIDPs look similar to the majority in terms of education, their labor market attachment shows resemblance to Roma instead of majority population. In all countries, RIDPs are considerably less likely to be employed compared to majority despite the fact that they are more likely to work relative to Roma. A comparison of wage income across groups yields similar conclusion further suggesting that RIDPs indeed face hurdles not only in finding jobs but also in finding well-paid jobs in their new destination.

143

238

177

41.39 44.28 23.53 35.59 4.79 10.31 83.61 33.90 16.39 54.24 0.00 11.86 76.47 70.62 48.74 59.89 5.94 4.15 1.78 0.62 13.45 37.85 133.53 191.60 (114.18) (112.66) 100.98 201.98 (200.80) (194.42) 202.98 267.07 (226.83) (168.24) 68

44.16 30.88 11.88 13.24 67.65 19.12 88.24 35.29 4.16 0.46 73.53 530.39 (315.81) 837.93 (615.34) 934.45 (557.33)

(6)

166

76

37.67 42.28 7.23 15.79 6.50 9.93 79.62 37.33 19.75 58.67 0.64 4.00 93.37 85.53 23.49 14.47 5.69 4.57 2.19 1.01 27.11 39.47 482.08 366.01 (903.79) (299.06) 183.02 351.50 (341.53) (402.80) 455.01 513.34 (308.96) (390.37)

(5)

94

47.29 12.77 12.83 6.38 57.45 36.17 87.23 36.17 4.15 0.30 80.85 336.29 (268.66) 491.31 (319.60) 552.37 (332.03)

(7)

70

Serbia

(9)

92

163

46.23 19.02 12.34 9.82 57.06 33.13 85.28 79.75 3.91 0.29 69.94 217.29 (145.79) 292.42 (227.83) 390.87 (261.77)

(10)

(12)

R&IDPs

203

215

41.86 43.39 9.85 14.42 7.06 10.98 72.91 25.58 25.12 51.16 1.97 23.26 87.68 86.98 73.89 66.05 4.94 4.59 1.28 1.08 31.53 36.74 156.82 130.52 (261.90) (98.53) 122.07 148.14 (218.03) (262.33) 181.47 210.36 (239.14) (294.91)

(11)


43.40 48.03 7.14 14.13 3.94 11.45 92.65 20.88 7.35 57.14 0.00 21.98 92.86 83.70 38.57 38.04 5.67 4.05 2.17 0.58 34.29 45.65 188.67 221.68 (106.02) (125.18) 162.13 230.97 (130.07) (202.92) 249.84 279.86 (186.87) (291.23)

(8)


Montenegro


N

45.13 18.88 11.59 13.99 65.73 20.28 86.71 70.63 3.98 0.39 62.24 262.12 (166.38) Household income-wage 365.08 (277.59) Household income-all sources 410.73 (266.43)

Age Female Years of schooling Primary school Secondary school Tertiary Married Urban Family size Number of children Employed Income

(4)

(3)

(1)

(2)


Majority Roma

Croatia

Summary statistics for household heads with children


Table 2.

426 Martin Kahanec and Mutlu Yuksel

87.25 12.51 0.24

27 16.25 20 36.75

Highest degree completed Primary school 17.98 Secondary school 58.62 Tertiary 23.4

Number of children Zero 61.63 One 19.55 Two 14.85 Threeþ 3.97

51.01 18.84 22.11 8.04

32.96 54.1 12.94

52.35 37.32 63.76 29.97

(3)

R&IDPs

64.17 16.93 15.75 3.15

13.58 57.92 28.51

52.87 37.9 63.64 57.58

(4)

Majority

13.1 15.87 23.8 47.23

84.63 13.76 1.61

49.44 31.92 84.82 18.22

(5)

Roma

Croatia

54.31 18.78 16.24 10.67

45.59 49.71 4.71

50.91 37.83 71.58 32.17

(6)

R&IDPs

58.65 23.06 16.79 1.5

11.96 57.95 30.09

51.02 38.89 64.73 52.28

(7)

Majority

29.57 19.05 26.07 25.31

78.12 20.68 1.2

48.04 36.22 79.75 16.46

(8)

Roma

Montenegro

Summary statistics for all adults

41.94 21.34 20.35 16.37

26.18 53.2 20.63

49 37.17 70.55 26.16

(9)

R&IDPs

48.48 24.75 19.7 7.07

8.29 59.68 32.03

50.14 36.56 66.38 53.23

(10)

Majority

50.75 13.07 11.56 24.62

94.58 5.42 0

46.49 34.52 75.52 17.61

(11)

Roma

Serbia

50.98 20.1 19.61 9.31

16.63 61.76 21.62

49.15 37.68 65.64 31.28

(12)

R&IDPs


49 33.5 69.13 7.07

(2)

(1)

52.42 38.72 68.82 41.97

Female Age Married Employed

Roma

Majority


Table 3.

Intergenerational Transfer of Human Capital under Post-War Distress 427

428


Tables 1–3 indicate that Roma people in our sample are generally younger than majority and RIDPs. Life Cycle Theory suggests that individuals’ earnings and employment profile exhibit U-shape pattern. At the initial stage of life cycle, both employment and earning increases with age. However, the reverse is true at the later stage, where employment and earning decrease as individual ages. Therefore, it is of interest to analyze whether the aforementioned differences between groups in employment and earnings are driven by the differences in age distribution across groups. Analyses by age groups are summarized in Figures 1–3. Figure 1 presents the average years of schooling by age groups for majority, RIDPs and Rome in four countries in our sample. In Bosnia and Herzegovina, Serbia, and Montenegro, in all age groups, RIDPs have similar educational attainment as majority, which is substantially higher than Roma residing in their community. In addition, Figure 1 suggests that there is a less discrepancy between groups in terms of education in Croatia, where RIDPs older than 55 have similar educational attainment as Roma population. Figure 2 illustrates the employment probabilities by age groups for majority population and the two vulnerable groups. In UNDP Survey, respondents were asked to report whether they are employed in formal or informal sector. This information is important since vulnerable groups are more likely to be employed in informal sector and focusing only on formal sector may yield to a downward estimate of employment among RIDPs and Roma households. Using this information in the survey, we coded individuals as employed if they have reported working in either formal or informal sector. To begin with, U-shape pattern emerged in Figure 2 confirms the life cycle theory. Indeed, probability of employment increases by age until the age of 40–45 and decreases afterwards for all groups. Note however that there are stark differences at the employment probabilities across groups. At all age groups, majority population is substantially more likely to be employed compared to RIDPs and Roma. However, even though employment probabilities of RIDPs lie between majority and Roma, they are more likely to resemble Roma than majority in terms of their employment behavior. Figure 2 shows that both RIDPs and Roma have weaker labor market attachment both in formal and informal sector which may lead to a higher poverty and vulnerability. However, focusing only on employment may be misleading since RIDPs and Roma are more likely to be on welfare and receive transfer payments compared to majority. In the survey, respondent were asked to report their household income from all sources including all kinds of wages, earnings, old age pension, disability pension, state transfer for children, unemployment, poverty and local assistance benefits, remittances or gifts received from friends and relatives and aids from NGOs, charitable or humanitarian contributions. To account for potential differences across households in labor market earnings, we generated a

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