Labor Market Discrimination

O R L D B A N K R E G I O N A L A N D S E C T O R A L S T U D I E S

Unfair Public Disclosure Authorized Advantage

Public Disclosure Authorized in Developing Countries

EDITEDBY Public Disclosure Authorized

NANCYBIRDSALL

AND

RICHARDSABOT Public Disclosure Authorized

/ 2-/lq

Unfair

Advantage

Labor Market Discrimination

in Developing Countries

WORLDBANK

REGIONALAND

SECTORALSTUDIES

Unfair

Advantage

Labor Market Discrimination in Developing Countries

EDITEDBY

NANCYBIRDSALL

AND

RICHARDSABOT

The World Bank Regional and Sectoral Studies series provides an outlet for work that is relatively limited in its subject matter or geographical coverage but that contributes to the intellectual foundations of development operations and policy formulation. These studies have not necessarily been edited with the same rigor as Bank publications that carry the imprint of a university press. The findings, interpretations, and conclusions expressed in this publication are those ofthe authors and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to the members of its Board of Executive Directors or the countries they represent. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address shown in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when the reproduction is for noncommercial purposes, without asking a fee. Permission to copy portions for classroom use is not required, although notification of such use having been made will be appreciated. The complete backlist ofpublications from the World Bank is shown in the annual Index ofPublications, which contains an alphabetical title list and indexes of subjects, authors, and countries and regions. The latest edition is available free of charge from Distribution Unit, Office of the Publisher, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A., or from Publications, The World Bank, 66, avenue d'Iena, 75116 Paris, France.

Nancy Birdsall is director of the World Bank's Country Economics Department. Richard Sabot is professor of economics at Williams College and senior research fellow at the International Food Policy Research Center.

Cover design by Sam Ferro.

Libraty of Congress Cataloging-in-Publication Data Unfair advantage: labor market discrimination in developing countries / edited by Nancy Birdsall and Richard Sabot. p. cm. - (World Bank regional and sectoral studies) Includes bibliographical references. ISBN 0-8213-1909-4 1. Disctimination in employment-Developing countries-Econometric models. 2. Equal pay for equal work-Developing countries- Econometric models. 3. Pay equity-Developing countries- Econometric models. 4. Sexual division of labor-Developing countries-Econometric models. I. Birdsall, Nancy. II. Sabot, R. H. III. International Bank for Reconstruction and Development. IV. Series. HD4903.U56 1991 331.13'3'091724-dc2O 91-29951 CIP Contents

Foreword ix

Preface xi

Introduction Nancy Birdsall and Richard Sabot 1

1. Labor Market Discrimination: Measurement and Interpretation T. Paul Schultz 15

2. Labor Market Discrimination and Economic Development Orley Ashenfelter and Ronald L. Oaxaca 35

3. Labor Market Discrimination in a Poor Urban Economy J. B. Knight and Richard H. Sabot 55

4. Discrimination in East Africa's Urban Labor Markets Jane Armitage and Richard H. Sabot 75

5. Earnings and Determinants of Labor Force Participation in a Developing Country: Are There Gender Differentials? Jere R. Behrman and Barbara L. Wolfe 95

6. Why Males Earn More: Location and Training of Brazilian Schoolteachers Nancy Birdsall and M. Louise Fox 121

7. Why Do Males Earn More Than Females in Urban Brazil: Earnings Discrimination or Job Discrimination? Nancy Birdsall and Jere R. Behrman 147

8. Job Discrimination and Untouchability Biswajit Banerjee and J. B. Knight 171

v vi Contents

References 197

About the contributors 205

Tables Table 2.1 Occupational distribution in Tanzanian manufacturing by sex 40 Table 2.2 Occupational distribution in Tanzanian manufacturing by race 41 Table 2.3 Earnings differentials by sex 47 Table 2.4 Earnings differentials by race or caste 48 Table 2.5 Interaction effects of female dummy variable with selected explanatory variables for log earnings 49 Table 2.6 Interaction effects of race/caste dummy variable with selected explanatory variables for earnings 50 Table 3.1 Earnings functions for males and females 60 Table 3.2 Effects of discrimination and personal characteristics on the gross difference in mean wages between men and women 62 Table 3.3 Effects of occupational differences and job discrimination by sex 63 Table 3.4 Earnings functions for Africans and non-Africans 66 Table 3.5 Effects of discrimination and personal characteristics on the gross difference in mean wages between Africans and non-Africans 68 Table 3.6 Effects of occupational differences and job discrimination by race 69 Table 3.7 Coefficients on the race variable in occupation- specific earnings functions 71 Table 3.8 Coefficients on the race variable in earnings functions disaggregated by firm ownership 71 Table 4.1 Mean wages, levels of education and experience: Tanzania's manufacturing sector 77 Table 4.2 Earnings functions for Tanzania's manufacturing sector, 1971 and 1980 79 Table 4.3 Earnings functions for Tanzania's wage sector and public and private subsectors, 1980 82 Table 4.4 Actual and simulated occupational distributions: Tanzania's wage sector, 1980 83 Table 4.5 Predicted wages in Tanzania's public and private sectors 84 Contents vii

Table 4.6 Earnings functions for Kenya's wage sector and public and private subsectors, 1980 86 Table 4.7 Form IV exam scores 89 Table 4.8 Wage functions: form IV or more leavers 91 Table 5.1 Sample breakdown by sex, region, labor force participation and reported earnings 97 Table 5.2 Means for major variables used in labor force analysis for national and three regional samples, Nicaragua 1977-78 98 Table 5.3 Labor force participation probit estimates for males and females in national and three regional samples, Nicaragua 1977-78 104 Table 5.4 Probits for reported earnings for males and females in national and three regional samples, Nicaragua 1977-78 108 Table 5.5 Ln earnings regressions with controls for hours and for double selectivity for males and females in national and regional samples, Nicaragua 1977-78 111 Table 5.6 Contributions of differential capital stocks to mean In earnings differential between the sexes with parameters for males and females in national and three regional samples, Nicaragua 1977-78 113 Table 6.1 Income and location of male and female teachers, 1970 126 Table 6.2 Mean characteristics of males and females 132 Table 6.3 Income equations: male and female teachers 133 Table 6.4 Income equations: male and female teachers 135 Table 6.5 Highest course completed, by sex and position 137 Table 6.6 Probit equations 139 Table 6.7 Probability of holding a secondary-school job 140 Table 6.8 Income differentials: male and female differences 141 Table 7.1 Percentages of males and females in labor force and mean monthly earnings, urban and rural Brazil 1970 150 Table 7.2 Distribution of males and females in labor force among job sectors, and mean earnings by sex and sector, urban Brazil 1970 150 Table 7.3 Education and potential experience of urban male and female labor force participants by sector, Brazil 1970 152 viii Contents

Table 7.4 Percentage distribution among job sectors by family position for male and female workers, urban Brazil 1970 153 Table 7.5 Variable classification and descriptive statistics, urban Brazil 1970 154 Table 7.6 Probit functions determining work participation 160 Table 7.7 Earnings functions, urban Brazil 1970 164 Table 7.8 Impact on In earnings and on sectoral weights of replacing female characteristics and coefficients by male, urban Brazil 1970 165 Table 8.1 Occupational distribution of wage earners in Delhi, by caste and residential status for selected educational groups, 1975 175 Table 8.2 Mean values of earnings and characteristics of all migrants and of scheduled and non-scheduled caste migrants in Delhi, 1975-76 176 Table 8.3 Regression analysis of earnings for the entire sample 178 Table 8.4 Decomposition of difference in earnings between caste groups 180 Table 8.5 Coefficients and asymptotic standard errors for the multinomial logit model of occupational attainment: non-scheduled caste workers 183 Table 8.6 Full decomposition of gross earnings difference between scheduled and non-scheduled caste workers 184 Table 8.7 Regression analysis of earnings of all production workers and by caste 189 Table 8.8 Selected results in earnings functions for production workers in the formal and informal sectors, and in the public and private subsectors of the formal sector 191

Figures Figure 8.1 Earnings-experience profile, full sample 187 Figure 8.2 Earnings-experience profile, production workers 188 Foreword

When government intervention in the markets of developing countries became pervasive, assessment of its consequences for efficiency and equity rose in priority on the World Bank's research agenda. That assessment contributed to reappraisal by policy makers of the wisdom of substituting bureaucratic control for market outcomes. That reappraisal has, in turn, helped shape the shift toward market liberalization in developing countries. Costs of intervention in excess of benefits do not imply, however, that free markets are free of imperfections. As market intervention declines, so research should increasingly focus on the imperfections of free markets and on low-cost means of ameliorating the impact of market imperfections. The aim is not to stimulate another cycle of costly intervention but, on the contrary, to help avoid overreaction to unpleasant surprises. This pioneering collection of studies of labor market discrimination in developing countries is therefore most welcome. The market for labor is of central importance in the development process; how well it functions can have a profound impact on economic growth and the distribution of income. Discrimina- tion on the basis of gender, race, ethnicity, or caste can impede the efficient functioning of the labor market and have adverse distributional consequences. The studies collected here apply methods developed for the analysis of discrimination in high-income countries to data generated in Africa, Asia, and Latin America. The authors demonstrate the usefulness of these techniques in these new settings and stake a claim for this topic to be given priority on the research agenda. The editors raise the question of whether discrimination is an issue about which only high-income countries can afford to be concerned. The analysis in this volume suggests that the answer is a resounding no. The authors' findings deserve consideration by optimists who believe in liberalization as a panacea. It is the Bank's hope that this book will improve understanding of a costly phenomenon that may not easily be eliminated in labor markets that have been liberalized. This book should also contribute to renewed concern by governments about the economic and social costs of discrimination and provide a foundation on which to build the next generation of applied research on policy steps that would reduce discrimination. LawrenceSummers Vice President DevelopmentEconomics and Chief Economist ix The World Bank

Preface

The essays collected here, work on some of which began more than a decade ago, are pioneering attempts to explore the nature and magnitude of labor market discrimination in low-income countries. The challenge of designing and generating the sets of individual and household-level data on which these studies are based is far greater in low- than in high-income countries, which explains why this collection follows the first collection of essays on discrimination in the U.S. labor market, edited by Ashenfelter and Rees, by almost twenty years. Nevertheless, this volume is timely. We believe that the link between underdevelopment and discrimination deserves, and will receive, increasing attention. The worldwide trend toward the liberalization of markets is likely to renew interest in the operation of labor markets and the economic and social costs of such labor market problems as discrimination. Growing awareness of the crucial role that human capital has played in the transformation of low- into high-income countries has increased sensitivity to the cost to developing countries of the inefficient use of labor. Our hope is that these essays inspire a new round of work on this subject. We could not have succeeded with this project without the support and encouragement of many of our colleagues in the World Bank, at the International Food Policy Research Institute and at Williams College. We would like to thank in particular Timothy King and John Mellor, who provided leadership in supporting systematic analysis of micro-data by World Bank and IFPRI researchers respectively and who helped make this collection of studies possible. Our thanks also go to Dennis de Tray and the World Bank Research Committee and, for their patience and commitment throughout a long gestation period, to the authors contributing to this volume.

Introduction

Nancy Birdsall and Richard Sabot

Our concern in this volume is with labor market discrimination. The studies collected here apply, in African, Asian and Latin American settings, econometric tools developed to measure and analyze discrimination in high income countries. The studies are among the first to attempt rigorous analysis of discrimination in low income countries. Included are analyses of discrimination by caste, ethnicity and gender. Two overview essays, specifically commissioned for this volume, are by economists familiar with the evolution of the theoretical and applied economic analysis of discrimination in the United States. The overviews summarize the findings of the country studies and compare them with each other and with the findings of research in high income countries. An attempt is made to arrive at some preliminary generalizations regarding the magnitude and nature of labor market discrimination in the developing world. The country studies represent a transfer of analytic technology from high income economies; the overview essays assess the appropriateness of the technology in this new setting. In the introduction we attempt to clarify what we mean by labor market discrimination. We specify some of the equity, efficiency and non-instrumental costs of discrimination to society which justify concern by policymakers. We then consider why, particularly in low income countries, competition among firms may not be sufficient to eliminate discrimination. The introduction closes with a brief overview of the chapters that follow.

1. What is labor market discrimination?

The theory of competitive labor markets predicts that two workers with the same endowment of productive characteristics will be paid the same wage. Observation of the labor market suggests that this prediction is often incorrect. Given two

1 2 Introduction workers with the same education, training and employment experience, but differing in some non-economic personal characteristic, one may earn substantially less. The theory of labor market discrimination was developed to help explain differences in wages among workers that do not appear to be justified on efficiency grounds. Why might workers be penalized in the labor market because of some characteristic unrelated to their productivity such as their race, ethnic background, religion, gender, social class or caste? The strand of the theory of discrimination associated with Gary Becker (1957) focuses on prejudice. Employers, or relatively influential groups of employees, may find it difficult to tolerate in the workplace those not like themselves. The distaste of the dominant for the subordinate group - be they blacks, watutsi, muslims, women or members of scheduled castes - may be reflected in a preference of employers for hiring less productive workers who are like themselves, over more productive workers who are different. The implication is that given two workers with the same productive capacity, the member of the subordinate group will be hired only if he is willing to work at a lower wage than the member of the dominant group. A variant of this strand of the theory of discrimination may be of particular relevance in low income societies where social traditions still exercise a powerful influence on economic behavior. Tradition may segregate castes by occupation or industry. Or it may confine women to family enterprises or household production activities while men are free to shift into firms or other relatively dynamic, high productivity organizations. Perhaps for a long period during which there was little economic change, the division of labor which the traditions reflect may have been efficient.t Adherence by employers to traditional roles in the labor market when hiring, promoting and making decisions regarding wages may, therefore, be indistinguishable behaviorally from indulgence of prejudice: a revealed preference, perhaps in the form of higher wages, for employing members of the traditionally appropriate group over members of the traditionally inappropriate group, even when the latter group are demonstrably more productive. Another strand of the theory of discrimination, associated with Edmund Phelps (1972), focuses on the likelihood that information available to employers about the skill endowments of individual job applicants will be relatively sketchy, while information about the average endowments of social groups will be (or will be believed to be) relatively complete. The victims of discrimination generally are from low income groups. The average member of a low income group will not be as well endowed with productive characteristics as the average member of a high income group; for example, the average child from a disadvantaged household will have received less "training" at home and less, and lower quality formal schooling. In the absence of complete information on individual job applicants, unprejudiced employers may give preference in hiring, or pay higher Introduction 3 wages, to members of privileged groups, and justify their preferences on statistical probability grounds. Discrimination against women may be consistent with this model. While women are not concentrated in low income households, the distribution of education and other investment in human capital is often skewed against them. Parents may have traditional views about the appropriate role of women, and therefore consider modem formal education unnecessary. Alternatively, parents may be as ambitious for their daughters as for their sons but have no local school to which to send them. Even if a school is available they may invest less in the education of their daughters because discrimination by sex in the labor market results in a rate of return lower than for investment in their sons. Therefore, on average, female labor market entrants are likely to be less well endowed with human capital than their male counterparts, to whom even unprejudiced employers may feel justified in giving preference. We return below to the concept of a vicious circle implicit in this explanation, as an explanation also for the persistence of discrimination. Alternatively employers may generalize to all women a high probability of traditional female employment behavior, i.e., of a woman leaving the labor force at the time of marriage or childbearing. In developing countries, use of contraception to control fertility is more recent and less widespread than in industrial economies. Even educated women in urban areas have more children than their counterparts in high income countries. Employers in low income countries may conclude that, despite the availability of low cost child care, a woman's career is more likely to be interrupted than a man's. In the imperfect information model the explanation for discrimination is shifted from tastes (prejudices) to beliefs. Arrow (1973), however, has suggested that this model may not be as distinct from the Becker model as it first appears. How do employers form their beliefs regarding the endowments of job applicants from privileged backgrounds (or males) relative to the endowments of applicants from less privileged backgrounds (or females)? While the averages of the endowments of the two population sub-groups may be well known, job applicants are unlikely to be representative of their population sub-groups. Expected retums to rural-urban migration and to urban wage labor force participation are markedly higher for the more educated. These processes, therefore, are highly selective of individuals with relatively high endowments of human capital. It is likely that the pool of urban job applicants is selective of the best endowed members of the less privileged group. This implies that the task of forming beliefs about job applicants is not trivial. It is complicated in most countries by a paucity of readily available evidence regarding the degree of selectivity of the applicant pool. If employers base hiring decisions on average endowments, is it because information-gathering about particular candidates is costly, or because employers simply wish to use averages to rationalize their prejudices? 4 Introduction

Arrow invokes the theory of cognitive dissonance - which holds that beliefs and actions will tend toward consistency - to explain why employers may come to hold beliefs regarding relative endowments at variance with the facts, but consistent with their actions. The likelihood of such divergence may be particularly high where there is a strong ethic, perhaps even shared by the employer, which is in conflict with discriminatory behavior.

2. Why should we be concernedabout discriminationin developing countries?

Is discrimination an issue about which only high income countries can afford to be concerned? Do poor countries not have more important development issues on which to focus their scarce policy-making resources? Is discrimination not primarily an equity issue without serious "efficiency" costs? Are not the costs it imposes on society more social and political than economic? Whether discrimination should be given high priority as a policy issue depends on the answers to such questions. These are empirical questions that cannot be resolved a priori. The studies in this collection are a first step toward the empirical assessment necessary to judge whether in low income countries the economic costs of discrimination are high or low. We believe, on the basis of a priori considerations, however, that in some countries the potential economic costs of discrimination may be quite high. Such considerations indicate, moreover, that in contrast to many other public policy problems, discrimination does not necessarily confront policymakers with an equity-efficiency tradeoff.

Discrimination, inefficiency and growth

How does discrimination affect overall efficiency and thus economic growth? The standard theoretical analysis, based on the work of Becker (1975) and Arrow (1973), brings out clearly the implications of discrimination for profits, wages and efficiency in the allocation of labor. The theory assumes that groups, for example, males and females, with the same human capital endowments are perfect substitutes in production; therefore any difference in wages is due to discrimination. Taking capital as fixed in the short run, and assuming a large number of firms all producing the same output with the same production function, profits are given by the value of the output produced by the two groups of workers minus the wages paid to each group. When making employment decisions, a utility maximizing employer will, as usual, equate the marginal product and the price of labor. The employer, however, derives utility not just from profits but from the gender composition of his labor force. Therefore, the price of labor of females, the group against which the employer is prejudiced, is not simply the market wage rate but includes, in addition, what Becker has called the "discrimination Introduction 5 coefficient." This is the disutility associated with employing females, or alternatively, the profits the employer is willing to forego so as to reduce by one the number of females in his employ. Since males and females are perfect substitutes, the implication is that in equilibrium the wage of males will equal the wage of females plus the discrimination coefficient, i.e., despite being equal in productivity, the wage of males will exceed the wage of females. There are two further implications regarding equilibrium. First, the marginal product of females will exceed their wage: from the perspective of profit maximization, fewer than the optimal number of women are employed. Second, the gaps between the wage of males and the wage of females and between the wage of females and their marginal product will be bigger the greater is the employer's discrimination coefficient. If all employers have the same taste for discrimination, then the sex composition of each firm's labor force will be the same. Moreover, the marginal products of males and females will be the same in all firms. In this case, discrimination results at the micro-level in a gain for male workers at the expense of females and a reduction of profits and at the macro-level in a likely reduction of savings, investment and growth. However, no reallocation of women across firms would increase output. But, employers are likely to differ in their taste for discrimination. If so, the gender composition of the labor force will vary among firms in relation to the size of the firm's discrimination coefficient: those with the largest coefficients will have the smallest proportion of females. Discrimination shifts the employer's demand for labor to the more costly of the perfect substitutes. Therefore, the average wage rate and the marginal product of labor will be greater, and the size of the labor force smaller, in the more discriminatory firms than in those firms which employ a relatively high proportion of women. The variation of the marginal product of labor among firms implies that the allocation of labor is no longer efficient: a reallocation of women from firms employing relatively more women to firms employing relatively fewer women - as would occur if discrimination were eliminated - would increase output. Discrimination may also have an impact on X-efficiency. How do female workers respond to discrimination? They are likely to learn that male co-workers who are no more productive are paid more on account of prejudice or adherence to tradition. They are also likely to become aware that, in contrast to other employees, their pay does not measure up to their contribution to the output of the firm. One plausible response is to reduce their commitment to the job and level of effort, hence their level of productivity. Marginal productivity in excess of the wage provides an incentive to the employee suffering discrimination to shirk, unless there is an expectation that future wages will compensate for the current gap. Over time the productivity of victims of discrimination may decline to the level of their wage, imposing further costs on the firm and the economy. The decline in productivity may then be used 6 Introduction to rationalize wage differences and trigger increases in discrimination, setting in motion a "vicious circle" of declining productivity and widening wage gaps. There is a third dimension of efficiency which may be influenced by discrimination. The occupational structure of wages signals the importance to the economy of workers in technical, managerial and professional jobs; workers in those jobs are generally among the highest paid. Society has an interest in having the best qualified individuals in those jobs. If access to those jobs is not determined by meritocratic criteria but by nepotism, class collusion or discrimination, then the resulting misallocation of workers among occupations is likely to reduce the performance of firms and of the economy. Similarly, labor market discrimination may limit intergenerational mobility, the movement of families in the socioeconomic hierarchy from one generation to the next. The current distribution of income may be highly unequal and yet there may be perfect intergenerational mobility, i.e., a child's ultimate place in the socioeconomic hierarchy may be completely independent of where his parents are in the hierarchy. This implies that a child from a low income household will have the same probability of gaining access to elite white collar jobs as a child from a high income family. The norm, even in the most open, least class-ridden societies, is for intergenerational mobility to be considerably less than perfect. Children from relatively privileged backgrounds are likely to be at an advantage, for example, in the competition for access to the best quality higher education. The distribution of ability may be the same for both groups and selection decisions by schools may be made solely on the basis of meritocratic criteria. Even so, the children from privileged backgrounds are likely to have acquired more of the relevant skills at home and, as a consequence of household and neighborhood effects, to have attended higher quality primary and secondary schools. In these circumstances, less than perfect mobility may actually be efficient, since the children of high income parents, with greater accumulated training, can profit more from formal schooling. However, an efficiency problem does arise when low intergenerational mobility is the direct result of discrimination by schools against children from the bottom of the socioeconomic hierarchy or the indirect result of labor market discrimination. In either case, there is misallocation: individuals whose potential productivity is relatively low are being favored, in access to education and then in jobs, over others with potentially high productivity. The retums to investment in education and to training and, ultimately, the rate of economic growth, therefore, are depressed. The magnitude of the static inefficiency that results from discrimination may be influenced by economic dynamics. Discrimination constrains mobility, hence the speed with which the labor force adjusts to labor market disequilibria generated by structural change. This suggests that the efficiency losses due to Introduction 7 discrimination are likely to be higher the more rapid the pace of structural change and economic growth. Since 1982 many developing countries, especially in Latin America and Africa, have suffered low rates of economic growth. However, this period is exceptional. Developing countries grew at an average rate of over 6 percent per annum in the 1960s and over 5 percent in the 1970s, compared to much lower rates of about 5 percent and 3 percent per annum in the industrial countries. Changes in the pattern of demand for labor across industries, occupations, modes of employment and size categories of enterprises are a corollary of modem economic growth. The supply side of the labor market has also been characterized by marked changes due to historically unprecedented increases in the size of populations and school enrollments. The pace of change in the labor markets of developing countries has been faster than in the high income countries at a comparable stage of their structural transformation (Squire 1981). These changes alter the allocation of labor that is efficient; the faster the change the greater the necessary alteration is likely to be. In sum, discrimination will tend to slow economic growth by reducing efficiency in the allocation of labor among firms and the economy by reducing the job commitment and effort of workers who perceive themselves to be victims of injustice; and by reducing the magnitude of investments in human capital, and the returns on those investments. The losses due to discrimination are likely to be greater the more rapid the pace of change in an economy.

Discrimination and ethnic and religious conflict

There is an additional cost to discrimination. By exacerbating current inequality between groups, by contributing to its perpetuation from one generation to the next, and by ensuring that at least a component of that inequality is rooted in injustice, discrimination may foster conflict. In the attempt to maintain order or palliate the aggrieved groups government may commit substantial resources which, otherwise, could have been put to more productive uses. The classical economists developed their models of growth and distribution during the British industrial revolution. Perhaps because economic life was relatively simple then, they confined their analysis of growth to three inputs- labor, land and physical capital - and their analysis of inequality to the functional shares of wages, rent and profits. Marx concluded that the relationship between the relatively small group that derives its income from profits and purchases labor services and the much larger group that derives its income from the sale of labor services holds the greatest potential for economic, social and political conflict. Some contemporary economists similarly expected conflict between capitalists and workers to characterize the economic transformation of poor countries. 8 Introduction

Most contemporary economists, however, have been more concerned with the effects of differences in human capital (i.e., in formal education, skills, inherent ability) on the inequality of pay, and thus on income distribution. The accumulation of human capital has been shown to make a substantial contribution to modem economic growth and to account for most of the inequality of pay. In high-income societies today as much as three-quarters of inequality is due to the inequality of pay. In developing countries, the contribution of the inequality of pay to total inequality is smaller, but with the growth of wage employment, its contribution is increasing. Oddly enough, however, economic inequality - either between workers and capitalists, or among workers with different human capital - does not appear to have been the major source of social and political conflict in developing countries. In fact, in many low- (and indeed high-) income countries, vertical cleavages, for example, between tribal groups in Nigeria, ethnic groups in Sri Lanka or religious groups in Lebanon, have been a greater source of conflict, and a greater apparent impediment to economic development, than have horizontal cleavages between capital and labor or between those workers who are well- endowed with human capital and those who are not.2 Arthur Lewis (1985), however, emphasizes that economic inequality between groups can itself be an important and independent cause of antagonism, contributing to and reinforcing ethnic and religious cleavages.3 Economic inequality between groups can itself be as source of prejudice and discrimination if the economically dominant group - the group with a disproportionate number with high incomes - comes to hold the subordinate group in low esteem. Lewis notes that "those we look down on we feel free to exploit." In a vicious circle, the discrimination that arises from inequality can itself contribute to greater economic inequality between groups, since the effect of discrimination will be to lower the income of the group discriminated against relative to the income of the other group.4 The contribution of labor market discrimination to racial and ethnic cleavages may be greater if discrimination not only increases the inequality of current income between groups, but also reduces intergenerational mobility. Albert Hirschman (1973) has hypothesized that the political tolerance for any given degree of inequality in the current distribution of income will be greater, the more perfect is intergenerational mobility. Parents at the bottom of the socioeconomic hierarchy will be less resentful of economic privilege the greater is the perceived probability of their children being among the privileged. Hirschman's hypothesis can be extended to economic inequality between groups. For any given degree of inequality in current income between, for example, one ethnic group and another, the antagonism between the dominant and the subordinate group will be less the stronger is the expectation that in the next generation the subordinate group will be less concentrated at the bottom of the socioeconomic hierarchy relative to the dominant group. Introduction 9

Discrimination may influence whether differences in socioeconomic status between the dominant and subordinate groups are reduced, maintained or even widened from one generation to the next. For example, discrimination against children of the subordinate group in education or credit markets, which directly limits their accumulation of human or physical capital, may ensure the perpetuation of the difference in socioeconomic status. As we noted above, labor market discrimination may affect, as well as be affected by, the rate of intergenerational mobility. The demand for education will be influenced by the expected rate of return to investment in education. If discrimination depresses the expected earnings associated with a given level of education for the children of the subordinate group relative to the expected earnings of the dominant group, then it is also likely to depress the relative demand for education of the subordinate group. Expectation of labor market discrimination may, therefore, result in a higher enrollment rate for children of the dominant group than of equally well qualified children of the subordinate group, even in the absence of discriminatory behavior by those who govem school admissions. The difference in enrollment rates may, in tum, help transmit economic inequality between groups from one generation to the next.

Non-discriminatory behavior as an end in itself

Reducing discrimination is worthwhile because it is likely to increase economic efficiency and growth, and to reduce inequality among individuals and between groups and the potential for conflict to which inequality between groups may give rise. At the same time, we should not lose sight of the fact that, irrespective of its impact on economic outcomes, and on social and political stability, eliminating consideration of such extraneous factors as race, religion, sex or ethnicity from the interactions between buyers and sellers of labor services is a worthy end in itself. Kenneth Arrow's 1973 declaration in the first collection of studies of discrimination in the United States is equally relevant here:

It may wellbe admittedthat the term "discrimination"has value implicationsthat can never by completelyeradicated, though they can be sterilizedfor specific empiricaland descriptiveanalyses. I have spokenof personalcharacteristics that are "unrelated to productivity" and not "properly.relevant." These terms imply definitions of product and of relevancy which are themselves value judgments or at any rate decisions by the scholar. The black steelworker may be thought of as producing blackness as well as steel, both evaluated in the market. We are singling out the former as a special subject for analysis because somehow we think it is appropriate for the steel industry to produce steel and not for it to produce a black or white work force. 10 Introduction

Non-discriminatory treatment of labor is a social goal on a par with ensuring equality before the law and closely related to the goal of equality of opportunity, which is generally taken to be a hallmark of a just society. 3. The free market solution and its limitations Predictions regarding the nature and incidence of the costs of discrimination are derived from neoclassical economic theory. The same theory predicts that, in highly competitive markets, discrimination will prove to be a transitory phenomenon. We have noted that the costs to firms of discrimination may be high. Neoclassical theory sees the resulting erosion of profits as a self-correcting dimension of discrimination.5 Employers who indulge their prejudices, or those of influential employees, face the ultimate sanction imposed by the market: banishment. If correct, this view of discrimination has obvious and important implications for policy. Discrimination increases th_ share in the firm's labor force of the more costly of the perfect substitutes. Firms with strictly meritocratic hiring practices will, therefore, be at a competitive advantage relative to firms that give weight to personal characteristics unrelated to worker productivity. They will gain market share and eventually force discriminatory firms to either become meritocratic or leave the business. Discrimination based on imperfect information should also prove to be a temporary phenomenon. The pay of victims of such discrimination is low because employers expect their productivity to be relatively low. Once employed, however, these workers will have an opportunity to demonstrate their skills. Evidence that the low paid group is equal in productivity to the better paid group confronts the employer with a choice: the employers will either eliminate the discriminatory wage gap or face the same market sanctions as employers whose discriminatory behavior is based on prejudice. Where discrimination is indeed self-correcting a laissez-faire approach might be more appropriate than an activist anti-discrimination policy. While an activist policy might accelerate somewhat the process by which discrimination is reduced, it runs the risk of mistakes in the identification of firms that discriminate, and of high administrative costs. There are, however, several reasons why labor market discrimination might persist over long periods. First, not all markets are highly competitive. The persistence over decades of labor market discrimination in high income countries attests to that. Indeed in developing countries, monopoly power - most obviously of state-owned enterprises - is pervasive. In many countries, a large portion of skilled jobs are found in govemment itself or in monopolistic state- owned enterprises, and neither of these is necessarily responsive to competitive pressures. Nor, as the studies in this volume suggest, is it safe to assume that government officials responsible for hiring, promotion and wage determination Introduction 11 are free of prejudice or immune to poor information and resulting statistical discrimination. Second, even perfect competition in all markets is not a sufficient condition for the elimination of discrimination if all employers are discriminators. It is arguable that if all employers are males and choose to discriminate against females this would give a competitive advantage to female employers who enter the market. In fact, this kind of competition from new entrants is seldom a threat because few members of the groups discriminated against have the capital and entrepreneurial skills needed to enter these markets successfully and some consumers may refuse to patronize them. We noted that some workers who have been discriminated against may have the opportunity to demonstrate their potential and to communicate information about their relative productivity at no cost to their employers. Other workers, however, may not have that opportunity, suggesting a third reason for the persistence of discrimination. Job candidates from disadvantaged groups who, on the grounds of statistical probability, were refused employment or who were shunted into low skilled jobs may have no means of either generating, or confronting employers with, the information necessary to demonstrate a gap between the individual's potential and the perceived mean productivity of the group. Because there is no real market for the correct informnation,an employer will have little interest in bearing the high immediate costs of acquiring information about the true productivity of job candidates from disadvantaged groups, particularly if employers do not realize they are uninformed. In industrial countries, nonprofit groups often represent the disadvantaged and lobby on their behalf. But it is not the operation of the market, rather its failure to operate properly, that leads groups to assume such a function. The concept of vicious circles suggests a fourth reason for the persistence of discrimination in competitive markets. Because discrimination can affect individual behavior, including decisions about education, job search, and the extent of shirking on the job, members of groups that are discriminated against may, at any given time, actually be less productive than members of groups that have not experienced discrimination. Thus, the lower wage or reduced job opportunities which initially resulted from discrimination actually appear to be justified. For example, discrimination against women in the labor market in period 1 may reduce some women's commitment to their jobs, hence their X-efficiency, justifying lower pay for those women in period 2. An employer, observing in period 2 the differential in productivity between male and female employees, and not understanding the source of that differential, is likely to continue to discriminate in decisions regarding hiring, promotion and wage determination. Just as the wage differential may appear to be justified, so too may decisions of parents, based on that differential, to invest less in the education or training of girls than of boys of equal ability. Discrimination may 12 Introduction therefore persist because the behavioral responses to discrimination may effectively disguise the phenomenon from all but the most astute observers.

4. An overview

In the first of two overview essays commissioned for this volume, T. Paul Schultz focuses on the methodological problems that arise in trying to measure sex and race discrimination in the labor market, and uses the country studies in this volume to illustrate. His critical review provides the basis for suggestions regarding analytic techniques, and their data requirements, to be used in the next generation of economic research on discrimination. The second essay, by Orley Ashenfelter and Ronald Oaxaca, compares the statistical results from studies of labor market discrimination in developed economies with the results of the studies of developing countries. The aimi is to shed light on the relationship between the process of economic development and labor market discrimination. The authors also assess how well traditional theories of discrimination, developed in high income countries, apply to relatively low income countries. In chapter 3, John Knight and Richard Sabot analyze the extent of race and sex discrimination in Tanzania's manufacturing sector in 1971. As in the United States they find that the mean wages of males were substantially higher than the mean wages of females. In marked contrast to the United States, however, the differences can be explained almost entirely by differences in economic characteristics. Only a small part of the gross difference in mean wages between Asians and Africans, however, can be explained by the markedly higher level of education and training attained by the former. Their finding of an absence of wage discrimination against females challenges the assumption that economic development brings social enlightenment, while their evidence of discrimination in favor of Asians challenges the belief that only groups with substantial political and economic power benefit from discrimination. In chapter 4, Jane Armitage and Richard Sabot extend the Knight-Sabot analysis of Tanzania, adding an intertemporal dimension as well as a comparison with urban labor market discrimination in Kenya. Tanzania's private manufacturing sector continues to be free of sex discrimination as does Kenya's wage labor market. The story regarding the public sector discrimination is curiously mixed. In Tanzania's public sector males eam a substantial wage premium relative to females with the same human capital endowments. With regard to race the premium enjoyed by Asians in Tanzania remained large in the private segment of the urban wage sector in both Kenya and Tanzania in 1980. However, the racial premium was markedly smaller in Kenya's public sector and had been eliminated in Tanzania's public sector. Jere R. Behrman and Barbara L. Wolfe analyze sex differentials in determinants of labor force participation and earnings in (pre-revolutionary) Introduction 13

Nicaragua in chapter 5. Because of the support provided by extended families, the burden of child care constrains female labor force participation less in Nicaragua than in more developed countries. The influence of schooling, prior employment experience, and household nutrition are all found to have a greater impact on female than on male labor force participation. However, poor health is less of a constraint on female participation. Work experience is the most important human capital variable in accounting for mean eamings differentials by sex but, the authors conclude, a combination of unobserved sex-associated traits, such as physical strength, together with sex discrimination account for a large proportion of the differential. They are unable to identify the relative importance of sex discrimination versus other unobserved factors. Nancy Birdsall and Louise Fox examine the substantial income differential between male and female schoolteachers in Brazil in chapter 6. They find that at least three-quarters of the differential is explained by differences in personal characteristics; possible differences in real income associated with differences in location and supply-determined differences in geographic mobility, also associated with location; and differences in training leading to a greater likelihood that males will hold secondary school jobs. They find evidence of some wage discrimination, but conclude that the real problem is rooted in the decisions of women to seek more restrictive training than men seek; the more restrictive training confines women to teaching in primary schools, where salaries and retums to education are lower. In chapter 7, the focus is on the urban labor market as a whole in Brazil. Nancy Birdsall and Jere Behrman seek to explain sex differences in labor force participation, sector of employment and in earnings. They find that the probability of working in the formal sector is more sensitive to the level of schooling among women than among men. Marriage and young children appear to constrain female participation in the formal and domestic sectors, but not in the urban informal sector. With respect to eamings, neither differences in returns to human capital nor differences in hours worked appear to be a major factor in the considerable male-female average eamings differentials. They are unable to determine to what degree unobserved sex-related factors or discrimination account for the residual difference in earnings. In chapter 8, Biswajit Banerjee and John Knight examine discrimination based on caste in the Indian urban labor market in light of govemment policies to eliminate the past disadvantages associated with caste; They find that despite the government's efforts, job discrimination and wage discrimination account for nearly half of the gross earnings difference between scheduled and nonscheduled castes. The scheduled castes, in relation to their characteristics, are disproportionately confined to unskilled, "dead-end" jobs, and in certain job categories, notably production jobs, are subject to wage discrimination. The authors note that the govemment has been more successful in addressing caste discrimination in occupations where jobs are filled through open advertisement, for example, in 14 Introduction

professional and skilled occupations, than when, as in the case of manual jobs, they are filled through personal recommendations, which tend to perpetuate caste preferences.

Notes

1. There is a parallel with traditional dietary rules, many of which now appear to be arbitrary. They may have been quite sensible when they were first adopted given prevailing methods of food storage and preparation. 2. Arthur Lewis (1985) has suggested that the decline and breakup of the empires of the nineteenth century is one reason for the apparent increase in racial, ethnic and religious antagonism. The larger the area governed, the more diverse the population is likely to be. The greater the diversity, the less feasible is the imposition of cultural, racial or religious homogeneity. Tolerance has, out of necessity, tended to flourish in large empires. Conversely, small nation states tend to be more homogenous and, consequently, less tolerant of minority groups. 3. He notes that equality between groups requires more than the two groups having the same average income. Equality requires that each group has the same proportion of its members as the other in every income category, high and low. The implication is that there can be substantial income inequality between groups even when aggregate inequality is low and, of course, income equality between groups even when the aggregate- distribution of income is highly unequal. 4. Conceivably the group discriminated against could have higher income than the other group in the absence of discrimination. In this case, discrimination might actually contribute to a reduction of inequality between groups. However, this appears to be implausible. It might apply where there is a small minority, such as the Chinese in various East Asian countries or Indians in various African countries, with relatively high incomes but little or no political power. The studies of discrimination in East Africa included in this volume, however, suggest that the Asian minority does not suffer from discrimination but may actually benefit from it. 5. In contrast, Marxist analysis views discrimination against minorities as a means by which employers increase their pecuniary profits (Rees 1973, page 182). 1

Labor Market Discrimination: Measurement and Interpretation

T. Paul Schultz

1. Introduction

The studies brought togetherin this book quantify wage, occupationand other statusdifferences between men and women and among racial and ethnic groups in low-incomecountries. Analogous issues have been studied with increasing sophisticationrecently in the United States and in a few other high-income countries.This pioneeringcollection of investigationsapplies these quantitative tools of analysisto clarifythe magnitudeand correlatesof economicdifferences betweengroups of workersin poorer societiesor in less developedeconomies. Mostof my reflectionsin this essay deal with conceptualand methodological problemsof quantifyingdiscrimination (section 3). The authorsof the studiesin this volume, and the broader literatureto which they contribute,are concerned with understandingthe origins of economicinequalities in society.In section4, I turn to the specific findings from the book. I discuss directions for future research in section 5. These studies illustrate the strengths and limitationsof recursivedecompositions of inter-groupincome differences as a toolfor studying discrimination.Before turning to a discussionof methodologicalissues, however, severalquestions of a more substantivecharacter deserve comment(section 2).

2. Substantiveissues

Alternativetypes of comparisonsof productivity

There are basic differencesbetween the contextin which incomeis observedfor men and women,on the one hand, and among racial and ethnic groups, on the other hand. The wages or productivityof men and womencan be quantitatively summarizedand compared insofar as one can measure their productivityin commonand comprehensiveterms. In practice, wage comparisonsof men and womenare limitedto the productivityof men.and womenwho work for wages

15 16 Labor Market Discrimination: Measurement and Interpretation

in the market labor force. Comparisons of the productivity of persons in nonmarket (home) activity are as yet primitive and not commensurable (Schultz 1988). In most societies, men and women work different proportions of their lifetime for wages in the labor force; women notably engage in home production to a greater degree than do men and invest more of their effort in the rearing of children, and their output in this sphere cannot be readily valued because it is not exchanged in the market. To the extent that there are significant returns to the accumulation of distinct skills that enhance the productivity of labor in either market or home production, women may be rewarded to specialize in home production skills and men to specialize in market production skills, if they pool resources by marriage or other means (Becker 1981). Comparisons of the labor market productivity of women and men are, therefore, likely to overstate the advantage of men in terms of the initial value of their lifetime productivity relative to women, because men have specialized to a greater degree in market-relevant skills. The degree of overstatement depends on many complex and poorly understood features of the economy and society, such as the returns to market-specific and home-specific on-the-job experience; the opportunity cost of parents' time as it impacts differentially on the lifetime market productivity of women and men; and the returns mothers and fathers derive from their children as a function of their investments in offspring. Different regions of the world exhibit different patterns of female and male participation in market and home production (Boserup 1970; Durand 1975). In sub-Saharan Africa and parts of South East Asia (for example, Thailand, Malaysia, Indonesia and Philippines), women have traditionally assumed occupational roles that place them on a more equal footing relative to men in terms of producing goods that have an unambiguous market value, even when much of what they produce, such as food, is consumed directly within the home (Boserup 1970). Nonetheless, the contact of men and women with the market is less unequal in these regions and less constrained by the hierarchical coordination of tasks within a nuclear family production unit than it is in many other developing countries. The more widely observed pattern in rural Latin America, South and West Asia, and North Africa is for women to confine their work to home production activities, the output of which is not often exchanged in the market place. Measures of market productivity of women in South East Asia and sub-Saharan Africa may, therefore, be closer to a par with men than in the other low-income regions, especially when education and rural/urban residence are held constant. If, however, the traditional occupational specialization of women and men has placed women in sectors where productivity is declining relative to those sectors occupied by men, or if the introduction of mass education has differentially favored men compared with women (as in much of Africa and Asia), then the market productivity of women can suffer relative to men as modem economic growth gains momentum and labor specialization proceeds apace. Labor Market Discrimination: Measurement and Interpretation 17

Measured differences in the market productivity of women and men, therefore, signify a complicated set of constraints in terms of traditional occupational roles; home human capital investment patterns in the health and acquired capacities of boys and girls; differential returns to and investments in the formal education of men and women; and cultural rigidities in the occupational and regional movement of male and female workers. All of these factors determine how the labor market and the family accommodate modem economic growth and how efficiently male and female workers are employed over the course of development. Although some of the same factors influence race and ethnic group differences in market productivity, the context is quite different, and the implications for policy are not the same. Differences in investments between children within a given family may affect female-male wage differences, but do not have a counterpart in investments among ethnic and racial groups. Life cycle specialization in market or home production activities is also less germane to race and ethnic-group income differences. Consequently, race and ethnic-group differences in market wages and incomes are in this sense easier to interpret as arising from traditional (home produced and family apprenticed) occupational skills and acquired education, intergenerational bequests of capital and land, segmentation or rigidities in labor markets, and pressures to reallocate labor among sectors and occupations due to the pace and composition of modem economic growth. In brief, interpreting female-male wage differences may be still more complex than interpreting race or ethnic group wage differences for only males. Both types of differentials, however, can reflect short-run rigidities associated with segmentation of labor markets; longer-run group differences in human capital investments, including occupational choice and mobility; and long-run group differences in values and culture that are transmitted primarily through the family socializing network. Parallel but not quite coincident to these three sources of group wage differences are the levels of decompositions examined in this book, which frequently start with wage differences, given occupation and education, then move to occupation differences given education, and then, implicitly, to education differences. These distinctions may help to interpret several empirical regularities uncovered in this book. Wage differences between men and women are smaller in Kenya than in the United States, and probably smaller than in India, Brazil, or Nicaragua, given occupation and education. This corresponds to the greater involvement of women in market production relative to men in sub- Saharan Africa (i.e., Kenya and Tanzania) compared with other developing countries. This is confirmed in a study of the Ivory Coast (Vijverberg and Van der Gaag 1987), which also deals explicitly with the sample selection process that determines who is observed to be a wage earner. In contrast, minority/majority race differences in income and wages, which have long been viewed as a worrisome feature of the U.S. labor market, appear 18 Labor Market Discrimination:Measurement and Interpretation to be equally large in some low-income countries, such as India by caste, but to favor some minority racial groups who have immigrated into a low-income society bringing with them special skills, for example, the Asians in East Africa (i.e., Kenya and Tanzania). Public sectors in both India and Kenya exercise the political power of the majority groups and have a tendency, therefore, to narrow these caste-income or race-income differences observed in the more freely functioning private sectors of these same economies. Analyses of other multiracial societies where racial minorities are relatively better educated also confirm the capacity of the public sector to "discriminate" in favor of the majority, such as in Malaysia (DeTray 1987; Smith forthcoming). Ideally, policy interventions to deal with discrimination are both efficient and equitable. A goal of public policy is to achieve an increase in output, while at the same time to narrow differences in the productive opportunities of similar groups of workers. Among the three potential levels of discrimination noted above -current labor market wages, human capital investments, and familial values equalization of opportunities at the first two levels appears generally advantageous. But there is a weaker presumption that equal opportunity justifies homogenization at the level of familial values, and undoubtedly such values will impact on which children families decide to invest in. This search for public policies that both advance the efficiency of the aggregate economy and promote a more equitable personal distribution of the rewards of the economy challenges the ingenuity not only of economists, but also that of moral philosophers and social engineers. More often, I suspect, public policy will represent a compro- mise between short-run costs or inefficiencies due to govemmental interventions designed to achieve benefits in terms of a more equal distribution of personal economic opportunities in the long-run.

Ec rnings and wage functions

Tabulations of individual earnings by educational attainment and age have frequently been used to approximate synthetically the earnings profile experienced by a birth cohort over its lifetime (Becker 1 964a). Conceptual refinements and empirically supported simplifications allowed Mincer (1974) to summarize unwieldy U.S. tabulations by a four parameter semi log-linear regression. The logarithm of the individual's wage (or earnings) is regressed on the level (years) of education and a quadratic in years of post-schooling general employment experience of the individual. Although theoretical derivation of this specification of the earnings function depends on several controversial working assumptions, this simple earnings function accounts for between a quarter and a half of the variance in log earnings in dozens, if not hundreds, of surveys and censuses around the world. Rarely is a theoretically founded empirical regularity replicated so widely in the social sciences (Schultz 1988). Labor Market Discrimination: Measurement and Interpretation 19

Nonetheless, this earnings function that is a building block in virtually all the studies in this book may be particularly hard to interpret in a casual sense, if additional variables are used to explain earnings that are themselves selected by the worker or the employer. In this case, the regressor is not exogenous and the estimated earnings relationship is subject to simultaneous equation bias (Griliches 1977). For example, the relationship between wages and tenure on the current job or time since migration presents such a problem of interpretation, as does occupation. Even the most simple wage function can be viewed alternatively as a structural relationship representing an equilibrium between individual human capital investments and the market returns on those investments (Mincer 1974), or as a reduced-form hedonic wage equation in which market imperfections and unanticipated developments also introduce substantial disequilibrium rents in the implicit valuation of different types of inelastically supplied labor (Rosen 1977). Yet, estimation of such earnings functions, despite some ambiguity, has become a powerful heuristic device, because of the growing stock of labor force and household surveys with income variables and the straightforward nature of their calculation. These parallel studies of earnings functions provide a growing basis for rich comparative studies across countries at different income levels and with different institutional arrangements. Increasingly, general descriptions are likely to emerge in the next decade in terms of the apparent mechanisms translating the endowments of a population into individual economic opportunities, and how this process responds under different institutions to disequilibria associated with the changing location, composition, and technology of production. The distribution of these individual economic opportunities for workers is, of course, the predominant factor determining their economic welfare. Understanding this process better will presumably help us identify where public policies have prevented certain groups from achieving more equal economic opportunities in the labor market, or these studies could direct our attention to barriers erected by private groups that could be dismantled by public policies. This is the justification I see for this volume.

Male-female wage comparisons

If all men and women work the same time for wages, wage functions could be calculated for each sex separately and readily compared. An efficient allocation of investment resources to the schooling of women and men would then tend to equalize these sex-specific market returns, to the extent that returns to schooling in nonmarket production within households were of a similar magnitude for women and men. But as already stressed, women generally participate less often, and when they do participate, they may work fewer hours in the market labor 20 Labor Market Discrimination: Measurement and Interpretation force than men (Layard and Mincer 1985; Durand 1975). Thus, the potential for a bias in the samples observed of men and women who earn wages would appear more obvious in the case of computing returns to women's education than in the case of men. Comparisons of wage rates across education groups by gender are, therefore, potentially distorted. But the nature of this bias, if it is substantial, remains to be empirically documented in the various cultural and economic regions of the world. Traditional labor market institutions may segregate different groups of workers by industry, occupation, or activity. Though such arrangements may have had at one time relatively little effect on the efficient allocation of labor, they may become increasingly inefficient as economic opportunities for individuals rapidly change with modem economic growth. Women in particular may be caught in family enterprises and household production activities that are progressively displaced by firns. The share of the labor force employed by firms increases with development, because firms appear to be better designed than families to exploit new technological production possibilities, some of which are realized from large scale. Until women can acquire the requisite schooling and transferable skills to find suitable employment in firms in expanding sectors of the modem economy, the opportunity value of women's time relative to men's time is likely to decline. Fragmentary historical and anthropological evidence supports the view that early industrialization reduced the relative value of women's contribution to the household. Evidence presented in this volume could be interpreted as supporting this historical generalization, for it suggests possibly less of a market wage disadvantage for women in low-income societies than in the contemporary United States (Schultz 1988).

Occupational-wage comparisons

For a variety of reasons, earnings functions are sometimes calculated within subpopulations. If these subpopulations are defined by exogenously given characteristics of workers, such as race, caste, or sex, interpretation is relatively straightforward, though as noted earlier, differential participation in the labor force may remain a source of bias in intergroup comparisons. But when the subpopulations are not closed, as in the case of regions, because interregional migration is substantial, or in the case of occupations, because unobserved abilities and education may be an important qualification for entry into an occupation, a complex problem of selection bias may be present that is difficult to deal with empirically. In an extreme instance, Eckaus (1973) calculated for the United States returns to education within a large number of narrow occupations, and drew the conclusion that educational returns (within occupations) were lower than other aggregated studies would have led us to expect. With hindsight, it is obvious that what Eckaus had shown was that much of the returns to education accrue through the changes in occupation that education facilitates. Labor Market Discrimination: Measurement and Interpretation 21

A similar problem arises in the studies by Wolpin (1977) and Riley (1976) that sought to determine whether the wage-education relationship differs across subsamples of occupations, for which schooling might be used to varying degrees as a screening device. Their evidence bearing on the screening hypothesis is ambiguous because of the possible self-selection of different types of persons into different occupations. A selection correction term (see Heckman 1979) is needed for occupational choices in occupation-specific earnings equations (for example, Hill 1987). A method proposed by Hay (1984) permits one to estimate the returns to each specialized vocational path for a representative individual, rather than the biased-by-selection returns for those who actually opted for a particular specialization. Vijverberg (1986) has also developed a method for analyzing jointly the occupational choice and wage equation, conditional on being a wage earner. Further empirical study of occupational choice and earnings will be needed before it can be confidently concluded how to interpret sex and race differences in income within different occupations. It is still tempting as a starting point to decompose the effects of such exogenous traits as race or gender on earnings via occupations and wages, and thereby appraise what portion of the group effect on wages is associated with an occupational sorting and what portion occurs within occupations because current wages differ between groups (Polachek 1979). Unfortunately, unobserved variables that might affect occupation and earnings could overlap, in which case this form of decomposition is not readily identified. In other words, a factor must be known that influences occupation but does not influence earnings, and that can thus be used to explain the sorting of people into occupations but can be justifiably omitted from earnings equations within occupations. The studies reported in this volume assume that occupational choice and earnings are stochastically independent or block recursive. How well these single-equation estimation methods approximate reality is not known. Possibly the most important occupational distinction is between wage and salary earners, on the one hand, and self-employed workers, on the other hand. Most research on the determinants of earnings focuses exclusively on the former class of employees, because labor earnings are more directly observed for them without first deducting the hard-to-reckon value of non-labor inputs from gross entrepreneurial income. When wage earners are a large fraction of the labor force, as in high-income countries, omitting the self-employed has become a standard, if indefensible, practice in empirical studies of the labor market. When employees are a small but growing fraction of the labor force, there is reason to suspect that the synthetic age-wage profile across education, sex, and race groups of employees may not be a satisfactory basis for estimating lifetime earnings for a representative individual in the overall economy. Yet, there are relatively few studies that analyze how selection into the employee subsample actually biases group comparisons of wages (for example, Anderson 1982; Griffin 1985; Van der Gaag and Vijverberg 1987; Hill 1987). 22 Labor Market Discrimination: Measurement and Interpretation

3. Methodologicalissues

Discrimination may be thought of in economic terms as differences in economic opportunities between groups that cannot be accounted for in terms of the skills and productive endowments of these groups. Explaining the existence and persistence of discrimination is a challenge that modem economics has struggled to shed light on, but without unqualified success. How is it that comparably endowed people working in the same market are rewarded differently? In a "spot" or short-run competitive labor market, people should be paid for what they produce, and firms are motivated to hire more labor until wages equal labor's marginal product. Regardless of how it comes about, discrimination, as defined above reduces the product of society by pricing the same commodity - labor - differently, and changes the personal distribution of that social product, often from less to more-powerful groups in a society (Arrow 1973). To get at the origins and dimensions of discrimination, let me recapitulate the difficult conceptual and measurement problems that qualify the conclusions drawn from statistical analyses. First, the marginal product of a worker is typically not observed by the researcher, except by structural statistical estimation for self-employed. Only wages are observed.' Many intertemporal incentive schemes can be postulated that would introduce a systematic divergence between wages and labor's current marginal product to motivate longer-term employment- investment behavior - such as deterring turnover and reducing shirking among employees and financing investments in firm-specific job training - all of which can be construed as potentially enhancing the efficiency of long-term contracts between firms and workers. Second, enumeration of the productive attributes of workers is inevitably incomplete and imperfect, and often even the recognized productive attributes of workers are unobserved by the researcher or are measured with much error. The limits of model specification and variable measurement are obvious in the explanation of worker wages. Differences in measured productivity between groups of workers that cannot be entirely attributed to quantified sources could arise from a variety of mechanisms that are in practice nearly indistinguishable. Third, as proposed earlier, the cascading effects of discrimination may operate in an accumulating (or less frequently, in an offsetting) manner over a lifetime, through access to and willingness to invest in human capital, first by parents in the home, then through school achievements, and finally by job-related productive experiences. At what level does one stop pursuing the origins of discrimination or call it something else? Characteristics of a worker, such as schooling, may first be taken as given in the determination of current wages, while schooling is also usefully approached as a choice variable confronting parents and youth. The longer-run issue of discrimination may therefore turn on why human capital investments differ between groups. Labor Market Discrimination: Measurement and Interpretation 23

Fourth, those who obtain a specified level of schooling or decide to enter the market labor force are to some degree self-selected for underlying reasons that may be related to the shadow value of wages they could earn in the labor market or in the home (Willis and Rosen 1979). Underlying sources of unobserved heterogeneity in the population may filter through such schemes of selection and be modified by families, schools, and labor markets in different ways for various groups, and thereby alter the final interpretation of measured differences in wages between groups. Fifth, the value of a person's time in home (nonmarket) activities has much to do with their anticipated and actual participation in the market labor force, and the permanence of their attachment to the market labor force. The family's endowments and economic environment may fundamentally influence some of these and other choices, such as fertility for women; these choices may, in turn, contribute to observed group wage differences, without signifying discrimination in the more narrow context of the current matching of wages to the productivity of workers. Intrahousehold allocations of human capital investment among household members may be critical for employing the future adult populations more effectively as economic development changes technological opportunities for home and market production. In this sphere, families may lag behind signals of change in the labor markets.

4. Specific studies in this volume

Most of the papers in this book analyze earnings of women and men in which distinct traditional factors, often play a relatively large and difficult-to-quantify role from society to society. The Banerjee and Knight study (chapter 8) considers caste income differences, in which different lifetime labor force participation and home production patterns probably play a relatively minor role. This paper may, therefore, suggest a pattern for further studies of ethnic, race, and caste differences, although analogous issues arise in many of the other papers. First, it is useful to know what the analysis seeks to measure. How is one to interpret the relationship reported, and how are the data for the study obtained? Does the information analyzed in this paper on lower and higher caste migrants to New Delhi, India, provide a representative sample of these groups in New Delhi, in urban India, or in all of India, or does the selection of only migrants yield a sample that is atypical in terms of earnings. within the caste groups throughout some larger population. This latter possibility needs to be considered, because migrants tend to be selected on the basis of their ability to receive higher earnings at destination relative to their prospective earnings at origin. Extreme impoverishment at origin may also deter outmigration, where the poor cannot finance the costs of outmigration. Thus, migration streams from quite different strata of an upper or lower caste might reflect different selection mechanisms. 24 Labor Market Discrimination: Measurement and Interpretation

The migrant members of the castes could have originated from predominantly different cultural regions, such that they might have spoken different languages, or come from predominantly urban and rural regions with qualitatively different educational opportunities. Unexplained differences in earnings between castes are attributed by Banerjee and Knight to discrimination in the labor market. Several variables they use to proxy labor productivity are held statistically constant, and the unexplained residual is their measure of group differences. One might argue that this is an upper-bound estimate of current labor market (wage) discrimination. The wage discrimination exercise is then elaborated in this study of New Delhi migrants to include also a level of occupational or job discrimination. We know, of course, that hereditary castes in India were at one time limited to particular occupations or trades. Although these occupational lines may be more strictly adhered to in rural than in urban areas of India, an intergenerational transmission of occupational skills within castes migrating to New Delhi is anticipated. Some of the discriminatory privileges of caste may thus survive intact in urban areas. Looking for caste differences in occupational composition of migrants, therefore, is to look for what must be a mixture of (customary) discrimination in the current labor market and the effects of the rural Hindu value system on the past accumulation of labor market trades and skills across castes. Documented occupational differences among these castes are not a surprise. As already noted, it is difficult to justify statistically methods to decompose group differences in earnings into those that arise from occupational segregation and those that can be traced to wage discrimination within occupations. Does the "occupational segregation" occur because individuals in different caste groups make different choices regarding the trades they think they are best suited to follow, or do employers and social institutions hire differentially from the caste groups, either for reasons of presumed worker economic productivity or because of "noneconomic" reasons? The differential allocation of jobs for reasons not associated with productivity is commonly defined as discrimination, but is not readily separated from other explanations. The next problem is how to combine the occupational and wage differences, if the unobserved attributes of workers that enter into the occupational choice decision also enter to some degree into the wage setting process within occupations. In order to hold constant by single equation recursive methods the occupation of the worker when estimating wage equations, it is implicitly assumed that the errors in the occupational choice process are independent of the errors in the wage equations within occupations. Here is another potential case of selectivity bias, for we only observe an unrepresentative sample of persons in each occupation to analyze for group wage differences. Nonetheless, the procedure adopted in these studies is a useful first approximation. The caste group dummy variables are permitted to impact sequentially on the occupation Labor Market Discrimination: Measurement and Interpretation 25 and wage relationships. The algebraic summation of the caste effects on wages can then be simulated. In many societies, a major difference between male and female workers is the number of years of experience they have accumulated in on-the-job training. Wage comparisons among males can be based on Mincer's (1974) approximation of post-schooling experience, i.e., age minus school-leaving age, granted the assumption that all males are in the market labor force and accumulating relevant vocational experience after leaving school. This is a less appealing assumption where there is substantial open unemployment and when labor force participation rates are substantially less than one. If the lower rate of labor force participation by women is associated with greater entry and exit from the labor force than for men, particularly after marriage and childbearing, the measurement of actual lifetime accumulated labor market experience may be important in any evaluation of male-female current wage differences (Mincer 1979). Unfortunately, it is not straightforward to control for past labor force participation or experience, because it is itself an endogenous choice variable that might plausibly respond to the latent labor productivity of the individual. Unless labor market experience is instrumented (proxied) by exogenous variables, such as age and schooling in Mincer's (1974) original formulation, controlling for actual experience in a wage equation can introduce simultaneous equation bias itself, particularly across groups that participate differently in market work, such as men and women. The paper by Birdsall and Fox (chapter 6) on the income of Brazilian schoolteachers analyzes the incomes of men and women in one occupation. This is indeed an occupation that in many countries has been an important professional field open to women. The salient fact that Brazilian men earn roughly twice what women earn is an indication that among schoolteachers who are employed primarily in the public sector (87 percent), there remains substantial room for what appears to be discrimination. Yet with all the ambiguity inherent in efforts to decompose income differentials among groups of workers, Birdsall and Fox conclude that 74-78 percent of this overall income difference between males and females is explained by differences in their observed personal characteristics (education and age) and locational distribution (rural/urban and region). Moreover, their analysis suggests that relatively subtle differences in the training received by men and women may also be an important factor in this pay differential. Men generally pursue an academic secondary-school route toward college, while women are more likely to follow the vocational (normal) teacher training course of study. Certification from a normal school is sufficient to qualify for teaching in the primary schools, but rarely in the secondary schools, at least not in the wealthier regions and in the better urban schools districts of Brazil. Males thus have an advantage in competing for these better paying teaching jobs in the secondary schools. But, as the authors note, the attribution of the income gap between men and women to their different training does not preclude the role of "discrimination." Rather, their study draws our attention to 26 Labor Market Discrimination: Measurement and Interpretation an earlier stage in the stratification process between male and female teachers. Self-selection of persons into teaching may also attract relatively more able women than men. The occupational selection bias may in this case understate the magnitude of the real discrimination within this occupation. On the other hand, I suggest it is more costly to attend a year of secondary academic school (colegio) than a year of teacher training school (normal) in Brazil. These differential costs may lie behind the enrollment figures reported in their table 6.5 that show male teachers five times more likely than female teachers to have completed secondary school in the academic stream (24 percent versus 4.6 percent) and female teachers four times as likely as male teachers to have completed secondary school in the teacher training stream (55 percent versus 15 percent). As a consequence, four times as many men as women teachers extend their education and complete a university course of study (28 percent versus 6.7 percent). These different educational distributions for men and women could arise from discrimination, costs and benefit differences by sex, or preferences of individuals related to their life-cycle anticipated roles in home and market production. The analysis of Birdsall and Fox has taken us several steps closer to the source of the male-female income difference among Brazilian teachers. Further analysis of the process determining training choices would now be in order to ascertain whether these outcomes occur because women choose to invest in lower-cost educational options with lower earnings trajectories, or because institutions restrict the entry of women into academic university training programs. The analysis of Birdsall and Fox extends our knowledge of the sources of inequality, but it should come as no surprise that they cannot allocate unambiguously the wage gap or the underlying training gap to a particular market failure or individual cause. One may also wonder whether part of the story may not involve the response of parents in investing in the training of their children, based on their understanding of the opportunities their offspring will confront as adults. When are these parent expectations for their children likely to be inaccurate? Are they likely to be inaccurate? Are these parent investment decisions particularly inefficient in today's labor market, when sex roles are changing across generations? If this form of gender discrimination within the family is to be modified, the starting point for analysis should be the family of origin, not the individual worker in the labor force. If there is social agreement that the training of labor is being inefficiently undertaken by the family, because of the manner in which parents allocate resources between boys and girls, the design of an effective social intervention will probably require a more precise understanding than we now have of the determinants of intrahousehold resource allocations (for example, Rosenzweig and Schultz 1982). Dealing only with the final stage of wage setting, or even occupational stratification, could produce policies that are ineffective and even counter-productive. Labor Market Discrimination: Measurement and Interpretation 27

The paper by Birdsall and Behrman (chapter 7) broadens the analysis of earnings to all of urban Brazil in 1970, while retaining the focus on male-female differences in earnings. The sample of men and women who report earnings need not be representative of the earnings opportunities open to the entire urban population. The primary reason is that only about half of the adult women are included, since only this proportion of Brazilian women work in the market labor force and report earnings to the census. Thus, a sample selection correction is required to compensate for this potential source of bias in analyzing female earnings, and the authors accordingly follow the scheme proposed by Heckman (1979). Rather than only measure the likelihood of reporting earnings, they distinguish between two sectors of employment for men and three for women. They estimate independently five probit equations accounting for whether or not men work in a formal or an informal sector job (and presumably report earnings) and whether or not women work in a formal, informal, or domestic job. Their sample includes more males (2,738) than females (1,572), which is puzzling since the number of adult females exceeds that of males in urban Brazil.2 The critical identifying exclusionary restriction that drives their selection correction procedure is their choice of variables that are included in the participation equations but are excluded from the earnings function. These restrictions should help us interpret their findings. Birdsall and Behrman identify their selection correction terms (1) on the basis of eight variables: (1) spouse income, (2) other household income, and dummy variables denoting that the individual is a (3) head of household, (4) spouse, (5) child of coresident with parent, (6) has a child under age six, and two interactions between variables of (7) other household income and child (i.e., variable (2) times variable 5), and (8) has child under age six and number of dependents over age 14. Only a few of these variables appear important in explaining participation by sector (table 7.6). Spouse income is associated with moving from informal and formal sector participation and decreased female participation. A family labor supply model (Smith 1980) would not admit such an earnings variable on the right hand side, however, since it contains the jointly determined hours-of-work decision of the spouse. Being a child in the parents' household increases a woman's likelihood of working in the formal and informal sectors, while, not surprisingly, it is inversely related to being a domestic servant, since one cannot be counted as a domestic servant to one's parents. Being a household head is also associated with being in the informal or formal sector, and seemingly incompatible with being a domestic servant. Similarly, being a spouse increases formal market participation and precludes employment as a domestic servant. Finally, having a child under age six reduces the likelihood a woman will be employed as a domestic servant. Many of these relationships are either definitional (married women with young children are rarely hired as domestic servants) or jointly determined with the labor force participation decision in a family labor supply model (Smith 1980). 28 Labor Market Discrimination: Measurement and Interpretation

Other choices of identifying restrictions for the Heckman selection correction might alter one's confidence in the meaning to attach to the lambda terms in the eamings equations (table 7.7). Why should men who are more likely to work in the formal sector receive markedly lower earnings in the formal sector? Predicting domestic servants from their housing and marital status could explain the tendency for women who are especially likely to be domestics and to receive lower (cash) wages. Most of the differences in earnings between men and women in urban Brazil are not explained in this exercise. In other words, the earnings gap resides in the differences between the constant terms summarized in table 8. It is interesting that the distribution of men and women between formal and informal sector employment is a relatively unimportant source of the earnings gap between men and women, compared with the more or less constant within- sector group differences in eamings. The Behrman and Wolfe study (chapter 5) should be read in the context of the authors' other papers based on their 1977 Nicaraguan Survey, in order to obtain a fuller view of these data, variable definitions, and their integrated analytical framework. Their objective in this paper is to estimate earnings functions for men and women that are not biased by unavoidable sample selection. The selection rule for inclusion in the sample is that the individual participates in the labor market. There is also the further selection of only persons who report labor earnings. There is still another selection mechanism underlying these data, for the authors include only accompanied men and women, which leads to a curious predominance of women in the sample, i.e., 60 percent according to table 5.1. How to treat missing variables in microeconometrics is an unresolved problem; to treat them as a potential source of selection bias is appealing in its generality. But this corrective strategy for dealing with bias due to missing data is most appealing when the structure of the selection decision rule is understood, and variables are observed that enter the decision rule but do not belong directly in the earnings equation. In the case of Nicaragua, reporting earnings is particularly difficult for the self-employed and for the farmer, who must exclude from income the value of intermediate inputs and the rental value of owned land and productive assets. Only 68 percent of all labor force participants in rural areas report earnings, while 91 percent report earnings in the central metropolis (table 5.1). The decision to report earnings may thus be largely a reflection of the individual's occupation or sector of employment. The variables that Behrman and Wolfe specify to identify the earning reporting selection process are (1) the participant's other (nonearned) income; (2) own-farm other income; and (3) the female respondent's participation in the formal or informal sector. As might have been anticipated, farmers report their earnings less often and formal sector respondents report earnings more often, particularly in the central metropolis of Managua (table 5.4). In the Brazil study, these occupational choices were explained in order to evaluate discrimination Labor Market Discrimination: Measurement and Interpretation 29

between men and women. Here, they are used as identifying variables to explain the propensity to report eamings. As in many corrections for selection found in the economics literature, there is a tendency toward definitional circularity, which can erode our confidence in the resulting estimates. It is possible that procedures to correct worrisome forms of sample selection bias, if not guided by economic theory or clear modeling logic, could add their own bias, particularly if the identifying variables in the selection rule are themselves endogenous and hence correlated with the earnings outcome. Nonetheless, the perennial problem of missing data and nonresponse bias is important and must continue to be studied from many perspectives in the hopes that we can learn how to live with this limitation of microeconomic data. The studies in this volume illustrate the variety of forms of potential sample selection bias that are present in most comparisons of male-female wages. Only a few such features of the data can be dealt with at the same time. The challenge in this field is singling out the potential sources of bias that are likely to be important and resolvable with existing data. The paper by Knight and Sabot (chapter 3) extends to Africa the empirical evidence on male-female and racial income differences. They analyze data from a 1971 establishment-based survey of workers in the manufacturing sector of Tanzania. As with the New Delhi migrant survey, one may ask how might group differences in wages be influenced by the special character of the population that has managed to obtain jobs in this "elite" sector.3 Clearly, this is not a representative cross section of the population. I would expect that the disproportionate representation of men and non-Africans (i.e., Asians) in Tanzanian manufacturing jobs would parallel their above average educational attainment. Unfortunately, there are apparently no data to probe the selection process that allocates jobs across these four groups of workers. Rather, the study is restricted to an examination of group wage differences within manufacturing as they are partially explained by worker characteristics and occupational categories. A notable finding from the Tanzanian analysis is the lack of residual wage differences between men and women, and the tendency for non-Africans to receive higher wages than Africans, given their productivity-related characteristics. An important factor for explaining the gross 37-percent differential between the wages of men and women is the shorter length of employment experience of women and their slightly lower educational attainment. It is the lack of comparable information on actual employment experience for women in the studies of Nicaragua and Brazil that suggests some part of the estimated unexplained wage differences by sex in these latter countries is not directly due to wage discrimination. But as noted earlier, prior labor force participation is also not strictly speaking exogenous, and holding constant this endogenous choice variable may conversely understate life cycle discrimination against women. Nonetheless, the racial minority of Asians in Tanzania appears "advantaged" in the labor market, while gender differences in wages are found to be insignificant. 30 Labor Market Discrimination: Measurement and Interpretation

The allied paper by Armitage and Sabot (chapter 4) adds a time and regional dimension to this important East African study. They contrast the same sex and race differences in wages in Kenya and in Tanzania in 1980 based on a broader sample of urban wage workers. A comparison is provided within Tanzania, therefore, on how these wage patterns evolved from 1971 to 1980. Another novel feature of the 1980 data is that the survey is matched to test scores for the workers, which allows the researchers to control in the wage function for standardized school achievement, in addition to the number of years completed. The refinement in human capital variables reconfirms the finding of the prior Tanzanian study that unexplained wage differences between men and women are small in East African countries. Another important allied paper tests the screening versus human capital hypothesis with these same data (Boissiere, Knight and Sabot 1985). The parallels and contrasts between Tanzania and Kenya are the basis for another paper by Armitage and Sabot (not included in this book) on educational expansion and discrimination. The radically different development policies pursued by these two initially combined countries suggests one can learn much about how varied educational expansion policies reverberate throughout the society and economy of a low-income country. The implications for discrimination in the labor market are explored here, while other papers by the authors assess their consequences for aggregate economic growth, income distribution, and intergenerational mobility (Annitage and Sabot 1986). Several theories involving selection could potentially account for the unusual group wage differences noted in Tanzania in 1971. Since relatively few women obtain education in this society, and still fewer obtain urban wage jobs, the exceptional females that obtain these jobs may be particularly able and highly motivated, and hence command wages on a par with their male counterparts despite discrimination. As the educational process is opened up to women on more equal terms, a growing share of urban wage jobs will probably go to women. As the impact of this selection process for urban jobs diminishes, one might expect the male-female wage differentials to increase, as the underlying heterogeneity between the groups in unobserved abilities diminishes over time. An analogous selection mechanism might account for part of the wage differential observed between African and Asian workers and, in particular, the tendency for this differential to diminish for the most skilled job categories. At higher occupational levels, there were relatively few African workers in 1971, and it might be expected that those who gained these top positions despite educational disadvantages would be exceptionally talented; this is reflected in the diminished wage gap between Africans and Asians in the high-skill occupations. In this case, changes over time may close the gap between educational advantages enjoyed by the Asians relative to Africans in East Africa. The Armitage and Sabot paper indicates that in the public sector (parastatal) manufacturing firms, which were politically encouraged to hire Africans, Asians no longer received a wage premium in 1980, whereas in the private sector, the Labor Market Discrimination: Measurement and Interpretation 31

same premium for Asian workers prevailed in 1980 as in 1971. The "free" market for workers in the private sector signals that the special (unobserved) skills acquired by the Asian community remain productive. Reflecting the political preferences of the majority, "reverse" discrimination by the public sector has the expected effect of narrowing this wage advantage of Asians. One might expect there to develop over time a response in terms of systematic sorting of Asians (and Africans) between the "rationed" public and unregulated private sectors. More highly motivated and talented Asians would, other things being equal, opt for jobs in the private sector, where their advancement opportunities are more attractive, and conversely for the more talented African. Thus, compensating variation leads to the expectation that one intervention in the labor market in the form of reverse discrimination in a covered (for example, public) sector is likely to lead to a more subtle selection process of workers between sectors.

5. Directionsfor future research:data improvementand analysis

This collection of studies uses earnings functions to evaluate the role of group membership to explain the level of individual wages and earnings in developing countries. The approach shows considerable promise, but also poses stubborn challenges if we are to rely on this literature to understand the roots of discrimination in the labor market and to design policy interventions. Certain variables must be available to the researcher, and the care assigned to the definition of these variables and their uniform reporting can greatly strengthen the quality of the evidence accumulated in such investigations. The core variables required are labor earnings per unit time worked; education measured in suitable units of relatively homogenous quality; work and training experience accumulated after leaving school; biological age; gender; and racial or ethnic group identifiers. Whenever it is practical, the survey respondent should report his or her own hourly wage, rather than have the researcher divide reported "eamings" by the responses to questions on "hours and weeks worked." The construction of an hourly wage by the researcher from reported eamings and hours introduces measurement errors of a systematic form. More serious are the problems that arise if an endogenous variable, such as hours worked, is treated as an exogenous explanatory variable in the earnings function (Schultz 1980). In this case, labor supply and labor productivity relationships cannot be disentangled without imposing more structure on the modeling enterprise (Schultz 1969). Labor force participation is not an easy concept to define. It requires particular care in analyzing the behavior of the young and old, and may be especially ambiguous in the rural sector of low-income countries, where the distinctions between home production for home consumption, part-time production for the market, and unpaid family work are hard to draw. Minor restatements of the definition of the labor force from one census to the next, or 32 Labor Market Discrimination: Measurement and Interpretation changes in the training of census enumerators can elicit large shifts in reported labor force participation, most frequently for rural women (Deere 1983). Thus, economic modeling of the participation decision by women may eventually help to bridge the analytical gap between the inevitable limitations in data sources as we evolve a clearer rationale for labor force definitions and survey methodolo- gies. It should prove possible in the future to adopt a standard specification of the selection rule that determines who is likely to be in the wage labor force based on family characteristics that need not enter as determinants in the woman's market wage rate, such as her husband's wage or nonhuman capital. With this structurally identified correction procedure as an aid in estimating consistent wage functions for women (who are not uniformly enumerated as reporting wages), it will be possible to more confidently compare earnings opportunities for women over time and across countries. But the specification of these selection rules, whether they are predicting labor force participation or employment in particular occupations and types of jobs that report a wage, should be based on restrictions suggested by the economic theory of household and individual behavior and not be determined by the local availability of data or idiosyncrasies of the researcher. The distinction between the formal and informal sectors of the labor market may be another concept in development economics that could be used in studies of discrimination and public policy. But at this time, the formal/informal distinction is not dictated by a consistent and workable theoretical framework. Consequently, the distinction means many things to many people, and there is no consensus on how to measure and exploit the concept. Until it is defined in an analytical framework that implies empirically falsifiable propositions, measures of these sectors will continue to vacillate from study to study. The studies in this volume are a first step toward measuring earnings differences associated with gender, race, and ethnic groups in low-income countries. Further progress in analyzing how eamings differ because of occupation and type of employment will require improved data. These improvements in data collection must be increasingly guided by a more unified framework for interpreting the interdependent labor market behavior of family members.

Notes

1. It is possible to estimate productionfunctions in which labor of differentgroups representdistinct inputs. The ratio of marginalproducts of any two labor inputs can then be calculated from the estimates of the production function and factor payments (marginal products) simulated in response to changes in factor supplies. For example, the recent stability of the wage of women to men in the United States can be compared with the predicted change in female-to-male wages attributable to the relative growth in female-to- male labor supply. This approach has not been undertaken widely because it calls for firm-level disaggregated input and output data and associated regional variation in input Labor Market Discrimination: Measurement and Interpretation 33 prices to explain why firms vary their input mix. Taylor (1982) accomplished this task for U.S. manufacturing by merging across industry and state units the labor force data from the Current Population Survey (CPS) on the demographic and educational composition of workers with data on capital and values added from the Annual Survey of Manufacturers. Translog production function estimates were thus obtained for labor disaggregated by sex, education, and age, and marginal products compared with actual group wage differences over time in the CPS. 2. The proportion of women aged 15-64 in urban areas of Brazil was 52.4 percent in 1980. UN Demographic Yearbook Historical Supplement. 1969. New York. Table 3, page 320. 3. According to the 1967 Census of Tanganyika and Zanzibar, about 1 percent of the Tanzania labor force was employed as wage or salary earners in manufacturing, and only 5.3 percent of these are women. Yearbook of Labor Statistics. 1977. International Labour Office, Geneva. Table 2A, page 58.

Labor Market Discrimination and Economic Development

Orley Ashenfelter and Ronald L. Oaxaca

1. Introduction

Traditionally, empirical research on labor market discrimination has been largely confined to advanced industrialized societies. This emphasis undoubtedly reflects the availability of micro data sets in the developed countries. With the advent of socioeconomic surveys in developing countries, however, it has become possible to generate data sets comparable to those normally associated with data collection efforts in the industrialized nations. As the papers in this volume amply demonstrate, these data sets can be used with traditional techniques to measure the extent of race, ethnic, and gender discrimination in the wage economies of developing countries. A number of interesting questions can be raised about the relationship between the process of economic development and labor market discrimination. Is the current degree of labor market discrimination found in mature economies an equilibrium that developing economies are moving toward? Is there any association between the public sector/private sector employment mix and the amount and kind of labor market discrimination one should expect to find? Are the traditional theories of labor market discrimination and conventional measurement techniques wholly transferable to the context of a developing economy? Partial answers to these questions can be found among the studies represented in this volume. In this paper, we seek to shed light on the relationship between the process of economic development and labor market discrimination by pulling together results that are common to studies of labor market discrimination in both developed and developing economies, as well as to contrast differences that exist within the community of developing nations and between developing nations and the United States.

35 36 Labor Market Discrimination and Economic Development

The paper is organized as follows: section 2 is a review and commentary on the traditional theories of discrimination as they might apply to the studies published in this volume. Section 3 is an overview of the available statistical evidence on earnings discrimination in the labor markets of the developing nations discussed in this volume compared with evidence from U.S. labor markets. Section 4 is a summary and conclusion.

2. Theories of labor market discrimination

An obvious task, which is logically undertaken prior to measurement or explanation of labor market discrimination, is defining the phenomenon. Discrimination must involve judgments on the demand side of the market about individuals identified by group characteristics, for example, gender, race, ethnicity, that are not related to the true productivity characteristics of the individual worker. Therefore, among workers of identical preferences and supply- side attributes, discrimination is manifested in the form of wage differentials that are solely associated with group identification. Such discrimination can produce economic inefficiency, or be the result of economic inefficiency that stems from a noncompetitive labor market. Discrimination based on tastes (utility maximization) need not even produce economic inefficiency, since the costs of discrimination to the discriminator are consumption costs. These views of discrimination are as applicable to a developing economy as to a mature economy. One of the earliest theoretical treatments of gender discrimination in labor markets was by Robinson (1965, first published in 1933). Robinson considers a monopsonistic labor market in which male workers and female workers are perfect substitutes in production. If the wage elasticity of labor supply is less for women than for men, a profit-maximizing monopsonistic employer would pay lower wages to women. This wage differential is a manifestation of the inefficiency of a noncompetitive labor market rather than the cause of the inefficiency. Studies of labor market discrimination in the United States do not often appeal to the monopsony argument to account for sex discrimination in earnings. The empirical evidence on labor supply in the United States indicates that the wage elasticity of labor supply for women is well in excess of that for men (see Killingsworth 1983). This, coupled with the fact that the economy-wide labor market cannot be characterized as monopsonistic, would suggest that the Robinson model is not applicable to a developed economy such as that of the United States. However, this conclusion may be premature because of the fact that constrained occupational choices of women may produce relatively inelastic female labor supply to different occupations and industries. In this event, there could be an element of monopsony power due to reduced market alternatives to women.1 Labor Market Discrimination and Economic Development 37

None of the papers in the present volume explicitly appeals to the monopsony argument to explain gender or race differentials. There is really no direct evidence provided on labor supply elasticities and on the extent of monopsony power in the developing economy labor markets studied in this volume. However, there may be some circumstantial evidence to suggest that the monopsony argument may play a role after all. For example, the Behrman-Wolfe study of sex earnings differentials in Nicaragua (chapter 5) finds that as much as 70 percent of the gross earnings differential by sex stems from the difference in the estimated constant terms. If monopsony power were a factor, it would seem that it would be manifested in a pure differential independent of human capital characteristics. Unfortunately, corroborating evidence is unavailable. The extent of labor market competition in the Nicaraguan economy at the time of the study is not known. The probit equations for labor force participation that are reported in the Behrman-Wolfe paper are reduced form equations, so that inferences about male- female differences in labor supply elasticities cannot be.made. In the Birdsall- Behrman study of male/female earnings differentials in Brazil (chapter 7), sex differences in the estimated constant terms in the earnings regressions account for virtually all of the measured discrimination. The estimated probit functions for the simultaneous choice of labor force participation and choice of job sector are reduced form in nature and, therefore, do not provide evidence about labor supply elasticities. Thus, the monopsony argument remains only a potential explanation for the unexplained residual. Thurow's (1969) analysis of discrimination is also of the market power/market imperfection genre. It is argued that the economic power of the discriminators is so pervasive that monopoly gains accrue from the collective practice of discrimination. An interdependent process of discrimination from education to labor markets to product markets ensures that the incremental costs of discrimination at each stage are minimized. Discrimination in the amount and quality of education reduces the cost of labor market discrimination because of the inferior human capital that those discriminated against bring to the labor market. Their consequent low earnings reduce the retail costs of discrimination in the availability of goods and services in product markets. This argument was advanced by Thurow to explain the experience of blacks in the United States. In their study of earnings differentials by sex among Brazilian schoolteachers, Birdsall and Fox found that male schoolteachers have approximately a 14-percent advantage over their female counterparts in average years of education (chapter 6). This works in the direction of widening the earnings gap on pure productivity grounds. The Thurow model would have some applicability to this case, if it could be shown that there were impediments to the amount of schooling received by females. The authors speculate that the amount and patterns of formal education undertaken by female Brazilian teachers could be rational responses to anticipated labor market discrimination. If this is true, then educational 38 Labor Market Discrimination and Economic Development differences by sex are not pre-labor market discrimination of the Thurow variety, but rather a feedback component of direct labor market discrimination. Apparently, the educational advantage of male teachers over female teachers in Brazil does not carry over to the general urban labor force in Brazil. The Birdsall-Behrman study indicates that urban female labor force participants have, on average, an additional year of schooling over their male counterparts. Furthermore, for the urban Brazilian adult population, the average educational attainment of females is only slightly below that of the males. This difference is not statistically significant. The monopoly power argument is probably most suited to racial discrimination -its originally intended field of application. Knight and Sabot (chapter 3) and Armitage and Sabot (chapter 4) present evidence of Asians' substantial educational advantage over Africans in the East African nations of Tanzania and Kenya. This educational disparity is undoubtedly the legacy of the socioeconomic hierarchy inherited from the former European colonial administration. With the transfer of political power to the African majority, one might have expected the educational disparity to diminish over time. Interestingly, Armitage and Sabot show that at least among workers in Tanzanian manufacturing, the absolute disparity in educational attainment has remained constant over the decade 1970- 80, although the political power of the African majority has had some narrowing effect on the adjusted earnings gap between Asians and Africans. The Banerjee and Knight study of caste discrimination among migrants in Delhi, India (chapter 8) reveals a 59-percent educational advantage of non- scheduled castes over scheduled castes in terms of average years of education. Even when a worker's caste affiliation either cannot be determined or is not taken into account in a non-discriminatory employment situation, on average there will be an earnings penalty due to pre-labor market discrimination against scheduled castes in the form of unequal educational opportunities. Segmented labor market/dual labor market theories have been advanced as explanations for the different career paths of women and minorities in the United States compared with those of males. The dual labor market theory in Doeringer and Piore (1971) has its roots in the dual economy model of economic development.2 Under the dual labor market theory, entry level job assignments are made on the basis of a worker's sexual, racial, ethnic and religious characteristics, in addition to a worker's observable human capital characteristics. Those whose non-human capital characteristics are in disfavor by the dominant social group are relegated to jobs characterized by employment instability, low competitive wages, and the absence of a ladder for occupational advancement. Such jobs are said to be designed to minimize disruption of the production process from the unstable habits that these secondary workers are conditioned to exhibit. In contrast to this secondary labor market, jobs in the primary labor market are characterized by employment stability, high wages determined within the firrn's internal labor market, and a well-defined ladder for occupational Labor Market Discrimination and Economic Development 39 advancement. These jobs are filled by workers from the dominant group. There is little or no job mobility between the secondary and primary labor markets. Different occupational distributions between groups of workers distinguished by race, sex, or ethnicity could be a manifestation of the hypothesized dual labor market process. Such differences are also consistent with supply-side differences in occupational preferences. However, the latter interpretation is more difficult to accept in the case of racial or ethnic differences than it is in the case of sex differences.3 The more aggregate the occupational classification used, the more homogeneous the occupational distributions will appear. In the case of urban Brazilian workers, Birdsall and Behrman find that male- female differences in estimated probit functions for sectoral employment have only a negligible effect on sex earnings differences. Since the sectoral employment categories are a tripartite division of employment into formal, informal, and domestic sectors, there is still the possibility that earnings differentials within these broadly defined sectors reflect job discrimination of the segmented labor market variety. In their study of Brazilian schoolteachers, Birdsall and Fox find evidence of job discrimination in terms of a lower probability that a female with the same characteristics as a male would have a secondary-school job. Sharp differences in occupational distributions by race and sex within Tanzania's manufacturing sector are reported by Knight and Sabot. What is apparent by inspection can be formalized through a series of chi-square tests on the distributional differences in occupational attainment. In table 2.1, we have worked up occupational attainment distributions for men and women based on the data reported by Knight and Sabot. Columns 1 and 2 report the observed frequencies for their regression sample. Columns 3 and 4 report the expected frequencies if gender and occupational attainment are independent. The value of the test statistic for the chi-square contingency table test is calculated to be 58.89, which far exceeds the critical value of 11.34 at the 1-percent level of significance. Thus, we can easily reject the null hypothesis that the observed distributions are independent of gender. Knight and Sabot predict the occupational attainment of females by evaluating their characteristics with the male occupational attainment structure. The corresponding expected frequencies are listed in column 5 of table 2.1. These figures are still quite different from the observed frequencies in column 2. The calculated chi-square statistic for the difference in these two distributions is 52.91, which is well above the critical value 11.34. Accordingly, one can easily reject the null hypothesis that the observed occupational attainment of females is the same as that which would obtain if females faced the same occupational attainment function as males. These figures suggest that in the absence of job discrimination, the presence of females in Tanzanian manufacturing would decline in the white collar and unskilled occupations and increase in the skilled and semi-skilled occupations. An interesting observation is that the predicted female occupational attainment 40 Labor Market Discrimination and Economic Development

Table2.1 Occupationaldistribution in Tanzanianmanufacturing by sex

Observed frequencies Expected frequencies

Occupation Men Women Men' Women' Womenb

White collar 120 13 122 11 10

Skilled 249 3 213 21 19

Semi-skilled 265 15 257 23 26 Unskilled 169 41 193 17 17 Total 803 72 803 72 72

Note: This table is derived from tables 3.1 and 3.3 in Knight and Sabot (chapter 3). Critical value of the text statistic: X3 o = 11.34. Test statistic for independence between sex and occupational

attainment: %2 = 58.89. Text statistic for the difference between the observed occupational attainment of females and the predicted attainment based on the male occupational attainment structure:

X3= 52.91. Test statistic for the difference between the expected occupational attainment of females in the presence of independence between sex and occupational attainmnentand the expected occupational attainment based on the male structure: X3 = 0.66. a. These are expected frequencies of the X, contingency-table test for independence between sex and occupational attainment. b. These are the predicted frequencies when the estimated male occupational-attainment structure is evaluated at the experience levels for females. based on female characteristics and the male occupational attainment function (column 5) is virtually identical to the expected female attainment if occupational attainment and gender are independent (column 4). These latter figures do not even control for male-female differences in personal characteristics. Therefore, sex differences in personal characteristics are apparently not important sources of sex differences in occupational attainment. Indeed, a chi-square test of the differences between columns 4 and 5 yields a value of 0.66, well below the critical value of 11.34. As it turns out, the occupational differences associated with sex differences in occupational attainment functions do not have a very large impact on sex earnings differentials in Tanzanian manufacturing. Occupational attainment distributions for Africans and non-Africans in the Knight-Sabot study are presented in table 2.2. These data are analyzed in the same manner as gender differences in occupational attainment in Tanzania. It is clear from table 2.2 that 6ne can easily reject the hypothesis that race and Labor Market Discrimination and Economic Development 41

Table2.2 Occupationaldistribution in Tanzanianmanufacturing by race

Observed frequencies Expected frequencies

Occupation Non-African African Non-African' Africana African'

White Collar 48 99 13 134 172 Skilled 25 252 24 253 466 Semi-skilled 7 343 30 320 146 Unskilled 1 167 14 154 77 Total 81 861 81 861 861

Note: This table is derived from tables 3.4. 3.6 and 3.7 in Knight and Sabot (chapter 3). Critical value of the test statistic:X3 ,, = 11.34. Test statisticfor independencebetween race and occupational attainment: X3 = 135.87. Test statistic for the difference between the observed occupational attainmentof Africansand the predictedattainment based on the non-Africanoccupational attainment structure: X3 = 500.27. Test statistic for the difference between the expected occupational attainment of Africans in the presence of independence between race and occupational attainment and the expected occupational attainment based on the non-African structure: x2 = 390.12. a. These are expected frequencies of the X2 contingency-table test for independence between race and occupational attainment. b. These are the predicted frequencies when the estimated non-African occupational-attainment structure is evaluated at the educational levels for Africans. occupational attainment are independent for Tanzanian manufacturing workers. The chi-square test value of 135.87 far exceeds the critical value of 11.34 at the 1-percent level of significance. Also, one can readily reject the hypothesis that the observed occupational distribution of African workers is the same as the distribution they would attain if they faced the same occupational-determination structure as non-Africans. Unlike sex differences in occupational distribution, one can also reject the hypothesis that the African occupational distribution that would exist if race and occupational attainment were independent is the same as that predicted from the non-African occupational-determination structure. The chi-square statistic is 390.12, compared with a critical value of 11.34. Evidently, African/non-African differences in personal characteristics, in addition to different structures, are important determinants of racial differences in occupational attainment. 42 Labor Market Discrimination and Economic Development

Not necessarily inconsistent with the dual labor market/segmented labor market approaches is the theory of statistical discrimination discussed in Aigner and Cain (1977). Given that information is costly to employers, a cost- minimizing employment strategy may be to adopt gender, race, family background, etc. as indicators of productivity. Those who are believed (correctly or incorrectly) to be in low-productivity groups would be offered relatively low compensation, most likely through job assignments that would be manifested as racial or ethnic differences in occupational attainment. With occupational segregation there may be little opportunity for employers to revise their priors about specific individuals or about entire demographic groups. Statistical discrimination can arise when a productivity-proxy indicator is more variable for some groups and, hence, less reliable for these groups. Under such circumstances, statistical discrimination could arise even if the underlying true productivity distributions were the same for all groups. This line of reasoning could hold promise in interpreting the results from Armitage and Sabot (chapter 4) regarding labor market discrimination against workers from uneducated famnilies.As will be discussed below, Armitage and Sabot find that basing discrimination measures upon years of formal schooling completed misses the considerable variation in human capital endowments associated with different family educational backgrounds of workers. If family educational background is a more variable predictor of unmeasured human capital endowments/productivity for some groups than for others, then statistical discrimination could be a reasonable way to look at earnings differentials associated with family educational background. Probably the most venerable of the theories of discrimination is the tastes and preference approach developed in Becker (1957). This theory permits the joint assignment of responsibility for labor market discrimination to employers, workers, consumers, and government. With the exception of the case of worker discrimination among perfect substitutes in production (in which segregation rather than wage discrimination occurs), the Becker theory can explain the existence of wage differentials among groups of workers after controlling for productivity differences. The persistence of these wage differentials in the long run is commonly believed to be impossible under conditions of perfect competition. In many cases, however, urban markets in developing economies are not very competitive (see Knight and Sabot, chapter 3). Even if these markets were competitive, the impossibility of long-run discrimination in competitive markets rests upon the assumption of constant costs. With increasing costs, a long-run equilibrium market discrimination coefficient different from zero is possible. The relationship of labor market discrimination to industry cost structures in developing economies is not investigated in any of the papers of the present volume, but is perhaps a worthy topic for future research. Labor Market Discrimination and Economic Development 43

3. The evidencefor earningsdiscrimination The simplest statistical means for detecting earnings discrimination is to run an earnings (wage) regression in which a worker's race, sex, or ethnicity is represented by a dummy variable. If the estimated coefficient is statistically significant in the presence of nondiscriminatory wage-determining variables, then earnings discrimination is said to be present. Taken at face value, this procedure characterizes earnings discrimination as a pure premium (positive or negative) that is independent of a worker's other wage-determining characteristics. A more general procedure allows for earnings discrimination to vary with worker characteristics. This is accomplished by interaction of the dummy variable with some or all of the wage-determining variables in the model. It is the full interaction between the dummy variable and all of the variables in the model that underlies the methodology in Blinder (1973) and Oaxaca (1973a, 1973b). When separate wage structures are found to exist, discrimination is measured as a residual obtained through evaluation of one group's mean characteristics with the estimated wage parameters for the other group. This procedure leads to the familiar index-number problem associated with the choice of estimated wage structure used to evaluate differences in mean characteristics. Further refinements in the measurement of labor market discrimination are possible. Brown, Moon, and Zoloth (1980) developed a means for deternining the extent to which discrimination in occupational attainment is responsible for discrimination in earnings. This procedure involves evaluation of one group's mean determinants of occupational attainment with the estimated occupational attainment functions for another group. The end result is a decomposition of labor market discrimination into job discrimination and wage discrimination. Job discrimination is measured as that portion of the overall wage gap that is attributable to different occupational attainment structures (holding personal characteristics constant). Wage discrimination is measured as that portion of the overall wage gap that is attributable to different wage structures within occupations (holding constant occupational attainment). All of the studies in this volume use one or more of the above-discussed methods for determining the presence and extent of labor market discrimn..ation. We will begin below with estimates of sex discrimination in developing economies. Knight and Sabot (chapter 3) find that for the Tanzanian manufacturing sector in 1971, the gross earnings differential between males and females was 29 percent (in natural logs). It turns out that only 5-17 percent of this gross differential can be attributed to different wage structures when the male wage structure is assumed to be the norm.4 The male advantage in terms of work experience is the single most important factor in explaining the wage gap. Furthermore, job discrimination is shown to account for no more than 23 percent of the differential (six percentage points) and perhaps as little as 10 44 Labor Market Discrimination and Economic Development percent (three percentage points). More recent data in Armitage and Sabot (chapter 4) finds a substantial reduction in the male/female gross differential (in natural logs) in Tanzanian manufacturing over the period of a decade. The estimated log differential was about 13 percent in 1980. By contrast with the Tanzanian manufacturing sector, a standardized male wage advantage of 12 percent (in logs) is found in the wage sector as a whole in 1980. For the overall wage sector in Kenya in 1980, Armitage and Sabot find little evidence of gender discrimination. The estimated standardized differential is not statistically significant. Behtrmanand Wolfe (chapter 5) find quite sizable gross earnings differentials between males and females in Nicaragua in 1977-78. For the nation as a whole, the log differential is 85 percent. Experience differentials account for the single largest part of the gross differential attributable to differences in personal characteristics. Nevertheless, the unexplained component accounts for about 70 percent of the gross differential. Most of this, as noted above, is the result of differences in the estimated constant terms in the earnings regressions. This leaves as an upper limit for measured discrimination a magnitude more in line with that found in U.S. studies. The study by Birdsall and Fox (chapter 6) of eamings differentials by sex among Brazilian schoolteachers reports a sizable differential of 69 percent (in logs). Differences in personal characteristics and regional location are shown to account for anywhere from 74 to 89 percent of the gross earnings gap. Therefore, from 11 to 26 percent of the gap is attributed to discrimination, most of which is identified as wage discrimination. In their study of urban Brazilian workers, Birdsall and Behrman (chapter 7) reveal male/female gross earnings differentials (in logs) of 27 percent in the formal sector and 79 percent in the informal sector. Adoption of the estimated male earnings structure for evaluation of female personal characteristics at the mean is estimated to raise the average log earnings of females by about 31 percent. This sizable estimate of discrimination is almost entirely attributable to differences in the constant term in the earnings regressions. As was discussed above, adoption of the estimated parameters for the probit model of sectoral employment among males has only a negligible effect on the average earnings of females. Racial earnings differentials in developing economies turn out to be substantially larger than earnings differentials by sex. Knight and Sabot find the non-African/African earnings differentials among Tanzanian manufacturing workers to be 112 percent in logs (which implies a proportionate difference in mean wages of 209 percent). Using the dummy variable approach, Knight and Sabot estimate a standardized wage advantage to non-Africans of 87 percent. Allowance for separate wage structures still yields an estimate of residual discrimination of between 24 percent and 78 percent of the logarithmic earnings gap. Interestingly, discrimination in occupational attainment is estimated to Labor Market Discrimination and Economic Development 45 account for only about 6 to 11 percent of the earnings gap, so that wage discrimination is the major source of the discriminatory differential. Armitage and Sabot find that the gross racial earnings difference in logs declined in Tanzanian manufacturing to about 86 percent in 1980. Log earnings regressions run for 1971 and 1980 reveal that the coefficient on the dummy variable for non-Africans declined from .722 to .547, which implies a reduction in a pure wage advantage from 106 percent to 73 percent. For the Tanzanian wage sector as a whole, the standardized wage premium for non-Africans in 1980 was estimated to be about 85 percent, which is somewhat larger than in the manufacturing sector. Standardized tribal differentials were also found: 23 percent for members of the Chagga tribe and 13 percent for members of the Haya tribe. For the Kenyan wage sector as a whole, Armitage and Sabot find a standardized wage advantage to non-Africans of about 118 percent, which is considerably larger than in Tanzania. In their study of caste discrimination in India, Banerjee and Knight (chapter 8) find a modest (about 17 percent) gross earnings differential in logs between non-scheduled and schedule castes. The estimated wage penalty to workers from the scheduled castes is close to 8 percent, based on the dummy variable, standardized differential approach. Further investigation reveals separate wage structures for the two categories of caste affiliation. Wage-decomposition analysis suggests that discrimination accounts from between 35 percent and 54 percent of the earnings gap in logs. Further refinement of the discrimination measure to include occupational discrimination shows that caste differences in occupational attainment functions is responsible for only about 10 percent of the earnings gap. Wage discrimination within occupations accounts for about 54 percent of the gap. Thus, wage discrimination is far more important than job discrimination in explaining caste discrimination. Of course, the greater scope for non-revelation of caste affiliation compared with the identification of race or sex means that measured caste discrimination could underestimate discrimination due to tastes. On the other hand, caste status could also reflect unmeasured productivity traits (which themselves could be attributed to some form of pre-labor market discrimination). A limitation of the conventional methodology for estimating labor market discrimination is illustrated by Armitage and Sabot. The context is that of labor market discrimination against workers from uneducated families in Kenya and Tanzania. In 1980 the gross earnings differential between workers from educated families and those from uneducated families was a negligible 30 shillings/month in Tanzania and 239 shillings/month in Kenya. The conventional wage decomposition approach implied that as much as 84 percent of the wage gap in Kenya was due to labor market discrimination. However, the use of formal years of schooling completed by the worker is too crude a measure of human capital endowments. Armitage and Sabot show that there is more variation in human capital endowments within educational classes in Kenya because of its expanded 46 Labor Market Discrimination and Economic Development educational system. Ignoring this factor causes some of the wage gap attributable to different human capital endowments to be imputed to measured labor market discrimination. By taking account of differential performance on exam scores in Kenya, the corrected discrimination figure is reduced from accounting for 84 percent of the wage gap to 62 percent of the gap. There still remains the question of how accessible the information on the educational background of a worker's family is to those in the labor market who are inclined to discriminate. Among other things, different earnings specifications and sectors studied make comparisons among the developing economies and between the developing economies and the United States somewhat tenuous. Nevertheless, an attempt is made here to make such comparisons among some of the studies in this volume for which comparable information is available. These comparisons are presented in tables 2.3, 2.4, 2.5 and 2.6. For purposes of comparison with the U.S. labor market, findings from Oaxaca (1977a, 1977b) are also presented. Table 2.3 lists estimated gross earnings differentials (in logs) between males and females and the percentages of these differentials attributable to labor market discrimination. The smallest differentials are in Tanzanian manufacturing (29 percent) and the largest are in the informal sector of the Brazilian economy (341 percent). The second largest differential is the formal sector of the Brazilian economy (120 percent). These Brazilian differentials were constructed from the unadjusted differentials reported by Birdsall and Behrman by deducting from the unadjusted gross differentials the differential due to sample selectivity bias estimated from their study. The gross differential in the United States (61 percent) was just below the median of the estimates presented in table 2.3. The percentage of the earnings gap attributable to labor market discrimination ranges from a low of 10 percent in Tanzanian manufacturing to a high of 113 percent in the formal sector of the Brazilian economy. As discussed above, the estimated earnings gap attributable to discrimination in the formal and informal sectors of the Brazilian economy is almost entirely the result of estimated differences in the constant term. The percentage of the earnings gap attributable to discrimination in the U.St. economy (79 percent) is just above the median of the estimates presented in table 2.3. The absolute magnitude of the earnings gap due to discrimination is of course determined by both the magnitude of the gross differential and the proportion of the differential due to labor market discrimination. Even so, the largest absolute gap attributable to discrimination is found in the Brazilian economy and the smallest is found in Tanzanian manufacturing. The United States occupies a median position with respect to the size of the gap attributable to sex discrimination. Estimates of race and caste discrimination are presented in table 2.4. The largest gross earnings differential (in logs) is between Africans (blacks) and non- Africans (Asians) in Tanzanian manufacturing (112 percent). The smallest gross earnings differentials (in logs) are found between scheduled castes and non- scheduled castes (untouchables) in Delhi (17 percent). The estimated white/black Labor Market Discrimination and Economic Development 47

Table 2.3 Earnings differentials by sex

Gross earnings differentials Attributable to discrimi- Study Country (Logs) nation (%)

Behrnan & Wolfe (chapter5) Nicaragua' 0.850 71.2

Birdsall & Behmnan(chapter 7) (formalsector) Brazila 1.20ob 113.3

(informal sector) 3 .410 b 98.5

Birdsall & Fox (chapter 6) Brazilc 0.690 10.0

Knight & Sabot (chapter 3) Tanzaniad 0.288 17.2

Oaxaca (1977a) United Statesa 0.613 79.3

a. National labor market. b. Gross differential adjusted for selectivity bias. c. Schoolteachers. d. Manufacturing. differential (in logs) in the United States occupies the median position of about 43 percent. Tanzanian manufacturing also exhibits the largest percentage of the differential attributable to racial discrimination (78 percent). The smallest percentage due to discrimination is found in India (54 percent). Again, the United States occupies an intermediate position, with about 60 percent of the racial earnings differential attributable to labor market discrimination. In absolute terms,' discrimination accounted for the largest earnings gap in Tanzania and the smallest in India. Under the residual/decomposition method of measuring labor market discrimination, the presence of measured discrimination implies structural differences in earnings models between groups of workers. It is perhaps instructive to compare sex, race, and caste differences in the estimated parameters on the human capital variables corresponding to experience and education as well as differences in the estimated constant terms. In making these comparisons across the various studies, the caveats regarding differing earnings model specifications and sectors studied still apply. 48 Labor Market Discrimination and Economic Development

Table 2.4 Earnings differentials by race or caste

Gross earnings differentials

Attributable to discrimination Country (Logs) (%)

Banerjee & Knight (chapter 8) Indiaa 0.170 53.5 Knight & Sabot (chapter 3) Tanzaniab 1.119 78.4

Oaxaca (1977b) United States' 0.425 59.9 a. Delhi. b. Manufacturing. c. Nationallabor market.

In table 2.5, we report the effects of interacting the sex dummy variable for females with the experience and education variables and the constant term from selected studies. These estimated interaction effects are of course the estimated coefficients for females minus the estimated coefficients for males. In four out of the six studies represented in table 2.5, the linear effects of experience on earnings are greater for females. These findings are all from developing economies. The two exceptions are the United States and the formal sector in Brazil. For the former, the negative effect from linear experience is negligible, while for the latter, it amounts to 2 percent per year of experience. Of the five studies that use quadratic experience terms, three show earnings effects of experience squared that are smaller for females. These same three studies have also exhibited larger linear experience effects on earnings for females. Thus the positive effects of linear experience on sex differences in the annual rate of return to experience is offset by the negative quadratic effects of experience. In all cases, the absolute value of the differences in quadratic experience effects is quite small. The estimated differences in the earnings effects of experience indicate that the annual rate of return to experience is lower for females in the United States and in the formal sector in Brazil. On the other hand, the annual rate of return is higher for females over most of the work-life horizon in the informal sector of Brazil, among Brazilian schoolteachers, and in Tanzanian manufacturing. In the case of Nicaragua, the annual rate of return to experience is higher for females only up to 10 years of experience and is smaller thereafter. The estimates in table 2.5 on sex differences in the earnings effect of education show lower returns for females in half of the studies. All of these were Labor Market Discrimination and Economic Development 49

Table 2.5 Interaction effects of female dummy variable with selected explanatory variables for log earnings

Study Exp. Exp.2 Ed. Ed.2 Con.

Behrman & Wolfe (chapter 5) 0.021 -0.001 -0.013 0.003 -1.140

Birdsall & Behrmnan (chapter 7) (formal sector) -0.021 0.270a 0.047 ... -1.370 (informal sector) 0.026 -0.470a -0.024 ... -3.530

Birdsall & Fox (chapter 6) 0.013 -0.230a 0.018 ... 0.016

Knight & Sabot (chapter 3) 0.061 ... -0.023 ... 0.230

Oaxaca (1977a) -0.005 0.040a 0.009 ... -0.496 a. x10-3. in developing economies. Higher educational returns for females are found in the formal sector of Brazil, among Brazilian schoolteachers, and in the United States. With respect to the contribution of estimated differences in the constant term to the residual measure of sex discrimination, four out of the six studies in table 2.5 (including the United States) show this to be a major factor accounting for discrimination. On the other hand, the study by Knight and Sabot suggest that a substantial constant wage advantage for females considerably reduces the estimated residual discrimination. The Birdsall and Fox study shows the slight constant wage advantage to females to be negligible. Table 2.6 lists the estimated interaction effects of race or caste with experience, education, and the constant term. These interaction effects are the estimated differences between the coefficients for Africans (scheduled castes) and the coefficients for non-Africans (non-scheduled castes). For the developing economies represented in table 2.6, the returns to linear experience are significantly smaller for scheduled-caste workers (8 percent per year) and African workers (2 percent per year). In the case of the United States, the linear returns are virtually the same for whites and blacks. For the two studies that employ quadratic experience terms, the estimated differences in coefficients are minuscule. These results indicate that among Indian workers with less than nine years of work experience, the annual rate of return to experience is smaller for scheduled-caste workers. After nine years of work experience, the annual rate of 50 Labor Market Discrimination and Economic Development

Table 2.6 Interaction effects of race/caste dummy variable with selected explanatory variables for earnings

Study Exp. Exp.2 Ed. Ed.2 Con.

Banerjee& Knight (chapter8) -0.070 0.004 0.022 -0.002 0.335 Knight & Sabot (chapter 3) -0.018 ... -0.046 ... 0.092

Oaxaca (1977a) 0.006 -0.090a -0.024 ... -0.098

3 a. x1O0 . return becomes larger for scheduled-caste workers. In the United States the annual rate of return to experience is higher among blacks for up to 33 years of work experience. The estimates presented in table 2.6 indicate that education has smaller earnings effects for Africans compared with non-Africans in Tanzanian manufacturing, and smaller earnings effects for blacks in the United States. Among Indian workers, education has smaller earnings effects for scheduled- caste workers after about five years of formal schooling. Differences in estimated constant terms do not appear to be as important for racial and ethnic earnings differentials as they are for sex earnings differentials. The one exception is that a relatively large constant earnings advantage for scheduled-caste workers significantly reduces the estimated residual discrimination. In advanced economies, it is expected that discrimination in the public sector is less than that found in the private sector. Studies in the United States do, in fact, show that standardized earnings differentials by race and sex are smaller in public sector employment than in the private sector.5 When one considers that the proportional representation of women and minorities is greater in the public sector than that of white males, the effect of public sector employment in the United States is to reduce economy-wide earnings differentials by race and sex relative to race and sex earnings differentials in the private sector. The modern economic history for most developed economies has been one of increasing growth (absolute and relative) in the public sector. On the other hand, the birth of newly independent nations has been accompanied by a significant public sector at the outset. What has been the effect of public sector employment in developing economies on race and sex earnings differentials? Some of the papers in this volume address the issue of the impact of public sector employment on labor market discrimination. According to Armitage and Sabot, over the decade 1970-1980 Tanzanian manufacturing establishments administered by the government eliminated the Labor Market Discrimination and Economic Development 51 wage premia to non-Africans. On the other hand, the standardized differential in favor of non-Africans has persisted in private sector Tanzanian manufacturing. For the wage economy as a whole in Tanzania, Armitage and Sabot have unearthed evidence of sex discrimination in the public sector but not in the private sector. The standardized premium paid to males in the public sector is on the order of 19 percent. As for racial discrimination, the effect of public sector employment in the Tanzanian economy is to reduce substantially the standardized earnings differential between non-Africans and Africans. In the Kenyan wage economy in 1980, there does not appear to be any systematic sex discrimination in either the public or private sectors. When allowance is made for separate wage structures for males and females, the structures are the same in the public sector but differ in the private sector. In the latter, a pure wage advantage appears for males, but is offset by female wage advantages in the returns to experience. As for race discrimination, the pattern in Kenya is much like that found in Tanzania - no significant discrimination in the public sector but a substantial amount in the private sector. Finally, Banerjee and Knight find labor market discrimination against scheduled-caste production workers in both the public and private segments of the formal sector. In fact, the standardized scheduled-caste wage penalty in the public sector is approximately twice the magnitude found in the private sector.

4. Concluding remarks

The large variety of samples and specifications encountered in the studies of labor market discrimination presented in this volume make any attempt at generalization a risky venture. Nevertheless, we shall attempt some comparisons and generalizations. Estimates of the extent of labor market discrimination against females and blacks in the United States tend to be in the middle of the range of the corresponding estimates for developing economies. From the one study that examines both race and sex discrimination in a developing economy (Knight and Sabot, chapter 3), the racial earnings differentials (adjusted and unadjusted) are larger than the sex earnings differentials. This is the opposite of the pattern found in the United States, where male-female earnings differentials tend to be larger than earnings differentials by race or ethnic group, at least for race discrimination among males and sex discrimination among whites. Race or sex differences in the structure of occupational attainment were found to have had, at most, a modest effect on eamings differentials in developing economies, which is probably in large part due to the broad occupational categories used. This result is consistent with the findings in Brown, Moon and Zoloth (1980) for sex discrimination in the United States. They find that adjustment for broad occupational distributional differences unrelated to productivity characteristics has little or no impact on wage differentials by sex. 52 Labor Market Discrimination and Economic Development

On the other hand, concurrent elimination of intra-occupational wage discrimination and occupational discrimination would make a significant difference. Another interesting aspect of measuring discrimination according to the residual/decomposition method has to do with the effects of estimated differences in the constant terms in earnings regressions. Differences in estimated constant tenns accounted for substantial portions of the male/female discrimination measures in both the United States and developing countries. The legitimacy of the contribution of estimated differences in the constant term to discrimination measures rests upon the interpretation of the constant term. If the constant term is believed to be capturing the average earnings effects of critical productivity traits that have been left out, then its role in measuring labor market discrimination is questionable. On the other hand, if the earnings model is believed to be correctly specified, the constant term is simply a parameter of a wage determination formula that should be included in any discrimination calculations. Blinder (1973) does recognize the possibly unique role of estimated constant terms by isolating this component from the rest of the wage decomposition calculations. Unlike the experience in the United States, public sector employment in developing economies is not unambiguously associated with less discrimination vis-a-vis the private sector. In Tanzania and Kenya, racial discrimination in the public sector has been pretty much eliminated, in contrast to its continuing presence in the private sector. Yet in India, there appears to be greater discrimination against scheduled-caste production workers in the public sector than in the private sector. In Tanzania there is evidence of sex discrimination in the public sector but not in the private sector. The evidence for Kenya suggests that little or no sex discrimination exists in either the public or private sector. To what extent do the measures of contemporary labor market discrimination in mature economies (such as in the United States) predict the probable examination of labor market discrimination in developing countries? Based on the evidence presented so far, the answer would seem to be that labor market discrimination in developed economies offers no information as to what developing economies can anticipate as they mature. The so-called mature or developed economies are not static, but rather are continuing to change and evolve with continued economic growth. Thus, the picture of economic development from nineteenth century classical economies is not useful here. The varied results from studies of labor market discrimination in developing economies bracket the measures found in the U.S. labor market. This, of course, invites the suggestion that there is no unique relationship between the stage of economic development and the extent of labor market discrimination. Structures can vary even among a set of economies thought to be comparable in overall economic development. These conclusions about the apparent lack of a universal relationship between economic development and labor market discrimination do not by any means imply that a universal methodology for measuring discrimination is inappropriate. Labor Market Discrimination and Economic Development 53

As long as one is interested in the same questions as those raised in regard to labor market discrimination in developed economies, traditional theories and measures of discrimination are as applicable (or inapplicable) to developing economies as they are to developed economies.

Notes

1. This is essentially the argument made in Madden (1975). 2. An early introduction of the analysis of dual sector economies in developing nations is found in Lewis (1954). 3. Polachek (1981) advances a hypothesis to explain why females might enter occupations with less depreciable human capital investment requirements. 4. If the estimated female wage structure is used as the norm, the percentage of the earnings gap attributed to discrimination is somewhere between a negative 3 percent and a negative 45 percent. 5. See, for example, Oaxaca (1973b, 1977a) and Smith (1977).

Labor Market Discrimination in a Poor Urban Economy

J. B. Knight and Richard H. Sabot

1. Introduction

Distinguishing between differences in earnings that can be justified on efficiency grounds and those that, by misallocating labor, are inefficient is relevant to the debate, hotly contested in many developing countries, over wages policy. According to this distinction, government interventions to alter the earnings structure on equity grounds may either improve or worsen allocative efficiency. Labor market discrimination generally means that some workers are paid more than others with the same endowment of productive economic characteristics by virtue of some non-economic personal characteristic. The existence of discrimination implies that governments have an opportunity simultaneously to improve the distribution of income and the efficiency with which labor is allocated. The perceived injustice of discrimination has spurred the growth of civil rights movements and remedial government legislation in the industrialized social democracies. Discrimination is thus an important political issue irrespective of the costs it imposes or its implications for the distribution of income. Despite the policy relevance of the phenomenon in poor countries, discrimination has been more intensively studied in rich countries, largely because of the lack of appropriate data in the former. In this paper, we apply to detailed data from Tanzania what in the rich countries have come to be the conventional tools of analysis of discrimination. The data set we use was generated by a 1971 establishment-based survey of some 1,000 randomly selected workers in Tanzania's manufacturing sector. The survey was specially designed by one of the authors to avoid the deficiencies in the labor force data in poor countries that usually preclude detailed analysis of wage structures and of what those structures reveal about the determinants of wages and the functioning of labor markets.'

55 56 Labor Market Discrimination in a Poor Urban Economy

Our concern is with discrimination by sex and by race. Our results are surprising. They run counter to two common presumptions about discrimination. One is that development can be expected to bring changes in attitudes and institutions that diminish arbitrary disadvantages inflicted on women by sex discrimination in the labor market: such discrimination is found to be far smaller in Tanzania than in the United States. The other presumption is that racial discrimination is practiced either by a majority group with power against a minority lacking such power or in some instances, such as South Africa, by a minority against a relatively powerless majority; we find evidence for Tanzania of discrimination in favor of the non-African minority group with apparently limited power. Section 2 outlines the methods we employ to analyze labor market discrimination in Tanzania. Section 3 assesses the extent of sex discrimination, and section 4, the extent of race discrimination. In section 5 we discuss some of the implications of our findings.

2. The method

Our aim is to decompose the difference in mean wages between males and females or between Africans and non-Africans into the component "explained" by differences in economic characteristics between the two groups and the "unexplained" component, which can be regarded as reflecting the extent of labor market discrimination. Within the unexplained component, we attempt to distinguish wage and job discrimination. The distinction is relevant where occupation is itself one of the variables determining earnings. Wage discrimination occurs if, even within occupations, group differences in mean personal characteristics cannot entirely explain group differences in mean earnings. Job discrimination occurs if, for non-economic reasons, the different sexes or races have unequal access to the higher-paying occupations. Our procedure for measuring discrimination follows that conventionally used in industrialized countries. (See Blinder 1973; Oaxaca 1973a, 1973b; Malkiel and Malkiel 1973; Brown, Moon and Zoloth 1980. For an application to a developing country using a similar methodology, see Birdsall and Fox, chapter 6.) The standard technique for measuring discrimination when two groups differ in their personal characteristics and in the function relating these characteristics to earnings is to compare their actual mean wages with their mean wages if all were paid according to the same "earnings structure," that is, the same constant term and set of coefficients on the independent variables. Assume that the mean wage of men is wi and that of women wf (we could equally use wa and w., denoting Africans and non-Africans, respectively). Assume that the mean wage of men is determined by the earnings function Labor Market Discrimination in a Poor Urban Economy 57

Wm. f (X.) (1)

where fm is the male earnings function, xm is a vector of characteristics applicable to men and xm denotes their mean values; there is a corresponding expression for women. The mean wage that women would receive if they were paid according to the male wage structure is fm(xf). . The gross difference in mean wages (G) between men and women can be divided into the part "unexplained" by different personal characteristics (om -f,(x,)), and the unexplained part (xf(7) -wf), reflecting differences in the constant terms and coefficients of the regression equations for men and women:

G = W - w = (w- fW.Xf)) + (yTf)-i;f) = E + D, (2) respectively, where E is the explained and D the unexplained part of the gross difference. The estimation of the wage received by men if they are paid according to the female wage structure permits a corresponding decomposition. That part of G that is due to labor market discrimination is, in turn, decomposable into the parts attributable to wage and job discrimination (Brown, Moon, and Zoloth 1980; Birdsall and Fox, chapter 6). The choice of occupation can influence the wage a worker receives; occupation may be one of the personal characteristics included among the independent variables in the earnings function. The finding of significant coefficients on dummy variables representing occupation would not affect the analysis if the determninantsof occupational attainment were the same for men and women. However, job discrimination may cause men and women who otherwise have the same characteristics to work in different occupations. Thus, for men the sample proportion in occupation i is given by Pimg,,m) m)(3) The probability of women attaining occupation i if their occupational attainment was determined in the same way as that of men would be g.(xf) The gross difference in mean wages can be decomposed as follows:

Wm Wf L m im

= £ (PJfm(Xm) - Pfff(-if))

= PVf ( (Xim) f4 (xf))

+ Efim(XJ(Pm - Pif)

= W + J, respectively (4a) 58 Labor Market Discrimination in a Poor Urban Economy

E(Pig'(Jid(if fif(Xif))

+ E Pi,fimXi) fm ()

E f(x I-M)(Pim g9(Xf))

+ f i(xj ) (gm (xf) - Pif)

= WD + WE + JE + JD, respectively. (4b)

WD represents wage discrimination, since it isolates the effect of sex differences in wage structure. WE is the part explained by differences in personal characteristics. since both wage structure and job proportions are being held constant. JE and JD together show the contribution of occupation to G. JE is the explained part, reflecting differences in occupational attainment that are due to differences in personal characteristics; the male occupational attainment function is applied to both men and women. JD represents job discrimination, since it isolates the effect of sex differences in occupational attainment that cannot be explained by group differences in personal characteristics. The alternative decomposition of G introduces EP (f (x f)) instead of E PDf(gm)) to derive 4/ and 4b (not shown).

3. Discrimination by sex

Female manufacturing workers are a rather select group in Tanzania. Fifteen percent of all urban adult males are employed in manufacturing, compared with only 1.6 percent of females. This difference is largely a result of the lower participation rate of urban women and the under-representation of female labor force participants in wage employment.2 Women wage-earners are not only under-represented, they are also subject to a higher degree of selectivity by level of education. Despite the markedly lower educational attainment of the female than of the male population and labor force, male and female wage-earners have roughly equivalent levels of education, even in the manufacturing segment of the wage sector; male manufacturing employees have, on average, 4.0 years of formal schooling and females, 3.9 years. When the equivalence of the educational levels of males and females is set alongside the difference in mean earnings between them, the comparison is suggestive of discrimination. The mean monthly wage of female employees in our manufacturing sample is 276 shillings; the mean for males is 379 sh., or 37 percent higher.. Labor Market Discrimination in a Poor Urban Economy 59

The mean values for males and females of the independent variables included in the earnings functions that will serve as the basis for our assessment of wage discrimination suggest that women are at a disadvantage in their occupational distribution. Whereas 35 percent of male workers are in supervisory or skilled occupations, only 4 percent of female workers are in these occupations; 57 percent of working women are in unskilled jobs (see table 3.3). Again, given the evidence of educational attainment, the differences in occupational distributions reinforce the impression of labor market discrimination. Qualifying this impression, however, are some other sex differences in personal characteristics. More male workers (13 percent) have had formal training than female workers (4 percent). The proportion of men aged 35 or older is higher than that of women (21 percent versus 7 percent) and, in part reflecting the age distribution, men have greater seniority in the firm (6.1 years versus 3.8 years) and considerably more experience in previous wage employment (3.0 years versus 0.5 years) than women. To what extent is the sex structure of wages the result of differences between men and women in personal characteristics other than education, and to what extent is it the product of discrimination? When an earnings function for the sample as a whole with the natural logarithm of monthly wages (Z) as the dependent variable and a full set of independent variables, including dummy variables for different occupations, is estimated, the coefficient on the dummy variable denoting male sex (Sl)3 is slightly positive (.042) but insignificant, being. smaller than its standard error. This preliminary result suggests that in Tanzania's manufacturing sector, men and women with the same personal economic characteristics and type of job receive roughly the same pay. However, the coefficients on the independent variables in the whole sample earnings function are constrained to be the same for both males and females. This may result in an underestimate of the independent impact of sex on wages if, for instance, the returns to education are higher for males. Estimates of separate earnings functions for males and females are presented in table 3.1. A comparison of regressions a and c suggests that coefficients do indeed vary with sex. The coefficients on education, formal training, and seniority are significant only for men. The impression that women, in marked contrast to men, are not rewarded for accumulating human capital, is nevertheless misleading. Seventy-eight percent of women are in unskilled or semi-skilled manual jobs. As the returns to education (for both males and females) are insignificant for semi-skilled and unskilled workers (Knight and Sabot 1980b), it is not surprising to discover that for women as a group the returns to education are low. Regression c indicates that the coefficient on the clerical occupation dummy is positive, large, and significant: on average, women in clerical jobs earn 101 percent more than unskilled women.4 Furthermore, the partial correlation coefficient between the education and clerical occupation variables is high, 0.43. Women reap returns to education mainly in the form of access to clerical jobs; 18 percent of female 60 Labor Market Discrimination in a Poor Urban Economy

Table 3.1 Earnings functions for males and females

Coefficient Males Females Independent variable a b c d

Non-African race (T2) 0.594** 0.711** 0.488** 0.834** Years of education (E) 0.053** 0.078** 0.030* 0.049** Years of current 0.029** 0.035** 0.029 0.013 employment (LI) Years of previous 0.028** 0.030** 0.089* 0.096** employment (L4) Formal training, 3 months 0.069 0.089 0.037 0.054 or less (Fl) Formal training, more 0.131* 0.191** -0.097 -0.092 than 3 months (F2) Age 15-19 (Al) -0.255** -0.331** -0.003 -0.018 Age 35-49 (A3) 0.158** 0.168 0.422** 0.415* Age 50 or more (A4) -0.005 -0.008 x x Regular employment 0.359** 0.392** 0.523 0.213 status (R2) Supervisory (01) 0.601** ... x ... Clerical (02) 0.480** ... 0.697** ... Headman/skilled (03) 0.238** ... 0.270 ... Semi-skilled (04) 0.029 ... 0.057 ... Constant term 4.681 4.633 4.911 4.849

R2 0.591 0.533 0.742 0.628 S.E.E. 0.393 0.418 0.268 0.321 F 78.3 85.1 16.700 13.3 N 803 810 72 74 Dependent mean (Z) 5.710 5.707 5.422 5.428

Note: The base sub-categories of the dummy variables in this and subsequent tables are Tl (African race), S2 (female sex), 05 (unskilled occupation), F3 (no formal training, RI (casual employment status), and A2 (age 20-34). * Indicates in this and subsequent tables that the coefficient is significant at the 5-percent level. ** Indicates in this and subsequent tables that the coefficient is significant at the I-percent level. x Indicates in this and subsequent tables that the coefficient could not be computed. Labor Market Discrimination in a Poor Urban Economy 61 employees hold such jobs. Estimates of the earning functions without the occupation dummy variables (regressions b and d) confirm that in regression c, the clerical occupation variable captures the returns to education. In regression d, the coefficient on education is positive and significant: on average each additional year of education adds 4.9 percent to a female employee's earnings.5 Although women do earn returns to their human capital, differences in rates of return nevertheless persist in regressions b and d. Table 3.2 presents the contributions to the gross difference (G) that are made by differences in personal characteristics (E) and by sex discrimination (D). These are estimated on alternative assumptions, namely, that the male "earnings structure" applies to females (corresponding to equation (2)) and that the female structure applies to males. The method of decomposing G is to estimate E as the sum of the product of coefficients and the sex difference in the average values of characteristics and to estimate D as the residual.6 As is to be expected, personal characteristics explain a somewhat higher percentage of G when the results are based on regressions that include occupation dummies among the independent variables than when based on regressions excluding occupation. Nevertheless, the largest single contribution to E is made not by the occupation variables but by the experience variables; the contribution of education is slight. The most important finding of the table is that the contribution of personal characteristics is overwhelming, and that of discrimination slight. The estimates of D based on the male earnings structure represent five or 17 percent of G; indeed, the estimates based on the female structure show the contribution of discrimination to be negative! The results based on the male coefficients are more reliable, however; the female regressions contain fewer than 80 observations, and their results are sensitive to the values of insignificant coefficients on the occupation dummy variables. We conclude from this exercise that sex discrimination in Tanzania is negligible. Our findings are at odds with the findings of an important role for sex discrimination in analogous studies for the United States. For instance, Oaxaca estimated that the proportion of the gross difference in mean wages attributable to sex discrimination was 74 percent for whites and 92 percent for blacks (54 and 45 percent, respectively, including occupation) (Oaxaca 1973a, pages 146-7). The inclusion of occupational dummy variables produces significant coefficients. There is also a marked difference in occupational distribution between men and women (table 3.3). The view that women are "crowded" into inferior jobs, and that this is the cause of their wage disadvantage, has. a long history in various countries (see, for example, Edgeworth 1922). These considerations raise two questions. To what extent is G due to occupational differences (JE + JD in equation 4)? To what extent is it due to occupational discrimination (JD alone) rather than occupational differences that can be explained by differences in group mean characteristics (JE)? Table3.2 Effectsof discriminationand personalcharacteristics on the grossdifference in meanwages betweenmen and women

Absolute values Natural logarithms As percentage of gross (sh.p.m.) sh.p.m. difference in mean wages (G)

Mean wage, male (W,) 5.710 301.9 Mean wage, female (1wf) 5.422 226.3

Gross difference in mean wages (W - wf)o 0.288 75.6 100.0 Wage structure Male Female Male Female Male Female Occupation dummies: Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl. Contribution of personal characteristics (E): fi (xm - xf) 0.273 0.232 0.391 0.295 72.2 62.6 109.7 77.8 95.5 82.8 145.1 102.9 O\ of which, due to: experience (LI, L4) 0.136 0.155 0.288 0.271 38.4 43.4 75.6 70.5 50.8 57.4 100.0 93.3 education 0.008 0.011 0.004 0.007 2.4 3.4 0.5 1.6 3.0 4.5 0.7 2.1 occupation (01-04) 0.054 ... 0.028 ... 15.9 ... 6.5 ... 21.0 ... 8.6 other variables 0.075 0.066 0.071 0.017 21.8 19.3 16.7 3.9 28.8 25.5 22.1 5.2 Residual contribution of sex discrimination (D): f,(x,) . f/x) 0.015 0.056 -0.107 -0.007 3.4 13.0 -34.1 -2.2 4.5 17.2 -45.1 -2.9

Notes: The absolute values of mean wages are geometric means; they differ from the arithmetic means (i7. - 378.7, wf = 276.3) but their ratios are very similar. Using the male wage structure, the contribution of personal characteristics is f, (x, - if) and the corresponding residual contribution of sex discrimination is fm (ii) - ff(.Tf). The contributions of particular personal characteristics (aggregated into the two experience variables, education, the four occupation dummy variables, and the remaining variables) sum to more (less) than the absolute value of E in the case of male (female) structure, because the divergence from iw, (wf) produced by each is measured in isolation. Labor Market Discrimination in a Poor Urban Economy 63

Table 3.3 Effects of occupational differences and job discrimination by sex

Mean wage (shilling) Occupational distribution Women, male struc- Men Women Men Women ture Occupation (wim) (Wf) (P-,) (Ptf) (g,(.Vf))

Supervisory 981 763 4 0 1 Clerical 783 609 11 18 12 Skilled 427 274 31 4 27 Semi-skilled 263 229 33 21 36 Unskilled 219 173 21 57 24 Total 390 267 100 100 100

Note: As there are no womenin the supervisoryoccupation, we assumethat the sex ratio applying to clericalworkers would apply also to supervisoryworkers. The followingresults are obtainedfrom the table: G = 123;J = 52, JD = 28, JE = 24 (from (4)); J = 27, JD = 12,JE = 15 (from(4')).

In answering the first question, we use the relation based on (4a):

JD + JE =,(i7m)(P,m P

We apply the mean wage of men to the proportions of men and women, respectively, in each occupation. The mean wage of males using female occupational weights is 338 sh. The resulting estimate of JD + JE (derived in table 3.3) implies that different job proportions explain 42 percent of G. The alternative decomposition of G indicates that different job proportions explain only 22 percent of G. The separation of JE and Jb requires an estimate of g,(is), that is, what the distribution of female occupations would be if it were determined according to the male occupational attainment function. A function must be estimated to predict the occupational attainment of men on the basis of their personal characteristics, which in turn involves earnings functions stratified by occupation and the application of a multinomial logit model.7 We adopt a similar approach, but data constraints necessitate a cruder method; our sample is too small to permit the estimation of regressions for sub-samples stratified by both occupation and sex. Education and occupation are highly correlated in Tanzania (Knight and Sabot 1980b), but the educational attainment of male and female employees in our sample is almost equal, despite the marked difference in their occupational distributions. The main sex difference in personal characteristics lies in the extent 64 Labor Market Discrimination in a Poor Urban Economy of employment experience. This difference can be taken into account in measuring g,,(xA). We estimate the proportions of men and women in the five occupations, stratifying by five experience groups, and then apply the male experience-specific occupational proportions to females in order to obtain an estimate of the female occupational distribution in the absence of job discrimination (column g,(xf) of table 3.3).8 Two estimates of JD can be made, according to (4b) and (4b'); they represent 23 and 10 percent of G, respectively. Even the higher percentage amounts to only 28 sh. per month. Our estimates of JD suggest that job discrimination by sex in Tanzania is not an important problem. There is another indication that the influence of sex differences in occupational attainment on the gross difference in mean earnings is minor. If it were major, we would expect the exclusion of the occupation variables from the earnings function to increase the significance of the sex dummy variable. When the earnings function for the sample as a whole is reestimated with the dummy variables denoting occupation excluded, the coefficient on sex remains very small (0.055) and insignificant, that is, males do not earn a premium even if there is no standardization for occupation. It is possible that sex differences in the personal characteristics of our sample may themselves be the result of sex discrimination. The main differences in the average values of characteristics concern current and previous employment experience, age, and training. The occupations that women attain affect the amount of human capital that they can accumulate in employment. Exclusion of women from the more skilled manual and supervisory jobs might explain why a smaller proportion of women than of men receive formal training. Lack of reward for employment experience could deter women from staying with an employer or even in wage employment. The response of employers in their hiring and promotion decisions would in turn contribute to a "vicious circle." It is difficult to assess the relative importance of the effects of discrimination and the effects of non-discriminatory social factors, such as childrearing, in explaining why women are at a disadvantage in the possession of these productive characteristics. It is not clear that the coefficients on employment experience are lower for women. The coefficient on previous employment experience is actually higher for them, and that on current employment experience is the same when standardized for occupation, and lower for women when occupation is excluded from the independent variables (table 3.1). This similarity, and the remarkable youthfulness of female employees (18 percent are under age 20 and 93 percent are younger than 35), suggest that the difference in employment experience is either a life-cycle phenomenon or a reflection of the recent entry of women to manufacturing employment.9 If the difference in employment experience is the result of sex discrimination, it must stem from unequal access to manufacturing employment in the 1960s. Labor Market Discrimination in a Poor Urban Economy 65

4. Discrimination by race

Eight percent of the sample were non-Africans, almost all of whom were Asians. (Europeans were excluded from the survey.) During the colonial period, Asians occupied the middle rung of the clearly demarcated racial hierarchy in Tanzania. The mean wage in 1971 of 994 sh. for non-Africans was more than three times that for Africans (321 sh.), indicating that, at least in the manufacturing sector, Asians have perpetuated their economic advantage. Is this dramatic difference in earnings, like that between males and females, to be explained by differences between the two groups in personal economic characteristics? The shift in the distributior of power between the sexes is not nearly so marked as the change in the racial balance of political and economic power that Independence brought. This suggests that Africans are less likely to be the victims of labor market discrimination than women. Certainly non-Africans are better endowed with human capital than Africans. While there was little difference between the two in length of employment experience (non-Africans averaged 9.2 years and Africans, 8.8 years), non- Africans averaged more than twice as much schooling (8.3 years versus 3.6 years), and a higher proportion of non-Africans had received formal training (17 percent versus 12 percent). The difference in education was reflected in the occupational distributions; nearly 60 percent of non-Africans were in the supervisory or clerical occupations, while over 60 percent of Africans were semi- skilled or unskilled workers (see table 3.6). When we estimate a full-sample regression equation, controlling for these differences in characteristics between Africans and non-Africans on the assumption that the coefficients on independent variables do not vary with race, the wage differential associated with race is neither reversed nor eliminated, contrary to expectations. The premium received by non-Africans is reduced but remains surprisingly large. The coefficient on the non-African race variable (T2)1Oindicates that the earnings of non-Africans with the same characteristics as Africans are 87 percent higher. The coefficient is highly significant, and the race variable is the first to enter a stepwise regression, that is, it explains more of the variation in earnings (just over a quarter) than any other variable. Apparently, the color of a worker's skin is a better predictor of his wage level than his educational attainment or employment experience. This striking result is not simply the consequence of inappropriate aggregation. Differences between Africans and non-Africans in retums to human capital are found when the earnings function is estimated separately for each ethnic group (table 3.4), but the advantage is not clearly with Africans. Whereas the constant term and the coefficients on current employment experience and formal training are higher for Africans, the coefficient on years of education is more than twice as large, and that on previous employment experience nearly twice as large, for non-Africans. 66 Labor Market Discrimination in a Poor Urban Economy

Table 3.4 Earnings functions for Africans and non-Africans

Coefficient Africans Non-Africans Independent variable a b a b

Male sex (SI) 0.039 0.05 x 0.035 Years of education (E) 0.045** 0.071** 0.091* 0.110* Years of current employment 0.030** 0.036** 0.017 0.019 (Li) Years of previous employment 0.022** 0.030** 0.040** 0.043** (L4) Regular employment status (R2) 0.287** 0.335** 0.694* 0.599* Age 15-19 (Al) -0.143** -0.217** -0.464* -0.435* Age 35-49 (A3) 0.147** 0.158** 0.258 0.224 Age 50 or more (A4) -0.017 -0.031 -0.046 -0.003 Formal training, 3 months or 0.050 0.059 0.065 0.288 less (Fl) Formal training, more than 3 0.128* 0.185** 0.037 0.158 months (F2) Supervisory (01) 0.652** ... 0.833* Clerical (02) 0.526** ... 0.451 Headman/skilled(03) 0.249** ... 0.474 ... Semi-skilled (04) 0.026 ... 0.147 ...

Constant term 4.724 4.645 4.632 4.907 R 2 0.478 0.391 0.544 0.527 S.E.E. 0.379 0.410 0.436 0.444 F 53.4 51.1 7.82 9.91 N 861 861 81 81 Dependent mean (Z) 5.602 5.602 6.721 6.721

The procedure for decomposing the race G into its E and D components is directly analogous to that used in the case of sex. When the African wage structure is used, D is found to be 69 percent (with the occupation variables included in the regressions) or 78 percent (with occupation excluded) of G Labor Market Discrimination in a Poor Urban Economy 67

(table 3.5). Occupation (in the former case) and education (in the latter) are the most important contributors to E. When estimated from the non-African wage structure, the contribution of D is much smaller (24 percent and 33 percent of G, respectively); but these results are less reliable, being based on only 81 observations and sensitive to a number of non-significant coefficients. Yet even when the non-African structure is used, the absolute size of D is very large in relation to the mean wage of Africans. The exercise implies that labor market discrimination based on race is an important phenomenon in Tanzania. Comparison of these results with the results of the analysis of sex discrimination is instructive. Using in each case the coefficients of the larger group, the means of the two estimates (including and excluding occupation variables) indicate that D as a proportion of G is 11 percent for sex and 74 percent for race. In addition, G is only 76 sh. for sex and 559 sh. for race. Thus, the mean absolute value of D for race is 50 times that for sex; after differences in personal characteristics are accounted for, men earn eight sh. more whereas non-Africans earn over 400 sh. more. We next examine the extent to which differences in occupational distribution account for the racial gap in mean wages" and the extent to which such differences are due to job discrimination. One indication that job discrimination may be important is provided by the earnings functions for the sample as a whole. The omission of the occupation dummies alters the coefficient representing race; the premium for non-Africans rises from 87 percent to 110 percent, and the statistical significance of the race coefficient increases, indicating that the influence of race on eamings is more important across than within occupations. We estimate the effects of race differences in occupational distribution using the same method as in the case of sex differences (see table 3.6). The estimate of JE + JD amounts to 33 percent of G using one set of weights and to 13 percent using the other. Occupation clearly plays some role in explaining why Asians fare better than Africans. Unlike males and females, Asians and Africans differ markedly in educational attainment. These differences in education should be taken into account in measuring g,(xi). Thus, we estimate the proportions of Africans and non-Africans in the five occupations, stratifying by four education levels, and then apply the non-African education-specific occupational proportions to Africans in order to obtain an estimate of the African occupational distribution in the absence of job discrimination (column g,(iT) of table 3.6). The two estimates of JD represent only 6 percent and 11 percent of G, respectively. These are likely to be overestimates of the effect of job discrimination because no standardization was made for group differences in the other productive characteristics, in which non-Africans were at a (slight) advantage. Wage discrimination by race is more important than job discrimination. In Tanzania, a minority, ostensibly without power, appears to be the beneficiary of discriminatory practices. The oddity of this phenomenon compels Table 3.5 Effects of discrimination and personal characteristics on the gross difference in mean wages between Africans and non-Africans

Absolute values Natural logarithms As percentage of gross (sh.p,m) sh.p.m. difference in mean wages (G)

Mean wage, non Africans (w,,) 6,721 829.6 Mean wage, African (w.) 5,602 271.0

Gross difference in mean wages (w - w) 1,119 558.6 100.0

Wage structure: African Non-African African Non-African African Non-African Occupation dummies: Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl. Incl. Excl.

Contribution of personal

characteristics (E): f, (Xn - ) 0.491 0.368 0.715 0.604 171.8 120.5 423.8 376.0 30.8 21.6 75.9 67.3 OfN of which, due to: °° experience (LI, L4) 0.002 0.007 0.038 0.040 0.5 1.9 30.9 32.5 0.0 0.3 5.5 5.8 education 0.202 0.335 0.429 0.519 64.0 107.8 289.4 335.9 11.4 19.3 51.8 60.1 occupation (01-04) 0.254 ... 0.206 ... 78.3 ... 154.4 ... 14.0 ... 27.6 ... other variables 0.023 0.026 0.042 0.045 6.3 7.1 34.1 36.4 1.1 1.3 6.1 6.5 Residual contribution of race

discrimination (D): fj(x;) - f(x.) 0.628 0.751 0.404 0.515 386.8 438.1 134.8 182.6 69.2 78.4 24.1 32.7

Note: The absolute values of mean wages are geometric means; they differ from the arithmetic means (wn = 994.4, i; = 321.2) but their ratios are very similar.

Using African wage structure, the contribution of personal characteristics is - x,(x,x)and the corresponding residual contribution of sex discrimination is fa (Xn) - fn (Xn). The contributionsof particularpersonal characteristics (aggregated into the twoexperience variables, education, the four occupationdummy variables, and the remaining variables) sum to more (less) than the absolute value of E in the case of non-African (African) structure, because the divergence fromwi(w,) produced by each is measured in isolation. Labor Market Discrimination in a Poor Urban Economy 69

Table 3.6 Effects of occupational differences and job discrimination by race

Mean wage (shillings) Occupational distribution Africans, non- Non- African Non-Africans Africans Africans Africans structure

Occupation (iiWa) (Pi,) (Pi;) (g,(i,a))

Supervisory 1,538 884 7 13 3 Clerical 1,107 570 52 8 17 Skilled 841 382 31 28 55 Semi-skilled 595 253 9 34 17 Unskilled 185 210 1 27 9 Total 1,001 323 100 100 100

Note: The following results are obtained from the table: G = 678, J = 225, JD = 39, JE = 186 (from (4)); J = 87, JD = 72, JE = 15 (from (4')). us to ask several questions: is the net wage differential in fact due to unmeasured differential productivity, that is, is it the result of our using inadequate proxy measures for productivity? Alternatively, if we find the evidence of discrimination persuasive, do non-Africans actually possess more power to discriminate than a superficial assessment would suggest?t" There are reasons why the productivity of Asians may have been underestimated in the empirical analysis. The education, experience, and training variables included in the earnings function are only proxies for the productivity of a worker, reflecting inputs to, not output from, the skill-formation process. Racial differences in the quality of education may account for part of the unexplained differential. We have no measure of "ability" among the independent variables. Whereas the most able non-Africans are unlikely to be excluded from the sample, the most able Africans may well have been drawn into the public services, in which preference is shown for citizens. We have no independent measure of such qualities as personal drive, acumen, and ambition; these are characteristics that are culturally influenced and that insecure minority groups often possess disproportionately. If employers pay some workers more than others simply on account of their non-economic characteristics, they can be said to be indulging their prejudices or their "taste for discrimination" (Becker 1971; Arrow 1973). If the marginal products of African and non-African workers are equal, the wage difference 70 Labor Market Discrimination in a Poor Urban Economy represents the psychic value that employers place on their prejudice; profits are not being maximized. In theory, such discrimination should be a temporary phenomenon: non-discriminating enterprises should drive out those that discriminate and should equalize wages. However, product markets in Tanzanian manufacturing are generally not competitive, and entrepreneurship is scarce. The cost to discriminating firms of satisfying their prejudices may simply be a lower return on their capital. Discrimination is not necessarily the result of prejudice. Where employers lack the information needed to measure personal productivity, they may find it profitable to use race as a statistical screening device in setting pay (Arrow 1973; Phelps 1973). It is then sufficient that employers believe that Asians are more likely to possess such unmeasured productive characteristics for Asians as a group to be paid more. Alternative stratifications of the sample into sub-samples, and the estimation of separate regressions that include race among the independent variables, help us to choose between these competing hypotheses.'2 A markedly higher proportion of parents of Asian workers are educated and work in white-collar occupations. Any advantage to Asian workers conferred by this difference in family background is more likely to be reflected in superior performance in white-collar than in blue-collar occupations, where strength and manual dexterity are more important determinants of productivity. If otherwise undetected racial differences in productivity were the explanation, we would expect the coefficient on the race variable to be higher in high-level occupations than in low-level jobs. The neo-classical theory of discrimination predicts the opposite relationship. Assuming that employers' taste for discrimination is equally strong in all occupations, it is easily shown that the size of the premiums paid to the preferred group in various occupations is determined solely by the relative supply of that group. In any given occupation, the greater the supply of the preferred group as a proportion of the total supply of qualified workers, the lower the premium paid. This is because in each occupation some employers will have a stronger taste for discrimination than others. In those occupations where the beneficiaries are least abundant, they will tend to be employed by those with the strongest discriminatory taste; where they are more abundant, some will be employed by those with weaker tastes for discrimination, and hence will receive a smaller premium. Table 3.7 shows the coefficients on the race variable declining as occupational level increases. This relationship contradicts the prediction based on the hypothesis that the premium is simply a reward for higher productivity. The table also shows the proportion of non-Africans in each occupation; there is a negative relationship between this proportion and the premium. This is consistent with the prediction of the neo-classical model of discrimination. The white-collar category has the highest proportion of non-African workers and the lowest ratio of non- African to African wages. Asian ownership of manufacturing enterprises may dispel the image of a powerless group benefiting from discrimination. Asian entrepreneurs were among Labor Market Discrimination in a Poor Urban Economy 71

Table 3.7 Coefficients on the race variable in occupation-specific earnings functions

Non-Africans as Coefficient on race Occupation Observations percentage of total dummy

White collar 147 32 0.45** Skilled 277 9 0.60** Semi-skilled 350 2 0.69**

Note: Theseresults are takenfrom equations that containall the personalindependent variables. The occupationalcategory "unskilled"is excludedbecause it containsonly one non-African.

the first to invest in manufacturing enterprises, and while with nationalization and increased foreign investment they controlled less than they had before Indepen- dence, they retained a substantial portion of the private sector in 1971. Was it in this sector that discrimination was concentrated? The estimation of three separate earnings functions for firms whose ownership is "wholly local and private," "foreign and private," and "mixed local and foreign government and private," permits us to answer this question (table 3.8). Asian owners of manufacturing enterprises are found in the first category; the coefficient on the race variable is markedly higher in the equation of this category than in those for the other categories. Asians working in wholly local and private finns receive a premium of 139 percent, while the premiums in the other two categories are 55 percent

Table 3.8 Coefficientson the race variable in earningsfunctions disaggregated by firm ownership

Non-Africans as percentage of Coefficient on Ownership category Observations total race dummy

Wholly local and private 218 12 0.87** Foreign and private 209 7 0.44** Mixed local and foreign; government and private 161 12 0.26**

Note: These results are taken from equationscontaining all the personalindependent variables. 72 Labor Market Discrimination in a Poor Urban Economy and 30 percent, respectively. It is certainly suggestive of racial favoritism: Asians are paid the highest premiums in firms owned by Asians. This finding does not entirely resolve the anomaly of a powerless group as the beneficiary of discrimination. The coefficients on the race variable are also significant in the earnings functions estimated for employees of foreign and private firms and of mixed local and foreign, government and private firms. In other words, while discrimination is marked in Asian-owned firms, Asians still receive substantial wage premiums in firms owned by foreigners or jointly by foreigners and the Tanzanian government. The difference between the premiums found in local private firms and in these other firm categories can be regarded as the minimum measure of discrimination.

5. Conclusions

Our analysis of labor market discrimination in Tanzania has confounded expectations. In Tanzania, as in every other country for which we have seen evidence, the mean wage of males is substantially in excess of the mean wage of females. In marked contrast to the other, almost exclusively industrialized countries where these sex differentials have been subjected to rigorous analysis, in Tanzania the premium paid to males is explained almost entirely by differences between males and females in economic characteristics. Men and women with the same level of education, employment experience, formal training, and other personal characteristics receive roughly the same pay. The mean wage of non-Africans in Tanzania is much higher than that of Africans. Given the vigor with which the governments of East Africa have attempted to redress the racial inequities of the colonial era, we expected to find this differential entirely accounted for by the markedly higher level of education and training attained by Asians. In fact, decomposition of the gross difference in mean wages reveals that only a small part is so explained. After standardizing for differences in economic characteristics, non-Africans are found to earn a net premium in excess of the mean wage of Africans. Moreover, our attempt to discover whether this premium is simply due to underestimation of the productive characteristics of non-Africans adds to the evidence that non-Africans are the beneficiaries of discrimination. They earn the largest premiums in the (manual) occupations where unmeasured human capital is likely to have the least impact on -productivity, but where their numbers are fewest, and hence their likelihood of finding an employer with a "taste" for non-Africans is greatest. The anomaly of a powerless minority benefiting from discriminatory behavior is resolved in part by the finding that the premium earned by Asians is markedly higher for local private firms, in which Asian ownership and management is concentrated. We can only speculate as to why, contrary to expectations, female wage earners in Tanzania are not subject to discrimination while non-Africans are the Labor Market Discrimination in a Poor Urban Economy 73 beneficiaries of such discrimination. The explanation may lie in the difference between women and non-Africans, collectively, in their wage-employment experience. Female participation in urban wage employment on a significant scale is a post-Independence phenomenon, whereas non-Africans have occupied a privileged position ever since an urban wage labor force emerged in Tanzania. While the govemment is avowedly opposed to both sex and race discrimination, it may have proved easier to prevent the emergence of sex discrimination than to erode the longstanding advantages of non-Africans. One aspect of our analysis does suggest that sex discrimination may yet become a problem. In our sample there are no women in high-level technical or managerial positions. Because of the relatively high wages paid to clerical workers, this occupational segregation did not explain much of the difference in mean wages between males and females. However, if such segregation persists as the supply of educated females increases, crowding of females into clerical positions may result. This, in turn, could depress the relative wage of this occupation; consequently, men may begin to earn more than women with the same economic characteristics. The problem of sex discrimination in Tanzania may not be as small nor the problem of race discrimination as large as our results imply. First, the under- representation of females in urban wage employment may, in part, be the result of discriminatory hiring practices. The higher educational attainment of the male than of the female population may similarly reflect discriminatory practices within the educational system. Discrimination in access to schooling and to wage employment have, however, been beyond our purview. Second, our evidence of racial discrimination is suggestive rather than conclusive. Unmeasured differences in economic characteristics, possibly in quality of education and culturally based, may yet be part of the story.

Notes

This paper, with minor editorial changes, is reprinted from the Journal of Development Studies. vol. 19, No. I (October 1982). 1. A detailed description of the data base and sampling procedures is available in Knight and Sabot (1980b). 2. While 86 percent of urban males are labor force participants, only 29 percent of females participate. Some 68 percent of male labor force panticipants are wage employees compared with 37 percent of female participants. See Knight and Sabot (1980b). 3. Female sex (S2) is the base sub-category. 4. Because the dummy variable enters the equation in dichotomous form, the coefficient must be adjusted to obtain its percentage effect. See Halvorsen and Palmquist (1980). 5. The case against the inclusion of occupation as an explanatory variable in earnings function analysis is that it biases downwards the coefficient on education, which is nonnally a determinant of occupation. However. the coefficient on occupation may 74 Labor Market Discriminationin a Poor UrbanEconomy

represent the influence of institutional wage determination or occupation-specific human capital acquired on the job. 6. All the independent variables representing personal characteristics, and not only the productive economic characteristics, are included as part of E, because our objective is to isolate the contribution of discrimination attributable solely to sex. 7. Brown, Moon and Zoloth (1980). The probability that an individual will enter a particular occupation was treated as a function of factors affecting the demand for a supply of labor, such as education, training, experience, stability of employment, and job preference. 8. This standardization for years of employment experience eliminates the effects of skill acquisition on the job and the advantages in access to jobs that earlier cohorts of labor market entrants possessed over later cohorts in a loosening labor market. See Knight and Sabot (1981). 9. Preliminary results of a corresponding survey conducted in 1980 by a team including the authors favor the latter interpretation; the attrition rate over the decade was only slightly higher for women than for men and the average length of female employment experience doubled to 8.8 years. 10. African race (Ti) is the base sub-category. I1. Differences in the supply prices of Africans and non-Africans would provide a third possible explanation of the net wage differential if non-Africans were foreigners. However, almost all of the non-Africans in the sample were of Asian extraction and generally long-established residents of Tanzania. 12. Constraints imposed by sample size prevent us from conducting a decomposition analysis for the sub-samples. However, the consistency of the sign and the significance of the coefficient on the race dummy variable with the findings of the decomposition analysis in the sample as a whole suggest that the race dummy can be used as a rough measure of the extent of discrimination in the sub-samples. 4

Discrimination in East Africa's Urban Labor Markets

Jane Armitage and Richard H. Sabot

1. Introduction

Our concernin this paper is with urbanlabor marketdiscrimination in Kenyaand Tanzania.We focus on discriminationbased on sex and on race, althoughwe also assess whetherworkers from certain tribal backgroundsare paid more than others with the same endowmentof productivecharacteristics. For Tanzania's manufacturingsector, we have rigorouslycomparable data for 1971 and 1980 that permit us to make intertemporalcomparisons. For 1980 we have data that permit comparisonsof discriminationin the urban wage sectors of Kenya and Tanzaniaand, within each country,comparisons of discriminationin the public and private sectors.The various comparisonsallow us to assess whether our findingscan be generalizedbeyond a particulartime period, sector, or country. They provide a basis for assessing the impact of public policy on racial and sexualpay premiumsas well. The 1980 data also permit a refinement of the measure of productive endowments.Because males and females, or Africans and Asians, differ in characteristicsother than their sex or race, differencesin mean wages are an inappropriateindicator of the presenceor magnitudeof labor marketdiscrimination. The standardtechnique for measuringdiscrimination, therefore, estimates what the wage differentialwould be if the two groups had the same productive characteristicsand were paid accordingto the same wage structure.' However, as Knight and Sabot (chapter 3) noted in their analysis of discriminationin Tanzaniain 1971, "the education,experience, and trainingvariables included in the earningsfunction are only proxiesfor the productivityof a worker,reflecting inputs to, not output from, the skill-formationprocess." Differencesbetween groups in the quality of the schoolingthey receivedor in out-of-schoolinvestments in humancapital (training in the home)may account

75 76 Discrimination in East Africa's Urban Labor Markets

for the earnings differential that the standard technique attributes to discrimination. For a sub-sample of respondents surveyed in 1980, we have a measure of the output of the skill formation process: scores on the exams administered nationwide on the completion of lower secondary school (Form IV). These scores allow a more precise measure of human capital endowments and, hence, of discrimination. We can estimate for Kenya and Tanzania the magnitude of the bias that results, when the standard technique is used, from errors in the measurement of the output of the education process. The 1971 data set we employ, comprised roughly of 1,000 manufacturing sector employees, is the same as the one analyzed by Knight and Sabot. The 1980 data were generated by surveys of a random sample of workers, roughly 2,000 in each country, selected from among the entire wage labor force of Nairobi and Dar es Salaam.2 The respondents of all three surveys were asked detailed questions about, inter alia, their educational, occupational, and wage histories, although, as was noted earlier, some information was generated by the 1980 surveys that was not generated by the 1971 survey. Section 2 examines whether labor market discrimination in Tanzania's manufacturing sector had increased or decreased during the 1970s. Section 3 broadens the focus to Tanzania's wage sector as a whole, and its private and public components. Section 4 examines discrimination in Kenya's wage sector. Section 5 is concemed with the extent to which the standardized wage differentials estimated in sections 3 and 4 are the result of our using inadequate proxy measures for productivity. Section 6 offers conclusions.

2. Intertemporal comparisons of discrimination in Tanzania

Our assessment of changes in labor market discrimination during the 1970s in Tanzania's manufacturing sector is based on comparisons of eamings functions, estimated for 1971 and 1980, of the following general form:

w = F(E,L L2, LI, T22,S,X 1 where w = log of individual earnings E = years of education L, = years of employment experience T, = a dummy variable signifying non-Africans (with Africans as the base) S, = a dummy variable signifying males (with females as the base) X = a vector of other independent variables defined below

The coefficient on the race dummy shows the effect on earnings of being an Asian, after standardizing for differences between Asians and Africans in other personal characteristics. The assumption is that Asians and Africans are paid Discrimination in East Africa's Urban Labor Markets 77

according to the same wage structure, i.e., that the coefficients on the other independent variables are the same for both groups. The coefficient on the sex dummy can be interpreted similarly; the same caveat applies.

Discrimination by sex

In Tanzania in 1971, the gross wage differential between males and females was 37 percent (table 4.1).3 Knight and Sabot (chapter 3) show that this male premium was due entirely to differences between males and females in productive characteristics, in particular, in length of employment experience. Despite the fact that the most highly educated females were confined to clerical occupations, whereas males with the same education were also found in technical and managerial positions, males and females with the same productive characteristics were paid the same wage. Knight and Sabot speculate, however, that sex discrimination "may yet become a problem." If the occupational segregation of females persists, then the expansion of educational opportunities for females may result in a "crowding" of women into clerical occupations and the consequent decline of earnings for female workers in those occupations relative to earnings of males with the same education level in technical and managerial occupations. The absence of sex discrimination is unusual, and unanticipated in the African context. This may also have influenced the prediction that the phenomenon will not endure. The gross wage differential between males and females in 1980 was 15 percent, less than half its 1971 level (table 4.1). Such a decline would be consistent with increased sex discrimination if males accumulated human capital during the 1970s at a less rapid pace than females. In fact, school enrollment

Table4.1 Meanwages, levels of educationand experience:Tanzania's manufacturing sector

1971 1980 Mean Mean Mean Mean Mean years years Mean years years wages educa- experi- wages educa- experi- Group (sh.p.m.) tion ence (sh.p.m.) tion ence

Asians 829 8.3 9.2 1,584 11.1 7.6 Africans 273 3.6 8.8 668 6.2 10.4 Male 308 4.0 9.2 694 6.4 10.6 Female 225 3.7 4.4 604 5.8 7.7 78 Discriminationin East Africa's UrbanLabor Markets

ratios rose faster for females than for males, although the mean years of schooling of male and female manufacturing workers rose at roughly the same rate. The wage functions presented in table 4.2 indicate that, contrary to the prediction, there is no evidence that in 1980 the remuneration of males exceeded that of females with the same productive characteristics. In the wage function for 1980 (equation 2), as in the wage function for 1971 (equation 1), the coefficient on the dummy variable signifying the sex of the worker is both small and statistically insignificant. Equations 1 and 2 were stratified by sex and subjected to Chow tests, which confirmed that in neither 1971 nor 1980 was the structure of male wages significantly different from that of females. The coefficient on sex in 1980 is actually smaller than it was in 1971. Is this difference statistically significant? To test for changes over time in returns to particular characteristics, we pooled the data for 1971 and 1980 and reestimated the wage function. The specification of the regression is changed as follows: a dummy variable, T, signifying Tanzania, 19804 is introduced (in equation 3) to capture changes in the wage level (constant term); this dummy is interacted with the other independent variables to capture changes in returns (coefficients). Reflecting the substantial increase in (nominal) wages during the 1970s, the coefficient on the additional dummy variable is large and highly significant. The coefficient on the interaction between this and the sex dummy, T * S, though negative, is small and insignificant, confirming that there has been no change over the decade in the standardized wage differential between males and females. In sections 3 and 4, we assess whether this remarkable absence of sex discrimination, which has characterized Tanzania's manufacturing sector since Indepen- dence, is also a characteristic of the urban wage sector as a whole and of urban wage employment in Kenya.

Discrimination by race

During the colonial era in East Africa, there were separate educational systems for Europeans, Asians, Africans, and Arabs. In Tanzania in 1960, govemment expenditure on education of European and Asian children was roughly 50 percent of the amount spent on education of African children, although Europeans and Asians constituted less than 1 percent of the population. In the civil service, there were separate salary structures for the different races. The colonial powers justified this racial structure, which was imitated in the private sector, on the grounds of a racial hierarchy of productivity. In 1971, 10 years after Independence, the gross wage differential between Asians and Africans was 204 percent (table 4.1). While the markedly higher levels of education and employment experience of Asians explained roughly two- thirds of the premium, equation I in table 4.2 indicates that a very substantial Discrimination in East Africa's Urban Labor Markets 79

Table 4.2 Earnings functions for Tanzania's manufacturing sector, 1971 and 1980

1971 1980 Pooled 1971 1980 Independent variables (1) (2) (3) (4) (5)

E .081*** .081*** .081*** .079*** .081***

L2 .082*** .054*** .082*** .082*** .054*** 2 (L2) -.002*** -.001*** -.002*** -.002*** -.001*** S, .068 .039 .068 .103*** .055 T2 .722*** .547*** .722*** .716*** .737***

E T,0 .000

L2 T -.027*** 2 (L "80 (L 2)2 .T" -.001***

SI TM -.029

T2 TM -.175**

T80 .835*** PS .069*** .055** PS T .260 -.749*** Constant 4.720 5.555 4.720 4.681 5.514 R2 .570 .441 .686 .573 .452 N 939 993 1,932 939 Dependent mean 5.705 6.528 6.128 5.705

* Significant at 10-percent level. ** Significant at 5-percent level. :** Significant at 1-percent level.

racial premium of 106 percent remained after standardization (see Halvorsen and Palmquist 1980). On the grounds that discriminatory behavior was deeply embedded in social and institutional structures during the colonial period, Knight and Sabot suggest that short of draconian measures, it would take considerable time for government action to erode the Asian wage premium. During the 1970s, the gross wage differential between Africans and Asians declined substantially to 137 percent (table 4.1). This change did not necessarily signal a decline in discrimination by race, since Africans accumulated human capital at a faster pace than Asians. While the absolute difference between the 80 Discriminationin East Africa's UrbanLabor Markets

races in years of education remained roughly constant, the education levels of both groups had increased markedly by 1980, implying a decline in the proportionate advantage of Asians. Nevertheless, equation 2 of table 4.2 indicates a decline in the coefficient on the race dummy from .72 to .55.5 The coefficient on the interaction term (T R) in the pooled regression (equation 3) is substantial, negative (-.17), and statistically significant, confirming that a decline in the racial premium has occurred. Since Independence, the school system has been integrated, separate wage schedules have been abolished, and a vigorous policy of Africanization, especially of the government administration and in the parastatals, has been pursued. Moreover, the government has increased the share of the manufacturing sector it controls -50 percent of workers were employed in parastatals in 1980, compared with 20 percent in 1971 - and reduced the autonomy of parastatals with regard to wage determination. One hypothesis, therefore, is that these actions of the central govemment, rather than changes in the behavior of individual managers, resulted in the decline of the premium paid to Asians during the 1970s. To test this hypothesis, we add a dummy variable, P, signifying whether a worker was employed by a parastatal, and a term, P R, that interacts this variable with the race variable. These allow us to compare the wage levels in the two sectors and the magnitude of the racial premium paid in each. The results (table 4.2, equations 4 and 5) confirm the hypothesis. The coefficient on the race variable now measures the premium paid to Asians in the private sector. There has been no change in this premium. The coefficient is roughly .7 in both 1971 and 1980. The magnitude of the Asian premium in the parastatal sector is derived from the sum of the coefficient on the Asian dummy and the coefficient on the P.R interaction term. Between 1971 and 1980, the premium paid to Asians by parastatals declined; in 1980 parastatals paid no premium whatsoever to Asians. These results imply that the entire decline from .72 to .55 in the average premium paid Asians in the manufacturing sector is due to policies regarding the size of the public sector and the remuneration of public sector employees. The persistence of the premium paid to Asians in private manufacturing firms may reflect persistent discriminatory tastes among private sector managers that managers in parastatals do not share or are unwilling, for political reasons, or are unable to indulge. Alternatively, the premium may reflect the sharing of profits by Asian owners with Asian employees. Or it may reflect the persistence of productivity differentials between Asians and Africans with the same levels of education and experience, which are rewarded in the competitive market sector but, for political reasons, not in the non-market sector. We return to this hypothesis below. Discrimination in East Africa's Urban Labor Markets 81

3. Discriminationin Tanzania'swage sector and some intersectoral comparisons

Our intertemporal analysis has been confined by data availability to the manufacturing sector and to discrimination by race and sex. In this section, we address the following questions about 1980: Is the wage sector as a whole also characterized by the absence of sex discrimination and by a large wage premium for Asians? Are there differences between the public and private sectors in the magnitude of discrimination? Is there evidence of wage discrimination on the basis of tribe?

Discrimination by sex

Equation 1 in table 4.3 indicates that in the wage sector as a whole, the coefficient on the variable signifying sex is positive, large, and highly significant: Standardizing for differences in other characteristics, females are paid roughly 14 percent less than males. This is in marked contrast to the manufacturing sector. As a higher proportion of manufacturing sector workers than other workers are employed by private establishments, the explanation for this wage differential may lie in differences in the sex premium between the public and private sectors. It has been demonstrated that due to public sector pay policy, there are differences between the private and public sectors in the level and structure of earnings (see chapter 3 and Lindauer and Sabot 1982). The difference in levels is reflected in the significant coefficients in equation 1 on the dummy variables signifying employment in government and parastatal establishments. When this equation was stratified by ownership category, a Chow test strongly rejected the null hypothesis that the structure of wages was the same in the public and private sectors.6 Equations 2 and 3 in table 4.3 allow a comparison of the premium earned by males in the private and public sectors. The comparison indicates that males earn a substantial (roughly 20 percent) wage premium in the public sector; they do not earn a premium in the private sector. The coefficient on the dummy variable signifying sex is small, negative, and insignificant in equation 2 and large, positive, and highly significant in equation 3.7 Since this is a surprising finding, we probe further. A high proportion of clerical jobs are concentrated in the public sector. Perhaps the "crowding" that Knight and Sabot predicted has occurred and has driven down the wage in clerical jobs relative to other occupations. Table 4.4 presents the occupational distribution of men and women in the Tanzanian urban labor market. The impression from a comparison of columns 1 and 2 is that women are being 82 Discrimination in East Africa's Urban Labor Markets

Table4.3 Earningsfunctions for Tanzania'swage sector and public and private subsectors,1980

Wage sector Private Public Private Public Independent variables (1) (2) (3) (4) (5)

E .080*** .076*** .080*** .036*** .042***

L2 .054*** .042*** .064*** .033*** .051*** (L2)2 -. 001*** -. 001*** -. 001*** -. 001*** -. 001***

S, .116*** -.066 .178*** -.001*** .169*** T2 .614*** .779*** .169*** .537*** .153 TR, .209*** .228*** .214*** .092* .158***

TR2 .125** .170** .098* .100 .095* G -.162*** ... -.231** ... -.298*** PS .092*** ...... 04 .015 .070** 03 .329*** .315*** 02 .502*** .327*** 01 .892*** .772*** Constant 5.478 5.755 5.209 5.807 5.603

R 2 .503 .508 .524 .635 .605 N 1,586 608 978 605 971 Dependent mean 6.614 6.537

* Significant at 10-percent level. ** Significant at 5-percent level. * Significant at 1-percent level. crowded into the clerical occupations. Women in the Tanzanian wage labor force have a higher level of education than men, so we would expect themto be in the higher occupations more often than men. Using the male education-occupation matrix, we can predict what the distribution of women across occupations would be if the relationship between level of education and occupational attainment was the same for men and women (column 3). A comparison with the actual distribution (column 2) confirms that women are still substantially over- represented in clerical occupations and somewhat under-represented in managerial/professional occupations. Discrimination in East Africa's Urban Labor Markets 83

Table 4.4 Actual and simulatedoccupational distributions: Tanzania's wage sector,1980

Predicted occupational distribution of Men Women women Sector (1) (2) (3)

Unskilledmanual 05 17.8 25.7 14.9 Semi-skilledmanual 04 27.7 12.6 24.3 Skilledmanual 03 24.9 3.6 24.9 Clerical 02 19.9 50.5 26.6 Managerialand professional 01 9.8 7.7 10.4

To test the hypothesis that their concentration in clerical jobs explains why women with the same productive characteristics as men are paid less in the public sector, we add occupational variables to the regression. The coefficients on these variables increase monotonically, they are all significant, and a comparison of equations 3 and 5 in table 4.3 indicates that, as expected, their introduction reduces the premium on education. Standardizing by occupation does not, however, have much effect on the premium earned by males. In equation 5, the coefficient on the sex dummy is constrained to be the same in all occupations. It is possible that the premium earned by males is particularly large in the clerical occupations into which females have been crowding. In another equation (not shown), the occupation and sex variables were interacted (S * 0,). The coefficient on the male dummy remained large and significant; an F test indicated we could not reject the null hypothesis that all the coefficients on the (S * 0,) interaction terms are zero. These results suggest that sex discrimination in the public sector is not the result of crowding; males are paid a standardized premium of 20 percent regardless of occupation. The evidence of discrimination against women in the public sector suggests labor market disequilibrium. Table 4.5 presents the predicted mean wages for the different occupational categories for employees in the base category (i.e., African from a tribe other than Chagga or Haya) with the sample mean level of education and experience. Wages are presented separately for men and women in the public sector, but not in the private sector, where there is no evidence of sex discrimination. It is clear that there is a very strong incentive for women in clerical jobs in the public sector to obtain jobs in the private sector, where predicted mean wages of females are 30 percent higher. This differential is.the result both of sex 84 Discrimination in East Africa's Urban Labor Markets

Table 4.5 Predicted wages in Tanzania's public and private sectors (shillingsper month)

Public sector Private Sector Males Females sector

Unskilled manual 05 584 493 540 Semi-skilled manual 04 626 530 548 Skilled manual 03 801 677 750 Clerical 02 810 683 891 Managerial and professional 01 1,264 1,066 1,317

discriminationin the public sector and of the Tanzaniangovernment's compres- sive pay policy,8 which has raised wagesat the bottomand loweredthem at the top of the occupationalhierarchy. However, the equilibratingpressures resulting from the movementof women into the privatesector are likely to be weakened by the relative scarcity of white collar jobs in that sector. For a woman contemplatingleaving a white collar job in the public sector, the relevant comparatoris likely to be the wage she couldearn in a manualjob in the private sector.The incentivesfor a womanin a manualjob to moveto the privatesector is minimalbecause the general level of wages in these occupationsis lower in the private than in the public sector as a consequenceof pay policy.

Discrimination by race9

Equation 1 in table 4.3 indicates that in 1980 in the wage sector, as in its manufacturingcomponent, Asians are beneficiariesof a substantial(standardized) wagepremium. However, the 85-percentpremium is somewhatsmaller than in manufacturing.As in manufacturing,the coefficienton the variable signifying race is large (.78)and highlysignificant in the marketsector (see equation2) and relatively small and just significantin the non-market sector (see equation 3). The smallerracial premiumin the wage sectoras a whole than in the manufacturing sector is, therefore, a reflection of the larger share of public sector employmentin non-manufacturingthan in manufacturingactivities. It is clear that the government's vigorous attempt to reduce the racial premium in the public sectorhas been successful. Discriminationin East Africa's UrbanLabor Markets 85

Discrimination by tribe

The wage functions include dummy variables signifying whether a worker is a Chagga (TR,) or a Haya (TR2). These are two of the 15 tribal groups included in -the enumeration of wage employees in Dar es Salaam. These two groups have been among the most productive agricultural producers in Tanzania and among the first to perceive the value of schooling. They are generally believed to be disproportionately represented in the upper levels of the occupational hierarchy in the wage sector. Mean wages confirm this impression. The gross wage differential between Chaggas and other Africans is 39 percent; between Haya and other Africans, it is 29 percent. Equation 1 in table 4.3 indicates that standardizing for differences between Chagga and others in characteristics other than tribe reduced the wage premium to 23 percent. Similarly, the net premium eamed by the Haya is reduced to 13 percent. Equations 2 and 3 in table 4.3 indicate that tribal premia are substantial, irrespective of the category of ownership of the establishment in which the worker is employed.'° This is in contrast to males and Asians, who eam a premium in one or the other of these sectors.

4. Discriminationin Kenya'swage sector and some intersectoral comparisons

How does labor market discrimination in Kenya compare with discrimination in Tanzania with regard to its magnitude in the aggregate and differences in magnitude between public and private sectors? Our focus continues to be on discrimination by sex, race, and tribe.

Discrimination by sex

In Tanzania males earn a standardized premium in the public sector but not in the private sector, and this premium was captured by the wage function for the wage sector as a whole. The aggregate wage function for Kenya is presented in table 4.6, equation 1. The coefficient on the dummy variable signifying sex is small and insignificant. The same result is obtained when the aggregate function is stratified by ownership category (equations 2 and 3):" In neither the public nor the private sector is the coefficient large or significant. Following our standard procedure, we assessed whether males and females are paid according to the same wage structure. If they are not, the coefficient on the dummy variable may yield a biased estimate of the male premium. In equations estimated for the public and private sectors, respectively, the sex dummy is interacted with the education and experience variables. In the public sector, these variables are insignificant, and the results of an F test indicate we 86 Discrimination in East Africa's Urban Labor Markets

Table 4.6 Earnings functions for Kenya's wage sector and public and private subsectors, 1980

Independent Wage sector Private Public Private variables (1) (2) (3) (4)

E .107*** .102*** .121*** .125***

L2 .051*** .053*** .042*** .096*** (L,)2 -.001 ** * -.001*** -'.0004* -.0024*** S, -.013 -.003 -.024 .421***

T2 .783*** .881*** .232 .860*** TR, .077*** .086*** .077* .087*** G .001 ... -.107** ... PS .100** ...... S, E ...... -.026*

Si L2L ...... -.047**

2 S, *(L 2 ) ...... 0016** Constant 5.506 .528 5.393 5.148 R2 .400 .380 .363 .383 N 1,673 1,238 435 1,238 Dependent mean 6.822 6.745 7.041

* Significantat 10-percentlevel. * Significantat 5-percentlevel. *** Significantat 1-percentlevel. cannot reject the null hypothesis of homogeneity of the male and female wage structures. In the private sector, however, the interactive terms are significant, as is the sex dummy, and the F test rejects the null hypothesis (see equation in table 4.6). The sex dummy is positive, significant, and large (.42), indicating that women with no education and no experience earn substantially less than comparable men. But the S * L2 term is negative and significant: The returns to experience are substantially higher for females than for males. This implies that the earnings of women will catch up with and overtake the earnings of men. The positive and significant coefficient on the S * L2 term further implies that the earnings stream of females peaks earlier (at 20 years) than the male stream (31 years) and declines more rapidly. Discrimination in East Africa's Urban Labor Markets 87

A simple simulation indicates that among workers with the mean level of education of the sample, it takes six years for the earnings of women to overtake the earnings of males who entered employment at the same time."2 Among comparable workers with 7-22 years of employment experience, females actually earn more than males."3 The female earnings stream again falls below the male stream for workers with 23 years of experience. The coefficient on theS E term is also negative, indicating that the returns to education are also higher for females than for males. This implies that the point at which the earnings of women overtake those of men will occur earlier than six years among workers with above average years of education and later among workers with below average years of education. The costs of acquiring firm-specific, and thus non-marketable, skills are borne by employers, who also reap the subsequent productivity benefits such training yields. This is a well-known proposition."4 It is also generally recognized that because of labor turnover and its adverse impact on training costs, the experience-earnings profile may not be flat. Without necessarily increasing the lifetime earnings of workers with firm-specific skills, the employer may increase wages with seniority as an incentive for employment stability. If there is a stronger tendency among females than among males to change employers or temporarily withdraw from the labor force, then the incentives for women to remain on the job must be correspondingly greater, i.e., the experience-earnings profile must be steeper. The hypothesis is that in Kenya's private sector, the difference in the structure of earnings of males and females is not a product of discriminatory tastes but a rational response by employers to differences between the sexes in their commitment to wage employment. Whether the discounted present value of lifetime earnings of comparable males and females is the same, despite differences in wage structure, is a test of this hypothesis. We estimated the present values of 40 years of earnings for males and females for a "reasonable" range (5-10 percent) of discount rates. The underlying assumption, of course, is that our wage-experience profiles, derived from cross-section data, are good approximations of earnings over the life-cycle. The present value of earnings is higher for males, but the difference between the sexes is very small, ranging from 1.0 percent to 3.9 percent. Despite differences between males and females in the structure of earnings, sex discrimination in Kenya's private sector appears to be negligible.

Discrimination by race and tribe

From equation I in table 4.6, it can be seen that in Kenya; as in Tanzania, Asians earn a substantial wage premium; the coefficient on the Asian dummy is large, significant and of similar size to that in Tanzania (see table 4.3, equation 1). The pattern of Asian premia in the public and private sectors is also identical 88 Discrimination in East Africa's Urban Labor Markets to that in Tanzania: The coefficient is very large (.88) and significant in the private sector and much smaller and insignificant in the public sector. The wage functions also contain a dummy variable (TRI) signifying that the employee is a Kikuyu. In Kenya the Kikuyu are not only the largest tribe, but also are generally perceived as the most privileged and powerful. They were the first Kenyans to be affected by social and economic change during the colonial regime and to become politicized. From equation 1, it can be seen that the Kikuyu earn a premium of around 8 percent compared with non-Kikuyu with the same levels of education and experience. There is little difference in the premium between the private and public sectors.

5. Discrimination or unmeasured human capital

Years of attendance is an important input into the skill formation process that occurs in schools. There are other inputs, however, such as individual ability, quality of schooling, and the quantity and quality of out-of-school investments in human capital. Therefore, years of schooling may be a poor proxy measure for the output of the school system. Among workers with the same years of schooling, there is likely to be substantial variance in skills acquired in schools and, therefore, in productivity and earnings. In the empirical analysis of labor market discrimination, this heterogeneity among workers with the same number of years of schooling is of no concern if there is no correlation between the non-productive characteristic under consideration - race, sex, or tribe - and the level of these other inputs into the skill formation process. If there is a correlation, then the residual premium for example of males relative to females, that remains after standardizing for differences in years of schooling is a biased measure of the magnitude of discrimination. The bias can go either way. If the group discriminated against has higher levels of these other inputs, then discrimination is underestimated. If the recipients of the wage premium have benefitted from higher quality schooling or more and better training in the home, then the magnitude of discrimination is overestimated. What appears to be discrimination may simply be unmeasured human capital. The heterogeneity among workers with the same years of schooling but different levels of complementary inputs can be captured by an appropriate measure of the output of the schooling process. Exam scores at the 0 level are such a measure for Kenyan and Tanzanian workers with Form IV or more education. They do not measure the affective skills of Form IV-leavers, but they do measure their cognitive skills, an important output of the school system. Table 4.7 presents for both countries the distribution across five grades of males and females and of Africans and Asians. In neither Kenya nor Tanzania does the performance of males differ substantially from the performance of females. Any tendency for males to benefit from more training within the household or from Discrimination in East Africa's Urban Labor Markets 89

Table 4.7 Form IV exam scores

Kenya Tanzania Males Females Africans Asians Males Females Africans Asians

Div 1 14.8 8.0 12.2 40 10.3 7.1 9.4 12.9 Div 2 22.9 28.7 23.0 40 38.6 25.0 35.4 45.2 Div 3 33.3 31.0 33.4 20 31.6 41.1 34.0 25.8 Div 4 20.7 25.3 22.8 ... 15.1 19.6 16.5 9.7 Fail/did not sit 8.3 6.9 8.6 ... 4.4 7.1 4.7 6.5

higher quality schooling is apparently offset by the more stringent acceptance criteria applied by secondary schools when filling the proportionately fewer places open to women. There is a marked, and predictable, difference between Kenya and Tanzania in the 0-level performance of Asians relative to Africans. In Kenya 80 percent of Asians placed in the top two divisions compared with 35 percent of Africans; in Tanzania the proportions of Asians and Africans in Divisions I or 2 are more similar, 58 percent and 45 percent, respectively. This difference between Kenya and Tanzania is a reflection of the marked difference in size and, therefore, in the selectivity, of their secondary school systems. The secondary enrollment rate is roughly six times greater in Kenya than in Tanzania. Nevertheless, in both countries a very high proportion of Asian primary-leavers progress to secondary school.'5 In Tanzania the small secondary system is selective of only the most accomplished of African primary-leavers who compete on an equal basis with Asian students. This is reflected in the 0- level scores of the two groups. The larger Kenyan secondary system is less selective of African primary-leavers. As a consequence, many Africans are at a marked disadvantage when competing with Asians, and this, too, is reflected in the 0-level scores. Workers with a given number of years of schooling are nevertheless heterogeneous. We expect workers with higher exam scores in both Kenya and Tanzania to have higher earnings. We do not, however, expect the addition to our model of wage determination of variables signifying exam scores to influence our assessment of the magnitude of sex discrimination in either Kenya or Tanzania or of race discrimination in Tanzania. This is because, for the reasons noted above, there is only a weak correlation between the Form IV-leaver's sex or (in Tanzania) race and performance on the exam. We predict that the change in 90 Discrimination in East Africa's Urban Labor Markets specification will reduce the magnitude of race discrimination in Kenya. The exams indicate that in Kenya, Asian Form IV-leavers are more skilled than African Form IV-leavers. Table 4.8 presents wage functions estimated for the strata of our 1980 samples in Kenya and Tanzania with Form IV or more education. In these equations, the continuous education variable is replaced by a dummy variable (E5) signifying whether the respondent progressed beyond Form IV education; four dummy variables (D1-D4) are added, signifying the 0-level division in which the respondent was placed, with "failed or did not sit" as the base; and three dummy variables (S2-S4) are added signifying the type of secondary school attended."6 The results confirm our expectations that among Form IV-leavers, earnings vary with the level of cognitive skills. In both countries, the earnings of secondary-leavers are shown to increase monotonically with their performance on the 0-level exams. All four coefficients on the division dummies are significant at the 1-percent level in Kenya; the coefficients on the top two division dummies are significant at the 5-percent level in Tanzania. Kenyan workers in Division 1 earn on average a 169-percent premium relative to workers in the base category; in Tanzania the comparable premium is 28 percent.'7 Our predictions regarding the impact of a more refined measure of human capital on the estimated magnitude of discrimination are also borne out. Comparisons of equations 1 and 3 in table 4.8, which do not include the exam score dummies, with equations 2 and 4 indicate that in neither Kenya nor Tanzania does taking account of the heterogeneity of Form IV-leavers influence our assessment of sex discrimination: The coefficients on the sex dummy are much the same in both sets of equations. Likewise for Tanzania, the addition of the exam scores does not alter the coefficient on the race dummy, which remains large and significant. As predicted, it is in Kenya that the standardized premium on race is affected by our more refined human capital measure. The premium Asian Form IV-leavers earn relative to their African comparators falls from 65.5 percent to 37.4 percent."

6. Conclusions

Sex discrimination is pervasive in the labor markets of high-income countries. Nevertheless, the absence of wage discrimination against females, first documented in Tanzania's manufacturing sector in 1971, proved to be the rule in East African urban labor markets rather than the exception. Based on evidence of occupational "crowding" in 1971, it had been predicted that such discrimination would emerge during the 1970s. Once differences in characteristics were taken into account, however, we could find no evidence that males were paid a premium in Tanzania's manufacturing sector, or in its urban private sector, in 1980. Nor was there evidence of sex discrimination in Kenya. In Kenya's private sector, women had lower starting wages but higher returns to experience. This Discrimination in East Africa's Urban Labor Markets 91

Table 4.8 Wage functions: form IV or more leavers

Kenya Wage sector Tanzania Wage sector (1) (2) (3) (4)

E5 .677*** .319*** .453*** .364*** L, .101*** .098*** .105*** .103*** 2 (L2) -.002*** -.002*** -.002*** -.002*** S, .076 -.085 .138** .115* T2 .504*** .318*** .587*** .610*** TR, -.006 -.028 .176*** .176***

TR2 ...... 018 .054 D1 ... .990*** ... .249** D2 ... .721*** .225**

D3 ... .474*** ... .104 D4 ... .271 ... -.028 S2 ... -.010 .054 S3 ... .018 ... .059 54 ... -.185* ... .197* Constant 6.562 6.164 6.124 6.026

R2 .440 .524 .596 .609 N 513 480 351 295 Dependentmean 7.212 7.189 7.033 7.071

* Significantat 10-percentlevel. ** Significantat 5-percentlevel. *** Significantat 1-percentlevel.

sex difference in the structure of earnings is consistent with the need to provide greater incentives for employment stability to women and did not appear to place women at a disadvantage with regard to life-time earnings. Tanzania's public sector proved to be the one exception to the rule. Among workers with the same education and employment experience, males earned a substantial (roughly 20 percent) premium. This standardized premium was found in all occupations. The neo-classical theory of discrimination may provide an explanation for this phenomenon. Managers of establishments in the public sector may simply be indulging their individual preferences for males when making hiring, promotion, and wage-setting decisions. For this explanation to apply, 92 Discrimination in East Africa's Urban Labor Markets however, either the preferences of these managers must differ from those in Tanzania's private sector and in the private and public sectors in Kenya, or the Tanzania public sector must be unique with regard to its ability to bear the costs resulting from the allocative inefficiency associated with discrimination. Alternatively, the wages of female workers may have absorbed relatively more of the impact of the severe budgetary constraints imposed on the Tanzanian public sector, because managers believed that the welfare consequences of such a distribution of costs to be minimal. Virtually all female public sector employees are secondary earners; they are married to workers with earnings equal to or greater than their own. The role of the government as discriminator against women is particularly odd in East Africa, given the effectiveness of policies aimed at reducing racial wage premia. Asians have a distinct labor market advantage because of their greater human capital endowments. However, after controlling for differences between Asians and Africans in educational attainment and employment experience, the racial premium remained large - 60 percent or more - in the private segment of Tanzania's manufacturing sector in 1971 and 1980, and in the private segment of the entire urban wage sector in both Kenya and Tanzania in 1980. The standardized racial premium is markedly smaller in the public sector in both countries. Indeed, in Tanzania's public sector the racial premium has been eliminated. The decline in the racial premium between 1971 and 1980 was entirely accounted for by public policy; there was no decline in the premium paid by private employers. The persistence of a racial premium in Tanzania's private sector does not appear to be due to differences between Asians and Africans with the same levels of education in human capital endowments. There is substantial variance in skills acquired in schools, but in Tanzania, because of the nature of the secondary school system, there is no correlation between race and skill level, as measured by scores on 0-level examinations. While exam scores are important determinants of the earnings of form IV leavers in Tanzania, their inclusion in the wage function does not reduce the magnitude of the standardized racial premium. In Kenya, however, refining the measure of productive endowments of employees substantially reduces the standardized racial premium. Not only are exam scores important determinants of earnings, but there is a strong correlation between race and performance on the exams. In sum, the absence of sex discrimination in much of the urban wage labor market in East Africa challenges the common presumption that economic development brings social enlightenment, although it should be recalled that women in East Africa still have relatively limited access to post-primary education. The persistence of racial discrimination challenges the notion that only groups with substantial political and economic power benefit from discrimination. The relatively small standardized racial premium in the public sector illustrates the power of the government to reduce the arbitrary disadvantages inflicted on Discrimination in East Africa's Urban Labor Markets 93 some groups in the labor market. The relatively large standardized sex premium in Tanzania's public sector serves as a warning that government power can also bestow arbitrary advantages. Finally, the large bias in the measurement of racial discrimination revealed in Kenya when more refined measures of human capital were substituted for the conventional variables should reinforce the tendency to be cautious in interpreting results when conventional methods of discrimination analysis are applied.

Notes

1. See Blinder (1973); Oaxaca (1973a, 1973b); Malkiel and Malkiel (1973); Brown, Moon and Zolath (1980). For developing countries, see Behrman and Wolfe (1982); Birdsall and Fox, chapter 6; Knight and Sabot (chapter 3). 2. The Tanzania survey included among the establishments selected in the first stage of the two-stage sampling process the manufacturing establishments surveyed in 1971.

3. G = W.w-w - Wf where wi signifies mean wages and m andf signify males and females, respectively. 4. The base, naturally, is Tanzania 1971. 5. For 1971 a Chow test rejected the null hypothesis that the wage structures of Africans and Asians was the same, but taking account of the differences did not much affect the standardized racial premium (see Knight and Sabot, chapter 3). In 1980 we could not reject the null hypothesis. 6. Further statistical tests within the public sector indicated that while there are differences in levels (constant terms) between government and parastatal establishments, we cannot reject the null hypothesis that their earnings structure is the same, justifying our treatment of the public sector as a unified aggregate. 7. The estimate of the premium earned by males is not altered by allowing for sex differences in the structure of wages. In other regressions, not presented here, the sex dummy was interacted with the education and experience variables. For both public and private sectors, F tests could not reject the null hypothesis that the returns to education and experience are the same for males and females. 8. Note that for clerical jobs, wages for males are lower in the public than in the private sector. 9. The estimate of the premium earned by Asians is not altered by allowing for racial differences in the structure of wages. In an exercise analogous to that described in note 5, we could not reject the null hypothesis that in both the public and private sectors, Asians and Africans do not differ with regard to the returns to education and experience. 10. Because of the small numbers in the two tribal categories that are the focus of our attention, it was not feasible to stratify the regression so as to assess whether the tribal premia are altered by allowing for racial differences in the structure of wages. 11. A Chow test was done to test for differences between the two categories in the structure of wages. The value of the F statistic was 2.16, which just exceeds the critical value of F.05 (6,1658) = 2.09. The null hypothesis of homogeneity of the two functions 94 Discrimination in East Africa's Urban Labor Markets is thus rejected. When the public sector function was further stratified into parastatal and government regressions and subjected to a Chow test, the null hypothesis was accepted. 12. The equation defining the female eamings profile, assuming she has the sample mean level of education E (where E = 7.5 years), is: logeWF = a + .125E + .096L - .0024 L2. For males with E years of education, it is: 2 logeWm = a + .421 + .099(E) + .049L - .0008L . where a = constant (same for males and females). Equating these two regressions we can solve L*, where L* is the number of years of experience at which men and women with the mean level of education earn equal wages. 13. Since the mean years of experience of women in the sample is 7.5, the representative female worker actually earns more than her male comparator. Nearly 50 percent of the women in the sample have between 7-22 years of experience. 14. See Becker (1964b) for a theoretical exposition and Knight and Sabot (1980a) for an assessment of the importance of firm-specific skills in Tanzania. 15. Because of the markedly higher educational levels of their parents, in both countries, Asian children tend to benefit from more and higher quality training within the household. Because of their concentration in urban areas, the quality of primary schooling Asian children receive tends to be higher than the quality of schooling received by African children. Therefore, Asian children tend to perform relatively well on the primary-leaving exams, which provide the basis for selection to government secondary schools. 16. The base dummy is government secondary; S2 signifies harambee, S3, private and S4, technical secondary. There is substantial variation in quality by type of school; the impact of this variation on wages appears to be indirect, through exam scores. 17. Cognitive skill levels could be used by employers as a sophisticated screen for "natural ability." For evidence that this is not the case in either Kenya or Tanzania, see Boissiere, Knight and Sabot (1985), who estimate wage functions with ability as well as cognitive achievement as independent variables. 18. Wage functions are presented for the whole wage sector and not stratified into public/private subsectors. Adding the government and parastatal dummies somewhat reduces the size and significance of the examination scores in Tanzania, suggesting some "creaming" by the private sector. 5

Earnings and Determinants of Labor Force Participation in a Developing Country: Are There Gender Differentials?

Jere R. Behrman and Barbara L. Wolfe

1. Introduction

In the past decade, interest in the socioeconomic roles of women in developing countries has exploded. In the burgeoning literature on this topic, numerous hypotheses are presented about gender differentials in earnings and determinants of labor force participation (Boserup 1970; Buvinic 1976). Most of this literature, however, is characterized by casual observations; there has been little effort at systematic empirical analysis or hypothesis testing regarding gender differentials in the labor force. In this paper, we present the first (to our knowledge) systematic analysis of gender differentials in determinants of labor force participation and earnings on a general level in a developing country.' An extended double selectivity model in the Mincer-Heckman tradition is used. This model is expanded beyond the standard formulation in two respects. First, an enlarged set of human capital variables that includes health and nutrition in addition to the standard schooling and work experience variables is considered.2 We do so because of the considerable emphasis in the development literature on the important effect of health and nutrition on productivity (for example, Leibenstein 1957). Second, the In earnings estimates control for the "report inclination" in addition to the more common "labor force participation inclination" in a double selectivity model. We make this extension because for over 12 percent of those who participate in the labor force in our sample, earnings are not reported; simply dropping such individuals from the earnings estimates might cause selectivity bias. For our application of this extended model, we consider, in addition to overall national

95 96 Earnings and Determinants of Labor Force Participation estimates, three subsamples defined by the degree of urbanization because of frequent hypotheses about regional segmentation of labor markets in developing countries.3 For all of our estimates, we allow for the possibility that additive terms and variable coefficient estimates differ between males and females. This approach allows the consideration of a number of interesting and important questions about possible gender differentials in labor force participation and earnings functions in a developing country: Does the more widespread presence of extended families mean that the impact of child care on labor force participation differentiates less between males and females in developing than in more developed countries? Does the pattern of the estimates of the impact of nutrition suggest that adult males are favored over adult females in the intrahousehold distribution of nutrients, as has often been claimed? Do generally lower earnings for women reflect lower human capital stocks, lower returns to those stocks, or both? Are there differences associated with the degree of urbanization in these and other pattems? We organize our presentation as follows: section 2 introduces the sample and male-female differences in earnings and in human capital stocks. Section 3 sketches the double selectivity model. Sections 4, 5, and 6 consider, respectively, the estimates of the labor force participation inclination, the report earnings inclination, and In earnings functions. Section 7 presents concluding remarks.

2. Data and male and female differences in human capital and earnings

Our data are from a nationwide stratified random sample of women aged 15-45 (excluding nonworking students), which we collected in Nicaragua in 1977- 1978.4 For this study, the sample is limited to 2,962 currently accompanied women and 1974 currently accompanied men.' Because of frequent hypotheses mentioned above about differences in labor markets with the degree of urbanization in developing countries, we distinguish among three regions by the degree of urbanization: the central metropolis with about half a million inhabitants (almost a quarter of the country's population); other urban areas with from 500 to 78,000 inhabitants; and rural areas. Table 5.1 gives the breakdown by sex and region of the overall sample, labor force participants, and those who report earnings. Table 5.2 presents means of major human capital variables, of the labor market variables of interest for males and females, and of some important household characteristics for the overall sample and for the three regions. We summarize these distributions first across regions and then between sexes. There are some important differences among the distributions for the three regions. Generally, they reflect the common stereotype of a positive association between urbanization and well-being, though there are some notable exceptions to this pattern. Among the human capital variables, mean schooling is substantially higher in the two urban areas than in the rural areas for both men and Earnings and Determinants of Labor Force Participation 97

Table 5.1 Sample breakdown by sex, region, labor force participation and reported earnings

Central Other Nation metropolis urban Rural

Originalsample (S,) 4,936 1,999 1,553 1,384 Females 2,962 1,167 945 850 Males 1,974 832 608 534 Labor force participation(S 2) 2,683 1,158 872 653 Females 893 406 333 154 Males 1,790 752 539 499

Reportearnings (S 3 ) 2,340 1,038 773 484 Females 827 386 299 142 Males 1,513 687 474 342

women; mean days ill are higher in the central metropolis than elsewhere for women, but are higher in rural areas than elsewhere for men; mean work experience for women is highest in other urban areas and lowest in rural areas, but mean work experience for men is highest in the rural region;6 and mean household nutrition as a proportion of international standards is highest in the other urban areas and lowest in the rural region (but below international standards for most households in all three regions). Among the labor force variables, current participation rates are almost twice as high for women in the urban areas as in the rural region, but do not vary much for men across regions; the proportions of labor force participants who report earnings reflect a sharp urban-rural dichotomy for males, but not for females; and mean In earnings are positively associated with urbanization for both men and women, with a greater difference between the central metropolis and other urban areas for women than for men.7 Among the other household variables, the presence of children under five is more common in rural than in urban households, but a relatively higher proportion of households with small children have home child care from relatives in the central metropolis than elsewhere; and mean other income is greater in the central metropolis than elsewhere, but with much greater variance in rural than in urban areas. There also are important differences between the distribution for women and men. Generally, but not always, they reflect the stereotype of greater human capital stocks and greater earnings for men than for women. Among the human 98 Earnings and Determinants of Labor Force Participation

Table 5.2 Means for major variables used in labor force analysis for national and three regional samples, Nicaragua 1977-78

Central Other Nation metropolis urban Rural

Female characteristics Age (years) 29.40 29.60 29.50 29.50 Schooling (grades completed) 4.00 4.60 5.00 1.50 Work experience (years) 5.70 5.30 6.00 4.90 Days ill in current year 4.20 5.00 3.40 3.30 Labor force participation rate .30 .35 .35 .18 Report earnings rate .93 .95 .90 .92 Ln earnings (cordobas per fortnight)a 5.46 5.62 5.42 4.98

Male characteristics Age (years) 34.00 32.70 34.40 35.00 Schooling (grades completed) 5.10 7.40 6.20 1.20 Work experience (years) 20.60 19.90 19.90 24.00 Days ill in current year 7.70 7.30 6.40 10.80 Labor force participation rate .91 .90 .89 .93 Report earnings rate .85 .91 .88 .69 Ln earnings (cordobas per fortnight)a 6.31 6.61 6.46 5.73

Household characteristics Nutrition (proportion of international standards) .63 .60 .75 .51 Presence of children under 5 (proportion) .73 .69 .70 .81 Home childcare (proportion) .29 .44 .20 .17 Other income (103 cordobas per fortnight) .573 .592 .558 .562 Primary own farm (proportion) .11 .00 .00 .39

Note: For more detailsconcerning the data, see Wolfeand others (1980). At the timeof the survey, seven cordobas equaled one U.S. dollar. The labor force participation and report earnings rates are calculated from table 5.1. a. For those who report positive earnings only. Earnings and Determinants of Labor Force Participation 99 capital variables, mean grades of schooling are 1.1 years greater for men than for women, with an increasing differential favoring men with greater urbanization;8 mean work experience is 15.2 years greater for men than for women, with an inverse differential with greater urbanization;9 and mean days ill are over 80 percent higher for men than for women, with the largest differential for the rural region. Among the current labor force variables, current rates of labor force participation for men are about triple those for women, with the largest difference in rural areas; the proportion of participants who report earnings is higher for women than for men, due in large part to the differential noted above for rural areas; and at the points of mean earnings, the level of earnings for men is 234 percent of that for women in the overall sample - and 269 percent, 282 percent, and 212 percent, respectively, for the three regions in order of decreasing urbanization. Thus, the distributions suggest substantial differences in human capital stocks and in labor market outcomes across regions and between sexes. Below we examine to what extent the differences in labor market outcomes reflect differences in capital stocks versus differences in the returns to or effects of those stocks.

3. Labor force participation, reported earnings and a double-selectivity model for In earnings

We begin with a Mincerian model in which ln earnings depend on linear and quadratic terms in formnaleducation and in actual work.10 As is noted above, we extend the definition of human capital variables to include nutrition and health status, since such factors are often emphasized as being important productivity determinants in developing countries (Leibenstein 1957). We also note that employment conditions in Latin American countries such as Nicaragua apparently satisfy at least one of the assumptions of most models of labor force supply better than do conditions in labor markets in the United States: hours worked can be adjusted to equate the market wage and the shadow wage (Heckman 1974). Casual empiricism suggests that there is much more flexibility in hours of employment in the labor markets under study here than is the case for most samples used from the United States and other developed countries. But such a ln earnings function can be estimated only for the individuals in our sample who report eamings. The subsample of such people is a non-random sample of people who are selected by rules pertaining to (1) "labor force participation inclination" and (2) "report inclination." In the overall sample of 4,936 people, 2,683 participate in the labor force and 2,340 participate and have eamings reported (table 5.1). This possibly leads to a double-selectivity framework. A number of studies consider the first selection rule. Generally, 100 Earnings and Determinants of Labor Force Participation however, the possibility of selectivity in reporting earnings has not been considered. Instead, those for whom earnings are not reported are assumed to be a random subsample. But our earlier work suggests that reporting data may cause selectivity (Behrman and Wolfe 1984a; Behrman, Wolfe and Tunali 1981). Therefore, we posit a double selectivity framework, which is formalized as follows. For the ith individual in our sample, we have:

"participation inclination" YI* X +i (1)

"reportinclination" Y4 = Xi +U 2 i (2)

"In earnings function" Y.= x3X + U3, (3) where X, is a K x I vector of regressors, pi is a K x 1 vector of unknown parameters, and

E(Ujd) = 0 j = 1,2,3; (4)

E(Up,Uj/i) = aj V j, j' 1,2,3; i =i (5) 0 j,j'= 1,2,3; i•i'

The main objective is to estimate the parameters of equations (1) and (3). For equation (3), the unobservable continuous random variables Y * and Y2 determine the subsample (or selected individuals) for which complete observations are available. We introduce the dichotomous variables Y, and Y2 to indicate the outcomes of the selection processes in equation (1) and (2):

individual participates in labor force: 1 if Y, > °

Y,i individual does not participate in O if Y* < O labor force: i (6) Earnings and Determinants of Labor Force Participation 101 individual's earnings reported (and individual participates in the labor 1 if Y. > 0 and Y, = 1, force):

Y2i individual's earnings not reported (even though individual participates a in the labor force): i (7) individual does not participate in the labor force: unobserved if Y, = 0.

Y3, is observed if and only if Y2.= 1, that is, if and only if:

Y1* > O and Y2 > 0. (8)

This sequential selection process partitions the original random sample into three mutually exclusive, non-random subsamples, namely, those with Y, = 0, those with Y2 = 0, and those with Y2 = 1. We denote the subsamples by S1, S2, and 53, respectively. Since S3 consists of individuals for whom Y3 is observed, the In earnings regression equation may be written as:

) =3 Xi + E(U3 jIY2 = 1)(9

Y3, {l 32iX, + E(U 3 , * >0,> >0°)

Therefore, if

E(U3.I Y3i > 0, Y2i > 0) 0,

ordinary least squares result in inconsistent parameter estimates, or "selection bias." Consistent estimation of the parameters in equation (9) requires knowledge of the form of the conditional expectation

E( U3; I Y, > 0, Y2i > 0),

hence the conditional distribution of the error term. This calls for imposing additional structure on the model. Earlier papers by Tunali and others (Behrman, Wolfe and Tunali 1981; Tunali 1983) discussed the maximum likelihood formulation of this double-selectivity problem, identification, estimation, and prediction, as well as the properties of a constrained model in which the two selection rules are assumed to be independent. The papers demonstrate that this constrained version is an extension 102 Earnings and Determinants of Labor Force Participation of Heckman's (1979, 1976) selectivity estimator with sequential (independent) selection rules. In this case, we can use S, to estimate the probability of labor force participation; S2 to estimate the probability of reporting earnings conditional on labor force participation; and the inverses of Mill's ratios from the two selection rules to control for selection in the estimation of the In earnings function with subsample S3 :

Y3i = 3 Xi + 71 72Y+-2 + W3 (10) where E(W3 IY* >0,Y 2 >°)=°

-= f___ as estimated from probit for work inclination from S,. I-F,

XI = f2 as estimated from probit for work inclination from S2- 1- F 2 We adopt this procedure for the present study."

Table 5.1 gives the S, S2 and S3 subsample sizes for men and women for the three regions and for the combined sample. In the next section, we discuss our estimates of the probability of labor force participation based on the S, subsamples. In the subsequent section, we turn to the probability of earnings being reported based on the S2 subsamples. Then in section 5, we consider the In earnings functions within this double-selectivity framework with the S3 subsamples.

4. Probits for labor force participation

Labor force participation rates for women average 0.30, but there is a sharp urban-rural dichotomy, with values of 0.35 for the urban regions and 0.18 for rural areas. As in many countries, labor force participation rates for males are much higher, with an overall average of 0.91 and not much regional variation (table 5.2). The determinants of the probability of labor force participation are posited to be a standard comparison between market and reservation wages, with some additions to reflect conditions in developing countries. Both market and reservation wages may depend on the standard human capital variables pertaining to schooling and experience and the additional nutrition and health variables emphasized in the development literature. The reservation wage also is posited to depend on the standard other-income and the need-for-child-care-for-children- under-five variables. In addition, other income is multiplied by a 0-1 dichotomous variable with a value of one if the household is primarily engaged in own- farm production, since agrarian income is subject to greater variations than are many other income sources and probably was below normal in the year of the Earnings and Determinants of Labor Force Participation 103 sample.'2 In addition, the presence-of-children-under-five is multiplied by a 0-1 dichotomous variable with a value of one if home child care is provided by children over age 14 or by adults in an extended family, since such forms of child care are thought to be relatively important for developing countries. Table 5.3 presents probit estimates for labor force participation (relation 6 above) for the overall sample and for each region. Both men and women are included in each estimate, but each parameter is allowed to differ for women from the value for men. Overall, the probits are significant nonzero and significantly different among regions at high levels. Therefore, we focus on the regional estimates. Also, the asymptotic t-tests indicate that the determinants of male versus female labor force participation differ in a number of respects. For men, schooling has no significant coefficient estimates at the standard 5-percent level.'3 Even at the 10-percent level among the regional estimates, the coefficient estimates are significant only in the rural areas (with a carryover of the significant positive quadratic effect to the national sample). Therefore, schooling does not seem to have a very important impact on male participation decisions. In contrast, the quadratic experience term has the standard significantly negative coefficient estimates for both urban regions and the national sample. The linear experience terms do not have significant coefficient estimates. Nevertheless, for the urban areas and the national sample, the estimates seem to imply the typical quadratic serial correlation of experience, with age representing the positive linear experience term (the two are highly correlated). The extended human capital variables apparently have some impact on male participation. Nutrition is significantly positively associated with participation in the other urban areas, and days ill is negatively associated with participation in the central metropolis and national samples. The common result that child care considerations do not affect male participation significantly is obtained. But there is some hint of an effect in that at the 10-percent level,-the presence of small children reduces male participation in the central metropolis unless offset by home child care (which, at the same level of significance, also has an effect in other urban areas and for the national sample). Finally, other income significantly reduces male participation in the other urban and national samples (and, at the 10-percent level, in the central metropolis), except for primarily own-farm households in the national sample. Thus, the comparison between market and reservation wage model with some extensions for the special conditions of developing countries has some explanatory power for males, at least in urban areas. For women the estimates differ significantly in a number of respects from those for men. Schooling has a significant positive quadratic effect for women 104 Earnings and Determinants of Labor Force Participation

Table 5.3 Labor force participation probit estimates for males and females in national and three regional samples, Nicaragua 1977-78

Central Nation metropolis Other urban Rural

Independent variables

Constant .71 (2.4)' .36 (0.7) -.80 (1.4) 2.47 (3.1)' Age .03 (2.4)' .05 (2.7)' .05 (2.1)' -.04 (1.2) Schooling -.04 (1.3) .05 (1.1) -.03 (0.6) -.20 (1.8)" 2 Schooling .004 (1.8)" -.001 (0.3) .002 (0.8) .003 (1 -6)b Experience .01 (0.7) .02 (0.7) .01 (0.3) -.01 (0.1) Experience2 -.001 (2.5)a -.001 (2.6)a -.001 (2.2)' .001 (1.3) Nutrition -.25 (1,1) -.79 (1.8)" 1.06 (2.2)' -.04 (0.1) Days ill -.01 (3.7)' -.01 (3.6)' -.003 (0.7) -.01 (1.6)

Children under 5 -.03 (0.3) -.27 (1.0)" .04 (0 .2 )b .11 (0.5) Home child care* children under 5 .16 (1.6)b .27 (1.8)b .31 (1.6)b -.10 (0.4) Other income -.24 (2.7)' -.19 (1.7)' -.46 (2.1)a -.45 (0.9) Primarily own-farm* other income .28 (2.8)' .45 (0.9) Female dichotomous variable times:

Constant -2.69 (7.7)a -1.62 (2.8)' -1.71 (2.5)a -4.49 (5.2)' Age -.03 (2.0)a -.06 (3.0)' -.03 (1.2) .04 (1.3)

Schooling .06 (1 7 )b -.10 (1.6)b .06 (0.9) .19 (1.5) Schooling2 -.001 (0.2) .01 (1.9)" .001 (0.2) -.02 (1.1) Experience .18 (10.1)a .18 (6.1)' .22 (7.6)' .12 (2.4)' Experience2 -.004 (8.1)' -.004 (4.3)' -.01 (6.3)' -.004 (3.0)' Nutrition .95 (3.3)' 1.51 (2.9)' -.58 (1.0) .77 (1.3) Days ill .01 (2.1)a .01 (2.1)' -.002 (0.3) .01 (1.0) Children under 5 -.07 (0.6) -.01 (4.9)' .08 (0.4) -.05 (0.2) Home child care* children under 5 .01 (0.1) -.10 (0.6) -.17 (0.7) .13 (0.5) Other income .13 (1.3) .05 (0.4) .25 (1.0) .59 (1.1) Primarily own-farm* other income -.26 (2.3)' -.71 (1.3) -2* Log likelihood function 2,744 1,030 842 943

Note: Table 5.1 gives sample size (S,) and numbers of participants (S2). Table 5.2 gives variable definitions and units. After the point estimates in parentheses are absolute values of asymptotic t- statistics. a. Asymptotically significantly nonzero at 5-percent. b. Asymptotically significantly nonzero at 10-percent. Earnings and Determinants of Labor Force Participation 105

(but not for men) in the central metropolis. Experience has strong quadratic impacts in all three regions (perhaps a little weaker in the rural subsample) on top of those suggested for men in urban areas. For nutrition, there is a significant positive impact for women in the central metropolis and on the national level beyond that for men, in addition to the equal impact on men and women in the other urban areas. For health, the significant positive coefficient estimate for the differential impact of days ill on female participation implies that less healthy women do not have lower labor force participation rates in the central metropolis (and national estimates) - in contrast to men. The significant negative effect of the presence of small children on female labor participation also is in contrast to the results for men and in accord with many other studies. But what is more striking is that in the other two regions and on the national level, there is no significant impact of the presence of small children on female labor force participation, and in none of the samples does home child care have significant effects. Child care considerations seem to play a much less important role in women's decisions about labor force participation in Nicaragua's developing economy than in the industrialized economies. The effects of other income on female labor force participation generally are the same as on male participation. The only exception is that women in primarily own-farm households in the national sample are significantly less likely to participate if other income is higher, in contrast to the absence of such an effect for men from the same type households. Finally, for women there is a significantly negative additive effect on labor force participation - larger for the rural than for the urban areas - in addition to those effects captured by our human capital variables and household characteristics. This may reflect tastes, since the rural areas are thought to be more conservative and therefore perhaps less accepting of women working in the paid labor force. But it also may just reflect the comparison of returns from market versus nonmarket activities, given the differential pattems across regions in the market returns estimated in the In earnings functions (section 6). Thus, the determinants of women's labor force participation differ significantly from those for men in all three regions and on the national level. Among the observed variables, the differential effects are strongest for experience, reflecting much greater serial correlation in women's labor force participation than in that for men's. This may be due to tastes or to gains from past on-the-job learning, which increase the retums to market versus nonmarket activities. Whatever the cause, in a context in which labor force participation for prime-age females is substantially less than universal, there apparently are strong persistent individual effects for females. There also are sharp differences across the regions. Except for experience, there are no significantly different effects of the observable variables for women than for men in the other urban and rural areas. In the central metropolis, however, not only do the effects of the coefficients of experience differ 106 Earnings and Determinants of Labor Force Participation

significantly for women, those for age, the quadratic term in schooling, nutrition, days ill, and the presence of small children also differ. Thus, in the most urban market, there seems to be the greatest difference in the effects of observable human capital and child care variables on female versus male participation. In contrast, with the exception of experience, these variables do not have significantly different effects elsewhere - though the unobserved variables that affect the constant estimate certainly do.

5. Probits for reported earnings

We estimate probits for reported earnings not because they are of great interest in themselves (as are the relations in sections 4 and 6) but because of possible selectivity bias in the estimated earnings function (section 3). For the sample as a whole, 87 percent of the labor force participants report earnings. With the exception of the much lower reporting rate for males in rural areas (i.e., 69 percent), there is not much systematic variance in reporting rates across regions or between the sexes (table 5.2). We conjecture that the much lower rate for males in rural areas reflects, in part, greater lack of information on the part of the women respondents because of the greater prevalence of seasonal migration for male participants in the rural paid labor force than for others. Earnings data may not be reported for at least four reasons."4 First, individuals may not have had earnings in the relevant period because they were ill, unemployed, or on vacation. In the case of illness or unemployment, we expect human capital stocks to be positively associated with employment and good health, and thus with reporting. However, human capital stocks and other income also may be positively associated with the probability of being on vacation, or with the probability that a given state of poor health will lead to not working, and thus to not reporting earnings for those reasons. Second, individuals who had earnings during the relevant period may not remember them. We expect that this is more likely for individuals with less human capital stock, both because such individuals may remember given information less well and because such individuals are more likely to have irregular jobs and wages and thereby more complex information to recall. Third, individuals who had earnings and remember them (or the women respondents, in the case of men) may choose not to divulge the information. Our priors on the association between such a tendency and human capital stocks and other income are ambiguous. Those who are relatively poor by such measures may fear more possible exploitation if such information is provided or may be ashamed of their low earnings. On the other hand, those who are relatively well off by such measures may have a more developed sense of individual privacy rights and a greater reason to be uncooperative because of possible tax implications of disclosure. Fourth, male earnings may not be reported by the women respondents because the women do not know them, even though the man in the household has earnings and Earnings and Determinants of Labor Force Participation 107 remembers them. We expect that this is more likely the lower the human capital stocks of the man and the woman. Also, as we note above, nonreporting probably is more likely for male seasonal agricultural migrants, since female respondents are less likely to know the magnitude of earnings obtained by their male companions while working away from home on their own. Unfortunately, our data do not permit us to distinguish among such possible reasons for not reporting earnings. However, this discussion of the possibilities points to the considerable ambiguity about the signs of coefficients of human capital and other income variables in the probits for reported earnings. Table 5.4 presents probit estimates for the report inclination in relation 7 above for the overall sample and for each of the three regions. The first part of the table refers to the characteristics of the participant; the second refers to the characteristics of the respondent (which are the same if the participant is the woman, but different if the participant is a man). All four of these probits are significantly nonzero at standard levels. Thus, there do seem to be some significant associations between the included variables and whether or not earnings are reported. Despite the overall significance, t-tests at standard levels indicate relatively few significant point estimates. Those that are significant imply a general inverse association between human capital stocks and earnings being reported, and thus suggest the dominance of the third, and perhaps the vacation part of the first, reason for nonreporting cited above. These include negative coefficient estimates for the participant's schooling in the central metropolis and experience in rural areas, and positive ones for the participant's days ill in other urban areas and for the national sample. The closest indication of a positive association with the human capital stock is for the respondent's nutrition status in other urban areas, which is significantly nonzero at the 10-percent level. Several of the other coefficient estimates also are significant. There is a strong positive association with other income and reporting in the rural region, but an equally strong negative association of this effect if the other income is from primarily own-farm activities. This pattern probably reflects the counter- acting effects of the various components of the third reason for nonreporting - the choice not to divulge the information above. There also is a significantly negative association with the respondent's age and reporting in the central metropolis and national samples, presumably because of a combination of reasons one, two, and four. Moreover, if the respondent works in the formal sector (and less so in the informal sector, with domestic work being the excluded sector, since participation is controlled for), the probability of reporting is higher in the central metropolis and the national sample. In regard to her own earnings, this probably is due partially to a greater ease of remembering earnings for those with regular pay periods or rates,'5 but for her or her companion's earnings, it also may represent unobservable dimensions of her motivation and capabilities. Finally, we note that there is not evidence of a significantly lower probability of Table 5.4 Probits for reported earnings for males and females in national and three regional samples, Nicaragua 1977-78

Central Nation metropolis Other urban Rural

Participant's characteristics: Age .01 (0.9) .01 (0.8) -.02 (1.3) .02 (1.0) Schooling .01 (1.2) -.05 (2.3)a -.01 (0.5) .02 (0.8) Experience -.01 (1.0) .01 (0.4) .02 (1.1) -.05 (2.2)a 2 Experience -.001 (0.2) .0003 (0.8) -.0005 (1.0) .0005 (1.2) Nutrition .31 (1.4) .47 (0.9) -.48 (1.0) .11 (0.3) 00 Days ill .004 (2.0)a .01 (1.6)b .02 (2.4)a .002 (0.8) Other income .03 (0.4) -.08 (0.8) .08 (0.5) 2.10 (3.1)a Primarily own-farm* other income -.21 (2.3)a -2.23 (3.3)a Female .41 (1.3) .04 (0.1) -.95 (1.6)b .72 (1.2)

Respondent's characteristics:

Age -.02 (2.4)a -. 03 (1.9)a -.01 (0.4) -.01 (1.0) Schooling .01 (1.0) .04 (1.4) -.01 (0.4) .01 (0.2) Experience -.00004 (0.0) -.01 (0.3) .02 (0.7) -.01 (0.4) 2 Expcricnce .0002 (0.4) .0001 (0.1) -.001 (0.8) .001 (0.6) Nutrition -.34 (0.8) .16 (0.2) 1.38 (1 .8)b -.84 (0.9) Days ill -.001 (0.5) -.004 (1.5) -.01 (1.1) .002 (0.3) Formal sector participation .68 (2.8)' 1.07 (3.3) .81 (1.3) .92 (1.3) Informal participation .23 (1.0) .52 (1.9) .25 (0.4) .41 (0.6) No participation .16 (0.7) .46 (1.5) .46 (0.8) .52 (0.8) Constant .96 (3.1) 1.18 (2.2)a 1.85 (2.4)' .41 (0.6) -2* Log likelihood function 158 36 40 89

Note:Table 5.1 gives the sample size (S2) and numbers of reported earnings (S3). Table 5.2 gives variable definitions and units. After the point estimates in parentheses are absolute values of asymptotic t-statistics. a. Asymptotically significantly nonzero at 5-percent level. b. Asymptotically significantly nonzero at 10-percent level.

'0 110 Earnings and Determinants of Labor Force Participation

earnings being reported for male participants in rural areas due to migratory labor, as suggested in reason four. These estimates are hard to judge, in part because of the considerable a priori ambiguities with regard to many signs. They do suggest some significant plausible impacts; for example, respondents who work in the formal sector in the central metropolis are more likely to report earnings, and labor force participants from households with higher (non-farm household) other income are more likely to have earnings reported in rural areas. But our success in capturing the determinants of earnings being reported is certainly limited. Of course, in regard to our interest in estimating the ln earnings functions, this limited success is not necessarily troublesome as long as it does not limit our ability to control for possible selection bias in section 6.

6. Ln earnings functions

Earnings differ substantially across regions and between sexes (section 2). Table 5.5 presents In eamings function estimates with an extended human capital definition with control for double selectivity (i.e., equation 10 in section 3), and with an additive control for hours worked.'6 While the control for hours is significant in all the estimates, the selectivity controls have significant coefficient estimates only for earnings being reported for the combined national sample and (at the 10-percent level) for the central metropolis. For each region (and for the overall sample), table 5.5 contains estimates in which all parameters (except for those for the controls for selectivity and for hours) might differ between the sexes. F-tests indicate that the estimates differ among regions, so attention is focused on the regional relations. For men, the estimates imply a number of standard human capital effects and some impact of the extended human capital variables. For the combined national sample, all of the coefficient estimates of the human capital variables are significantly nonzero with the a priori expected signs. On the regional level, however, the extent to which the estimates are significant seems to be associated with the degree of urbanization. All but the coefficient for days ill are significant at the 5-percent level for the central metropolis (and days ill has a significant coefficient estimate at the 10-percent level); none of the coefficient estimates is significantly nonzero for the rural regions;'7 and the other urban region is in- between. Thus, the extent to which variances in eamings seem to reflect variations in human capital stocks appears to be positively associated with the degree of urbanization. Apparently in the less urban (more traditional) areas, other factors (family ties) are relatively more important in labor markets than are productivity-related human capital stocks. Of course, this pattern may induce migration across regions. Other things being equal, those in rural areas with higher capital stocks have greater expected returns if they move to urban regions, and those in other urban areas with greater capital stocks in terms of Earnings and Determinants of Labor Force Participation 111

Table5.5 Ln earningsregressions with controlsfor hours andfor doubleselectivity for males and femalesin nationaland regionalsamples, Nicaragua 1977-78

Central Nation metropolis Other urban Rural

Constant 4.90 (33 4)' 4.73 (25.7)a 4.28 (15.9)a 5.24 (15.7) Schooling .068 (5.1)a .047 (2.1)a .071 (3.0)a -.005 (0.1) Schooling' .0020 (2.2)a .0038 (2.7)a .0018 (1.3) .0038 (0.8) Experience .038 (6.0)' .039 (4.3)' .031 (2.5)' .006 (0.3) Experience2 -.0005 (4.3)a -.0006 (3.0)a -.0004 (1.6)b -.0002 (0.6) Nutrition .37 (3.1)a .67 (3.5)' .90 (3.9)a -.003 (0.0)

Days ill -.003 (2.5)a -.003 (1.8)b -.001 (0.2) .000 (0.1) Females times: Constant -1.14 (2.2)a -.98 (2.0)' -1.36 (2.4)a -1.19 (0.7) Schooling -.013 (0.6) .048 (1.3) -.044 (1.1) .05 (0.9) Schooling2 .0026 (1.6)b -.0018 (0.7) .0053 (2.1)a -.0013 (0.2) Experience .021 (0.6) .025 (0.7) .051 (1.1) -.022 (0.3) Experience2 -.0006 (0.7) -.0012 (1.2) -.001 (1.0) .0016 (0.8) Nutrition .21 (0.9) .25 (0.8) .10 (0.3) .65 (0.9) Days ill .001 (0.5) .003 (1.3) -.005 (1.0) -.013 (1.5) Controls for: Participation selectivity .19(0.7) -.00 (0.0) .35(1.2) .05(0.1) Reporting selectivity -1.04(4.7)a -.59 (1.8)b -.23(0.6) -.11(0.3) Hours .01I(9.0)a .010(5.9)a .012(6.3)a .011 (2.8) Other statistics R2 .435 .459 .503 .142 SE .736 .683 .692 .891

Note: The model is outlined in Section 2. The numbers in parentheses are the absolute values of t-statistics. Table I gives the sample sizes (S2). a. Significant nonzero at 5-percent level. b. Significant nonzero at 10-percent level. schooling, experience, and perhaps health have greater expected returns if they move to the central metropolis." 8 Such incentives are strongest for those with relatively rich endowments of capital stocks, so analysis of migration is more fruitful on the micro level, for which data on individual (and not just average) characteristics can be used (also see Behrman and Wolfe 1984b; Schultz 1978). 112 Earningsand Determinantsof Labor Force Participation

For women, F-tests imply that earnings functions differ significantly (at high levels) from those for men for the national and two urban samples, but only at the 10-percent level for the rural region. However, the estimated marginal returns to human capital stocks do not differ much for women compared with men. The only significant difference is the positive coefficient estimate for the quadratic term in schooling (which carries over at the 10-percent level in the national sample). Thus, in this region, increasing returns to schooling for women are implied (i.e., 4.1 percent at grade one, 9.8 percent at grade five, 16.9 percent at grade ten).)9 The implied higher marginal returns for more-schooled women than for more-schooled men suggests some labor market segmentation by sex in this region. This presumably implies, other things being equal, stronger incentives for more-schooled women than for more-schooled men to migrate from rural to other urban areas, but fewer incentives for more-schooled women than for more- schooled men to migrate from other urban areas to the central metropolis. In fact, these estimates imply that for more-schooled women, the marginal returns on earnings incentives are to migrate from the central metropolis to other urban areas. That the only significant difference in the estimated marginal returns between men and women is in favor of women, moreover, implies that there is no evidence of discrimination against women in the sense of their receiving lower marginal returns to their human capital stocks. Even though there is not evidence of discrimination against women in regard to marginal returns for the observable human capital variables, there are large, significant negative additive effects in the estimated earnings functions for both urban regions and for the nation as a whole.2 0 These imply lower earnings for women than for men with the exception of relatively highly educated (i.e., nine or more years of schooling) individuals in other urban areas. Such a pattern may be due to discrimination against women or other unobservable variables that affect productivity and are associated with sex (i.e., physical strength). Available data do not permit identification of the importance of discrimination versus other omitted variables in these results. It is interesting to note, however, that this effect does not weaken significantly with greater urbanization, as might be expected if it represents discrimination and discrimination is stronger in the more traditional, more rural areas, or if it represents physical strength and physical strength is more important in jobs in less urban areas.

Decomposition of male-female In earnings differences

Within each region and on a national level, there are differences in male versus female earnings, capital stocks, and ln earnings functions. How important are differences in capital stocks versus differences in the In earnings functions in explaining ln earnings differentials by sex? Earnings and Determinants of Labor Force Participation 113

Following Oaxaca (1973b), the difference between mean In earnings for males versus females is:

In YM In YF = 3M (XM - XF) -A'3 XF (12)

l 3F (XM XF) -A 3XM where the variables are defined above in section 3 except that bars refer to means, the subscripts M and F refer to males and females, and

A3 =3F M

The first terms of the two alternative expressions are the weighted differences in mean male versus female human capital stocks. Oaxaca suggests that the use of the estimated parameters for males and for females as alternative weights should bracket the weights that would exist if there were no discrimination. This seems plausible if all of the differences between the male and female parameters are due to sexual discrimination. Unfortunately, as we note above, there is no way that we - or Oaxaca for that matter - can be sure about the extent to which such differences reflect sexual discrimination or reflect sex that is a proxy for omitted variables, altering the marginal returns to the observable human capital variables. Nevertheless, these seem to be interesting weights, so we follow Oaxaca and use them. Table 5.6 gives the estimated percentage contributions of observed differences in mean human capital stocks between males and females to the mean In earnings differences in the overall sample and in each region. Both sets of estimated weights are used. The percentage decomposition is somewhat sensitive to the choice of weights, particularly for the rural region. The percentage decompositions in this table suggest four points: * Sex differences in schooling do not account for a very large proportion of the In earnings differentials, though they probably are more important in more- urban regions. Nevertheless, schooling differences account for a larger proportion of the differentials in the urban areas than in most of the results for whites in the United States (Oaxaca 1973a) or for manufacturing in Tanzania (Knight and Sabot, chapter 3). * Health differences as measured by our days-ill variable are even less important, except possibly in the rural region (depending on the weights). * Differences in work experience are the most important of our three measured individual human capital variables in accounting for mean In earnings differentials by sex. The relative contribution of experience differentials is less for the central metropolis than for the other urban areas (the rural estimates range so broadly that it is not clear what their relative magnitudes are). These relative contributions also are much larger than reported for the United States (Oaxaca 114 Earnings and Determinants of Labor Force Participation

Table 5.6 Contributions of differential capital stocks to mean In earnings differential between the sexes with parameters for males and females in national and three regional samples, Nicaragua 1977-78 (in percentages)

Central Nation metropolis Other urban Rural Male-female differentials in human capital stock of M F M F M F M F

Schooling 2.1 2.4 10.3 13.2 6.2 7.3 -0.3 -2.8 Experience 27.3 31.2 21.2 12.7 17.1 36.1 -0.9 56.6 Days ill -1.1 -0.7 -0.4 0.0 -0.3 -2.0 0.0 -11.3 Other difference 71.2 67.1 68.9 74.1 77.0 58.6 101.2 57.8

Note: Based on point estimates in table 5.5 and mean human capital stocks for males and females who report labor market earnings.

1973b), but this reflects in substantial part Oaxaca's use of potential experience (i.e., age-schooling-sex) rather than actual experience, which biases downward substantially the experience differential by sex. On the other hand, they are not as large as reported for manufacturing in Tanzania (Knight and Sabot) in which case, apparently, actual experience is the variable used (as in the present study). The "other" category is quite large and quite possibly larger for the less- urban areas than for the more-urban ones. A small part of this category is due to differential estimated marginal returns to human capital stocks for females versus males, but only a small part. For an explicit example, the only significantly different coefficient estimate for capital stocks in table 5.5 (i.e., for schooling in other urban areas) accounts for only 7-8 percent of the male-female discrepancy of the In earnings in that region. A much larger share is due to the difference of the constant between the sexes. In fact, this difference in itself is 134 percent of In earnings between men and women in the national sample, with some evidence of it being of increasing importance in less-urban areas (i.e., 99 percent for the central metropolis, 131 percent for other urban areas, and 158 percent for rural areas). This contrasts with the small role of such additive sex tenns in accounting for the difference in wages for whites in the United States and for manufacturing in Tanzania, though an effect of the same order of magnitude is reported for blacks in the United States (Knight and Sabot 1982, 1991; Oaxaca 1973b). Earningsand Determinantsof Labor Force Participation 115

In Nicaragua, therefore, a combination of omitted traits and sex discrimination accounts for a large proportion of the mean In earnings differential between the sexes. Furthermore, the perception of differential average returns to males versus females may have altered relative human capital investments and thus current relative human capital stocks. If the relevant unobservable sex-associated traits become less important over time (as might be expected if physical strength is one of them) and/or sexual discrimination lessens (as may occur as attitudes change with development), the dominance of this "other" category may lessen.

7. Concluding remarks

In the introduction, we raised a number of important questions about possible differentials between the sexes in labor market participation and earnings determinants in developing countries. Our analysis permits some empirical answers to these questions for the case under examination. Is child care less of a constraint on female labor force participation than in developed economies? The answer to this question is positive. The presence of small children significantly reduces the probability of labor force participation only for women in the central metropolis (and at the 10-percent level, also for men there), but not for women (nor for men) in the other two regions, in which three-quarters of the country's inhabitants reside. Therefore, the impact of the trade-off between home production related to child care and market production is not as widespread as it apparently is in industrial economies. We find limited evidence that the presence of home child care by extended family members or older, children increases labor force participation of both males and females in families with small children in the two urban regions. The relatively widespread existence of such home child care options may be one important difference between labor force participation determinants in developing and developed economies, but the relevant coefficient estimates are significantly nonzero only at the 10-percent level, so this possibility is only marginally supported by our analysis. Apparently in addition to more widespread home child care, neighborhoods are seen as more benign for small children or resources and preferences are sufficiently different, so that the major empirical representation of home versus market productivity for the industrialized countries is not very important outside of the central metropolis. Are there differences in the impacts of the standard human capital variables of schooling and experience on male versus female labor market relations? For the labor force participation relations, the answer is affirmative. Schooling has a significantly stronger positive quadratic impact on women's participation in the central metropolis than it does for men (though the significantly negative, at the 10-percent level, coefficient estimate of the linear term may be partially offsetting). In all of the regions, experience has a significantly stronger quadratic 116 Earningsand Determinantsof LaborForce Participation

impact for women than for men. Apparently, such serial correlation in participation is much more important for women because of differential tastes, resource constraints, nonmonetary returns to work associated with experience, and/or on- the-job training. But in a way, the male-female distinction is misleading in regard to the impact of experience on participation. Prime-age males tend to have much higher participation rates than do females (table 5.2), so there is not much variance in their participation rates in the sample. As a result, much of the difference in the association between current participation and experience for the combined sex samples is captured by the additive constraint adjustment for sex, which is substantial and significant in all of the estimates in table 5.3. Were this constant suppressed, past experience probably generally would have significant coefficients for males.21 In the earnings relations, in contrast, there generally is not evidence of significantly different effects of the standard schooling and experience human capital variables for males versus females. The one exception to this statement is that females are estimated to receive a significantly higher quadratic return to schooling in the other urban region than do males. Thus, there certainly is not evidence of discrimination against women in the form of lower marginal returns to the standard human capital variables. Are there differences in the effects of the extended human capital variables of nutrition and health on male versus female labor market relations? The answer to this question is that the evidence is very limited. For participation in the central metropolis, the estimates imply that better household nutrition is associated with a significantly greater increase in female than in male participation, but that poor health does not deter female participation as it does male participation. The nutrition result is consistent with the possibility suggested by many that intra-household allocation of nutrients on the average favors males. If so, females may be more malnourished, and marginal improvements in average household nutrition may have a greater impact on their energy and productivity in market activities than is the case for males. However, the support for such a hypothesis is hardly conclusive. Neither the nutrition nor the health proxy have significantly different coefficient estimates for women than for men in the earnings function for the central metropolis, as would seem to be implied by this line of reasoning. Moreover, neither has significantly different estimated effects for men versus women in the labor market relations for the other two regions. For the standard human capital variables, therefore, we conclude that we have no strong evidence of sex discrimination in the labor market in the form of differential marginal returns for observable extended human capital variables. How important are differences in human capital stocks in explaining gender differences in average earnings? The gender differences in the standard human capital variables, particularly experience, but to a lesser extent also schooling, Earnings and Determinants of Labor Force Participation 117 account for a larger proportion of the gender differences in In earnings than has been reported in most other studies of the decomposition of eamings differentials. But the health and nutrition differences do not seem to be very important. Is there evidence of discriminiation against women in these labor markets? Our empirical evidence regarding discrimination in regard to differential marginal returns to observable human capital variables suggests almost no significant differences between the sexes. Those few differences that do exist imply that, if anything, women are favored. And yet there are large differences in mean earnings by sex. In part, these reflect differential human capital stocks. But much more important are differences in the average, as opposed to the marginal, returns to men versus women. The substantial and significant additive gender effects in the In earnings function, as we noted above, may reflect sex discrimination or simply unobservable, sex-associated, productivity-associated traits, such as physical strength. We cannot identify the relative importance of sex discrimination versus such other unobservable factors. We can say that some sex-associated factor is very important in these earnings differentials, and discrimination may account for large earnings differentials.

Notes

This paper is one of a series resulting from a survey and research project to investigate the social, economic and demographic roles of women in the developing country of Nicaragua. Funding for 1977-1978 was provided by the Ford and Rockefeller Founda- tions, for 1977-1981 by Agency for International Development Negotiated Contract AID/otr-C-1571 and 1980-1983 by National Science Foundation Grant SES-8025356. Behrman was a Guggenheim Foundation Fellow for the 1979-1980 academic year, a Compton Foundation Fellow for part of the 1980-1981 academic year, and an Academic Visitor, ICERD, London School of Economics and Political Science for parts of 1979- 1982. The project is being conducted jointly by the Universities of Pennsylvania and Wisconsin, the Centro de Investigaciones Sociales Nicaraguense (CISNIC), and the Banco Central de Nicaragua. Humerto Belli, Director of CISNIC, and Antonio lbarra, forner head of the Division of Social Studies and Infrastructure, Banco Central de Nicaragua, were co-principal investigators with Behrman and Wolfe for the early stages of the project. Belli supervised the collection of all survey data. The authors would like to thank, but not implicate, the funding agencies, T. Paul Schultz, the editors, their principal co- investigators, and associates in the project, especially Insan Tunali, Kathleen Cairns and Nancy Williamson. Behrman and Wolfe equally share responsibility for this paper. 1. We are aware of two other systematic analyses of some of these questions (Birdsall and Fox, chapter 6; Knight and Sabot, chapter 3). However, these studies are for more specialized samples (i.e., schoolteachers in Brazil and manufacturing workers in Tanzania) and do not consider the determinants of labor force participation. 118 Earnings and Determinants of Labor Force Participation

2. We assume that these human capital variables are exogenous in this paper. Of course, this is the standard (though not universal) assumption for schooling and experience in labor market studies. Other investigations we have undertaken suggest that nutrition and health are not affected substantially by the labor market outcomes under investigation here (Behrman and Deolalikar 1988; Wolfe and Behrman 1984, 1983, 1987). However, there still may be simultaneous bias so we wanted to see if the other estimates change substantially if nutrition and health are precluded a priori from the specification. They do not. We also assume that the human capital variables represent what they are purported to represent. For industrial countries, there is some evidence that this might not be the case. Schooling, for example, may be a proxy for unobserved abilities and motivation (for example, Behrman and others 1980). We are unaware of similar studies for developing economies except for ones we have made (Behrman and Wolfe 1984d, 1987a, b, 1988; Wolfe and Behrman 1986, 1987). 3. Such segmentation long has been hypothesized. See Behrman and Wolfe 1984a for estimates that are consistent with such segmentation in the labor markets for women. 4. For more details conceming this data set, see Wolfe, Behrman, Belli and Gustafson (1979) and Wolfe and others (1980), as well as all of the studies involving both Behrman and Wolfe in the references. 5. That is, to treat males and females symmetrically, we exclude women who currently are not accompanied. However, because more critical data, such as schooling, are missing for males than for females, there are more females than males in the overall sample. There may be some selectivity involved in the exclusion of individuals without important information, but there is no way of testing for such selectivity since, if it exists, it probably depends in part on some of the missing information -once again, for example, schooling. 6. For men, but not for women, mean age is inversely associated with urbanization- but not enough to account for all of the differences in work experience among the regions. 7. For women mean In earnings imply urban levels 190 percent and 155 percent of rural earnings, respectively. For men the comparable figures are 241 percent and 208 percent. 8. 2.8 grades in the central metropolis; 1.2 in other urban areas; and 0.3 in rural areas. 9. In part this reflects differential age patterns across regions by sex (see note 5), as well as the fact that, on average, men are 4.6 years older than are women. 10. In the usual Mincerian model, experience is the only human capital variable for which nonlinear terms are included. However, in some applications, the possibility of nonlinear effects of schooling has been included (for example, Oaxaca 1973b). We also allow for such nonlinearities in regard to our other human capital variables, but report estimated relations below only with linear terms for health and nutrition, because the coefficients of the nonlinear terms are not significantly nonzero and their a priori restriction to zero does not alter substantially other coefficient estimates. 11. For further details see Behrman, Wolfe and Tunali (1981); Tunali (1983). 12. Furthermore, the measurement of other income in such households probably is subject to greater error than in other households. This variable is included only in the overall sample and rural region probits. 13. Unless otherwise qualified, below we use "significant" to refer to the standard 5-percent. Earnings and Determinants of Labor Force Participation 119

14. These reasons all may cause reporting to be systematically associated with characteristics of the respondent or her male comparison. In addition, there may be nonreporting due to recording and processing errors, but such cases would not seem to be systematically associated with the characteristics of the respondent or her comparison. 15. Because we do not have a similar sectoral break down for males, we cannot explore whether the sector in which they work makes a difference. 16. We control for hours in case the lower earnings of women reflect simply a lower distribution of hours worked. We note, however, that part-time work for women is much less common in this sample than in more industrialized countries, so it is much less likely to be a problem comparing earning functions for males and females for this sample than for those from industrialized economies. Nevertheless, some control seems desirable just in case. We control for hours by an additive variable instead of the alternative of dividing through by hours to obtain an average wage, instead of earnings, as a dependent variable. We choose to control for hours in this manner because we know our data for hours has substantial measurement error in it (for example, for domestics who often are on call all day and evening, we do not have direct observations on hours but assume that they work the modal hours of 45 hours per week of other women workers), and we do not want to divide the earnings by such a noisy variable. We note, however, that whether or not hours are controlled for does not change substantially the estimates or interpretations of table 5.5. 17. For other rural labor markets in developing countries, some available studies also indicate very limited, if any, returns to human capital variables (for example, Ryan 1980). 18. For nutrition status, the point estimate is larger for the other urban areas than for the central metropolis, although not significantly so even at the 25-percent level. For low schooling levels, the estimates also are higher for other urban areas (though again not significantly so), but for higher schooling levels, the estimates are larger for the central metropolis because of the significant positive quadratic term (which is not significant for other urban areas). For days ill, the coefficient estimate is significantly negative at the 10- percent level for the central metropolis, but not even at the 25-percent level for other urban areas. 19. One of the two studies mentioned above in note 2 reports a similar result for Brazilian schoolteachers in that the marginal return for schooling is significantly higher for women than for men (Birdsall and Fox, chapter 6). In the other, however, the opposite result is obtained for Tanzanian manufacturing (Knight and Sabot, chapter 3). 20. For both studies mentioned in notes I and 19 above, the point estimates for the constant terms are bigger for females than for males, though the differences are not very large and standard errors are not provided so we can not tell if the differences are significant. 21. We do not suppress it, however, because it is representing other factors than just experience (for example, sex-related tastes for labor market participation).

Why Males Earn More: Location and Training of Brazilian Schoolteachers

Nancy Birdsall and M. Louise Fox

1. Introduction

The persistence of a substantial differential between the wages of men and women is a continuing source of interest to economists. The fact that much of the differential is not easily explained is perplexing to those who believe that labor markets function reasonably well. There is a large literature concerDedwith explaining what appear to be other anomalies of labor markets in poor countries; it deals with geographical segmentation, the formal versus the informal sector, and the public versus the private sector.' Little, however, has been written on female employment and on male-female wage differentials in poor countries. In this analysis, we examine the income differential between male and female schoolteachers in Brazil, using information on their income, hours of work, educational background, and other characteristics from a 1 percent sample of the 1970 Brazil census. (The issues arising from use of income rather than wage are discussed below. Data on wage rates were not available.) The income differential is substantial: the mean income of female teachers in Brazil is less than one-half the mean for males. We are particularly concemed with isolating the contribution to the differential of two factors often ignored in similar studies: the locational distribution of male and female teachers and differences between males and females in job position due to differences in the types of job training they have pursued. Both factors appear to favor males -location because males are more highly concentrated in high-income regions and because interregional income differences in Brazil are substantial (possibly reflecting geographical segmentation); and job position because males are much more likely to hold secondary-school positions rather than primary-school positions and there is a substantial income premium associated with the former. By restricting our study to one occupation - schoolteachers, for whom we have extensive education data

121 122 Why Males Earn More

- we can study carefully the effects of these differences in a developing country context. As it turns out, incorporation of these factors into the conventional function used to explain individual income reduces the amount of the unexplained residual and thus the amount of the income differential conventionally attributed to discrimination. As a result, we find little evidence of discrimination as it is usually defined, despite the large differential we observe initially.2 We are not able to distinguish between two possible contributors to the locational effect: cost-of-living differences and the possible constraint on their geographic mobility that most female teachers face because they are married. However, we note that in neither case should these be associated with discrimination, as they would be had we not controlled for them in our analysis. We are able to explain much of the advantage males have in gaining secondary-school jobs because of the unusual educational background detail we have on types of courses male and female teachers have taken. The course data indicate that the male advantage is a largely supply-side and nondiscriminatory phenomenon, at least in the short run, though it is obvious that the difference in the types of training men and women take may be due to differences in opportunities and expectations that are themselves attributable to longstanding discrimination. This aspect of our analysis suggests how misleading the use of education, measured simply as years attained, as is common in many studies of earnings differentials, can be for comparing the personal characteristics, or stock of human capital, of males and females. The paper is organized as follows. In section 2 we list and briefly explain some major theoretical explanations of differential wages that have been defined and discussed in the existing large literature on the subject. The treatment of each theory in this paper and its relevance to the Brazilian schoolteacher data are explained. Section 3 gives a brief description of the Brazilian educational system. The educational attainment of teachers and their income by region, type of job (primary or secondary), and sex are shown. In section 4 we explain our method for decomposing the income differential into portions attributable to differences in personal characteristics, to differences in the locational distribution of male and female teachers, to job segregation effects, and to pure wage discrimination. In section 5 we apply the methods to the Brazilian data and present estimates of the contribution of various factors to the total male-female income differential. We conclude in section 6 by suggesting some of the implications of our findings for the educational system in Brazil.

2. Causes of the male-female wage differential

In this paper, we attribute to discrimination the portion of the income differential between men and women we cannot explain by other causes. The most obvious Why Males Earn More 123 nondiscriminatory cause of the differential is that men and women differ in their personal characteristics or in the amount of human capital they possess (Becker 1975). In the Brazilian data, male teachers are somewhat more educated and more experienced than are female teachers, and they report on average more hours of work per week (see table 6.2 below).3 A second nondiscriminatory cause is that women as a group may be less mobile geographically than men and are therefore more likely to accept jobs for which they are overqualified.4 A simple family decision rule, if households with two workers of differing skills seek to maximize their total income, and given that on average males have greater potential income than females, is that the wife accompanies her husband, locating wherever his income is maximized, and simply does the best she can in that location. This so-called male chauvinist rule could easily apply to female teachers in Brazil, only 6 percent of whom are heads of households (compared with 62 percent of male teachers) and 85 percent of whom are married to heads of households or live with their parents (compared with 23 percent of males who live with their parents; all males who are married are heads.)5 Our finding below is not inconsistent with such a family decision rule, although the existence of the rule is not directly tested. A third nondiscriminatory cause of the differential, associated with any difference in the locational distribution of males and females, is the possibility of cost-of-living differences among regions. Spatial price differences are substantial in Brazil; estimates range from 30 percent to 100 percent variation between the lowest and highest-price regions.6 Failure to control for cost-of- living differences could lead to an underestimate or overestimate of discrimination - for example, if there were differences in the distribution of male and female workers between urban and rural areas because women are less likely to be agricultural wage workers. In Brazil the critical difference for schoolteachers is the much greater concentration of males in the high-income, high-price metropolitan regions of Rio de Janeiro and Sao Paulo. Still another cause of a male-female wage differential is job segregation or a segmented labor market. Job segregation alone need not lead to wage discrimination; however, it is likely to do so if relatively few occupations are assigned to women and consequent crowding results in monopsonistic exploitation (Madden 1975; Cardwell and Rosenzweig 1980). In these data, women are much less likely than men to be secondary-school teachers, and secondary-school teachers (male and female) receive a significant income premium, controlling for personal characteristics. In our analysis, we decompose the differential associated with the males' greater probability of having a secondary-school job into that due to differences in personal characteristics, including that due to different pattems of training; and that which is unexplained and which we label explicitly "job discrimination." 124 Why Males Earn More

Finally, if there is "taste" discrimination on the part of employers, women will be paid less to compensate employers for the disutility of hiring them (Becker 1975). The existence of such taste discrimination is difficult to test empirically and would be surprising in the context of institutional setting of salaries analyzed here. In this paper we do, however, attribute to such discrimination the portion of the income differential we cannot otherwise explain. To interpret the residual as discrimination requires that any unobservable differences between males and females in the sample have no effect on their eamings; thus the residual represents an upper bound on discrimination. In this respect, the restriction of our sample to the single occupation of schoolteachers requires some comment. First, to a large extent schooling is a publicly provided service; 87 percent of all teachers in this sample work in public schools. Within public schools in a given location, salaries are institutionally set on the basis of prior education and experience. It is illegal for a school system to offer different salaries to men and women with the same qualifications for the same position. Thus, individual unobserved characteristics (for example, ability) probably have less bearing on salaries than they would in a competitive industry (though ability may affect access to the job), and the portion of any income differential not explained by observable characteristics will be minimized. Similarly, though the characteristics of the "firm" (i.e., schools) will differ and will affect the marginal productivity of individual teachers, they will not affect salaries (at least within given locations); the problem of a segmented labor market, in terrns of access to jobs in the formal sector, for example, is thus nonexistent in looking at teachers' earnings. Second, teaching school traditionally is a female occupation (at least in Brazil and elsewhere); in this representative nationwide sample of primary and secondary teachers, 89 percent are women. Because the occupation is a traditionally acceptable one for women, female teachers are not as likely to have unobservable characteristics (greater or lesser ability, drive, motivation) or unobserved preferences (for example, for work rather than home and family) that make them different from women in general, including nonworking women, as might female lawyers, construction workers, or electricians. Male teachers, on the other hand, may be an unusual group - selected on preferences or skills that make them peculiarly attracted to teaching and better at it than their observed characteristics can measure; or, alternatively, selected on characteristics that made them losers in a male world and escapees into this "female" occupation. In looking at any single occupation, the possibility of bias due to this sort of sample selectivity cannot be completely avoided. But by looking at a "female" occupation, we minimize the possibility that the women are an unusual group among women. Thus, if male teachers are "losers" we underestimate discrimination, and vice versa. Why Males Earn More 125

3. The setting: education in Brazil

The educational system in Brazil in 1970 included primary school (four years); lower-level secondary school (four years); higher-level secondary school (four years); and university. Until 1971, secondary school at both levels had three types of curricula: academic, technical, and teacher training. Admission to a university required completion of higher-level secondary school, taking the academic, as opposed to the teacher training or vocational, curriculum.7 Primary and secondary education is organized and financed at the state and municipal levels. Private schools also exist in most major cities, primarily at the secondary level. In the public sector, qualifications and salaries for the state schools are set within each state; they can differ across states. Salaries for teachers at the same level of training and experience vary by region and by type of job - secondary or primary-school teacher. The regional variation is due to two factors: (1) differences in cost of living and (2) real income differences across regions, which reduce poorer regions' public expenditures for education (Souza 1979). Mean salaries for all teachers also differ by region because poorer areas have teachers who are much less qualified.8 Table 6.1 indicates mean monthly income in cruzeiros of male and female teachers by region (regions are ranked according to average income of all teachers from high to low), the percentage distribution of male and female teachers by region, and the proportion of secondary-school teachers among all teachers by region. In every region except the poorest (Maranhao and Piaui), male teachers' mean incomes are substantially higher than female teachers' mean incomes. In addition, male teachers are a somewhat higher proportion of all teachers in the high-income regions, particularly in the southem states of Sao Paulo, Parana, Santa Catarina and Rio Grande do Sul and in the region that includes the capital city of Brasilia (Distrito Federal). The poorer regions tend to have not only a higher proportion of female teachers, but a lower proportion of secondary-school teachers.

4. The method

The purpose of the method employed is to decompose the difference in the mean income of male and female schoolteachers and to isolate that portion of the differential explained by each of the three factors discussed above: personal characteristics; the locational difference, which may cause income differences because women are constrained from moving to the locations where the retums to their personal characteristics are highest or may underlie income differences merely associated with differences in the cost of living; and job discrimination. The remainder of the differential not explained by the application of our decomposition is our upper-bound estimate of pure wage discrimination. The Table 6.1 Income and location of male and female teachers, 1970 (regionsranked in order of averageteacher income)

Secondary Mean Mean Mean male Mean female school- household teacher teacher teacher Percentage Male % teachers as income income income income distribution divided by % of all Region Area' (CR$) (CR$) (CR$) (CR$) Males Females female % teachers

Sao Paulo 7 608 667 1,080 593 28 20 1.4 22.8 Rio de Janeiro and Guanabara 6 613 532 1,267 448 11 12 0.9 17.2 Parana and Santa Catarina 8 337 337 601 288 10 7 1.4 12.8 Rio Grande do Sul 9 337 313 508 285 14 13 1.1 15.5 Minas Gerais and Espirito Santo 5 294 254 666 223 10 17 0.6 9.4 Mato Grosso, Goias and Distrito Federal 10 314 254 394 231 7 5 1.4 14.3 Sergipe and Bahia 4 229 237 404 220 5 6 0.8 9.1 Rondonia, Acre and other North 1 300 217 429 195 3 4 0.8 6.3 Ceara and other Northeast 3 218 187 560 154 9 13 0.8 12.0 Maranhao and Piaui 2 172 110 78 112 5 6 0.8 6.8 Total Brazil 393 370 757 323 100 100 1.0 14.5

Note: Incomesare in cruzeiros(CR$) per month.Mean teacher income (CR$370) in 1970represented about $900per monthin constantpurchasing power (1970) dollars, a. The numbersidentify the areasin the regressionresults of table6.4. Why Males Earn More 127 standard technique for comparing wages across groups that differ in personal characteristics is to compare not their actual wages but their "would-be" wages, as if all groups were paid according to the same earnings structure (i.e., received the same returns to their actual characteristics) (Malkiel and Malkiel 1974; Blinder 1973; Oaxaca 1973a). We explain this method starting with the simplest case. Assume that each person is paid according to the function

W, =(X;.), j = male (m), female (f), (1) and

N E f (X..) W= N

where Wij = the wage of the ith person in the jth group, X.. = a vector of characteristics for the ith person in the jth group, and WJ= mean wage of the group.' The function (fj) is estimated by regressing the (X) variables on the wages for each group, so that WJcould be written as

Al X,j (2) W.= i N where Aj = the coefficients estimated in the earnings regression, interpreted as the returns to the characteristics variables. If both groups receive the same returns to their characteristics, then i=

(that is, the vector of regression coefficients A1) is the same for both groups and no measurable discrimination exists. The would-be wages of the ith person in a group can be written

wij.= f,(XR), j, k = m,f; j • k, so that, for example, the constant term and the coefficients on the independent variables of the males are applied to the female characteristics. Then the mean wage of one group if it were paid according to the other's structure is 128 Why Males Earn More

Throughout the paper, we will use the symbol (W) to represent a wage paid according to own structure and (co) to represent a wage paid according to the other group's structure. The gross differential in mean wages between any two groups - in our data, wm and f(W,. - W1 f) can be decomposed into the portion explained by differences in individual characteristics, represented by the X; j vectors, ( W (f); and the unexplained portion due to differences in the returns of those characteristics, (of - Wf); the latter is the estimate of discrimination.'" Most authors who have used this method have defined the vector of characteristics, X,' as the traditional human capital variables, such as education and experience. We wish to focus more carefully on two other factors that appear to affect teachers' incomes in Brazil: location and type of job - secondary or primary. To do so, we make two central assumptions. The first is that the locational distribution of female teachers is independent of their own characteristics and their opportunities in the labor market -in other words, female teachers' location is constrained only by the family location rule." The second is that job position (primary or secondary) is not itself a function of location. This is justified in that 82 percent of female teachers compared with 79 percent of male teachers reside in urban areas, where secondary schools are overwhelm- ingly concentrated; female teachers do not live in areas where secondary-school teaching jobs are unavailable. We incorporate the effects of differential locational distribution by dividing the vector of characteristics (Xi) into two types of characteristics, personal (human capital) characteristics and locational characteristics. We can rewrite (1) as follows:

WIi= f(ZiJ7L), (la) where Z, = a vector of personal characteristics such as education and experience and Li= a vector of locational characteristics.(an indicator variable for urban vs. rural residence and an indicator variable indicating region of the country). This allows us to rewrite equation (2) for the would-be wages of the ith person in the jth group as Why Males Earn More 129

fD = i (X ) = (Z, L ). (2a)

However, given our assumption regarding the location of female teachers, we do not want only to apply the male returns to the female locational distribution; we want to simulate what female wages would be if females not only received the male returns but also had the male locational distribution. Since the location variables are actually a set of indicator or dummy variables, we can apply to females the male locational distribution simply by weighting the matrix of the females by the locational distribution of the males. This procedure changes the locational distribution of females to match that of males. Thus, for our sample, females who live in region 1 have a weight applied to their (L) vector of dummy variables equal to the proportion of males in region 1 divided by the proportion of females in region 1. The would-be wage can then be written as follows:

0),_- = fi (Z,,L,,) (3) where oo,j, = the would-be wage of the ith person in the jth group if he or she had the locational advantage or disadvantage of the other group and were paid according to the other's earning structure. We can now decompose the gross differential into three portions:

the portion due to differences in personal characteristics: (W. -

the portion due to differences in locational distribution: (W0½,-wf);

the unexplained portion as before: (0o - Wf).

Having taken location into consideration, the next step is to account for any portion of the wage differential that is due to job discrimination."2 It is legitimate to include actual job position (in this case, an indicator variable representing whether an individual teaches in a secondary school) in the vector of individual characteristics only if males and females face equal probabilities, for given characteristics, of receiving a secondary school job. If they do not, an alternative approach is required. The presence or absence of a variable representing secondary-school job can be thought of as having a probability distribution, conditioned on the vector (Z) of personal characteristics. Calling this conditional probability a,,, we can write a,, = gj(Z,), where gj is the probability function of the jth group. In the absence 130 Why Males Earn More

of job discrimination, a,,c Z, = j 7 -or, in other words, gm = gf for all Zi - a woman with the same credentials as a man should have an equal probability of obtaining a secondary-school job. We can incorporate this element aj. into our previous expressions for individual and mean wages and,.rewrite (la) above as

WiJ= fi (Zi.,Lij;a1j). (lb)

The mean wage now is the sum over the group (j) of all Wdffrom (lb) divided by the number in group (j). Since aij is itself a function of the Z-vector of variables, its inclusion changes the relative mean wages of the two groups only if the g function for the two is different. We can now incorporate the argument a,} into our wage decomposition. The expression for the portion of the mean differential due to differences in personal characteristics is

( W,,- (° l,),f (2b)

£V i (Of-d,;im N

N and

i. gm (Zi)

The complete set of "would-be" wages (using the females as the base group) is now:

the portion due to differences in personal characteristics:

(W - r);

the portion due to job discrimination, that is, differences in access to better- paying jobs that are not explained by differences in personal characteristics:

where Why Males Earn More 131

Sf[(Zf,,f z;cif) N

aif = gf (Z,4); the portion due to differencesin locationaldistribution:

the unexplained difference, which we label pure wage discrimination:

(Of - Wf).

5. The empiricalestimates

The mean characteristicsof male and female schoolteachersare shown in table 6.2. In these censusdata, actual wage informationis not available;we knoweach individual's income and have an estimate of his or her hours worked."3 The income differentialis substantial;females' mean incomeis less than 50 percent that of males. Females are also much less likely than males to have secondary- school jobs. Table 6.3 showsthe results of estimatingincome functionscombining male and female teachers. The regressionsare based on the standard human capital model, ln(yi)= cx+ P3(Z,)+ e', where y is reported income and the f3are the estimatedcoefficients on the human capital variables.The specificationis log linear, embodying the assumptionthat percentage incrementsin income are proportionalto the absolute differencesin time spent acquiringhuman capital (Mincer1974). The averagehours of work per weekis added as an independent variable;this providesa partial correctionfor the use of income,rather than the wage rate, as the dependent variable. Of course, hours worked can itself be viewed as a choice variable,but to predicthours would have been difficult,since the availableinformation puts individualsonly intobroad categories (worked less than 15 hours per week, 15-39 hours, etc.).'4 For a sampleof teachers, in any event, it is not unreasonableto view hours worked as fixed within somerange and independentof the wage rate.'5 Education is defined simply as reported years of study completed. The experienceproxy is age minus 21 for those who completeda universitycourse and age minus 18 for those who did not. The experience proxy overstates experiencefor persons who have not worked continuously,and it is generally thoughtto do so more for womenthan for men, since women,because they have childrearingresponsibilities, are more likely to leave the labor force. For this 132 Why Males Earn More

Table 6.2 Mean characteristicsof males and females (standard deviation in parentheses)

Males Females (N = 718) (N = 5,870)

Monthly income 757 323 Log income 6.13 5.44 (1.06) (0.88) Education 12.0 10.5 (4.2) (3.6) Experience 14.2 11.5 (11.1) (9.3) Hours worked per week 36.5 32.3 (10.8) (9.3) Proportion urban 0.79 0.82 (.41) (.40) Proportion in secondary schools 0.54 0.10 (.50) (.30) Log income, secondary teachers 6.66 6.36 (0.87) (0.72) Proportion heads of households 0.62 0.06 (.48) (.24)

Note: The one-percent sample of the census was not self-weighting; weights were supplied with each individual record. These N's are the weighted numher of cases. The actual (unweighted) N's were 701 males, 5,749 females.

sample, however, the experience variable does not seem to be overstated for women as a group compared with men. An attempt was made to correct for the deficiency of the experience proxy by including number of children in the regression;1 6 however, the variable was not significant for either males or females.'" The low relative price of servants in urban Brazil -could make childrearing and work outside the home easy to combine. In addition, since teachers can work part-time in Brazil, teaching school may be compatible with childrearing, so that female teachers are less likely to leave the labor force for several years than are women in other occupations. The experience proxy is entered as a squared term as well to test if the eamings function is parabolic in the experience term. Why Males Earn More 133

Table 6.3 Incomeequations: male and female teachers (dependentvariable: natural log of income)

Males and females Males Females (N = 6,588) (N = 718) (N = 5,870) Independent variables (1) (2) (3) (4)

Constant 3.34 3.35 3.08 3.14 Education .170 .159 .167 .170 (.002) (.0022) (.006) (.002) Experience proxy .0456 .0459 .0331 .0448 (.00223) (.0022) (.0082) (.0023) Experience proxy squared -.000742 -.000757 -.00060 -.000694 (.00006) (.00006) (.00018) (.00006) Hours worked per week .00600 .00547 .01425 .00458 (.00008) (.0008) (.002) (.00084) Head, dummy .200 .183 .426 .0987 (.029) (.029) (.064) (.034) Secondary school, dummy ... .288 ...... (.025) Female, dummy -.255 -.157 ...... (.029) (.030) R2 .57 .58 .59 .54

Note: Standarderrors are inparentheses. All variablesare significantat least at the five-percentlevel.

The variable head is entered to measure attachment to the labor force as a proxy for higher productivity; it also helps correct for any unearned income included in the income variable.'" The secondary-school dummy (column 2) captures any premium paid to secondary-school teachers not accounted for by the other variables. The negative sign on the female dummy variable suggests that, controlling for the personal characteristics of individuals, females receive lower income, presumably due to discrimination. However, there are several difficulties with these estimates. The first is that, by combining male and female observations (columns 1 and 2), we restrict the coefficients on individual variables to be identical for the two groups; only be separating the estimates is it possible to analyze the underlying sources of the income differential in terms of differences in male and female retums to their characteristics. The separate estimates 134 Why Males Earn More

(columns 3 and 4) are more typical of studies of income differentials. However, they may overestimate discrimination given that males tend to be located in higher-income regions, if their higher nominal income is due merely to cost-of- living differences, or if the female distribution is supply determined, due to a constraint on female mobility. Finally, none of the estimates. is satisfactory with respect to clarifying the possible importance of job discrimination and its effect on the income differential, as females may receive a different premium than males to having a secondary-school job and may have different probabilities of obtaining such a job - possibly related to variables not already in the estimated function, in particular, type of education. Table 6.4 shows the results of separate estimates for males and females that include a series of variables indicating where teachers are located, and a dummy variable for secondary-school job. It is thus of the form ln(y,) = a + O(Zi)+ y(L) + 2j, where y is the vector of coefficients on the locational dummies. The first point to note regarding the table 6.4 results is that the constant terms and the coefficients on education, experience, and the secondary dummy variable are actually slightly higher for females than for males. This suggests little discrimination against females. The coefficients on hours worked and on the dummy variable for head are much higher for men."9 The higher coefficients for the latter two variables may measure greater attachments to the labor force of males, or at least employer perception of greater attachment. These higher coefficients for males will contribute in the decomposition method to the unexplained portion of the income differential and thus to the portion considered to reflect discrimination. However, the amount contributed will be relatively small, because of the low proportion of females who are heads and the lower hours of most females. The coefficients on the locational variables are less easy to interpret. For both males and females, urban location adds significantly to income, even controlling for the higher average education of teachers in urban areas and their much greater likelihood to be in secondary school jobs. Some portion of this urban premium is likely to be due to a cost-of-living difference between urban and rural areas.20 The urban premium is much greater, however, for males; the difference between the male and female urban premium is probably not due to cost of living (except insofar as the distribution of males and females within urban areas is different, should males be more likely to be in the central urban areas). The higher male premium will contribute to the unexplained portion of the income differential. The base group for the area dummy variables is the highest-income area of Sao Paulo (area 7). It is clear that both male and female teachers have lower incomes elsewhere. If there were no differences in cost of living, no migration costs, and no geographical segmentation in the labor market, we would expect none of the coefficients on the area variables to be statistically significant, at least for the males. We could then easily interpret any significant (negative) Why Males Earn More 135

Table 6.4 Income equations: male and female teachers (dependentvariable: natural log of income)

Males Females Independent variables (N = 718) (N = 5,870)

Personal characteristics (Zi): Constant 3.97 4.13 Education .089315 .10690 (.00849) (.00252) Experience proxy .02514 .03858 (.00707) (.00190) Experience proxy squared -.0004041 -.000634 (.00016) (.00005) Hours worked per week .01488 .00596 (.00212) (.00068) Head, dummy .3650 .1391 (.05573) (.02768) Secondary school, dummy .3199 .3614 (.05861) (.0228) Locational characteristics (Li): Urban, dummy .3888 .1924 (.0781) (.02045) Area 1, dummy -.6889 -.6028 (.13643) (.03597) Area 2 -1.376 -1.1026 (.1602) (0.04010) Area 3 -.8602 -1.1651 (.08587) (.02397) Area 4 -.8025 -.8110 (.11163) (.02941) Area 5 -.5669 -.6715 (.08141) (.02142) Area 6 -.1272a -.2141 (.03811) (.02280) Area 8 -.4136 -.4228 (.08311) (.02787) Area 9 -.3644 -.4396 (.07731) (.02295) Area 10 -.6447 -.5656 (.09496) (.0310) R2 .69 .70

Note: Standarderrors are in parentheses. a. Indicatesthe coefficientis not significantat the five-percentlevel. 136 Why Males Earn More coefficients for the females as due to discrimination within areas, perhaps in turn due to crowding of female teachers and resultant monopsonistic exploitation. (This is particularly plausible since most teachers are females.) The female coefficients do tend to be more negative in the four highest-income areas after Sao Paulo (areas 5, 6, 8 and 9) - the difference is not significant for area 8 and only so at the 10-percent level for the other three; however, they are less negative in the other areas (again, usually not significantly so). The net contribution of the locational differences effect to the unexplained (discrimination) portion of the male-female income differential is slightly positive, primarily due to the higher concentrations of both male and female teachers in the higher- income areas and the greater relative weight of the coefficients for those areas. However, though positive, this contribution is small, particularly compared with the effect of the urban dummy variable. There is, however, a "locational" effect that is not associated with discrimination. The distinction between the "locational" portion of the income differential and the unexplained "discrimination" portion that is associated with the location variables arises in the decomposition in the following way: The first is due to the imposition on the males (females) of the female (male) distribution across areas; the second is due to the differences in the returns males and females receive within locations, as reflected in the coefficients. The secondary-school coefficient indicates that this job does indeed carry a premium for both men and women, equivalent to more than three years of education in contributing to higher income. Recall (table 6.2) that a much higher proportion of men have secondary-school jobs. Male and female secondary- school teachers also have different levels of educational attainment. Table 6.5 indicates that female secondary-school teachers are more likely to have completed university than their male counterparts; male secondary school teachers are more likely to have completed only secondary school. This raises the question of whether there is job discrimination. To distinguish between wage and job discrimination requires, first, some theory regarding the job choices of potential teachers. We assume that any differences in preferences for type of job between males and females will be completely captured by differences between them in the type of training they have. In other words, if women as a group prefer to teach young children and men to teach older children, those preferences are assumed to have influenced their prior choices regarding training. The difference in the probability of holding a secondary job not accounted for by differences in training and in the other human capital variable, experience, we attribute to job discrimination. This assumption is justifiable given the apparent wage premium both men and women receive in secondary-school jobs; given that secondary-school jobs require more and different training, anyone who acquires the necessary training is assumed to be at least indifferent between the two jobs, in the absence of different returns. Table 6.5 Highest course completed, by sex and position (percent)

Primary Secondary All Males Females Males Females Males Females

No formal education 9.6 6.7 0.5 0.2 4.7 6.0 Primary 26.3 17.8 0.8 0.4 12.3 16.0 First-level secondary: academic 15.8 9.2 3.7 3.5 9.3 8.6 First-level secondary: teacher training 3.1 1.9 0.0 0.3 1.3 1.7 Second-level secondary: academic 13.3 4.1 33.6 10.1 24.0 4.6 Second-level secondary: teacher training 24.6 58.0 7.0 29.0 15.1 55.1 Second-level secondary: other 2.4 0.8 7.0 1.8 4.8 0.9 University 4.9 1.6 47.4 54.8 27.7 6.7 Average years of education 9.2 10.1 14.4 14.3 12.0 10.5 N 333 5,298 385 572 718 5,870 138 Why Males Earn More

As outlined in section 4, the probability of having a secondary-school job can be estimated for males and females as ajj = gj(Zij),j = m, f, where Zi is a vector of variables representing years of education, type of education (i.e., course followed), and experience. Table 6.5 shows the distribution of male and female teachers by job position and type of course taken. The table indicates that women not only have slightly fewer total years of education than men, but have taken different types of degrees. Job discrimination occurs when, for the ith person in the j group, cx < aik' or, in other words, given any vector of characteristics, one group has a higher probability of receiving a secondary-school job. Probability density functions for males and females were estimated separately using a probit equation.2 ' The results are shown in table 6.6. The estimated probability density function is different for males and females. Education measured simply as years of schooling is an important determinant of the outcome of a secondary-school job for both but is much more so for females. However, controlling for years of schooling, the effect of type of course is markedly different for males and females. Not surprisingly, for both males and females, university training increases the probability of having a secondary- school job. For males, however, completion of secondary-school second-level academic or "other" secondary-school curriculum also significantly increases the probability of having a secondary-school job; this is not the case for females. At the same time, completion of the teacher training type of secondary-school, which is designed to train future primary-school teachers, has a negative effect on females' chances of holding a secondary-school job but not on males' chances. This result is surprising because the omitted category represents even less training (possibly explained by the fact that education measured in years is also included). This is particularly important since more than half of all female teachers went to this type of secondary-school, compared with only 15 percent of male teachers (table 6.5). Table 6.7 shows the resulting a's for males and females given their own and the other group's estimated density function. Under either structure of conditional probabilities, males have a significantly greater chance of receiving a secondary- school job, due to their higher group mean education and their greater proportion with university training. However, women's probability of teaching secondary- school is 20 percent higher using the male structure, indicating there is some job discrimination. Table 6.8 shows the results of the decomposition of the income differential; it indicates the proportion of the gross differential between males and females attributable to various causes. It is based on the table 6.4 regression, in which the secondary-school dummy has been adjusted for females using the male probability structure derived from the table 6.6 results. The calculated proportion depends on which structure is taken as the base, the familiar index number problem; two calculations are therefore shown for each category. Why Males Earn More 139

Table 6.6 Probit equations (dependentvariable is presenceof a secondary-schooljob; t-valuesare in parentheses)

Males Females (N = 701) T (N = 5,749) T

Constant -3.036 -4.399 (7.8) (14.8) Education .1672 .2590 (3.3) (8.08) Experience proxy .02297a OOOv0 (1.18) (.017) Experience proxy squared -.00003a .00038a (.05) (1.723) Highest course completed:' First-level secondary: academic .3461a -.01203a (1.02) (.06) Second-level secondary: academic 1.296 .1896a (2.9) (.74) Second-level secondary: teacher training -.1299a -.5078 (.30) (2.2) Second-level secondary: other 1.42 .2547a (2.9) (.81) University 1.367 1.002 (2.3) (3.1)

Note:This regressionwas applied to an unweightedsample. a. Indicatesthe coefficientwas not significantat the 5-percentlevel. b. The missinggroup includesthose with no schooling,primary schooling only and first-levelnormal secondary schooling (teacher training).

Between 74 percent and 89 percent of the overall income difference between males and females is explained by differences in their personal characteristics and their locational distribution. The remaining 11 to 26 percent we label discrimination. Of that, a small proportion is clearly attributable to job discrimination; the rest, which is unexplained, we attribute to wage discrimination. The proportion explained by location is relatively small; what portion of this is simply due to cost-of-living differences and does not reflect differences in real income, and what portion is due to a constraint on the geographical mobility of 140 Why Males Earn More

Table 6.7 Probability of holding a secondary-school job

Males Females

Estimated: With male probabilitydensity function 0.55 0.12 With femaleprobability density function 0.47 0.098 Actual 0.54 0.097

women, we cannot say. Since cost-of-living differences in Brazil have been estimated in the range of 15 percent between the northeast and the southeast urban areas (Thomas 1982), it is possible that the entire proportion of the differential explained by location is not a real income difference. In neither case, however, should this proportion be associated with discrimination, as it would be if our correction had not been made. Without this correction, the unexplained wage discrimination proportion of the income differential would be in the range of 18 - 36 percent rather than 10 - 25 percent. In either case, the proportion due to discrimination is low but above the range for teachers in developed countries. One set of estimates reported for the United States was that the unexplained proportion of the wage differential between male and female teachers was 5 percent for white teachers and zero for black teachers (Antos and Rosen 1975). The more interesting comparison between the Brazil and United States results is the much greater size of the gross differential in Brazil. In Brazil the female- male income ratio is 50 percent (57 percent taking into account differences in personal characteristics and location); in the U.S. study the ratio was much higher: 87 percent for whites and 95 percent for blacks (95 percent and 101 percent taking into account differences in personal characteristics and school variables). Thus, the absolute differential to be explained or to be attributed to discrimination is much smaller in the United States. Why is there such a difference? In part, it is because the distinction between qualifications for and salaries of primary versus secondary-school teachers is not nearly so great in the United States; in Brazil, the distinction is marked and a higher proportion of males have secondary-school jobs. The contribution of job segregation to the explanation of the male-female wage differential is relatively small. Although the estimated probability density function indicates there is job discrimination, the differences in returns to various types of courses between men and women do not account for much of the overall income difference because the differences are also great between the sexes in the Why Males Earn More 141

Table6.8 Income differentials:male and femaledifferences (percent)

Males as base Females as base

Explained difference: Individual characteristics:

(Wm - OfZ)/(WM - Wf) 81.3

(Wmc-f o,,,,)/(Wm - Wf) ... 61.7 Locational distribution:

((Ofam- Cf)(W, - Wf) 7.7 (Co., - Wf)(W, - Wf) 12.7 Job position (job discrimination):

- Wf)(wm - Wf) 1.0 ...

(CO. W.af)(Wm - Wf) .. 3.4 Total explained

(Wm,,- (W,, - Wf) 90.0 Total explained

(0~m WfM(wm - Wf) 77.8 Unexplained difference (wage discrimination):

(cOf Wf)/(W - Wf) 10.0 (Wm 0-).t (W, Wf) 23.2 Gross difference

(Wm - Wf) 100.0 100.0

types of training actually acquired. As table 6.5 shows, female teachers were much more likely to go to a secondary school with a teacher training curriculum that qualifies graduates only for teaching jobs in primary school. Females were also less likely to have attended university, the principal entree to a secondary- school job.

6. Conclusion

Female teachers earn about half of what male teachers earn in Brazil. Although this income differential appears at first glance quite large, at least 74 percent of it can be explained by nondiscriminatory causes: differences in personal 142 Why Males Earn More

characteristics; possible differences in real income associated with differences in location and supply-determined differences in geographic mobility, also associated with location; and finally, differences in training leading to a greater likelihood that males will hold secondary-school jobs. We find some evidence of job discrimination, but it makes only a tiny contribution to the overall differential. Some wage discrimination is found between the two groups after all differences in characteristics, location, and job position are taken into account. The finding of relatively little discrimination in the conventional sense requires some additional comment, however. First, given the highly institutional setting, it is not surprising that we can "account for" much of the total income difference. The interesting point is that even in this largely female and institutional occupation, females eam systematically less. Why? This brings us to a second comment. Most of the explanation comes on the supply side: females do not locate themselves to maximize individual income, and they make choices regarding training early in life that restrict them to certain jobs. In the long run, however, these factors themselves could be demand-determined. We have attributed differences in the courses taken by males and females to differences in preferences; this is probably much too simplistic. It may be that there are perceived barriers to entry for women into secondary-school jobs that prejudice their early decision regarding training - so that they are less likely even to consider attending academic secondary-school and university. Our estimated probit equation does indicate that women face slightly lower probabilities of receiving secondary-school jobs for given characteristics. It may be likewise that the men who teach secondary-school without a college degree went to academic secondary-schools expecting to continue on through university and did not, so they became teachers. This would suggest that the process of selection into teaching is different for males than for females, with male teachers more likely to come from a lower portion of the overall ability distribution. If this is so, wage discrimination is likely to be underestimated. In short, without more information on how job preferences and job and income expectations affect choices regarding the acquisition of human capital, we cannot know the extent to which women's choices are constrained by perceived and real barriers to entry both to secondary-school jobs and to jobs in other fields. By ignoring what might be called cumulative discrimination, which discourages women from investing in their own human capital, we may understate the true level of discrimination in the labor market. Should there be an expansion of job opportunities for women in Brazil, and particularly for educated women similar to the expansion in the United States in the past decade, the market for schoolteachers could change in important ways. Poorer areas may find fewer women willing to teach primary school at the salary levels currently offered; over the longer run, more girls will seek education and training that prepare them for a wider choice of jobs. This potential shift of Why Males Earn More 143 women out of lower-paying teaching jobs could further strain the capacities of poorer states in Brazil to continue to expand opportunities in education, and poses for the long run the issue of how to finance education in poor areas.

Notes

This paper was prepared as part of a World Bank Research Project "Studies on Brazilian Distribution and Growth." It is reprinted with minor editorial changes, from Economic Development and Cultural Change 33:533, 1985. 1. The seminal article on such fragmentation between rural and urban areas is Lewis (1954). For a review and critique of the literature positing imperfect labor markets in poor countries, see Berry and Sabot 1978. 2. For a finding of relatively little discrimination by sex in one of the few other analyses of this type using developing country data, see Knight and Sabot (chapter 3). 3. More hours obviously will increase total income for any given wage rate. It would also increase male rates if, over time, more hours worked implies greater accumulated job experience for any given number of years in a job and more investment in job skills. The latter can cause a problem in interpreting the estimated return to years of experience, which we discuss below. 4. Frank (1978). Frank's income maximization rule actually requires that the household locate in a labor market where the degree of overqualification expected for each spouse is inversely related to the potential income of each. 5. Many schoolteachers in Brazil are still living in their parents' house and thus are listed as "children" in the census. Twenty-three percent of male teachers and 34 percent of female teachers fall in this group. 6. Some estimates are provided in Thomas (1982). Thirty percent is the maximum variation of food prices; estimates including nonfood prices are higher. 7. After the reform of 1971, the primary and lower secondary were combined, and a common curriculum of more studies was required. Enrollment in the first eight years of education became mandatory, replacing the prior requirement of only primary school. 8. For example, mean years of schooling of primary-school teachers in 1970 in some poor areas of northeast Brazil was less than five years, compared with about 12 years in the south. See Birdsall (1985). 9. When Blinder (1973) first developed this method, he applied it to mean differences between groups. That is to say, he defined W. = Fj(Xj), Xj = mean of X for group (j). Due to rounding errors in calculating W. Blinder's W. may differ slightly from ourW. which is calculated using the individual data. Since the absolute difference in the dependent variable, the natural logarithm of the wage, is very small, we did not use Blinder's WJ in this paper.

10. Or ((Dm - Wf) and (W, - °) respectively, if females are used as the base group. Results using both indices are shown below. 11. To the extent there is assortive mating and male and female labor market opportunities are positively correlated, the family location rule may not reduce female returns. But 144 Why Males Earn More

even under these assumptions, it is unlikely that one or the other family member will not be at some disadvantage if they reside in one location. 12. To isolate the effect of job discrimination, Brown, Moon and Zoloth (1980) estimated a function to determine occupation (job); this permitted them to compute the separate effect of occupational attainment on wages. We adopt a similar procedure below to distinguish within the occupation of schoolteachers between primary and secondary-job positions. 13. The hours variable is explained below. Unfortunately, the reported income can include income from assets as well as earned income and is probably more likely to do so for heads of households than for nonheads. Among males, 62 percent are heads; among females, only 6 percent are heads (table 6.2). The implicit value of owner-occupied housing is not included in individuals' incomes, however, and most of the population would not have other income-earning assets. Moreover, male heads were not positive outliers in an income function that did not include an indicator variable for head, suggesting that any contribution to income of unearned income is minor, or at least not associated with being a male head of household. 14. Because the hours variable is a categorial one, in intervals, there is considerable measurement error. Hours were entered in the regression by setting less than 15 equal to 12, 15 to 39 equal to 27, 40 to 49 equal to 44, and more than 50 equal to 55. The assumption made in using this procedure is that hours worked are linear in the natural logarithm of wages. If that is not the case, the proper specification would be dummy variables to capture non-linearities in the relationship between the categorial hours variable and the dependent variable. We also estimated the regression shown in tables 6.3 and 6.4 using the dummy variables for hours worked. The estimated returns to increased participation were for the most part linear in hours and not significantly different using the categorical specification for either males or females. 15. In fact, even among teachers, it is probable that the wage elasticity of hours worked is positive, explaining why men, given their higher wage, work more hours. For evidence that the wage elasticity is positive, and where reported to be negative has been incorrectly estimated, see Borjas (1980). 16. Oaxaca enters number of children in female wage equations run on U.S. urban data in a rough attempt to correct for the problem of lost experience among females not reflected in the experience proxy based on age, and the resulting possible overestimate of discrimination if the estimator of the coefficient on the experience variable were biased downward for females. In the U.S. sample including women from many occupations, the children variable did have the expected negative effects. See Oaxaca (1973b). 17. In regressions (not shown) for all females and for female heads, no variable for number of children - including born last year, children currently at home, all children alive, children aged 0-5, and children aged 0-10, entered in separate regressions - had a negative effect on female earnings. In fact, among all females, the number of children currently at home and currently alive (which would include older children) had a significant positive effect on earnings. Among female spouses, some children variables did have a negative effect, but spouses are only 50 percent of all female teachers. It is of course also possible that many female schoolteachers who have children leave their jobs and never return, so that the sample includes primarily the stayers, for whom the effect of children is different. 18. See note 13. Why Males Earn More 145

19. One possible explanation for a lower coefficient on hours worked for women is that men moonlight - work in another, more lucrative occupation on the side, which is reflected in their higher income. However, we rejected this explanation since over 90 percent of our sample reported only one occupation, schoolteaching. 20. Ideally, we would have deflated the measured income variable by a regional cost of living index in order to separate the mobility effect from the cost-of-living effect. Unfortunately, reliable estimates of cost-of-living differences among areas in Brazil are not available for the period of our data set. Thomas (note 6 above) provides some estimates using a 1974 expenditure survey for poor and average income households. Unfortunately, the most reliable portion of the data used for his index is the food basket; quantities and values for nonfood items were not collected very reliably. Where food is a large portion of the household budget, Thomas' estimates are applicable. For our sample, however, income levels are high enough so that nontradables and consumer durables are important in the commodity basket; we could not justify applying his estimates to our sample. 21. The probit equation was estimated using PROBIT IV, a program written by Michael Hartley and Eric Swanson of the World Bank. In the transformed probit equation estimates, a predicted dependent variable (outcome) less than zero is assigned to primary- school teaching, greater than zero assigned to secondary-school teaching.

Why Do Males Earn More Than Females in Urban Brazil: Earnings Discrimination or Job Discrimination?

Nancy Birdsall and Jere R. Behrman

1. Introduction

The purpose of this paper is to explore and explain differences in labor force participation rates and in earnings income between men and women in a large developing country, Brazil. The differences in income by sex are substantial, in both urban and rural areas and in the so-called formal and informal sectors.' Therefore, the question naturally arises whether there is discrimination, and if so, how and why it persists. The theoretical and empirical literature on the economics of discrimination is now extensive (for example, Becker 1975; Madden 1975; Blinder 1973). But empirical work has been confined for the most part to developed countries,2 for which the assumption, at least, is that labor markets are competitive, and where the phenomenon of an informal sector is not normally explicitly considered. Yet for explaining earnings differences between men and women, consideration of the informal sector is particularly important; several characteristics of that sector - including low costs of entry, and thus lower losses in exiting temporarily, and flexibility in hours worked - make it an apparently appropriate one for women, assuming they have or expect to have considerable family-related responsibilities that limit or interrupt their labor force participation. In developing countries, the informal sector is usually relatively large, and the proportion of workers in it who are women is considerable; thus, examination of male-female job and earnings differentials in a developing country with a large informal sector should provide new insight into the causes of such sex differentials.

147 148 Why Do Males Earn More Than Females in Urban Brazil?

In this paper, we use a selectivity model to examine differences between men and women in what determines labor force participation and job sector (formal, informal, and, for women, an extra category of domestic service); we then proceed to the standard Mincerian formulation of earnings determinants, controlling for selection. The resulting estimates provide a basis for decomposing earnings differentials between males and females into various components, including: differences in their human capital; differences in their returns to human capital; differences in their supply of labor, some perhaps associated with family responsibilities; job discrimination; and an unexplained income difference that provides an upper bound on wage discrimination. The remainder of the paper is organized as follows. In section 2, we describe our sample and its characteristics, outline the family model of labor supply that underlies our analysis, and describe our procedure for exploring the determinants of the eamings differential between males and females. In section 3, we present and discuss the estimates determining labor force participation and job sector. In section 4, we present the estimated earnings functions. In section 5, we present our investigation of the total male-female earnings differential, based on the two sets of estimates, and discuss the implications of our analysis.

2. Data, family model, and procedure for exploring causes of male-female earnings differentials

We use a random subsample of 4,310 men and women between the ages of 15 and 65 from the 1-percent Public Use sample of households from the 1970 Brazilian census. For these individuals, we have data on their own income3 and on their age, education, residence, occupation, and a rough indication of their weekly hours worked. In addition, we know the individual income and characteristics of other members of their households and the number of children and other dependents in the households. The availability of this extensive household information allows us to embed each individual in our sample in his or her family, and to examine whether and in what type of job they are working and their income in the light of their family's, as well as their own, characteristics. For Brazil and other developing countries, such an approach is particularly important. Recent studies demonstrate that poorer populations spend relatively larger amounts of time in home production activities, including child care, food preparation, and home maintenance (for example, King and Evenson 1983; Da Vanzo and Lee 1983). For the unskilled, in particular, the tradeoff between earning a low wage in the labor market and the product of time spent directly satisfying family consumption needs is less likely to result in labor force participation; this is especially true for women, who, by choice or custom, normally have specialized in work at home. From the household's point of view, Why Do Males Earn More Than Females in Urban Brazil? 149 the issue is to maximize"full income,"including returns from work outside the home for cash and from work in the home for direct consumption;4 thus, individuals'decisions on whetherto work and on what type of work to do will usuallybe part of a family-maximizingstrategy. That strategydictates that the personfor whomthe differencebetween the potentialmarket wage and marginal home productivityis greater specializein work outsidethe home; this is usually the man. When wagerates are lowerfor women,even given equivalentskills as those of men - be it due to discriminationor employers' expectationof their having lower attachmentto the labor force - the tendency for women to specializeat home is reinforced. Ornthe other hand, many adult women in Brazil (and increasinglyin other developing countries) are not members of husband-wifehouseholds, but are single, widowed,or unmarriedmothers. In 1970, for example, 13 percent of Brazilianhouseholds were headed by women;the percentagewas much higher in urban areas (Merrick and Schmink 1983, page 246). For these women, particularlythose with children,a full-incomestrategy requires someaccommo- dation between work in and outside the home. Tables 7.1 through 7.4 present descriptiveinformation on our sample and provide an introductionto the issues. Table 7.1 shows the percentagesof men and women in our samplewho are in the labor force and their mean earnings, for urban and rural areas. Eamings for men are almost twice as great as for women in urban areas and more than twice as great in rural areas. Only 25 percentof urbanwomen and 13 percentof rural womenare reportedto be in the labor force, comparedwith 78 and 92 percent of men, respectively.The rural figureis almostcertainly an underestimatefor women;it reflectsfailure to report agriculturalwork on family farms and nonagriculturalwork that is not for cash income or is viewed as secondaryto women's main activity of housewifery.In the remainderof this paper, we therefore analyze only the data for urban men and women. Table 7.2 shows the distributionamong three categoriesof occupationsfor men and women:the formal and informalsectors and domesticservice. It also shows eamings within categories. We distinguish between the formal and informalsectors using the detailedinformation on individuals'occupations, their productivesector, and whetherthey are self-employed.All self-employedpersons are categorized as informal, unless they work in the public sector or in occupationsthat are clearly technical,administrative, or professional.These last occupations,along with persons who reported themselvesto be employers,are categorizedas formal. The separate category of domesticsis an awkwardbut importantone, comprising 28 percentof all workingwomen in urbanareas. Their work is not informal,given that they have an employer;but we were reluctant to classifyit as formalbecause it does not have the "modern"characteristics of most formal-sectoractivities. As shall be seen in the analysisbelow, the separate categoryis justifiedfrom an empiricalpoint of view. 150 Why Do Males Earn More Than Females in Urban Brazil?

Table 7.1 Percentagesof males and females in labor force and mean monthly earnings,urban and ruralBrazil 1970 (in cruzeiros)

Urban Rural Percent Earnings Percent Earnings'

Males 78 425 92 119 Females 25 224 13 52

Note:Mean earnings are for participantsonly.

Table 7.2 indicates that earnings for both men and women are highest in the formal sector. Men in the formal sector average 27 percent higher earnings than men in the infonnal sector. Women in the formal sector on average earn 56 percent more than women in the informal sector and 254 percent more than women in the domestic sector. Within the formal sector, men's mean earnings are 48 percent higher than those of women, and within the informal sector, men's mean earnings are 81 percent higher than women's. Indeed, men in the informal sector on average earn more than women working in the formal sector. The proportion of working women in the formal sector is lower than that of men. Tables 7.1 and 7.2 suggest two possible mechanisms of sex discrimination: (1) entry into the urban formal sector, where wages are higher, may be more

Table 7.2 Distributionof males and femalesin labor force amongjob sectors, and mean earningsby sex and sector, urbanBrazil 1970

Formal Informal Domestic

Males Distribution 80 2 1 0 Earnings 440 350 ... Females Distribution 57 14 29 Eamings 301 193 85

Note: Maynot sumto 100percent due to roundingerror. Why Do Males Earn More Than Females in Urban Brazil? 151

difficult for women than for men; and (2) within sectors, there may be wage discrimination. These mechanisms are explored systematically below. Table 7.3 shows mean education and potential experience for labor force participants by sector. We do not know actual paid labor force experience, so we use potential experience, defined as the number of post-schooling years the individual has been age 15 or older.5 To the extent that actual work experience, not just general maturity, is relevant, potential experience is probably a better proxy for the actual experience of males than females, since males participate more continuously in the labor force. Working women have more education on average than men; the sectoral data indicate that this difference is due to the large discrepancy in the formal sector. They have less potential experience (and, as noted above, probably even less actual experience), reflecting a lower average age than male workers. Within the formal sector, however, women may have greater human capital, since their greater education may compensate for their lower apparent experience. Outside of the formal sector, it is clear that women who work bring less human capital to their jobs than do men. Domestics, in addition to being the least educated, also are on average the youngest workers. Table 7.4 shows the distribution among job sectors for working males and females by their position in their household. A "child" is any person, regardless of age, who lives with a parent. All married men were classified as heads; thus, there are no males in the spouse category. Note that female heads of household are less likely than male heads to work in the formal sector. For both sexes, work in the informal sector is more likely the greater the presumed family responsibilities (assuming a decline in such responsibilities from head to single); however, in contrast to men, most unmarried women, particularly those in the category of single women, work not in the formal sector (as we have defined it), but as domestics. The results in tables 7.3 and 7.4 are consistent with the possibility that women are less likely to be in the formal sector because those who already have family responsibilities are less able to accept jobs with inflexible hours; and those who are young but foresee future family responsibilities seek work as domestics because of the sector's low entry and exit costs. The possibility that women's labor supply functions are different across sectors from men's provides an explanation for an earnings differential that does not rely on the existence of discrimination. Again, this possibility is explored systematically below. In short, both the family model and the descriptive tables indicate the importance of analyzing the male-female earnings differential taking into account factors determining whether women (and, indeed, men) work at all, and if they do, what type of jobs they have.6 In order to estimate eamings taking into account these factors, we use a selectivity model in the Heckman tradition (Heckman 1976). We estimate probit functions determining the probability that Table 7.3 Education and potential experience of urban male and female labor force participants by sector, Brazil 1970

Formal' Informal Domestic All Education Experience Education Experience Education Experience Education Experience

>" Males 4.7 17.7 2.9 25.3 ...... 4.3 19.2 Females 7.2 13.5 2.6 22.5 2.4 10.8 5.2 13.9

Note: For definitionof sectors,see text. Why Do Males Earn More Than Females in Urban Brazil? 153

Table 7.4 Percentage distribution among job sectors by family position for male and female workers, urban Brazil 1970

Formal sector Informal sector Domestic

Head of household Males 75 25 ... Females 46 29 25 Spouse Females 69 23 9 Child Males 92 8 ... Females 75 6 18 Singles Males 92 8 ... Females 29 4 67

males work in the formal sector and informal sector, and that females work in the formal, informal, and domestic sectors. The samples include all males and females between the ages of 15 and 65, whether they are working or not. Our approach thus simultaneously controls both for selection in the decision to participate in the labor market and for selection in the type of job. It also allows for the possibility that people decide whether to work in a particular job - not necessarily first whether to work, and then where;7 we think this is particularly important, since women with family responsibilities, either current or anticipated, may well decide to work only conditional on obtaining a certain type of job. In addition to providing the empirical estimates for the selection control, the probit function also allows us to decompose into human capital, family, and unexplained factors the probability that men and women end up in one or another job sector. The means and standard deviations of variables used in the probit and earnings functions are shown in table 7.5. We classify the schooling and experience variables as human capital variables. We classify as family variables the variables that indicate an individual's status in the household (as head, spouse, child, or as a single person), as well as variables representing income of other household members and the presence or absence of young (under six years of age) children of the individual. Table 7.5 Variable classirication and descriptive statistics, urban Brazil 1970

Males Females (n=2,738) (n=1,572) Standard Standard Data for probit functions Mean deviation Mean deviation

Human capital variables: Schooling 4.44 3.91 4.03 3.76 Experience 18.9 15.1 18.4 15.6 > Experience squared 582.0 790.0 583.0 846.0 Family variables: Spouse income 165.0 604.0 360.0 732.0 Other household income 19.5 92.7 26.8 120 Dummy: resides with parents 0.285 0.452 0.237 0.425 Dummy: head of household 0.629 0.483 0.115 0.319 Dummy: spouse ...... 0.506 0.500 Dummy: has a child aged 6 or less 0.314 0.464 0.297 0.457 Interaction of: resides with parents and other household income 137.0 560.0 108.0 479.0 Interaction of: has child aged 6 or less and number of dependents over 14 .283 .451 .0362 .187 Males Females Formal Informal Formal Informal Domestic (n=1,706) (n=438) (n=227) (n=54) (n=114) Data for earning functions Mean S.d. Mean S.d. Mean S.d. Mean S.d. Mean S.d.

Ln earnings 5.49 1.23 5.46 .853 5.22 1.29 4.67 .922 4.21 .700 Human capital variables: Schooling 4.72 4.13 2.91 2.81 7.22 4.36 2.59 2.76 2.39 2.04 Experience 17.7 12.3 25.3 13.4 13.5 11.9 22.5 12.7 10.8 12.2 u, Experience squared 465.0 566.0 822.0 806.0 323.0 547.0 666.0 669.0 264.0 537.0 Family variables: Dummy: works in agriculture .0897 .286 .237 .426 .0352 .185 .0926 .293 ... Dummy: works less than 15 hours weekly .00528 .0725 .0137 .116 .0176 .132 ...... 0175 .132 Dummy: works 15 to 39 hours weekly .0692 .254 .0685 .253 .317 .466 .278 .452 .0790 .271 Dummy: works 50 hours or more weekly .212 .409 .269 .444 .123 .330 .204 .407 .342 .477 .839 .257 1.77 .292 1.62 .529 2.39 .397 1.66 .574 156 Why Do Males Earn More Than Females in Urban Brazil?

Earnings functions are estimated for the various groups, controlling for selection; an extended Mincerian formulation is used, with the natural logarithm of earnings as the dependent variable. Table 7.5 also indicates that we include dummy variables using survey information on hours worked; unfortunately, the data are only available in the intervals shown. On average, women work fewer hours than men. Finally, we also control for working in agriculture (as a few urban residents do), since the agricultural labor market differs from the urban labor market. Our examination of possible causes of differences between female and male earnings is based on these two sets of estimates. For the jth sex, the average In earnings is the weighted average of In earnings for that sex in each of the i sectors:

Y = wJ'(bhJ,Xi) Y'(aj' Z)

where Wj' = proportion of labor force participants of sex j in sector i as

dependent on vectors of parameters (b1i) and mean observed

variables (Xj1) for selection of type j into sector i; Yj' = mean ln earnings for sex j in sector i as dependent on vectors of parameters (a,') and observed characteristics (zj,) Y. = mean In earnings for labor force participants of sex j in all sectors.

We observe Xji and Zj', and we obtain estimates of bj1 and aj) for both sexes.8 Therefore, we can use our estimates for females (say j = 2) as a base and explore what would happen to females in terms of job outcomes and incomes if mean observed characteristics for males (X,', Z,') or parameter values for males (b,', a,.) were substituted for mean observed characteristics for females or parameter values for females, under the assumption that the parameters of the participation-sector selector probits and of the sectoral In earnings functions remain constant except for the hypothesized change. Of course, if all of the male- observed variable values and parameters are substituted into relation (1) at the same time, the result is the mean In eamings for males. However, by substituting subsets of observed male characteristics or parameters one at a time, we can explore the relative importance of male-female differences in parameters and in observed characteristics on female In earnings.9 To disentangle these effects, we consider separately the impact on the In eamings within each sector and on the distributional weights among sectors. This dichotomy permits identification of the importance of various male-female Why Do Males Earn More Than Females in Urban Brazil? 157 differences on earnings levels within sectors versus the distribution of participants among sectors. For the In earnings within each sector, we estimate the impact of each of the following changes: * replacing observed female human capital of schooling and experience by male human capital levels; * replacing observed female hours worked by male hours worked; * replacing coefficients of observed female human capital variables by male coefficients; * replacing coefficients of observed female hours by male coefficients; and * replacing the female constant by the male constant. An upper-bound estimate of the earnings impact of discrimination within sectors combines the last three effects, that is, the differential impact of all parameter differences.10 This is an upper bound, however, because the coefficient of experience may be only adjusting for systematic sex-related differences between our potential experience variable and the true experience"1 and the constant may reflect unobserved sex-related traits, such as strength. Therefore, a lower-bound estimate of the earnings impact of discrimination within a sector is provided by the impact of differential schooling and hours coefficients alone. For the weights that allocate labor force participants among the sectors, we estimate the impact of each of the following changes: * replacing observed female human capital by male human capital; * replacing observed female household background characteristics by male characteristics, - replacing female coefficients of observed human capital variables by male coefficients; - replacing female coefficients of household background characteristics by male coefficients; and - replacing the female constant by the male constant. Job discrimination may lower female average earnings by making it more difficult for females than for males to participate in higher-earnings sectors. By analogy with the case of within-sector earnings discrimination, an upper-bound estimate of the impact of job discrimination would combine the impact of all of the coefficient differences for the observed human capital variables and the constant. This would be an upper-bound estimate because the coefficient differences may reflect systematic sex-related supply differences due to sex- related specialization decisions rather than only demand effects. This is obviously true for the coefficients on the household background variables. It is also true even for the coefficients of schooling, since such coefficients incorporate the effect of labor force participation in a sector versus all other activities, and the latter include household production in which there may be specialization by sex for biological reasons (for example, Becker 1981) or because of custom. Moreover, even if there is no job discrimination, the coefficients in the sectoral 158 Why Do Males Earn More Than Females in Urban Brazil?

weights may differ because earnings discrimination makes some sectors relatively less attractive for females than for males.'2 Therefore, the lower-bound estimate of the impact of job discrimination would be zero. However, there is a further complicating factor in regard to job discrimination, which means that the upper and lower bounds discussed in the previous paragraph are not the true bounds. This factor is that job discrimination may alter not only the sector of work, but also decisions about labor force participation. A change from female to male coefficients in the participation-sector selection probits to represent the elimination of hypothesized job discrimination, for example, may increase the proportion of females who participate in the labor force and the absolute number of females in the formal sector, but simultaneously reduce the proportion of female participants in the formal sector. In such a case, the average earnings of female labor force participants would fall even though females in the aggregate are better off due to the elimination of the hypothesized job discrimination. That is, the average female earnings may decline even though a female with any given set of characteristics has as much or more earnings than she would have had without the hypothesized change precisely because a number of females with limited stocks of characteristics in the labor market are induced to participate in the labor market by the hypothesized change. As a result, the change in the average female earnings due to the elimination of job discrimination may be negative rather than positive.

3. Determinantsof labor force participationand job sector

In table 7.6, we present probit estimates of the probability of working in the formal and informal sectors (males and females) and as domestics (females only).'3 In each case, the sample includes all members of the relevant sex. For the formal sector (columns 1 and 3), the estimated coefficients of the observed human capital variables for both groups have the expected signs, assuming formal sector returns to these variables exceed returns in other activities. For females, the estimated coefficients on schooling and experience are significantly more positive than for males. Also for women, in contrast to men, being married has a negative effect on working in the formal sector. Note the negative (though not quite statistically significant) effect of the spouse variable; the more positive effect (compared with men) of nonmarital status, i.e., of being a household head or living with parents; and the negative effect of spouse income (for men it is positive). For men, but not women who live with their parents, the effect of parents' income is negative (see the interaction term). This may be because women remain in their parents' households longer and are less likely to be students. In addition, the presence of a child under six has a more negative effect on working in the formal sector for women than for men (though, again, the coefficient is not quite statistically significant.) Finally, note that the constant term for women is significantly more negative than for men. Part of this Why Do Males Earn More Than Females in Urban Brazil? 159 difference is probably due to differences in overall labor supply between men and women, rather than to differences in demand for women's labor on the part of formal-sector employers. Such differences in supply could be due to women's expectations regarding future family responsibilities (though not to current family position to the extent that it is captured by the observed family variables), or to past family responsibilities that prevented job continuity. As noted in section 2, the relative size of the constant terms across sectors for women compared with men might reflect, in part, job discrimination, i.e., differences in employer demand controlling for overall labor supply of women. However, even in terms of relative sizes, it is possible that differences are due to the greater willingness of women to work in the informal or domestic sectors, where entry and exit costs are low, because they foresee future family responsibilities. For the informal sector, the coefficients on the schooling variables are negative for both men and women; but as in the formal sector, the coefficient is less negative (or more positive) for women than for men. In general, the greater their schooling (except as maids, see column 5), the more likely women are to work, all other things equal.'4 But like men, the greater their schooling, the less likely they are to work in the informal (and domestic) sector. Another important difference between the formal- and informal- sector results for women is in the dummy variable for spouse: Being married has a negative effect on formal-sector work, but not on informal-sector work, which suggests that family responsibilities reduce women's labor supply to the formal sector, but not to the informal sector. This is consistent with our earlier observation that work in the informal sector probably has lower interruption costs. Similarly, the presence of a child under six does not discourage work in the informal sector, as it appears to do in the formal sector. On-the-job child care apparently is much more possible in the informal sector. Married women and women with a child under age six are also unlikely to work as maids, presumably because many maids live in their employer's households or work long hours. In this respect, the domestic sector appears to place demands on women's time similar to those of the formal sector. Finally, note that for the informal sector, the constant term is again more negative for women than for men, though the difference is much less marked than in the formal sector. What do these results imply for the investigation of male-female eamings differences in section 5? Three points can be foreseen. First, our estimate of job discrimination related to observed human capital is likely to be negligible, since the likelihood that women work in the formal sector as a function of their schooling and experience is greater than for men.'5 Second, the effect of family variables is clearly different for men and women. We assume that this reflects differences in labor supply (rather than in demand - i.e., we assume formal- sector employers do not discriminate against women because they are spouses). The results indicate that family-related supply differences are important in Table 7.6 Probit functions determining work participation (t-statistics in parentheses)

Urban males Urban females (n=2,738) (n=1,5 72) Formal sector Informal sector Formal sector Informal sector Domestics (1) (2) (3) (4) (5)

Constant -.0362 -1.63 -2.15 -2.51 -.524 (-.329) (-10.1) (-12.26) (-8.28) (2.95) > Years of completed schooling .036 -.058 .147 -.0104 -.148 (4.75) (-5.92) (11.3) (-.441) (-5.80) Experience .049 .039 .067 .0682 -.030 (6.59) (4.56) (5.29) (3.43) (-1.91) Experience squared -.00132 -.000594 -.00142 -.00128 -.000164 ( -9.76) ( -4.23) ( -5.67) ( -3.44) ( -.547) Spouse income .000636 -.000963 -.000165 -.00112 -.00252 (2.04) ( -2.13) ( -1.69) ( -2.12) ( -1.50) Other household income .000165 .0000205 -.000240 -.00000342 -.000474 (.577) (.059) ( -.723) ( -.00507) ( -.834) Dummy: resides with parents ("child") -.134 .076 .512 .723 -.986 ( -1.29) (.450) (3.23) (2.29) ( -5.17) Dummy: head of household .144 .606 .372 .696 -.312 (1.31) (4.12) (1.92) (2.65) ( -1.53) Dummy: spouse ...... -.323 .247 -.918 (1.80) (.836) (-2.66) Dummy: has a child aged 6 or less -.184 .171 -.209 -.120 -1.0017 -1.06) (.901) ( -1.49) ( -.631) ( -3.75) Interaction of: resides with parents and other household income -.000849 .000859 -.000118 -.00142 .00166 (-2.70) (1.77) (-.752) (-1.09) (.993) Interaction of: has child aged 6 or less and number of dependents over 14 .240 -.238 -6.77 .477 -6.01 (1.37) ( -1.24) (-.139) (1.56) (-.124) Chi squared 337 251 280 63 281 162 Why Do Males Earn More Than Females in Urban Brazil? understanding labor force participation and sectoral selection. Third, the constant terms tend to be more negative for women, with the greatest difference for the formal sector. Unfortunately, much of the differences in total earnings due to sectoral composition of participation thus is likely to be unexplained.

4. Determinants of earnings

In table 7.7, we present the estimated In earnings functions for males working in the formal and informal sectors and for females in the formal and informal sectors and working as domestics. These include controls for selection (the shift parameter, X) in an attempt to assure consistent estimates of the effects of the other variables (at least with respect to sample selectivity.) For all five groups, this extended Mincerian formulation accounts for a substantial portion of the within-group variance in In earnings. The impact of schooling is positive and significant for all five samples.)6 For the formal sector, females have a significantly higher estimated schooling coefficient than do males; in the informal sector, the estimated schooling coefficient for males is higher, though not significantly so. For females, the estimated schooling coefficient is significantly greater in the formal sector than in the informal and domestic sectors; other things being equal, this pattern may suggest disequilibrium with job rationing in the formal sector. For males, the estimated schooling coefficient does not differ significantly between the sectors. Finally, under the standard interpretation" it is interesting to note that both males and females receive fairly substantial returns to schooling in the informal sector (and females do as domestics), wherein schooling would seem to be representing primarily productivity effects and not screening nor credentialism. Experience has a significant positive impact for both males and females in the formal sector and for female domestics; the quadratic effects are negative in all cases, but significantly so only for domestics. Though the formal-sector coefficient estimate is higher for males than for females, the difference between these is not significant; therefore, these results suggest that our potential experience variable is associated with general maturity, not actual labor market experience. In contrast, workers of both sexes receive no significant return from experience in the informal sector; this is consistent with our earlier observation that entry and exit is relatively easy in this sector, in part because there is little scope for acquiring valuable human capital on the job."8 The hours variables have no significant coefficient estimates, which is a surprising result that suggests there is considerable measurement error even in the crude representation of hours by intervals that is available. Nevertheless, the sign pattern of the estimates for men (though not always for women) is that expected a priori. Why Do Males Earn More Than Females in Urban Brazil? 163

The coefficients on the selectivity controls (X) indicate surprisingly little sample selection bias among the women'9 except for domestics. For domestics, the sign is negative, which suggests that unmeasured individual factors in the probit function have a negative effect on earnings. Those women with greater quantities of these factors apparently tend to be domestics. For males, the coefficients on the selectivity control terrmsare negative for both the formal and informal sectors (though significant at the 5-percent level only for the formal sector); this pattern seems to reflect the fact that more capable males (particularly younger men) tend to select themselves out of the labor force more to continue schooling, and if they do participate in the labor force, to spend more time searching for a job.20 As in the probit functions, the constant terms are much higher for males than for females. This is particularly true for the informal sector, where most workers are self-employed and thus employer discrimination in the usual sense cannot really exist. What are the implications? It appears that differences in returns to human capital and to hours worked are not a major source of earnings differences between men and women; there is little obvious "wage" discrimination. A large portion of the earnings differential is reflected in the constants and is thus unexplained. Though conventionally this unexplained portion is attributed to discrimination (for example, Oaxaca 1973b), we are hesitant to do so because it is difficult to imagine the mechanism by which such discrimination would operate within the informal sector, where most workers are self-employed.2"

5. Sources of differences between male and female average In earnings

Table 7.8 summarizes the impact of replacing female mean variable values and parameters by male values on: sectoral female In eamings, sectoral weights for female participants, and overall female In earnings. Each row refers to the replacement of certain female values by male values. The upper half of the table refers to In eamings, with percentage changes in the formal, informal, and domestic sectors, and in the overall level, indicated in the four columns. The bottom half of the table refers to the estimated distribution of female participants among sectors, with the weights for the formal, informal, and domestic sectors, respectively, in the first three columns. The fourth column in the bottom half of this table gives the changes in overall average female earnings if there were to be the changes in weights indicated in the first three columns (but mean sectoral earnings were to remain constant). The In eamings estimates suggest that by the broadest definition of eamings discrimination given in section 2, elimination of discrimination might result in considerably higher earnings for females: increases in In earnings of about 22 percent in the formal sector, 72 percent in the informal sector, 44 percent in the domestic sector, and 31 percent overall. These are quite large percentage changes for In earnings. But they originate in very little part in the coefficients of the Table 7.7 Earnings functions, urban Brazil 1970 (dependentvariable: natural logarithmof income;t-statistics in parentheses)

Males Females Formal sector Informal sector Formal sector Informal sector Domestics (1) (2) (3) (4) (5)

Constant 4.88 6.00 3.51 2.47 3.87 (11.6) (6.71) (5.82) (1.71) (15.0) Years of schooling .125 .155 .172 .131 .125 (10.9) (5.18) (5.91) (2.05) (3.12) Experience .062 .017 .0413 .0425 .0650 (3.37) (.768) (2.43) (.792) (3.77) Experience squared -.00066 -.00026 -.00039 -.00073 -.00071 (-1.56) (-.765) (-1.03) (-.717) (-1.92) < Dummy: works in agriculture -.659 -.366 -1.46 .193 ... (-5.44) (2.56) (-3.68) (.342) Dummy: works less than 15 hours weekly -.158 -.0183 -.0804 ... .402 (-.348) (-.040) (-.150) (.768) Dummy: works 15 hours to 39 hours weekly -.0894 -.311 -.286 -.280 -.0176 (-.669) (-1.42) (1.69) (-.736) (-.07) Dummy: works 50 hours or more weekly .0557 .208 -.234 -.0407 .0156 (.675) (1.58) (-1.06) (-.097) (.107) -.859 -.658 .131 .611 -.294 (-2.70) (-1.68) (.549) (1.25) (-1.98) R2 .41 .31 .37 .29 .27 Corrected s.e. 1.17 .927 1.03 .960 .65 Why Do Males Earn More Than Females in Urban Brazil? 165

Table 7.8 Impact on In earnings and on sectoral weights of replacing female characteristicsand coefficientsby male, urbanBrazil 1970

Total Formal Informal average In sector sector Domestics earnings Ln earnings At female mean value 5.22 4.67 4.21 5.03 Percentage change if change to male values of: Human capital levels: Schooling -8% 1% 1% -7% Experience 2 0 13 3 Hours worked -1 2 0 -1 Human capital coefficients: Schooling -7 1 2 -5 Experience 4 -5 -10 I Hours worked coefficients 3 0 1 2 Constant 26 76 51 33 Distribution of female participants Weights at mean female values .80 .16 .04 Weights if change to male values of: Human capital levels: Schooling .82 .14 .03 0 Experience .80 .16 .04 0 Household background variables .05 .95 .00 -14 Human capital coefficients: Schooling .59 .18 .23 -4 Experience .39 .10 .51 -8 Household background coefficients .39 .04 .56 -8 Constant .89 .11 .00 2

Note:.The basisfor this table is describedin sections2 and 5 in the text.

observed human capital characteristics and hours worked, which are associated with our lower-bound estimates of earnings discrimination. In fact, if the female coefficients of these observed variables alone are replaced by the male coefficients, our estimates, if anything, imply slight declines in In earnings! In other words, the constants account for almost all of the gains to females if discrimination, as measured by our upper-bound estimates, were to be eliminated. This raises sharply the question of what the differences in the constants are representing. The answer, of course, is the average effect of any unobserved 166 Why Do Males Earn More Than Females in Urban Brazil? factors that are associated systematically with sex. These may include sex discrimination by employers, including conventions that lead to occupational segregation within sectors and lower pay for the "female" occupation (for example, in the formal sector, lower pay for typists than for machine operators). But it is also possible that unobserved factors include differences in work experience or attachment to the labor force not captured by the experience and hours variables, or differences in training not captured by the education variable.22 Finally, the constant could also reflect sex-specific attributes, such as physical strength. In section 4, we questioned whether the constant differential in the informal sector is likely to be associated with labor market discrimination, given the self- employment in that sector. If none of the informal-sector male-female constant differential originated in earnings discrimination, our estimate of the upper bound of the impact of eliminating earnings discrimination would change to 20 percent of In earnings (as compared with the 31 percent cited above). This estimate still assumes that all of the male-female constant differential in the formal sector is due to discrimination. The weights for the distribution of female participants among sectors in the bottom part of table 7.8 suggest that differences by sex distribution among sectors are less important than ln earnings differences within sectors in the determination of overall male-female average In earnings differentials. Though some of the weights change a fair amount if male coefficients are used instead of female coefficients, the impact of these changes alone on average female In earnings is not large. Moreover, due to the induced increased labor force participation by women with lesser stocks of characteristics rewarded in the labor market (as discussed at the end of section 4), such changes in the coefficients of the observed variables would tend to reduce average female In earnings, though the opposite would be the case for the constant.2 3 Thus job discrimination, as defined in section 2 to be related to different probabilities of entry into the formal sector, apparently is not very important in the male-female average In earnings differentials in urban Brazil.24 What have we learned about the determinants of the considerable male-female average earnings differentials in urban Brazil? Based on our estimates, neither differential hours of work nor differential human capital stocks are important explanatory factors. Among current participants, in fact, the higher schooling of females more than offsets their lower experience. Likewise, neither job discrimination by our simple definition nor earnings discrimination related to observed variables such as schooling, experience, and hours worked seems to be very important. The major source of the difference is unobserved sex-related factors that determine earnings and alter the constant in the In earnings relations. If our argument that the difference in these constants for the informal sector is not likely to reflect sex discrimination in the labor market is right, we are able to narrow significantly the range between our estimated lower and upper bounds Why Do Males Earn More Than Females in Urban Brazil? 167 of the impact of labor market discrimination on female earnings. Nevertheless, the remaining range of from zero to 20 percent of female In earnings is quite substantial - and indicates that our remaining ignorance also is quite substantial.

Notes

This paper was prepared as part of a World Bank Research Project, "Studies on Brazilian Distribution and Growth." 1. We define the informal sector below. 2. But see Knight and Sabot (chapter 3), Banerjee and Knight (chapter 8), Behrman and Wolfe (chapter 5), and Birdsall and Fox (chapter 6), for studies of wage differentials in Tanzania, India, Nicaragua and Brazil, respectively. 3. Information on individuals' wages would be better; we include information available on ranges of hours worked to correct for differences in reported income between men and women that may be due to differences in hours worked. We cannot correct for the fact that for some individuals, income may include unearned income. There has been some controversy about the census income figures, since they imply lower national income than do national accounts (see Lluch 1981); however, any underreporting is not likely to vary systematically between males and females and thus should not affect our results. 4. For a discussion of the relevance of the full-income approach to income differentials between male and female headed households in Belo Horizonte, Brazil, see Merrick and Schmink (1983). 5. For those who have nine or more years of schooling, this is identical to Mincer's (1974) measure of potential experience. For those with less schooling, however, we include only "adult" experience acquired when one is 15 or older. We do so because post- schooling experience when one is eight or nine would seem to be irrelevant, though the cutoff between 14 and 15 is arbitrary. 6. Given that a relatively small proportion of women work, the possibility that they have certain unobserved characteristics that influence their earnings cannot be ignored. If, for example, women who work have on average greater drive or ambition than men who work (and this drive is not correlated with schooling or any other variable explicitly included in the earnings function), then their human capital is in effect understated, and any estimate of discrimination based only on measured differences in human capital will be biased downward. On the other hand, if women who work do so on average because of short-term needs for cash income and with little expectation of remaining for long periods in the labor force, they may invest less on the job and have less human capital than is measured. Moreover, they may choose types of jobs where entry and exit costs are low. Since in Brazil women in both the urban formal and informal sector are younger than other working women, it is not implausible that women invest less and choose jobs with low interruption costs. 7. Brown, Moon and Zoloth (1980) estimate a function determining occupational outcomes for females but do not take into account the possibility that the decision to work is, in part, a function of the type of work that can be obtained. 8. We do not observe values for males in the domestic sector (see table 7.2). Therefore, we use observed values and parameters of males in the informal sector when substituting to obtain the effects for females in the domestic sector (table 7.8). 168 Why Do Males Earn More Than Females in Urban Brazil?

9. Because of nonlinearities, there are interaction effects in addition to the effects captured by this procedure. 10. Differences in schooling may reflect earlier gender discrimination in the provision of schooling, but they also may be due to differential expected returns or costs. In any case, we do not include such differences in our estimation of possible direct labor market discrimination. 11. If actual work experience, rather than just maturity, is important, the differential coefficient estimates for males versu's females, in part, may be correcting for sex-related differences in the relation between actual and potential experience. If this were all that were occurring, and if the cross-section participation rates for 1970 in table 7.1 can be assumed to represent participation over time for individuals of the two sexes, then the estimated male coefficient of experience would be about 3.1 (=78/25) times the estimated coefficient for females. In fact, there is no such difference. 12. For this reason, there is some ambiguity about our distinction between earnings and job discrimination. Nevertheless, we think this dichotomy is useful in helping to distinguish between two somewhat different phenomena. 13. We present probit estimates rather than multinominal logit estimates because we did not have at our disposal at the World Bank software for the multinomial logit estimates. The probit estimates are less efficient than multinomial logit estimates, but are equally consistent. 14. A probit function estimating the probability that women are in the labor force indicates that the overall effect of education for women is positive and highly significant; the positive effect is twice as great for wives compared to all women. 15. Such a comparison depends both on the relative size of the schooling and experience coefficient estimates and on the value of the rest of the function since the function is nonlinear. Both of these effects work in the same direction in this case. 16. The Mincerian interpretation of the schooling coefficients as the private earnings rate of return to the opportunity cost of time spent in school does not hold here because part of the earnings return to schooling is in the sectoral allocation discussed in section 3. Since the more schooled are more likely to be in the higher earnings sectors, other things being equal, the schooling coefficients in table 7.7 are lower-bound estimates of the return to schooling under the standard assumptions. However, we note that our work suggests that the standard assumptions cause overestimates of the returns to schooling because of the failure to control for school quality and family background differentials and geographical aggregation biases. See Behrman and Birdsall (1983); Behrman and Wolfe (1984d); Birdsall and Behrman (1984). 17. But see note 16. 18. For a discussion of the effects of on-the-job investment as reflected in the experience terrn, see Mincer (1974). 19. Only if the sample is confined to wives is the sign on lambda positive, and then only significant at the 10-percent level. 20. The male labor force participants who are searching for work are 6.0 percent of 15- 20 year olds, 1.3 percent of 21-25 year olds, and 0.8 percent of those over age 25. The mean schooling for nonworking males in the 15-20 age range is 5.8 years compared with 4.4 years for those working. For 21-25 year olds, there is a similar pattern: 7.1 versus 5.2 years. However, for those over 25, the pattern is reversed: 3.0 versus 4.3 years. Why Do Males Earn More Than Females in Urban Brazil? 169

21. A possible alternative discriminatory mechanism would be discrimination by consumers, if many informal-sector workers provide goods and services directly to consumers. The question then would be, for example, are consumers willing to pay more to a male "tailor" than a female "seamstress" for the same service? 22. For an example from Brazil indicating that for the same years of schooling, male teachers on average have taken more "academic" as opposed to "teacher training" courses, see Birdsall and Fox (chapter 6). 23. The induced changes in female labor force participation are considerable in some cases. For example, for the cases considered in the bottom half of table 7.8, the percentage change in female labor force participation rates are simulated to be +9 with male schooling, +6 with male experience, -38 with male household background variables, -49 with male schooling coefficients, +15 with male experience coefficients, +354 with male household background coefficients, and +938 with male constants. 24. It is possible, of course, that it is job differences within sectors that actually matter more.

Job Discriminationand Untouchability

Biswajit Banerjee and J. B. Knight

1. Introduction

Most of the empirical analysis of discrimination in the labor market is concerned with discrimination by race or by sex. The historical importance of caste in India offers scope for the study of discrimination by caste. The analysis of caste has, however, been the preserve of social scientists other than economists.' If discrimination by caste exists, it is of interest to know whether it takes the form of wage discrimination, commonly found in empirical studies of discrimination by race and by sex, including others in this volume, or of job discrimination, the traditional function of caste. The problem of caste in India is an age-old one. Life in the traditional Indian village was based on caste. Under the jajmani system, each caste had a traditional occupation, regarded as its sacred duty. The caste structure was thus a labor structure, fixing the supply of any kind of labor through heredity; such occupational mobility as occurred was collective rather than individual.2 The lowest castes (the Untouchables or Harijans) occupied the lowest jobs such as agricultural laborer and sweeper. There was also a religious and a social basis to their condition. The notion of untouchability separated the Harijans from the main body of Hindus. Their landlessness and their abject poverty, together with the justification provided by religion, in turn contributed to their social ostracism. Those who promulgated the Constitution after Indian Independence were determined to provide a framework which would help to rid India of caste discrimination and, in particular, of untouchability. Not only was the practice or enforcement of untouchability made an offence but provision was also specifically made for reverse discrimination. This takes the form of preferential access to education and preferential treatment in recruitment to employment in public services. The castes scheduled for favorable treatment corresponded to the Untouchables. As a result of these policies, the percentage of scheduled caste members in central government employment rose from less than 1 percent in

171 1.72 Job Discrimination and Untouchability

1951 to 4.8 percent of senior administrative and 7.4 percent of other administrative posts in 1979, and from 3.2 percent to 12.6 percent of clerical posts.3 Nevertheless, the scheduled castes, representing 14.6 percent of the Indian population in 1971 (8.8 percent of the urban and 16.0 percent of the rural population), were still under-represented in all but the lowliest class of public service employment. This was partly because of their lack of education: although educational access was improving, in 1971 the scheduled castes constituted only 3.2 percent of people with matriculation or above and 7.1 percent of people with primary or middle schooling.4 At the social level, also, progress was slow. The Committee on Untouch- ability concluded in 1969 that untouchability was still being practiced throughout village India.5 The persistence of caste may be due to the fact that the caste system represents a voluntary stable equilibrium based on consensus, not coercion.6 The urbanization accompanying economic development tended to weaken the system. It became less rigid in the cities owing to the greater anonymity, and the diminishing correlation between occupational or economic stratification and the traditional ranking of the castes. Nevertheless, the greater economic and social mobility of the scheduled castes in the urban areas was not achieved without resistance. Caste riots - such as the Gujarat riots of 1981, resurfacing in 1985 - reflect the hostility of the middle castes with whom the upwardly mobile members of the scheduled castes compete.' Resentment of preferential treatment may reinforce prejudice against the scheduled castes. Economic discrimination against the scheduled castes operates powerfully in the rural areas and it predominantly takes the form of discrimination "prior to the market," i.e., in access to land, property and education. In this paper our interest is narrower, being concemed with discrimination in the cities and "within the market." We first ask whether there is caste discrimination in the urban labor market after standardizing for the economic characteristics of workers, and then attempt to explain our affirmative answer. Section 2 deals with the data and with the techniques of analysis to be used. Section 3 measures the extent of discrimination by caste. Section 4 attempts to explain the discrimination observed, both by distinguishing empirically between wage and job discrimination and by pinpointing the incidence of discrimination. Section 5 concludes.8

2. The data and the method

The empirical basis of the paper is a survey conducted in Delhi by one of the authors from October 1975 to April 1976. As the primary objective of this survey was to test the empirical validity of economic models of migration, detailed data were collected only from male migrant heads of household. The first stage of the survey involved a stratified random sample of households in Delhi, and at the second stage a total of 1,615 migrant heads of households, all Job Discriminationand Untouchability 173 of whom had come as voluntary labor migrants and not as dependents or students and 1,408 of whom had come from rural areas, were interviewed. The monthly earnings of salaried employees include the basic wage, all allowances and bonuses before tax. For those who were paid daily wages or worked on a piece-rate basis, monthly earnings are calculated on the assumption that they worked for 25 days at the wage rate indicated by them.9 No account is taken of earnings from overtime work. Occupation is classified into six broad groups: professional, clerical, production, service, skilled and unskilled workers. Professional workers include also managerial, executive and administrative workers, and clerical and production workers are the same as those classified under these headings in India's National Classification of Occupations. Service workers consist of policemen and security guards (chowkidars), and skilled workers include motor vehicle drivers and skilled construction workers. Unskilled workers comprise all other occupational groups, including shop and sales assistants, sweepers, loaders, unskilled construction workers, and "laborers not classified elsewhere.' Twenty-nine percent of the migrants from rural areas and 13 percent of the migrants from urban areas in the sample claimed to be members of castes which fell in the scheduled caste group. These percentages are almost twice as high as those in the areas from which the migrants originated. Our sample selection criteria may be partly responsible for this result. The percentage of scheduled caste members would have been lower had the second stage sample not excluded migrants who had been transferred by their employers and those who had come as students: in the first stage of the survey, when these groups were included, the scheduled castes constituted 22 and 9 percent of migrant household heads from rural and urban areas respectively. Even after this adjustment for sample selectivity bias, however, it would seem that the scheduled castes have a higher propensity to migrate than other castes. The survey question about caste could give rise to bias. Given the practice of "Sanskritization"'° - the tendency among those in the lower levels of the caste hierarchy to emulate the ways of the upper castes and reduce the social distance between them - it is possible that some scheduled caste migrants concealed their true caste affiliation and claimed to be of a higher caste which belonged to the non-scheduled caste spectrum. Because of the difficulty of establishing the accuracy of an individual's response on caste affiliation, and to avoid the risk of terminating the interview or losing the rapport of the respondent, no attempt was made during the fieldwork to probe the issue of caste affiliation. The high incidence of scheduled caste members even in the first stage sample suggests that misreporting of caste affiliation was low. However, scheduled castes reporting to be non-scheduled would generally produce a downward bias in the standardized caste differential in earnings, and correction for misreporting would thus strengthen, rather than weaken, any evidence of discrimination against scheduled castes. Even if the misreporting were concen- 174 Job Discrimination and Untouchability trated among the highest wage-earners in the scheduled caste group, whereas this might raise the observed mean wage of the non-scheduled relative to the scheduled castes, such misreporting could raise the standardized differential only if the relatively high wages of these workers were due to lack of discrimination against them and not to their possession of productive characteristics. Insofar as those who misreported in the survey also "passed" as non-scheduled in the labor market, misreporting would not produce bias if the estimate was of discrimination conditional upon the caste information of employers. The nature of the survey could also give rise to a form of bias. Members of the scheduled castes from rural areas have already been exposed to caste prejudice and discrimination in their place of origin. This is likely to influence their attitudes and expectations in the urban environment. The response of scheduled castes who have been born and brought up in the city may be different. However, our examination of occupational distribution based on the more limited data collected in the first stage of the survey shows that caste differences in occupational attainment among urban natives are remarkably similar in qualitative terms to those observed among rural migrants (table 8. 1). 1 We decompose the difference in mean wages between scheduled and non- scheduled castes into the component "explained" by differences in economic characteristics between the two groups and the "unexplained" component, which can be regarded as reflecting the extent of labor market discrimination. We use the standard decomposition technique for measuring discrimination when two groups differ in their personal characteristics and in the function relating these characteristics to earnings. Thus our equations (1) and (2) correspond to (1) and (2) in Knight and Sabot (chapter 3, page 57), except that s (scheduled castes) and n (non-scheduled castes) replace f (female) and m (male) respectively. An OLS earnings function is again estimated with the logarithm of wages as the dependent variable. Similarly, for distinguishing wage and job discrimination, our equations (3) and (4a) correspond to their (3) and (4a). Reflecting the method of estimation, our (4b) differs slightly from (4b) in Knight and Sabot."2 Denoting pi, as the actual proportion of scheduled caste workers and PI, as the proportion of scheduled caste workers who would be in occupation i if scheduled castes faced the same occupational attainment function as non-scheduled castes, our decomposition becomes:

Wn s pip(fint Xi,n))

EPj(f,(Ti-) fi/(x`J))

+ Wrn(Pin Pid

W m (-ii Pis

= WE WD + JE + JD, respectively. (4b) Job Discrimination and Untouchability 175

Table 8.1 Occupational distribution of wage earners in Delhi, by caste and residential status for selected educational groups, 1975

Urban natives Rural migrantsa Scheduled Non-scheduled Scheduled Non-scheduled caste caste caste caste

Illiterates

Professional 0.0 2.7 1.8 1.7 Clerical 2.2 3.2 1.7 3.5 Production 23.7 49.3 22.9 36.8 Service 8.6 3.4 3.9 4.1 Skilled 15.5 10.5 9.5 6.1 Unskilled 50.0 30.9 60.2 47.8 Total 100.0 100.0 100.0 100.0

Graduates Professional 19.5 35.2 16.7 42.1 Clerical 30.5 57.4 77.7 50.3 Production 0.0 3.1 5.6 3.4 Service 0.0 4.3 0.0 4.2 Skilled 0.0 0.0 0.0 0.0 Unskilled 0.0 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0

All educational groups Professional 4.9 15.7 3.5 11.1 Clerical 13.8 20.6 9.2 21.8 Production 27.4 33.6 26.9 32.4 Service 6.0 3.7 4.4 4.4 Skilled 12.0 4.8 9.7 5.7 Unskilled 35.9 21.6 46.3 24.6 Total 100.0 100.0 100.0 100.0

Note: The figures are population estimates, obtained by scaling the sample observations using the appropriate weights. The data were collected in the first stage of the survey. a. None of the selectivity criteria used to select the second stage sample, on which this paper is based, apply here. 176 Job Discrimination and Untouchability

Whereas data constraints necessitated a cruder method in Knight and Sabot (page 58), we estimate the determinants of occupational attainment by means of multinominal logit analysis.

3. A measure of discrimination by caste

The average monthly earnings of scheduled castes and non-scheduled castes in 1976 were Rs. 272 and Rs. 337 respectively. A part of this difference of Rs. 65 per month, which is statistically significant at the 1-percent level, could be attributed to differences between the two groups of workers in several characteristics that are associated with earnings (table 8.2). The two groups are similar in various characteristics, including the average length of urban experience. However, the non-scheduled castes are at an advantage in three main respects: in the average number of years of education they possess, in the proportion who are salaried, as opposed to daily-wage workers, and in their occupational

Table 8.2 Mean values of earnings and characteristics of all migrants and of scheduled and non-scheduled caste migrants in Delhi, 1975-76

Non-scheduled Variables Entire sample Scheduled castes castes

Monthly earnings (Y) 317.32 271.94 337.14 In Y (W) 5.589 5.471 5.641 Years of education 5.500 3.892 6.202 (Education)2 53.012 37.282 59.883 Age on arrival 22.703 22.713 22.699 (Age on arrival)2 564.934 569.619 562.887 Urban experience 5.737 6.010 5.617 (Urban experience)2 44.001 47.471 42.485 Professional workers 0.064 0.044 0.072 Clerical workers 0.141 0.094 0.161 Production workers 0.398 0.345 0.421 Service workers 0.053 0.044 0.057 Skilled workers 0.048 0.083 0.034 Unskilled workers 0.296 0.389 0.255 Salaried employees 0.727 0.643 0.764 Formal sector employees 0.676 0.661 0.683 (N) (1115) (339) (776) Job Discrimination and Untouchability 177 distribution. The notable occupational difference is that the proportion in unskilled manual jobs is greater by 13 percentage points for scheduled than for non-scheduled caste workers, and the proportion in white-collar jobs - professional and clerical work - is lower by 9 percentage points. The independent variables in the reduced form earnings function, listed in table 8.3, include both human capital and other variables. The former are represented by years of education, years of urban experience and - less certainly - age on arrival in the city, and their squares. The latter include dummy variables for sector of employment (formal sector, with the informal sector as the omitted variable), employment status (salaried employees, with those employed on a daily-wage basis as the omitted variable) and occupation (a set of five dummy variables, with professional workers being the omitted group). The occupational classification is based as far as possible on skill level: a justification for including occupation variables in the earnings function is that they are a way of identifying occupation-specific skills acquired in the job. The estimated earnings functions for the entire sample and separately for the two caste groups are presented in table 8.3, the dependent variable being the natural logarithm of monthly earnings. The equation for the entire sample (column 1), shows that years of schooling have significant positive and increasing effects on earnings, whereas both pre-migration "experience" and experience in the city have significant positive but diminishing effects. A comparison of the regression equation including occupation dummies with that excluding them (not reported in table 8.3) indicates that occupation is a significant variable in explaining earnings: the test for the significance of the set of occupation dummies yielded an F-ratio of 38.2. The ordering of the coefficients for the occupation dummies is consistent with a priori expectations. Compared to professional workers, the earnings of clerical workers, production workers and unskilled workers are lower by 32 percent, 40 percent and 50 percent respectively. Skilled workers have slightly higher earnings than clerical workers, and service workers earn marginally less than production workers. The coefficients for the salaried worker and the formal sector dummies are both positive and significant. When the earnings function is re-estimated (column 2) with membership of the scheduled castes included as a dummy variable (membership of the non- scheduled castes being the base sub-category), that coefficient is negative and significant at the 1-percent level: membership of the scheduled castes reduces pay by 7.7 percent. However, this method of estimating the impact of caste on earnings is inappropriate because there are significant differences in coefficients on the other explanatory variables between the two caste-groups, as revealed by the introduction of slope dummy variables representing the products of each independent variable and the dummy variable for scheduled castes. Column 3 thus shows the constant term and coefficients for the non-scheduled castes, and column 4 the difference which membership of the scheduled castes makes to 178 Job Discrimination and Untouchability

Table 8.3 Regression analysis of earnings for the entire sample

Non- Scheduled- scheduled castes inter- castes action terms

Column (1) (2) (3) (4)

Education -0.021 -0.025 -0.029 0.022 (0.007)* (0.007)* (0.009)* (0.016)

(Education)2 0.004 0.005 0.005 -0.002 (0.001)* (0.001)* (0.001)* (0.001) Age on arrival 0.044 0.044 0.045 -0.0001 (0.007)* (0.007)* (0.010)* (0.015) (Age on arrival)2 -0.001 -0.001 -0.001 -0.0001 (0.0001)* (0.0001)* (0.0002)* (0.0002) Urban experience 0.071 0.072 0.098 -0.070 (0.011)* (0.011)* (0.014)* (0.022)* (Urban experience)2 -0.002 -0.003 -0.004 0.004 (0.001)* (0.001)* (0.001)* (0.002)**

Clerical workers -0.390 -0.387 -0.368 -0.005 (0.057)* (0.057)* (0.066)* (0.133)

Production workers -0.503 -0.503 -0.457 -0.196 (0.060)* (0.060)* (0.069)* (0.142)

Service workers -0.534 -0.534 -0.481 -0.240 (0.073)* (0.073)* (0.083)* (0.173)

Skilled workers -0.285 -0.266 -0.244 -0.132 (0.077)* (0.077)* (0.096)** (0.171) Unskilled workers -0.706 -0.699 -0.655 -0.165 (0.062)* (0.062)* (0.071)* (0.145)

Salaried employees 0.131 0.123 0.070 0.132 (0.026)* (0.026)* (0.033)** (0.053)**

Formal sector employees 0.083 0.085 0.120 -0.128 (0.024)* (0.024)* (0.029)* (0.052)**

Scheduled castes -0.081 (0.024)* Job Discrimination and Untouchability 179

Table 8.3 (cont.)

Non- Scheduled- scheduled castes inter- castes action terms

Column (1) (2) (3) (4)

Constant 4.888 4.929 4.805 0.335 (0.127)* (0.127)* (0.156)* (0.272)

R2 0.575 0.579 0.591

K2 0.570 0.574 0.581 F 114.415 108.030 58.150

Residual sum of squares 137.541 136.152 132.283

(N) (1115) (1115) (1115)

Note: The dependent variable is the natural logarithm of monthly earnings. The table shows the coefficients of the independent variables, with the standard errors in parentheses beneath. Columns 3 and 4 refer to the same regression equation, with column 3 showing the coefficients on the independent variable and column 4 the coefficients on the scheduled-caste interaction terms. The reported significance of the occupation dummies is with respect to the omitted category, professional workers. * Significant at the 1-percent level, using a two-tailed test. ** Significant at the 5-percent level. *** Significant at the 10-percent level.

each of these terms. A Chow test performed on the separate earnings functions for the two caste groups indicates that they are significantly different at the 1-percent level.13 Following the standard procedure, we decompose the observed difference into two components: that due to differences in characteristics (E) and that due to differences in earnings functions (D); we also indicate the contribution of education, occupation and urban experience to the difference in earnings (table 8.4). The decomposition is conducted using the earnings functions for non- scheduled and for scheduled castes; intermediate estimates are then calculated as the geometric mean of these two. When expressed in terms of natural logarithms, the gross earnings difference between scheduled and non-scheduled castes is 17 percent. Less than one-half of this is accounted for by differences in characteristics, if the wage structure of non-scheduled castes is used. If the wage structure for scheduled castes is used, 180 Job Discrimination and Untouchability

Table 8.4 Decomposition of difference in earnings between caste groups

Non-scheduled Scheduled Geometric mean Earnings function used: castes castes of results

Earnings difference due to: characteristics (E) 0.079 0.110 0.093 of which: education 0.047 0.048 0.047 occupation 0.035 0.045 0.039 urban experience -0.0 18 -0.011 -0.0 14

earnings function (D) 0.091 0.060 0.077 of which: education -0.002 -0.002 -0.002 occupation 0.154 0.144 0.149 urban experience 0.222 0.216 0.219

the difference in characteristics explains slightly less than two-thirds. Virtually the entire effect of differences in characteristics can be attributed to education and occupation, whereas urban experience and occupation are the important variables in explaining the difference due to coefficients. The lower rate of return to urban experience for the scheduled castes gives rise to an earnings difference (0.22) which actually exceeds the gross difference in earnings (0.17).

4. Explaining caste discrimination

Caste differs from sex and race in that it is less readily identified. This means that caste discrimination is less likely to be found in the city than in the village, and that the caste of a worker is unlikely to be known to a customer, and possibly not even to an employer, in the city.'4 Whether the caste of a worker is known to the employer depends partly on whether the worker wishes to disclose it, in order to take advantage of the reservation policy, or to conceal it, in order to avoid discrimination. It also depends on the method of application. For instance, caste is likely to be revealed where hiring takes place through contacts of the same caste. These considerations will help to explain the incidence of discrimination which we observe. We recognize that our apparent measure of discrimination is not necessarily the result of discrimination against the scheduled castes. It is possible that certain personal economic characteristics not measured in the survey - for instance, Job Discrimination and Untouchability 181

"drive, determination and doggedness" or other attitudes which are likely to be associated with a "culture of poverty" - are correlated with caste. The caste variable in the earnings function may be acting simply as a proxy for unidenti- fied productivity differences. It is similarly possible that profit-maximizing employers use caste as a screening device for differences in productivity in the absence of perfect information."5 Employers may use caste as a criterion for hiring if they believe that the two caste-groups have different distributions of the imperfectly observable economic characteristics. Scheduled caste workers wishing to be hired must accept a lower wage in order to offset the perceived lower probability of their having the desired economic characteristics. Provided that the scheduled caste group is indeed less efficient on average, such discrimination can persist. Although we do not expect unmeasured productivity differences to provide a full explanation for our results, the possibility of such differences nevertheless qualifies the analysis below. According to the neoclassical theory of labor market discrimination, wage differences arise among equally productive workers because group-specific characteristics are valued in the market, and the values placed on these characteristics are determined by a "taste for discrimination" exercised by employers, employees or consumers.'6 Scheduled and non-scheduled castes receive different pay even in the same jobs because of the psychic cost attached to the presence of scheduled caste employees. Given the extent of social discrimination found by the Committee on Untouchables,'7 a "taste for discrimination" on grounds of caste could plausibly be assumed to exist in the urban labor market. However, the neoclassical theory of discrimination is question- begging in three respects. First, it overlooks the mechanism by which a taste for discrimination is likely to be exercised. Secondly, it is a prediction of the theory that discrimination does not continue in the long run: competition should eliminate wage differences by driving out the discriminators or producing segregated firms.'8 For the theory to hold, it must be the case that caste prejudice is too pervasive, or competition in the product market too weak, for discrimination to be eliminated in India. Thirdly, in simply postulating a taste for discrimination, the theory fails to delve into the economic basis for that taste and the economic interests which it consciously or unconsciously serves. We attempt to develop a coherent altemative explanation which is based on job discrimination. It is in line with segmented labor market explanations of discrimination, according to which wage differences arise because group differences in political or economic power produce unequal access to earnings opportunities.'9 Thus Untouchables may be at a disadvantage in pay because they are allocated to less well-paid jobs. We begin from the premise that it is in the allocation of workers to jobs that discrimination is most likely to be practiced. An employer would have no aversion to employing an Untouchable provided that he worked in an Untouchable's job. The village customer might dislike being served by a scheduled caste shop assistant but he would be unlikely 182 Job Discrimination and Untouchability to complain if the more menial jobs in the shop were performed by the scheduled castes. Non-scheduled caste employees in a firm might object to the scheduled castes being employed in work of higher or equal status, but they would have no objection to the scheduled caste doing work of lower status. We argue that the occupational attainment of Untouchables is influenced by discrimination in three possible forms. First, there is a combination of social prejudice and economic self-interest of fellow-workers, through the pressures which they can bring to bear on employers. Secondly, owners, managers and recruitment officers may discriminate by caste for reasons of prejudice or caste loyalty or to avoid the costs which non-scheduled workers might otherwise impose on them. Finally, Untouchables may suffer from the constraints imposed by historical discrimination. While not denying a role for social prejudice, we argue that job discrimination may well serve an economic function. In the standard analysis of discrimination conducted above, that part of the gross earnings difference which is due to differences in earnings functions is regarded as the measure of discrimination, and that due to differences in characteristics as being "explained" in the sense that they are economically justified. This approach ignores the potential discriminatory nature of differences in characteristics. While it is possible that the different occupational distributions of scheduled and non-scheduled castes are the result of differences in economic characteristics and in "tastes," e.g., attitudes and aspirations, discrimination in hiring cannot be ruled out. Given the size and significance of the occupation coefficients obtained in the earnings function analysis, discrimination in access to occupations is potentially an important cause of caste differences in earnings. Moreover, if tastes are responsible, they may have been conditioned by past experience of discrimination.2 0 In order to take into account the possibility of "unexplained" differences in characteristics, we introduce a separate model of occupational attainment. The exercise involves estimating occupational attainment functions and within-occupation earnings functions. We analyze occupational attainment within the framework of multinominal logit model, setting the coefficients for skilled workers to zero for purposes of normalization. We are constrained by the nature of the data set to restrict the independent variables to education, age on arrival, urban experience, and, for the model based on the entire sample only, caste. Maximum likelihood estimates of the model based on observations for the entire sample indicate that there is differential access to certain occupations according to caste. The coefficient for the scheduled caste dummy is significantly different from zero in all occupation groups except professional workers. To measure the impact of occupational discrimination on occupational distribution and ultimately on wages, we estimate a separate model of occupational attainment for non-scheduled caste workers. Employing these estimates - shown in table 8.5 - we obtain the predicted distributions for scheduled (J5) and Job Discrimination and Untouchability 183 nonscheduled (P.) castes as follows: substitute the sample data for each caste group into the estimated model, producing for each individual a vector of predicted probabilities of belonging to each of the six occupations, and calculate the mean of the predicted probabilities for each occupation after summing over observations. For non-scheduled castes this estimation procedure yields a predicted distribution which is identical to their actual sample distribution, i.e., Pn = Pn. The difference in the predicted distributions (P, - P) is the "explained" component due to differences in characteristics, and the residual difference. (6,, - P,), is the "unexplained" component due to differential access, i.e., discrimination. Except for professional and clerical workers, the residual component accounts for the major part of the observed differences (table 8.6). For production workers the residual difference (0.11) is actually greater than the observed difference (0.08). If there were no differential access to occupation by caste, the proportion in production occupations would be higher for scheduled than for non-scheduled

Table 8.5 Coefficients and asymptotic standard errors for the multinomial logit model of occupational attainment: non-scheduled caste workers

Independent variable Age on Urban Occupation Constant Education arrival experience

Professional -11.323 0.924 0.074 0.145 (1.747)* (0.105)* (0.141)*** (0.092) Clerical -1.792 0.353 -0.0004 0.126 (1.043)*** (0.062)* (0.029) (0.070)*** Production 4.324 -0.026 -0.063 -0.038 (0.885)* (0.052) (0.025)** (0.063) Unskilled 3.463 -0.054 -0.046 -0.017 (1.901)* (0.053) (0.026)*** (0.065) Service -1.343 0.090 0.020 0.145 (1.109) (0.064) (0.029) (0.077)*** Log likelihood -929.029 (N) (773)

* Significant at the one-percent level, using a two-tailed test. ** Significant at the five-percent level. *** Significant at the 10-percent level. Table 8.6 Full decomposition of gross earnings difference between scheduled and non-scheduled caste workers

Observed occupational Predicted occupational Observed Explained Residual distribution distribution difference difference difference

Pn PS P fis PPs4P PS4 f•sP5 Occupation (1) (2) (3) (4) (5) (6) (7) Professional workers .0721 .0443 .0721 .0427 .0278 .0294 -.0016 Clerical workers .1611 .0944 .1611 .1110 .0667 .0501 .0166 Production workers .4214 .3451 .4214 .4556 .0763 -.0342 .1105 Unskilled workers .2552 .3894 .2552 .2970 -.1342 -.0418 -.0924 Service workers .0567 .0442 .0567 .0574 .0125 -.0007 .0132 Skilled workers .0335 .0826 .0335 .0363 -.0491 -.0028 -.0463

G E D (P, -p)3 ( P5 ) = mY nmY =f,,C-is) (>f) P, x E Ps x D x lnY4 x InY Occupation (8) (9) (10) (I1) (12) (13) (14) Professional workers .2607 .1329 .1278 .0059 .0057 .1988 -.0108 Clerical workers -.1154 -.0438 -.0716 -.0041 -.0068 .2954 .0979 Production workers .1235 .0174 .1061 .0060 .0366 -. 1895 .6122 Unskilled workers .0922 .0039 .0883 .0015 .0344 -.2226 -.4921 Service workers .3296 .1154 .2142 .0051 .0095 -.0040 .0751 Skilled workers .0240 -.1275 .1515 -.0105 .0125 -.0161 -.2654 Total .0039 .0919 .0620 .0169 WE WD JE JD Job Discrimination and Untouchability 185 castes. The exercise ghows that the scheduled castes were at a particular disadvantage in entering production jobs and were as a resu]t - in relation to their characteristics - disproportionately confined to unskilled jobs. Within-occupation earnings functions are needed to complete the decomposition based on the full model outlined in equation (4b). The dependent variable and the independent variables, with the obvious exception of the occupation dummies, are the same as those which appear in the earnings functions presented in table 8.3.21The regressions show that returns to education and experience vary substantially among different occupational groups. For each occupational group we decompose the actual earnings difference into the "explained" (E) and "unexplained" (D) components, assuming that the earnings functions for non- scheduled castes also apply to scheduled castes (columns 8 and 10 of table 8.6). Among clerical workers the mean earnings of scheduled castes are greater than those of non-scheduled castes, and scheduled castes have a more favorable earnings structure. In all other occupational groups the earnings functions favor non-scheduled castes. With the exception of professional workers, "unexplained" differences account for the bulk of the gross differences in earnings. The final calculations of the decomposition exercise (columns 11 and 14-of table 8.6) involve using the figures for "explained" and "unexplained" components of differences in earnings within occupations, and in occupational distributions, in the manner indicated by equation (4b). They leave no doubt that the actual earnings difference between the two caste groups is indeed largely the result of discrimination. Of the gross earnings difference (G) of 17 percent, the explained wage differences (WE) accounts for 0.39 percentage points, the explained occupational difference (JE) for 6.20 percentage points, wage discrimination (WD) for 9.19 percentage points, and occupational discrimination (JD) for 1.69 percentage points.2 2 Thus, discrimination accounts for two-thirds of the gross earnings difference, with wage discrimination being considerably more important than job discrimination. It might appear that, with our estimate of wage discrimination five times that of job discrimination, the neoclassical explanation is the more important. But such a conclusion would be too simple. The relative importance of wage and job discrimination depends on the degree of occupational disaggregation. If job discrimination occurs within as well as between the broad occupational skill categories used in the decomposition analysis, part of the measured wage discrimination is in fact attributable to job discrimination. Relative importance depends also on the degree of explanation achieved in the earnings function and in the occupational attainment function. Had it been possible to include as many independent variables in the latter as in the former, the results would presumably have been more favorable to job discrimination. Other evidence on the competing explanations must therefore be examined. We attempt to throw further light on the nature of caste discrimination by examining its incidence according to certain worker characteristics - occupation, 186 Job Discrimination and Untouchability employment status and sector - drawing on tables 8.3, 8.7 and 8.8. The most striking difference between the earnings functions for the two caste groups is the earnings-urban experience profile for scheduled caste workers is flatter than that for non-scheduled castes. An additional year of urban experience increases earnings by 2.8 percent for scheduled castes and 4.8 percent for non-scheduled castes, when evaluated at six years of residence in the city (table 8.3). The returns are similarly lower - 2.6 versus 7.4 percent per annum -within the occupation group "production worker" (table 8.7). The difference between the two caste groups is seen clearly in figures 1 and 2. These show the earnings- experience profile of a representative worker and production worker respectively, i.e., someone possessing the mean characteristics of all workers, or of production workers, except in respect of caste affiliation and length of urban employment experience. The difference between the curves for scheduled and non-scheduled castes is due to the difference in the coefficients in their respective earnings functions, and is thus attributable to discrimination. The caste difference is small on entry to urban employment but becomes important later in the career.2 3 The significantly lower return to urban experience for scheduled castes could reflect their confinement to "dead-end" jobs. For instance, scheduled caste workers may tend to be machine helpers whereas non-scheduled castes may tend to be machine operators. Even if there is no discrimination on entry or if the reservation policy assists entry to an occupational category, subsequent promotion to more responsible or supervisory posts within that category may be impeded by discrimination. This is consistent, for instance, with evidence from Morris' historical study of the Bombay cotton mills: "The exclusion of untouchables from weaving jobs may well have operated as a device to preserve the monopoly of particularly well-paying jobs for all Muslims and clean-caste Hindus against all untouchables more than it constituted a carryover of traditional ritual barriers into the factories."2 4 We regard the caste differences in the returns to experience as strong evidence of job discrimination occurring within broad occupational groups. Our measure of JD is therefore likely to understate the effects of job discrimination. Even in the absence of discrimination in the present, discrimination in the past could be responsible for our results. Historical patterns of employment may influence the scheduled castes' choice of occupations: because their expectations and aspirations are lower, they accept lower status jobs and lower pay. This interpretation is consistent with the view expressed that the scheduled castes have generally accepted their economic lot and their place in society without protest.2 5 The occupation-specific earnings functions provide helpful clues to the causes of the wage differences found between the two caste-groups. The gross earnings difference between scheduled and non-scheduled castes is significant at the 5- percent level or better only among production, unskilled and service workers. Job Discrimination and Untouchability 187

Figure 8.1 Earnings-experience profile, full sample

y 500

480 _ _ _

460 -

440 - Non-Scheduled

420 -

400 -

380 -

360 -

340 -

320 -

300 -

280 -4-

260 _260 Scheduled

240

220

200

I I I I I I I I I I I L 0 1 2 3 4 5 6 7 8 9 10 11 12 188 Job Discrimination and Untouchability

Figure 8.2 Earnings-experience profile, production workers

300 _

- Non-Scheduled

280 /

260

240 /

_ ~~~~~/r

220

Scheduled 200_ /

180

160

II 2 3 I I I I I L 0 1 2 3 4 5 6 7 8 9 10 11 12 Job Discrimination and Untouchability 189

Table 8.7 Regression analysis of earnings of all production workers and by caste

All production Non-scheduled Independent variable workers Scheduled castes castes

Educationa 0.018 0.020 0.016 (0.004)*b (0.007)* (0.005)* Age on arrival 0.070 0.051 0.071 (0.015)* (0.022)** (0.020)* (Age on arrival) 2 -0.001 -0.001 -0.001 (0.0003)* (0.0004)** (0.0004)* Urban experience 0.091 0.024 0.123 (0.017)* (0.030) (0.020)* (Urban experience) 2 -0.004 0.0005 -0.006 (0.001)* (0.002) (0.002)* Salaried workers 0.091 0.192 0.036 (0.038)** (0.063)* (0.046) Formal sector workers 0.042 -0.121 0.108 (0.037) (0.061)** (0.045)** Constant 4.066 4.478 3.968 (0.201)* (0.305)* (0.259)* R2 0.234 0.238 0.274 IF 0.222 0.189 0.258 F 19.074* 4.863* 17.199* SEE 0.360 0.317 0.364 (N) (444) (117) (327) Residual sum of squares 56.577 10.959 42.259 a. Education was entered linearly because in the quadratic specification both the linear and squared terms become insignificant. b. Figures in parentheses are standard errors. * Significant at the 1-percent level, using a two-tailed test. ** Significant at the 5-percent level. *** Significant at the 10-percent level.

Despite the large share of the "unexplained" differences in earnings in all occupations, the Chow test reveals that earnings functions for the two caste- groups are significantly different only among production workers and service workers.26 There is thus no significant difference in earnings functions between the caste-groups in the non-manual, i.e., professional and clerical, occupations. Indeed, the earnings function for clerks actually favored the scheduled castes. 190 Job Discriminationand Untouchability

The sub-sample of service workers is too small for detailed analysis; that of production workers is considerable larger - 444 workers of whom 117 are scheduled castes. The earnings functions for production workers are presented, separately for the two caste groups, in table 8.7. Scheduled and non-scheduled castes differ significantly in three respects. Firstly, the urban experience variables are statistically significant for non-scheduled but not for scheduled castes: the result interpreted above. Secondly, among scheduled castes salaried workers earn 21.1 percent more than those who are paid on a daily-wage basis, but among non-scheduled castes daily-wage employees are not at a disadvantage. This may be because scheduled caste workers paid on a daily basis - which includes all piece workers - are less effective than others on piece work. It could, however, be due to the exclusion of daily paid scheduled caste workers from the lucrative piece-rate jobs. Thirdly, for scheduled caste workers the coefficient on formal sector employment has an unexpected negative sign and is significant at the 1-percent level. Ceteris paribus, scheduled castes employed in the formal sector have 11.4 percent lower earnings than those in the informal sector. In contrast, among non- scheduled castes formal sector employees earn 11.4 percent more than informal sector employees. These findings suggest that caste discrimination may be a formal sector phenomenon. This is confirmed by the earnings functions estimated separately for the formal and informal sectors (table 8.8). The coefficient for the scheduled caste dummy in the equation for all production workers in the informal sector is not statistically significant. A Chow test shows that informal sector earnings functions estimated separately for the two caste groups are also not significantly different.2 7 On the other hand, in the equation for the formal sector, the coefficient for scheduled caste membership is negative and significant at the 1-percent level. Ceteris paribus, among formal sector production workers, scheduled castes have 17.3 percent lower earnings than non-scheduled castes. Over four-fifths (86 percent) of production workers in the formal sector are employed in privately owned establishments. Caste discrimination clearly takes place in these. An interesting question, therefore, given the public sector reservation policy, is whether wage discrimination also exists in public sector establishments. Regressions are estimated separately for the public sector and the private formal sector (table 8.8). The coefficient for the scheduled caste dummy is negative and significant at the 1-percent level in both the estimated equations, and the coefficient for the public sector (-0.34) is twice that for the private formal sector (-0.16). However, we cannot place much weight on this finding as only 7 of the 40 production workers in the public sector are members of the scheduled castes. It is a prediction of the neoclassical theory of discrimination that a taste for discrimination cannot be indulged in the long run if product markets are competitive. The informal sector is a part of the economy in which both product Job Discrimination and Untouchability 191

Table8.8 Selectedresults in earningsfunctions for productionworkers in the formal and informalsectors, and in the publicand privatesubsectors of the formal sector

Entire sample Formal sector (sectorsa) (subsectors) Formal Informal Public Private

Coefficient on scheduled caste dummy (standard -0.193 0.020 -0.337 -0.164 error) (0.053) (0.058) (0.136) (0.055) JF 0.248 0.201 0.556 0.194 (N) (287) (157) (40) (247) Computed F ratio to test for equality of coefficients for scheduled and non-scheduled castes 2.633 1.522 2.707

Note: The dependentvariable is againnatural logarithm of earnings. Othervariables included in the equation,but not reported,are as in table 8.7. a. Separateequations could not be estimatedfor the two caste-groups,as only sevenobservations belongedto scheduledcastes.

and labor markets approach conditions of perfect competition. Imperfections are more likely to be found in the formal sector. The evidence that caste discrimination exists in the formal but not in the informal sector is therefore consistent with the neoclassical theory. However, it is also consistent with job discrimination. It is in the formal sector that the institutions of the labor market are likely to generate economic rents in certain jobs, and for which workers will attempt to compete. The fact that discrimination is observed in the formal but not the informal sector, and no less in public than in private employment, suggests three possibilities. Either a taste for discrimination is indulged where lack of competition permits, or formal sector jobs are prized jobs, and hence resistance to hiring scheduled castes is greater, or non-scheduled castes have historically monopolized this sector, with its prized jobs, and scheduled castes have not been able to break their way in. There are three possible explanations for the distinction found between the manual and non-manual occupations, each which suggests that the disadvantage of the scheduled castes will not be easily removed. Firstly, the explanation may lie in a difference in recruitment patterns. Non-manual jobs are filled mainly through open advertisements and manual jobs mainly through contacts. Virtually all professional workers and two-thirds of the clerical workers had responded to 192 Job Discrimination and Untouchability advertisements in newspapers or obtained information from the employment exchange. In contrast, 58 percent of production workers, 53 percent of skilled workers, 47 percent of unskilled workers and 31 percent of service workers reported that they had obtained their jobs through relatives or co-villagers.2 8 Contacts are almost invariably from within the same caste-groups29 and the job secured through contacts is rarely better than that of the contact.30 Scheduled caste workers may be at a disadvantage because they lack contacts in the occupations sought. In these circumstances it is apparent why, even after the elimination of discrimination, the scheduled castes attempting to rise above unskilled manual jobs could be trapped in a vicious circle. Reverse discrimination is required to overcome the discrimination of the past. Scheduled caste workers recruited through contacts are also more prone to discrimination because they are less able to conceal their caste affiliation. A second possible explanation lies in differing expectations. It is plausible that the relative expectations of scheduled caste members are lower, the lower their level of education, i.e., education brings with it a growth in self-confidence and assertiveness. If that were the case, we would expect to observe discrimination more in manual than in non-manual occupations. Thirdly, the government reservation policy may have been more successful within the clerical, professional and service occupations. The policy may more readily be applied, and be observed to apply, when jobs are filled through open advertisement than when, as in the case of manual jobs, they are filled through personal recommendations. The fact that 60 percent of non-manual employees, and only 21 percent of manual employees, were in the public sector also means that the government reservation policy had more leverage in the non-manual labor market. Finally, we consider whether a "taste for discrimination" is an adequate explanation of the discriminatory practices which we have observed. The relation between caste and class is recognized as a central question in Indian sociology.3 ' Whatever the answer, it is clear that an effect of caste prejudice in the labor market and more generally is to promote the economic interests of some groups and to harm those of others. The caste system is thus functional to the generation and maintenance of economic inequalities. Job discrimination produces caste differences in the price of labor and therefore in incomes. It can be expected that these economic interests contribute to the preservation of the caste system. Being generally accepted by all castes, the system is more subtle and stable than systems of discrimination in which one group more overtly exercises power over another in the pursuit of its economic interests.32 Nevertheless, the caste riots referred to in section 1 - which were riots by non-scheduled against scheduled castes -suggest that those who benefit from the caste system would not meekly accept a perceived threat to their interests. Job Discrimination and Untouchability I'93

5. Conclusion

The objective of this paper was to measure the extent of discrimination by caste in the urban labor market in India, and then to explain the reasons for, and mechanisms of, discrimination. Use of the standard methodology showed that there is indeed discrimination by caste, although it is minor in comparison with the extent of discrimination by race or sex observed in some contributions to this volume and elsewhere. Our explanations of discrimination against Untouchables are suggestive rather than conclusive. We cannot, for instance, rule out the possibility that caste is simply acting as proxy for unmeasured differences in productivity. The strength of caste prejudice observed in rural India suggests that discriminatory preferences may well have an influence in the urban labor market. The neoclassical theory of discrimination, based on a "taste for discrimination," therefore appears relevant to this case study, just as it has been applied in many other cases, including some in this volume. The neoclassical theory is weak, however, on the motivation underlying the taste and on the mechanism by which it is exercised, and in its long run theoretical predictions. Moreover, since the traditional function of caste is to assign workers to occupation, it is worth exploring an alternative interpretation based on the notion of job discrimination rather than wage discrimination, and not subject to these criticisms. Our attempt to distinguish between job and wage discrimination through the estimation of occupational attainment functions suggests that the latter is quantitatively more important. This is in line with other findings on job discrimination in this volume.3 3 However, our exercise may understate the degree of job discrimination, which can occur within as well as among broad occupational groups. A pointer to this is the significantly lower retums to employment experience of scheduled caste workers. Discrimination appears to operate at least in part through the traditional mechanism, with Untouchables disproportionately represented in poorly-paid "dead-end" jobs. The discriminators are likely to be other workers and employers. Although the discriminatory decisions are no doubt taken by employers, they may be responding to pressures from non-scheduled caste employees. Even if discrimination is no longer practiced, the effects of past discrimination could carry over to the present, for instance in the choice of occupation. This may help to explain why discrimination is greatest in the operative jobs, in which contacts are important for recruitment, and not in white collar jobs, recruitment to which involves formal methods. The economic function which the system performs for the favored castes suggest that their "taste for discrimination" is based, consciously or unconsciously, on economic self-interest, so making prejudice more difficult to eradicate. 194 Job Discrimination and Untouchability

Notes

The survey which provides the data base for this paper was financed by a grant (RF71078, Allocation no. 16) from the Rockefeller Foundation, and was conducted by the first author when he was visiting the Institute of Economic Growth, Delhi, during 1974- 76. The paper was completed at the Institute of Economics and Statistics before he moved to the International Monetary Fund. We are grateful to Gordon Hughes for his help with the estimation of the logit model, and to the editors and referees of this volume and seminar participants at Williams College for helpful comments. 1. For instance, Berreman (1979); B6teille (1989); Cox (1948); and Wadhwa (1975). 2. Cox (1948), chapter 5. 3. Commissioner for Scheduled Castes and Scheduled Tribes (various years). 4. Taken from the Census Reports of 1961 and 1971; corresponding data are not available for 1951. 5. Committee on Untouchability (1969), page 15. 6. This argument is developed by Cox (1948), chapters 1-2 and supported by Moffatt (1975). 7. Bose (1981). 8. For a fuller account of institutions, data, methods and results see our paper on the same subject (Banerjee and Knight 1985). 9. An overwhelming majority of wage employees indicated that they had worked on all working days during the week preceding the interview day. 10. See Srinivas (1962), pages 42-62. 11. The analysis also omits the unemployed - defined as those who had not worked at all during the week preceding the day of enumeration. This should not bias our findings as only 0.8 percent of the scheduled castes and 1.2 per cent of the non-scheduled castes reported being unemployed. 12. In line with Brown, Moon and Zoloth (1980), and, in its measure of job discrimination, corresponding also to Birdsall and Fox (chapter 6). 13. F-ratio = 3.09 (critical value: F .,, = 2.07). 14. In the rural context, Srinivas (1969, pages 90-91) notes that when a non-scheduled low caste person became wealthy he tended to follow it up by Sanskritizing his style of life and ritual and claimed to be of higher caste, but similar mobility was extremely difficult for untouchable castes who became wealthy by local standards. Sociologists expressing a view to us on this unresearched question expect that some dissembling does occur in the urban context. 15. Arrow (1973); and Aigner and Cain (1977) who argue that screening based on correctly perceived group differences in mean productivity cannot be regarded as discrimination. 16. See Knight and Sabot (chapter 3) and the references cited therein. 17. Committee on Untouchables (1969), page 15. 18. Arrow (1973); Marshall (1974); and Stiglitz (1973). 19. Bonacich (1979); Cain (1976); Darity (1982); Knight and McGrath (1977); Lal (1984); Marshall (1974); and Swinton (1977). 20. B6teille (1974), page 65. Job Discrimination and Untouchabilitv 195

21. Because of the endogenous nature of occupational affiliation, within-occupation earnings functions should ideally include a selectivity correction factor (inverse of Mills' ratio). While the selectivity bias correction is likely to reduce the estimated effect of occupation on earnings, its impact on the caste differential in wages cannot be predicted a priori. Studies estimating wage differentials in the framework of simultaneous equations specification are still rare in the literature on developed countries when our research was conducted (Robinson and Tomes 1984; Birdsall and Behrman, chapter 7). 22. Owing to rounding errors these components do not add up exactly to the gross earnings differences between the two groups. 23. Urban employment experience is shown over the limited range 0-12 years because the survey covered only migrants who had arrived in Delhi after 1963. The mean values of urban experience were 5.8 and 5.3 years for all workers and production workers respectively, but the maximum value in the sample was 12. A projection of the curves beyond 12 years would not be reliable. 24. Morris (1965), page 201. 25. For instance, in Cox (1948), chapters 1-2 and Fukutake (1967), page 41. 26. The computed F-ratios are 3.38 for production workers and 2.14 for service workers. 27. F = 1.52 < critical Fo,o = 2.01. 28. For a more detailed discussion of the role of contacts see Banerjee (1981). There was no significant difference in the importance of contacts as between scheduled and non- scheduled castes. 29. Banerjee (1983). 30. In general prospective candidates hear about the same job that their contact is engaged in, and this tendency increases inversely with the status and skill of the contact's job. See Banerjee (1981). 31. See, for instance, Dumont (1966). 32. See, for instance, Knight and McGrath (1977) and Bonacich (1979, especially pages 42-45) on racial discrimination in the South African labor market. 33. See Birdsall and Behrman (chapter 7); Birdsall and Fox (chapter 6); and Knight and Sabot (chapter 3).

References

Aigner, Dennis J., and Glen G. Cain. 1977. "Statistical Theories of Discrimination in the Labor Market." Industrial and Labor Relations Review 30 (Jan.): 175-187. Anderson, K.H. 1982. "The Sensitivity of Wage Elasticities to Selection Bias and the Assumption of Normality." Journal of Human Resources 17(4): 594-605. Antos, Joseph R., and Sherwin Rosen. 1975. "Discrimination in the Market for Public School Teachers." Journal of Econometrics 3: 123-50. Artnitage, Jane, and Richard Sabot. 1986. "Educational Policy and Intergenerational Mobility." Paper presented at Consortia in Development Economics, Yale University (May). Arrow, Kenneth J. 1973. "The Theory of Discrimination." In 0. Ashenfelter and A. Rees, eds. Discrimination in Labor Markets. Princeton: Princeton University Press. Banerjee, Biswajit. 1983. "Social Networks in the Migration Process: Empirical Evidence on Chain Migration in India." Journal of Developing Areas (17)2: 185-96. . 1981. Some Aspects of Rural-Urban Migration in India: A Case Study of Delhi. Oxford: University of Oxford. Processed. Banerjee, Biswajit, and J.B. Knight. 1985. "Caste Discrimination in the Indian Urban Labour Market." Journal of Developing Economics 17: 277-307. Becker, Gary S. 1981. A Treatise on the Family. Cambridge, MA: Harvard University Press. . .1 976. The Economics of Discrimination, 2nd ed. Chicago: University of Chicago Press. . 1975. Human Capital, 2nd ed. New York: Columbia University Press. . 1964a. Human Capital. New York: Columbia University Press. . 1964b.Human Capital, A Theoretical and Empirical Analysis with Special Reference to Education. Princeton: Princeton University Press. . 1957. The Economics of Discrimination. Chicago: University of Chicago Press. Behrman, Jere R., and Nancy Birdsall. 1985. "The Quality of Schooling: Reply." American Economic Review 75(5): 1202-1205. . 1983. "The Quality of Schooling: Quantity Alone is Misleading." American Economic Review 73(5): 928-946. Behrman, Jere R., and Anil B. Deolalikar. 1988. "Health and Nutrition." In Hollis B. Chenery and T. N. Srinivasan, eds. Handbook on Development Economics 1:631-711. Amsterdam: North Holland Publishing Co.

197 198 References

Behrman, Jere R., and Barbara L. Wolfe. 1988. "Does More Schooling Make Women Better Nourished and Healthier? Adult Sibling Random and Fixed Effects Estimates for Nicaragua." Journal of Human Resources 24(4):644-663. . 1987a. "Investments in Schooling in Two Generations in Pre- Revolutionary Nicaragua: The Roles of Family Background and School Supply." Journal of Development Economics 27(1-2): 395-420. . 1987b. "How Does Mothers's Schooling Affect Family Health, Nutrition, Medical Care Usuage, and Household Sanitation?" Journal of Econometrics 36:185- 204. . 1984a. "Labor Force Participation and Earnings Determinants for Women in the Special Conditions of Developing Countries." Journal of Development Economics 15(1-3): 259-288. . 1984b. "Micro Determinants of Female Migration in a Developing Country: Labor Market, Demographic Marriage Market, and Economic Marriage Market Incentives." In T. Paul Schultz and Kenneth I. Wolpin, eds. Research in Population Economics 5: 137-166. Greenwich, CT: JAI Press, Inc. . 1984c. "More Evidence on Nutrition Demand: Income Seems Overrated and Women's Schooling Underemphasized." Journal of Development Economics 14(1- 2): 105-128. . 1984d. "The Socioeconomic Impact of Schooling in a Developing Country." Review of Economics and Statistics 66(2): 296-303. . 1982. "Labor Force Participation and Earnings Determinants for Women in the Special Conditions of Developing Countries." Philadelphia: University of Pennsylvania. Processed. Behrman, Jere R., Barbara L. Wolfe, and Insan Tunali. 1981. "Determinants of Women's Earnings in a Developing Country: A Double Selectivity, Extended Human Capital Approach." Madison: University of Wisconsin. Processed. Behrman, Jere R., Z. Hrubec, P. Taubman, and T.J. Wales. 1980. Socioeconomic Success: A Study of the Effects of Genetic Endowments, Family Environment and Schooling. Amsterdam: North Holland Publising Co. Berreman, Gerald D. 1979. Caste and other Inequities. Delhi: Folklore Institute. Berry, Albert, and Richard H. Sabot. 1978. "Labor Market Performance in Developing Countries: A Survey." World Development Report 6: 1199-1242. Beteille, Andre. 1974. Studies in Agrarian Social Structure. Delhi: Oxford University Press. _ . 1969. Castes Old and New. Essays in Social Structure and Social Stratification. Bombay: Asia Publishing House. Birdsall, Nancy. 1985. "Public Inputs and Child Schooling in Brazil." Journal of Development Economics. 18: 67-86. Birdsall, Nancy, and Jere R. Behrman. 1984. "Does Geographical Aggregation Cause Overestimates of the Returns to Schooling?" Oxford Bulletin of Economics and Statistics 46(1): 55-72. Blinder, Alan S. 1973. "Wage Discrimination: Reduced Form and Structural Estimates." Journal of Human Resources 8 (Fall): 436-55. Boissiere, M., John B. Knight, and Richard H. Sabot. 1985. "Earnings, Schooling, Ability, and Cognitive Skills." American Economic Review 75 (6) 1016-1030. References 199

. 1983. "Earnings, Schooling, Ability, and Cognitive Skills: An East African Comparison." Washington, D.C.: World Bank. Processed. Bonacich, Edna. 1979. "The Past, Present and Future Split Labor Market Theory." In C.B. Marrett and C. Leggon, eds. Research in Race and Ethnic Relations. A Research Annual. Volume 1. Greenwich, CT: JAI Press, Inc. Borjas, George J. 1980. "The Relationship between Wages and Weekly Hours of Work: The Role of Division Bias." Journal of Human Resources 15 (Summer): 409-23. Bose, Pradip Kumar. 1981. "Social Mobility and Caste Violence. A Study of the Gujarat Riots." Economic and Political Weekly (Apr. 18): 713-16. Boserup, E. 1970. Women's Role in Economic Development. New York: St. Martin's Press. Brown, Randall S., Marilyn Moon, and Barbara S. Zoloth. 1980. "Incorporating Occupational Attainment in Studies of Male-Female Earnings Differentials." Journal of Human Resources 15 (Winter): 3-28. Buvinic. M. 1976. Women and World Development: An Annotated Bibliography. Washington, D.C.: Overseas Development Council. Cain, Glen G. 1976. "The Challenge of Segmented Labor Market Theories to Orthodox Theory: A Survey." Journal of Economic Literature (Dec.): 1215-57. Cardwell, Lucy A., and Mark R. Rosenzweig. 1980. "Economic Mobility, Monopsonistic Discrimination and Sex Differences in Wages." Southern Economic Journal (Apr.): 1102-17. Commissioner for Scheduled Castes and Scheduled Tribes. Various Years. Annual Report. Delhi: Government of India Press. Committee on Untouchability. 1969. [Rest to come from authors]. Cox, Oliver Cromwell. 1948. Caste, Race and Class. A Study in Social Dynamics. New York: Monthly Review Press. Darity, William A., Jr. 1982. "The Human Capital Approach to Black-White Earnings Inequality: Some Unsettled Questions." Journal of Human Resources, 17(1): 72-93. Da Vanzo, Julie, and Donald Lye Poh Lee. 1983. "The Compatibility of Child Care with Market and Nonmarket Activities: Preliminary Evidence from Malaysia." In Mayra Buvinic, Margaret A. Lycette, and William P. McGreevey, eds. Women and Poverty in thte Third World. 62-91. Baltimore: Johns Hopkins University Press. Deere, C.D. 1983. "The Allocation of Familial Labor and the Formation of Peasant Households in the Peruvian Sierra." In M. Buvinic, Margaret Lycette, and William P. McGreevey, eds. Women and Poverty in the Third World. Baltimore: Johns Hopkins University Press. De Mello e Souza, Albert. 1979. Financiamento da Educacao e Acesso a Escola No Brazil. Rio de Janeiro: Instituto de Planejamento Economico e Social. De Tray, D. 1987. "Government Policy, Household Behavior and the Distribution of Schooling." In T. Paul Schultz, ed. Research in Population Economics 6. Greenwich, CT: JAI Press, Inc. Doeringer, Peter B., and Michael J. Piore. 1971. Internal Labor Markets and Manpower Analysis. Lexington, MA: D.C. Heath and Co. Dumont, Louis. 1966. "A Fundamental Problem in the Sociology of Caste." Contributions to Indian Sociology 9 (Dec.): 17-32. 200 References

Durand, J.D. 1975. The Labor Force in Economic Development. Princeton: Princeton, University Press. Dushkin, Lelah. 1979. "Backward Class Benefits and Social Class in India, 1920-1970." Economic and Political Weekly (Apr.): 661-67. Eckaus, R.S. 1973. Estimating the Returns to Education: A Disaggregated Approach. Technical Report of Camegie Commission on Higher Education, Berkeley, California. Edgeworth, F.Y. 1922. "Equal Pay to Men and Women for Equal Work." Economic Journal 4 (Dec.): 431-57. Frank, Robert H. 1978. "Why Women Earn Less: The Theory and Estimation of Differential Overqualification." American Economic Review 68 (June): 360-73. Fukutake, Tadashi. 1967. Asian Rural Society: China, India, Japan. Tokyo: University of Tokyo Press. Government of India, Department of Social Welfare. 1969. Report of the Committee on Untouchability, Economic and Educational Development of the Scheduled Castes and Connected Documents. Delhi. Griffin, C. 1985. "Methods for Estimating Value of Time in Low-Income Countries with an Application to the Philippines." New Haven: Yale University. Processed. Griliches, Z. 1977. "Estimating the Returns to Schooling." Econometrica 45 (1): 1-22. Halvorsen, Robert, and Raymond Palmquist. 1980. "The Interpretation of Dummy Variables in Semilogarithmic Equations." American Economic Review 3: 474-5. Hay, Joel W. 1984. "Occupational Choice and Occupational Earnings." In T.P. Schultz and K.I. Wolpin, eds. Research in Population Economics 5, Greenwich, CT: JAI Press, Inc.. Heckman, James. 1979. "Sample Bias as a Specification Error." Econometrica 47 (1): 153-162. . 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models." Annals of Economic and Social Measurement (Dec.): 475-492. . 1974. "Shadow Prices, Market Wages, and Labor Supply." Econometrica, pp. 153-161. Hill, M.A. 1987. "Investment in Human Capital in Pre-Oil Shock Japan." Research in Asian Economic Studies. Killingsworth, Mark R. 1983. Labor Supply. Cambridge, England: Cambridge Univeristy Press. King, Elizabeth, and Robert E. Evenson. 1983. "Time Allocation and Home Production in Philippine Rural Households." Mayra Buvinic, Margaret A. Lycette, and William Paul McGreevey. eds. Women and Poverty in the Third World, pp. 35-61. Baltimore: John Hopkins University Press. Knight, John, and M.D. McGrath. 1977. "An Analysis of Racial Wage Discrimination in South Africa." Oxford Bulletin of Economics and Statistics, 39 (4): 245-71. Knight, John, and Richard H. Sabot. 1981. "The Returns to Education: Increasing with Experience or Decreasing with Expansion?" Oxford Bulletin of Economics and Statistics. 43 (Feb.): 51-72. . 1980a. "From Migrants to Proletarians: Employment Experience, Mobility and Wages in Tanzania." Oxford Bulletin of Economics and Statistics (Aug.). References 201

. 1980b. "Why Wages Differ: A Study of Tanzania's Manufacturing Sector." Washington, D.C.: World Bank. Processed. Hirschman, Albert 0. and Michael Rothschild. 1973. "Changing Tolerance for Inequality in Development," The Quarterly Journal of Economics 4:544. Lal, Deepak. 1984. "The Political Economy of the Predatory State." Development Research Department Discussion Paper (June). Washington, D.C.: World Bank. Layard, R., and J. Mincer. eds. 1985. "Trends in Women's Work, Education, and Family Building." Journal of Labor Economics 3 (1) (Supplement). Leibenstein, H. 1957. Economic Backwardness and Economic Growth. New York. Lewis, A. 1985. Racial Conflict and Economic Development. Cambridge: Harvard University Press. Lewis, W. Arthur. 1954. "Economic Development with Unlimited Supplies of Labour." Manchester School of Economics and Social Studies. May. Lindauer, D., and Richard H. Sabot. 1983. "The Public/Private Wage Differential in a Poor Urban Economy." Journal of Development Economics. Lloyd, C.B., E.S. Andrews, and C.L. Gilroy, eds. 1979. Women in the Labor Market, Part Four. New York: Columbia University Press. Lluch, Constantino. 1982. "Sobre Medicoes de Renda a Partir dos Censos e das Contas Nacionais No Brasil." Pesquisa e Planajamento Economica 12 (1): 133-148. Madden, Janice Fanning. 1975. "Discrimination - A Manifestation of Male Market Power?" In Cynthia B. Lloyd, ed. Sex Discrimination, and the Division of Labor. New York: Columbia University Press. Malkiel, Burton G., and Judith A. Malkiel. 1973, "Male-Female Pay Differentials in Professional Employment." American Economic Review 4: 693-705. Marshall, Ray. 1974. "The Economics of Racial Discrimination: A Survey." Journal of Economic Literature (Sept.) 849-71. Merrick, Thomas W., and Marianne Schmink. 1983. "Households Headed by Women and Urban Poverty in Brazil." In Mayra Buvinic, Margaret A. Lycette and William Paul McGreevey, eds. Women and Poverty in the Third World, pp. 244-271. Baltimore: Johns Hopkins University Press. Mincer, Jacob. 1979. Comments in C. Lloyd, E. Andrews and C. Gilroy, eds. Women in the Labor Market. New York: Columbia University Press. . 1974. Schooling, Experience and Earnings. New York: Columbia University Press. Mincer Jacob, and Solomon Polacheck. 1974. "Family Investments in Human Capital: Earnings of Women." Journal of Political Economy 82: 76-108. Moffatt, Michael. 1975. "Untouchables and the Caste System: a Tamil Case Study." Contributions to Indian Sociology (N.S.) 9(1): 111-22. Morris, Morris David. 1965. The Emergence of an Industrial Labor Force in India. A Study of the Bombay Cotton Mills, 1854-1947. Berkeley and Los Angeles: University of California Press. Oaxaca, Ronald L. 1977a. "The Persistence of Male-Female Earnings Differentials." In F. Thomas Juster, ed. The Distribution of Economic Well-Being. Cambridge, MA: National Bureau of Economic Research, Inc. 202 References

. 1977b."Theory and Measurement in the Economics of Discrimination." In Hausman and others, eds. Equal Rights and Industrial Relations. Madison, WI: Industrial Relations Research Association. . 1973a. "Sex Discrimination in Wages." In Orley Ashenfelter and Albert Rees, eds. Discrimination in Labor Markets. Princeton: Princeton University Press. 1973b. "Male-Female Wage Differentials in Urban Labor Markets." International Economic Review 3 (Oct.): 693-709. Phelps, E.S. 1973. "The Statistical Theory of Racism and Sexism." American Economic Review 4 (Sept.): 659-61. Polachek, Solomon W. 1981. "Occupational Self-Selection: A Human Capital Approach to Sex Differences in Occupational Structure." Review of Economics and Statistics 63 (Feb.): 60-69. . 1979. "Occupational Segregation Among Women." In C.B. Lloyd, E.S. Andrews, and C. L. Gilroy, eds. Women in the Labor Market. New York: Columbia University Press. Portes, A., and L. Benton. 1984. "Industrial Development and Labor Absorption." Population and Development Review 10 (4): 589-612. Rees, A. 1973. The Economics of Work and Pay. New York: Harper and Row. Riley, J.G. 1976. "Information Screening and Human Capital." American Economic Review 66(2): 254-260. Robinson, Chris, and Tomes, Nigel. 1984. "Union Wage Differentials in the Public and Private Sectors: A Simultaneous Equations Specification." Journal of Labor Economics 2(1): 106-27. Robinson, Joan. 1965. The Economics of Imperfect Competition. New York: St. Martin's Press. Rosen, S. 1977. "Human Capital: A Survey of Empirical Research." In R.G. Ehrenberg, ed. Research in Labor Economics 1, Greenwich. CT: JAI Press, Inc. Rosenzweig, M., and T. P. Schultz. 1982. "Market Opportunities, Genetic Endowments, and the Intrafamily Distribution of Resources." American Economic Review 72 (4). Ryan, James G. 1980. "Wage Functions for Daily Labor Market Participants in Rural South India." Hyderabad, India: INCRISAT. Processed. Schultz, T. Paul. 1988. "Educational Investment and Returns in Economic Development." In H. Chenery and T. N. Srinivasan, eds. Handbook of Development Economics, Vol. I (Chapter 14). Amsterdam: North Holland Publishing Company. . 1980. "Estimating Labor Supply Functions for Married Women." In J.P. Smith, ed. Female Labor Supply. Princeton: Princeton University Press. . 1978. "Notes on the Estimation of the Micro Economic Determinants of Migration." Economic Growth. Center Discussion Paper no. 283, New Haven: Yale University. Processed. . 1968. Returns to Education in Bogota, Colombia, RM-5645. Santa Monica, CA: The Rand Corporation. Schultz, T.W., ed. 1974. Economics of the Family. Chicago: University of Chicago Press. Smith, J.P. (Forthcoming.) "Labor Markets and Economic Development." In T.P. Schultz, ed. Research in Population Economics 7, Greenwich, CT, JAI Press, Inc. , ed. 1980. Female Labor Supply. Princeton: Princeton University Press. References 203

Smith, J.P., and M.P. Ward. 1985. "Time Series Growth in the Female Labor Force." Journal of Labor Economics 3:S59-S90. Smith, Sharon. 1977. Equal Pay in the Public Sector: Fact or Fantasy. Princeton: Princeton University, Industrial Relations Section. Squire, L. 1981. Employment Policy in Developing Countries: A Survey of Issues and Evidence. Oxford: Oxford University Press. Srinivas, M.N. 1969. Social Change in Modern India. Berkeley: University of California Press. Srinivas, M.N. 1962. Caste in Modern India and Other Essays. Bombay: Asia Publishing House. Stiglitz, Joseph. 1973. "Approaches to the Economics of Discrimination." American Economic Review, Papers and Proceedings 63(2): 287-95. Swinton, David H. 1977. "A Labor Force Competition Theory of Discrimination in the Labor Market." American Economic Review, Papers and Proceedings 67(1): 400-4. Taylor, A. 1982. "Labor Substitution and the Male-Female Earnings Gap." New Haven: Yale University. Processed. Thomas, Vinod. 1982. "Differences in Income, Nutrition, and Poverty within Brazil." World Bank Staff Working Paper no. 505. Washington, D.C.: World Bank. Thurow, Lester C. 1969. Poverty and Discrimination. Washington, D.C.: Brookings Institution. Tunali, F. Insan. 1983. "A Common Structure for Models of Double Selection." SSRI Working Paper no. 8304. Madison: University of Wisconsin. UN Demographic Yearbook Historical Supplement. 1969. New York: United Nations. Yearbook of Labor Statistics, 1977. Geneva: International Labour Office. Van der Gaag, J., and Wim Vijverberg. 1987. "Wage Determinants in C6te d'Ivoire." Living Standard Measurement Study Working Paper no. 33. Washington, D.C.: World Bank. Vijverberg, W.P.M. 1986. "Consistent Estimates of the Wage Equation When Individuals Choose Among Income Earning Activities." Southern Economic Journal 52 (4): 1028- 1042. Wadhawa, Kamlesh Kumar. 1975. Minority Safeguards in India. Delhi: Thomson Press. Willis, R.J., and S. Rosen. 1979. "Education and Self Selection." Journal of Political Economy 5 (2, Part 2): 57-S36. Wolfe, Barbara L., and Jere L. Behrmnan.1987. "Women's Schooling and Children's Health: Are the Effects Robust with Adult Sibling Control for the Women's Childhood Background?" Journal of Health Economics 6 (3): 239-254. . 1986. "Child Quantity and Quality in a Developing Country: The Importance of Family Background, Endogenous Tastes, and Biological Supply Factors." Economic Development and Cultural Change 34 (4): 703-720. . 1984. "Determinants of Women's Health Status and Health-Care Utilization in a Developing Country: A Latent Variable Approach." Review of Economics and Statistics 56 (4): 696-703. . 1983. "Is Income Overrated in the Determination of Adequate Nutrition?" Economic Development and Cultural Change 31 (3): 525-550. 204 References

Wolfe, Barbara L., and Humberto Belli, Kathleen Cains, and Nancy Williamson. 1980. "Demoeconomic Characteristics of Women and Different Degrees of Urbanization in a Developing Country." Madison: University of Wisconsin. Processed. Wolpin, K. 1977. "Education and Screening." American Economic Review 67 (6): 949- 958. About the contributors

Jane Armitage is Senior Country Officer/Economist in the Southern Africa Department of the World Bank. Before joining the Bank in 1985, she spent two years in Brazil evaluating the determinants of educational achievement among rural primary school children. Her current work is focused on food security and poverty alleviation in the low-income countries of Southern Africa.

Orley Ashenfelter is Professor of Economics and Director of the Industrial Relations Section at Princeton University. His areas of specialization include labor economics, econometrics and the analysis of dispute-settlement systems. He has been Director of the Office of Evaluation of the U.S. Department of Labor, a Guggenheim Fellow, the Benjamin Meeker Visiting Professor at the University of Bristol and the Meyer Visiting Research Professor at the New York University School of Law. He edited the Handbook of Labor Economics and is currently the Editor of the American Economic Review. His current research includes the evaluation of the effect of schooling on earnings and several empirical studies of the impact of various dispute-resolution systems.

Biswajit Banerjee is a Senior Economist in the European Department of the International Monetary Fund. He was previously Research Officer at the Institute of Economics and Statistics, Oxford and Lecturer in Economics at Pembroke College, Oxford. He has published numerous papers on migration and labor markets in leading journals.

Jere R. Behrman is the William R. Kenan, Jr. Professor of Economics and Co- Director of the Center for the Analysis of Developing Economies (CADE) and of the Center on Household and Family Economics (CHAFE) at the University of Pennsylvania. He has held visiting positions at Princeton University, Boston University, the University of Wisconsin, the London School of Economics, Williams College and the Catholic University of Chile. He also has served as a consultant for the World Bank and many other international and national institutions concerned with the analysis of developing countries, as well as a principal investigator on numerous research grants related to economic development. His primary research interests are on a wide range of applied

205 206

empirical issues related to economic development, in recent years primarily related to human resources, household behavior and labor markets. He has authored or edited 18 books or monographs and over 150 professional articles, the majority of which are on dimensions of economic development.

Nancy Birdsall is Director, Country Economics Department of the World Bank. She has previously been Chief of the Environment Division, Latin America and Caribbean Region; of the Human Resource Operations Division, Brazil Department; and of the Policy and Research Division, Population, Health and Nutrition Department. She was also Staff Director of the World Bank's World Development Report 1984, Population Change and Development. She has published papers on the economics of population, education and health in developing countries, on public finance and political economy issues in the social sectors, and more recently on environmental issues in low-income countries. She received her Ph.D. in economics from Yale University.

M. Louise Fox is an Economist in the Human Resource Operations Division, East and Central European Departments, The World Bank. At the Bank, she has also held assignments in the Development Research, Country Operations and Brazil Operations Departments, and has recently published research on labor markets and poverty during the 1980s in Brazil. Her current area of inquiry is poverty and social service delivery during the transition to a market economy in the formerly Communist countries of East and Central Europe.

John B. Knight is a member of the Institute of Economics and Statistics and fellow of St. Edmund Hall in the University of Oxford. His research interests include labor markets, human resources and income distribution in Africa and Asia. Among his recent publications is Education, Productivity and Inequality: The East African Natural Experiment, written with Richard Sabot.

Ronald L. Oaxaca is Professor of Economics at the University of Arizona. He has previously served on the faculties of the University of Massachusetts- Amherst and the University of Westem Ontario. He has also taught at Smith College and Stanford University. He has published numerous articles spanning the areas of labor market discrimination, the effects of unions on wages, the effects of unemployment insurance benefits on job search, the minimum wage, the national and local effects of government programs on local economies and laboratory tests of job search models. His current research areas are laboratory tests of job search models, laboratory evaluation of econometric estimators, forecasting the market for scientific and engineering personnel and estimation of labor market discrimination. 207

Richard H. Sabot is Professor of Economics at Williams College and Senior Research Fellow at the Intemational Food Policy Research Institute. He had previously been on the staff of the Development Research Department of the World Bank. He has taught at Oxford, Yale and Columbia Universities. His theoretical and applied research has focused on labor markets in low-income countries and on the determinants and consequences of human capital accumulation in the process of economic growth. He has published numerous articles in leading joumals. His most recent book is Education, Productivity and Inequality: the East African Natural Experiment (with J. B. Knight) which has just been published by Oxford University Press for the World Bank. Human capital accumulation in post-green revolution rural areas is his major current area of inquiry.

T. Paul Schultz is a Professor of Economics at Yale University and Director of the Economic Growth Center. He has previously been on the faculty at the University of Minnesota and before that Director of Population Research at the Rand Corporation. He has studied and taught economics of individual and family behavior, including time allocation to the labor force and wage determination, migration, marriage, fertility, schooling and health, particularly as related to modem economic growth in low income countries. He has published many articles on these subjects, a textbook entitled Economics of Population, edited The State of Development Economics (Blackwell 1988), and Research in Population Economics (JAI 1984, 1988, 1991). He also participated in two National Academy of Sciences studies of population growth and development (1971, 1986). The causes and consequences of increased human capital investment in women is a main focus of his current research.

Barbara L. Wolfe is a Professor in the Departments of Economics and Preventive Medicine at the University of Wisconsin-Madison. She is also a Research Associate at the National Bureau of Economic Research and a Research Affiliate for the Institute for Research on Poverty at the University of Wisconsin- Madison. Professor Wolfe is the author or co-author of numerous publications including articles on the effects of Medicaid on welfare dependency and work, the changing economic status of the aged and children in the United States, and disability and labor force participation. She is a member of the Expert Group of the European Economic Community Project on the Distributive Effects of Cost Containment in Health Care. She is a co-editor of the Journal of Human Resources. Professor Wolfe received her Ph.D. in economics from the University of Pennsylvania.

T H E W O R L D B A N K

Books of Related Interest from the World Bank

Does Education Pay in the Labor Market? The Labor Force Participation, Occupation, and Earnings of Peruvian Women, Elizabeth M. King

Education, Productivity, and Inequality: The East African Natural Experiment, John B. Knight and Richard H. Sabot

Gender, Education, and Employment in Cdte dI'voire, Simon Appleton, Paul Collier, and Paul Horsnell

Gender and Poverty in India

Women, Poverty, and Productivity in India: Issues and Opportunities, Lynn Bennett

Women's Work, Education, and Family Welfare in Peru, edited by Barbara K Herz and Shahidur R Khandker