Schooling, skills, and the returns to government investment in education : an exploration using data from Ghana

LSM - 76 LESErPES MARCH 1991 Living Standards Measurement Study Working Paper No. 76 Schooling, Skills, and the Returns to Government Investment in Education An Exploration Using Data from Ghana Paul Glewwe  LSMS Working Papers No. 6 Household Survey Experience in Africa No. 7 Measurement of Welfare: Theory and Practical Guidelines No. 8 Employment Data for the Measurement of Living Standards No. 9 Incomne and Expenditure Surveys in Developing Countries: Sample Design and Execution No. 10. Reflections on the LSMS Group Meeting No. 11 Three Essays on a Sri Lanka Household Survey No. 12 The ECIEL Study ofHousehold lncome and Consumption in Urban Latin America: An Analytical History No.13 Nutrition and Health Status Indicators: Suggestions for Surveys of the Standard of Living in Developing Countries No.14 Child Schooling and the Measurement of Living Standards No.15 Measuring Health as a Component of Living Standards No. 16 Procedures for Collecting and Analyzing Mortality Data in LSMS No.17 The Labor Market and Social Accounting: A Framework of Data Presentation No.18 Time Use Data and the Living Standards Measurement Study No.19 The Conceptual Basis of Measures of Household Welfare and Their Implied Survey Data Requirements No.20 Statistical Experimentation for Household Surveys: Two Case Studies of Hong Kong No. 21 The Collection of Price Data for the Measurement of Living Standards No.22 Household Expenditure Surveys: Some Methodological Issues No.23 Collecting Panel Data in Developing Countries: Does It Make Sense? No.24 Measuring and Analyzing Levels of Living in Developing Countries: An Annotated Questionnaire No.25 The Demandfor Urban Housing in the Ivory Coast No. 26 The C6te d'Ivoire Living Standards Survey: Design and Implementation No.27 The Role of Employment and Earnings in Analyzing Levels of Living: A General Methodology with Applications to Malaysia and Thailand No.28 Analysis of Household Expenditures No.29 The Distribution of Welfare in Cote d'Ivoire in 1985 No.30 Quality, Quantity, and Spatial Variation of Price: Estimating Price Elasticities from Cross-Sectional Data No.31 Financing the Health Sector in Peru No.32 Informal Sector, Labor Markets, and Returns to Education in Peru No. 33 Wage Determinants in COte d'Ivoire No.34 Guidelines for Adapting the LSMS Living Standards Questionnaires to Local Conditions No.35 The Demandfor Medical Care in Developing Countries: Quantity Rationing in Rural Cote d'Ivoire No.36 Labor Market Activity in C6te d'Ivoire and Peru No.37 Health Care Financing and the Demand for Medical Care No.38 Wage Detenninants and School Attainment among Men in Peru No.39 The Allocation of Goods within the Household: Adults, Children, and Gender No.40 The Effects of Household and Community Characteristics on the Nutrition of Preschool Children: Evidence from Rural Cote d'Ivoire No.41 Public-Private Sector Wage Differentials in Peru, 1985-86 No.42 The Distribution of Welfare in Peru in 1985-86 (List continues on the inside back cover)  Schooling, Skills, and the Retuns to Government Investment in Education An Exploration Using Data from Ghana  The Living Standards Measurement Study The Living Standards Measurement Study (LsMs) was established by the World Bank in 1980 to explore ways of improving the type and quality of house- hold data collected by statistical offices in developing countries. Its goal is to foster increased use of household data as a basis for policy decisionmaking Specfically, the 1.MS is working to develop new methods to monitor progress in raising levels of living, to identify the consequences for households of past and proposed gov- ernment policies, and to improve communications between survey statisticians, an- alysts, and polighmakers: The LSM Working Paper series was started to disseminate intermediate prod- ucts from the IsMs. Publications in the series include critical surveys covering dif- ferent aspects of the LSMS data collection program and reports on improved methodologies for using Living Standards Survey (us) data. More recent publica- tions recommend specfic survey, questionnaire, and data processing designs, and demonstrate the breadth of policy analysis that can be carried out using IS6 data.  ISMS Worldng Paper Number 76 Schooling, Skills, and the Returns to Government Investment in Education An Exploration Using Data from Ghana Paul Glewwe The World Bank Washington, D.C.  Copyright C 1991 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing March 1991 To present the results of the Living Standards Measurement Study with the least possible delay, the typescript of this paper has not been prepared in accordance with the procedures appropriate to fcomal printed texts, and the World Bank accepts no responsibility for errors. 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ISSN: 0253-4517 Paul Glewwe is an economist in the Welfare and Human Resources Division of the World Bank's Population and Human Resources Department. Library of Congress Cataloging-in-Publication Data Glewwe, Paul, 1958- Schooling, skills, and the returns to government investment in education: an exploration using data from Ghana / Paul Glewwe. p. cm.-(LSMS working paper, ISSN 0253-4517; no. 76) Includes bibliographical references. ISBN 0-8213-17644 1. Education-Economic aspects-Ghana I. Title. II. Series. LC67.G45G57 1991 338.4'337'09667-dc2O 91-8098 CIP?  ABSTRACT Investments in schooling are often regarded as essential for economic development, which implies that such investments have high rates of return in developing countries. This paper examines the accuracy and usefulness of estimates of rates of return to formal schooling based on the standard human capital model of Becker and Mincer. Regarding accuracy, it investigates whether failure to account for differences in ability and school quality across a random sample significantly biases estimates of the private return to schooling derived from estimates of wage equations. This is done using an unusually rich data set from Ghana. When years of schooling are used to measure the accumulation of human capital, there are virtually no returns to schooling in the private sector. Replacement of years of schooling by reading and mathematical ability does show positive returns to acquired skills. However, these rates of return may be of little use to governments when making schooling investment decisions because such decisions are much more complex than the investment decisions of individuals. In particular, many government investments in education are designed to raise rates of return to schooling by raising school quality, but decisions by individuals assume that both rates of return and school quality are exogenous.  - vi - ACKNOWLEDGEMENTS The data used in this paper could not have been collected without the cooperation of Ghana's Ministry of Education and Culture and the Ghana Statistical Service, for which I am deeply grateful. Thanks also go to, Nick Bennett of the World Bank for help and advice in the data collection. Finally, I would like to thank Jere Behrman, Jacques van der Gaag, John Ham, Robert E. B. Lucas, George Psacharopoulos and David Ross for comments on earlier drafts of this paper. Of course, I alone am responsible for any shortcomings.  - vii - TABLE OF CONTENTS I. Introduction ...................................................... I II. Rates of Return to Schooling Investments .......................... 3 The Human Capital Model ....................................... 3 Innate Ability and School Quality ......... ................... 4 Other Schooling Models .... ............ III. Estimation ................8.. IV. Private Rates of Return to Schooling in Ghana . .. . 10 Conventional Estimates ... ...................... . ..*.1l1 Estimates Using Observed Cognitive Skills .................... 16 V. Further Examination of the Human Capital Model in Ghana .......... 26 Is There Evidence of Screening or Credentialism? ...........26 How are Workers Allocated Between the Public and Private Sectors? ........... ..... ................................... 28 Does Experience Matter in Ghana? ......... .................... 30 Rates of Return to Education by Schooling Level .............. 32 VI. Rates of Return to Schooling Investments Reconsidered ............ 37 Benefits of Schooling .... .......... .. .................................... 37 Costs of Schooling . .... .................. ...... . 39 Social Investment Decisions ............................. .41 VII. Summary and Conclusion ....45.. .. ............ LIST OF TABLES Table 1: Variable Definitions and Means in Wage Equations .............12 Table 2: Earnings and Schooling in Ghana: Government and Private Wage Estimates .........e. ...............13 Table 3: Means of Test Scores and Other Variables by Wage Sector .......17 Table 4: Wages and Cognitive Skills ........1............18 Table 5: Determinants of Cognitive Skills..*.. ...................... 20 Table 6: Impact on Math and Reading Skills from One Year of Schooling..24 Table 7: Rates of Return to Schooling....... ......................*..24 Table 8: Testing for Credentialism ...... .. 27 Table 9: Government Employment vs. Private Sector.... 30 Table 10: Occupation of Private Sector Wage Earners .....................32 Table 11: Determinants of Cognitive Skills with Separate Effects by Level of Education ..... Table 12: Marginal Impact on Math and Reading Skills from One Year of Schooling by.Level.of Ed.c.t.on....35 Table 13: Private Rates of Return to Schooling by Level of Education ...36  - 1 - I. INTRODUCTION Education is a key factor in economic development. At the aggregate level, Lau, Jamison and Louat (1990) estimate that a one year increase in the average education level of the adult population can lead to increases of 3-5% in real GDP in East Asia and Latin America. From an individual point of view, investments in education could well be more profitable than other types of investments. Education is also promoted as a means of reducing inequality, of making other investments more productive, and as an avenue for social and political development (Haveman and Wolfe 1984). Yet recent concern about the quality of education in developing countries, particularly in Africa (World Bank, 1988), complicates the issue. In fact, a poor quality education could well be a poor investment.-/ Investments in education, like all investments, are largely evaluated in terms of their rates of return. Human capital theory provides a general methodology for estimating the rates of return to investments in education (Becker, 1975; Mincer, 1974). Application of this methodology to developing countries has produced apparent high rates of return which have been put forth as evidence of the need for giving priority to investments in education, particularly primary education, in developing countries (Psacharopoulos, 1985; World Bank, 1986). This paper critically examines the extent to which rates of return to investments in education can be estimated using this methodology, with particular attention to the case where the quality of education may be Lau, Jamison and Louat found no relationship between adult education and real GDP in sub-Saharan Africa, which reinforces concern about the quality of education in Africa.  - 2 - uneven. An unusually rich data set from Ghana, which includes tests of ability and cognitive skills administered to survey respondants, allows one to distinguish between the returns to years of schooling and the returns to human capital as measured by cognitive skills. It turns out that calculating rates of return to schooling investments is more complex than many applications of human capital theory seem to assume. Further, recommendations regarding which types of educational investments governments should undertake based on this methodology are inappropriate and potentially misleading. The plan of the paper is as follows. Section II reviews the standard human capital theory underlying estimates of rates of return to investments in education, with particular attention to the impact of variation in ability, variation in skills attained and variation in school quality. Section III presents an econometric model to estimate private rates of return. Section IV uses household data from Ghana to demonstrate how "straightforward" application of human capital theory may lead to misleading results regarding the private returns to education. Section V examines specific hypotheses regarding the returns to investments in education in labor markets in Ghana. Section VI returns to the question of whether it is useful to estimate rates of return to education based on the standard human capital model, and Section VII concludes the paper.  -3- II. RATES OF RETURN TO SCHOOLING INVESTMENTS: THE HUMAN CAPITAL MODEL AMONG OTHERS The Human Capital Model. How can one estimate the returns to investments in schooling? If one assumes that wage earners are paid their marginal product and that this marginal product rises as more human capital is accumulated, one might estimate private rates of return to additional years of schooling from wage data among persons who have different levels of education.2- The usual procedure is to assume that the logarithm of wages received by an individual i (wi) is a function of the years of schooling (Si) and years of experience (Ed) of that individual: ln(w.) f(Si.Ei,ui) (1) 2 ao + aiSi + a2Ei + a3Ei u. where the second line is a polynomial expansion of f that follows the common practice of dropping certain higher order terms and ui represents other factors which affect wages but are assumed to be uncorrelated with schooling and experience. One can then interpret al as the private rate of return to schooling by appealing to the pioneering work on human capital by Becker (1975) and Mincer (1974). Their arguments for interpreting al as the private rate of return to schooling are for the most part simply arguments for the functional 21 Social rates of return, which adjust private rates by including costs of schooling borne by the government, will be discussed in section VI. That section will also discuss whether returns to additional years of schooling are appropriate tools for government investment decisions.  -4- form given in (1).3/ If one accepts that functional form (including the assumption on ui) for any reason, empirical or theoretical, all one needs to assume further is that the cost of additional schooling is simply forgone wages, and then straightforward differentiation (or simple algebra) will yield a1 as the private rate of return to schooling. Specifically, the annual private rate of return is the annual increase in income (w - ws) divided by the cost of the investment (w 1): w - w w ai+a S+aE +a E2+ u s s-l = s+ - 1 = ea 3 1 = 1 a1 (2) ws1 wss-l ea a (S-1) + a2Ei + a E. + U. 1 e 0 1 2 i 31i 1 Innate Ability and School Quality. Estimating a1 in (1) can be complicated by two potentially important factors: differences inn ability among individuals and differences in school quality, both among individuals and across time.4/ To see this, modify model (1) to explicitly recognize that it is human capital, not years of school attendance, which makes workers more productive and thus leads to higher wages: ln(w.) = g(Hi,E. A.,u ) = S + a H + B E + 2 E i 1I 1111U 0 l i 2 i 83E.+84A.+u ( where Hi is human capital accumulated by individual i, ui is a random error term, and most higher order terms are omitted for expositional convenience. Differences in ability that may contribute directly (i.e. in addition to any 3/ For details see Appendix I. 4/ See Criliches (1977) for a discussion of the impact of ability on estimates of rates of return and Behrman and Birdsall (1983) on school quality.  - 5 - indirect impact via human capital H) to increasing wages are captured in the ability variable Ai' To see the effect of school quality on attempts to estimate the private returns to schooling using (1), it is useful to specify the process by which human capital is acquired. Assume that years of schooling, the quality of that schooling (Q), ability and family background characteristics (B) are the main factors which determine the acquisition of human capital: Hi = h(Si,Qi,Ai,Bi.vi) = YO + yTSi + Y2Qi + Y3Ai + Y4Bi + vi (4) where quadratic and interaction terms are omitted for ease of exposition and vi accounts for unmeasured factors which are not correlated with the other variables. If a1n(wi)/asi can be interpreted as the private rate of return to schooling, then in the 2-equation system of (3) and (4) it is given by: awi/aHi x aHi/aSi = lYl(5) When does al in equation (1) equal lyl? Substitute (4) into (3): 2 ln(w~ = BO + $(Yo + yiS.+ Y2Qi+ Y3Ai+ Y4Bi+ vi) + 82Ei+ 83Ei+ B4Ai+ u (6) = 0+ aYo) + a + ,y2Q+ (y3 + 4)A + a y4B + 62E. + 23E+(81V+ u1) The reduced form estimate in equation (6) is essentially (1) with additional variables for ability, school quality and family background. If any of the coefficients of these variables are non-zero and that variable is correlated with years of schooling (or experience), then estimates of (1) by OLS will  -6- suffer from omitted variable bias. It is likely that these variables are positively correlated with years of schooling, so that omitting them will tend to overestimate Bly, and thus overestimate the private returns to education. Note that the impact of ability works in two ways; even if it has no direct effect on wages (i.e. 64=0) it may have an indirect effect by raising the amount of human capital attained for a given number of years (via y 3) of schooling, which would still lead to omitted variable bias. Other Schooling Models. The discussion so far assumes that the human capital model is the correct interpretation of positive correlation between schooling and wages. If wage employees were not paid their marginal products, or if schooling did not increase their marginal products, a private rate of return to schooling could be calculated but it would not represent the returns to investments in human capital. Two other models which purport to expLain correlation between schooling and wages are the credentialist model (Spence, 1976) and the screening model (Arrow, 1973). The former argues that workers may be paid on the basis of years of school attained or diploma held regardless of whether or not they are more productive workers. Of course, firms in the private sector which operate in this way are likely to have lower profits than firms which pay according to actual productivity of workers, and thus would tend to go out of business. However, government employers could conceivably pay workers according to a credentialist wage structure since they do not need to be profitable to survive. If credentialism exists in either sector one should find that years of schooling or diplomas obtained have a positive impact on wages even after one controls for human capital and ability. Thus if one adds years of schooling and dummy variables for diplomas obtained to equation (3) one would get a significantly positive coefficient (cf. Boissiere, et al, 1985).  - 7 - The screening model argues that education does not necessarily impart productive skills to workers, but instead provides information by ranking workers according to their innate ability, which is the true productive asset which workers have. Employers can then judge the innate productivity of potential workers by observing their years of schooling, and although they will be paid according to their innate ability it may appear that human capital, as measured by schooling, is being rewarded. The best way to test this hypothesis is to examine the coefficients on ability and human capital in equation (3); if that on ability is significantly positive the screening model has some suppport, but otherwise its validity would be in doubt.5/ 5/ Entering an ability variable in equation (1) would be misleading because a positive effect of ability on wages may arise via the positive impact it has on human capital independent of schooling level [y3 in equation (4)] and consequently [a1y3 in equation (6)] even if there is no direct effect of ability on wages (i.e. B4 in equations (3) and (6) equals zero.  - 8 - III. ESTIMATION This section presents appropriate econometric methods for estimating equations (1) and (3) of Section II. The results will be presented in Section IV. In most developing countries many adults do not work in the wage sector. Thus estimates using ordinary least squares (OLS) may suffer from selectivity bias. Further, one would like separate estimates of returns to schooling for private wage earners and government wage earners, since perhaps only the former has a wage structure which reflects the impact of education on worker productivity, which is what government investment decisions must be based on. This suggests a model with three possible activities: wage employment in the private sector, wage employment in the government sector, and a residual category which includes self-employment and no employment. One observes wages for the first and second categories, but not the third. It is convenient to model these labor market outcomes as the result of two binary events. The first divides individuals into those who work in the wage labor market and those who do not. For those in the former category, a second split is observed, separating those who work in the private sector from those in the public sector. This model can be expressed as follows: ln(w ) = X 9 + u government wage (7) ln(w ) =X a+ u private wage (8) 1 = Z a + v govt. vs. private wage work (9), I = Z a + e wage work vs. other activity (10) 2 2 2  -9- where Z, and Z2 are vectors of exogenous variables (which may contain some or all of the variables in Xi and X2), and I and I2 are unobserved variables which correspond to observable indicator variables (I, and I2) which take the value of 1 if the respective unobserved values are greater than or equal to zero and take the value of 0 if they are negative. I2 (wage employment vs. other activity) is observed for the entire population, but I, (government vs. private wage work) is only observed if I2 > 0. Finally, w is only observed if I > 0 and I2 > 0 while wp is only observed if I2 '