Labor market participation, returns to education, and male - female wage differences in Peru

Policy, Research, and External Affairs WORKING PAPERS Women In Development Population and Human Resources Department The World Bank July 1990 WPS 461 Labor Market Participation, Returns to Education, and Male-Female Wage Differences in Peru Shahidur R. Khandker Private schools are more effective than public schools in in- creasing productivity -- and returns on female education are at least as high as returns on male education, so governments must find ways to imuprove the puiblic schools and increase girls' schooli ng. I'hc Poicv, R escarch. and 1 vterr a! A rdair, C,npRx dr,inr1tes PRF \ ork:ng Par: todi is%,rr..n'aLc the findings of t n prorgems arid to eneounrge tLh ex, orgcf .fdie ar-niig I} k e:d!f and a: "Nrs :nIervtced in dNe!C,rt"en i t,uc he T laric canr the -.an'e of Lie aujh"'n irKc ,r' h .' s i' e I . .... k .iO , J*i ti. a-w,4;rgi 'Ihe f;rining. :rterpret .n. and -iciuSlins are the a.i,< .ts rs.v I -i eho. it- <. arr. iso.:n! ! ri \k ir.: Ikavr .:' k c . f Imaixv'r . : , rn..g : r ar *-!a if L. mr.tn n r ci,ni:ncs  Policy, Research, and External Affairs Women in Development WPS 461 'This paper - a product oi' thc Womnen in Developimtent Division, Population and Hlunian Resources Department -- is pairt ol' a larger ef'for in PRE to deteniine if and how womcn's productivity (and Illthus family welfarc) are improvedwhen womcn are givenmore accesstoeducation, extension, training, credit, health care, and other public resources. Copics are available free from the World Bank, 1818 If Street NV, Washington DC 2(033. Please contact Bclinda Smith ,room S9-125. extension 35 108 (51 paces with tables). Using household survey data t'rom Peru, * Investments in education and training for Khandkcr estimates dit'fercnces between male girls increase their participation anti productivity and femalc participation in the labor market, in the labor market more than a similar invest- productivity (measured by wages), and econiomic nerit in boys' education incrcases dicirs. T'hose returns to schooling. investments also reduce fertility andi improve the education of children and the health and nutiritionl fIe trics to identify characteristics that enable ol' all f;amilv members. Returns are high on some women, althoughl not manv, to participate human capital investments in women - at lea;st in the labor market; to determine whether the as high as an equivalent investment in mein. 'I'hc private retumns ltO eucaLion vary by genider andi governmcnt must identify ways to chonncl imore influcnce sch0ool einrollmiint: and to evaluate thc resources to women's education. extentt to \Alhich thc male-emrale wA age gap is caused b\ dil'f'ernces in humnan capital. * Ilousehiolds anid communities are probahi the main sources of enider bias in parental KhandkQr reaches threc poe licy conclusions: invest-inent in child(irel's Lducation. so tlh oo\ eminiicit ne ust i(elti I ' As s to i Il IucIcL the Public s,chools are less etfcfcti\ e than private household's diccisionls about education. Policv schools ini raising producti i tI and rectucinlg the re'C;arch is needed to identil\ hoA households w.agoe g;ap. Policmnt akers shlouldt manke thle public and commuilnuiities aflect parental diecisioins and slchool systcnm Imore eflfective. hoA thc government Calln intcr\en c c ti\ elv to aif'ct t fis decisionm aking. IP. WIIIi' I .q.r P,ipic tlS'cnun, itw ' ien o!gs ef Ark urwtdr Aa in tiro Banik l 'Polh,, Rk,C,irthi, MTSl IL\trAtl AffairI (om i;bx An 0 ti% Cot O) C 'r; 11 it .! gt I fIitni ', Li t qlliukl\ . e'1 i I)I ;ic ICT1: I tcs tim le I lUhtl I'llt X$ff~~~~~~~! 7il!:.! rFf( ,!,11< i t' >;T 'OT,)>t^sr rCj1r-'- 1T. *%.1' 11.1lk 1-11! I't.~~~~~~~~~~I cr.t:t iR.I.,\u.itV<  Table of Contents 1. Introduction 3 2. Model Specification and Estimation Strategy 5 2.1. Labor Market Participation 6 2.2. Returns to Education 8 2.3. Male-Female Wage Differences 15 3. Data Characteristics 19 4. The Results 23 4.1. Determinants of Labor Market Participation and Productivity 23 4.2. Estimates of Returns to Schooling 29 4.3. Returns to Education and School Enrollments of Children 33 4.4. Determinants of Male-Female Wage Differences 36 5. Discussion 44 Note: I wish to thank Barbara Herz, John Newman, Marcia Schafgans, Paul Schultz, Jacques van der Gaag and seminar participants at Yale and World Bank for helpful comments. I am indebted to Marcia Schafgans for her excellent computer assistance. I also wish to thank Belinda Smith for typing the manuscript and Elinor Berg for editorial assistance.  3 1. Introduction This paper uses household survey data from Peru to estimate the differences between males and females in participa.ion in the labor market, productivity (measured by wages) and economic recurns to schooling. The purpose is to (a) identify those characteristics that enable some women, though not many, to participate in this sector, (b) determine whether the private economic returns to education vary by gender and influence school enrollment, and (c) evaluate the extent to which the male-female wage gap is caused by differences in human capital. Identifying the constraints to the labor market participation and productivity of women is particularly important in countries like Peru that have an underutilized female labor force.' Results suggest that male-female differences in human capital (for instance, education) account for some observed differences in labor market participation and productivity. The results on returns to education show that the private rate of return is generally higher for women than for men, which appears inconsistent with lower school enrollment for girls than for boys, especially at the secondary level. When unobserved family characteristics that influence wage and return estimates are controlled, however, the results indicate that parents may have reasons, at least in rural areas, for investing less in daughters than sons. More research is necessary on the social and private benefits and costs of schooling to quantify the factors that influence this decision. The paper uses a human capital model to analyze wages and labor market participation in the formal sector that was developed by Becker (1964) 'See appendix table Al. Note that women's average labor force participation rate is lower i. Latin Americe. than in Asia and Africa (IDB 1987).  4 and Mincer (1974). The focus is on human capital -- especially education -- as a determinant of la-r market participation and productivity. Because the amotunt of schooling imparts different skills -- and hence 'ifferent wages -- this model provides a framework to look at differences in the iages and labor market participation of men and women in terms of levels of schooling. The wage estimates help determine the private rate of return to education for men and women. By comparing differences between men and women in school returns and school enrollments, we can see whether there is an underinvestment in the education ot either gender. Furthermore, using the wage estimates, we can identify how much variation in wages is due to differences in human capital. The wage function and the estimates derived from this may suffer from two sources of bias. The first source is unobserved variable Problem bias, which arises in the event that some variables may affect wages but are not included in the wage regression. A satisfactory analysis, therefore, requires identifying potentially observable characteristics other than human capital that can affect an individual's wage. These are not clearly understood and thus difficult to incorporate in the analysis (Schultz 1989). There are, however, ways to reduce the impact of unobserved characteristics on wages and other related estimates. This paper uses a household fixed-effect method to quantify the severity of the bias in the estimates due to the unobserved variable problem. The second source produces a sample selection bias, which arises due to restricting the analysis to wage workers, thus ignoring information on workers who do not participate in the wage market. The paper identifies whether sample selection correction modifies the wage estimates and hence the difference in the economic returns of men and women to education and productivity.  Two earlier studies using the same household survey data from Peru have investigated the wage rate function for men and women (Stelcner and others 1988, King 1988). They did not, however, assess differences in wages and returns to schooling, or evaluate whether correcting for sample selection or unobserved variables systematically modifies the comparison of the estimated returns to education and productivity of men and women. The paper is structured as follows. Section Two explains th model specification and estimation strategy. Section Three discusses the data and highlights the differences between males and females in terms of wage- related characteristics. Section Four reports the results. Policy implications are in the concluding sect:on. 2. Model SRecification and Estimation Strategy This section outlines a model framework to address participation in the labor market, the private economic returns to education, and a wage gap between males and females that is influenced by differences in job-related characIteristics. It also discusses ways to reduce the impact of unobserved variable and sample selection bias from the wage estimates.  6 2.1. Labor Market ParticiRation What influences women's participation in the labor market? Do women differ from men in responding to labor market opportunities? Does humar, capital (for instance, education) help women more than men to participate in the wage sector? Do women face different constraints? Identifying these factors will help policymakers promote the rarticipation of women in the labor market. The decision to join the labor market, given the constraints, is based on an individual's income-leisure trade-off. A household model framework can help identify the constraints that affect an individual's allocation of time (Becker 1965). This model identifies those individual characteristics, such as education and experience, household characteristics, including landholding and unearned income, and market conditions, such as wa-es, that influencze an indi.'idual's allocation of time. Thus the time allocated to different activities, including leisure, can be drawn as function of individual, household, and market characteristics. The tine allocation data can produce a discrete choice structure of whether to participate in the wage market. The decision can be estimated using a orobability functioin independently for males and females as follows: Y. - Tom + XTrnlm + ZmT2m + em (1) Yf _ 7of + XfTlf + ZfT2f + ef (2) where: Y3(j-m,f) are binary dependent variables with 1 if jth individual participates in the wage labor market and 0 otherwise; X is a vector of individual characteristics that influences an individual's time allocation; Z is a vector of household and market factors which also explains why an individual participates in the labor market; r is the vector of coefficients  7 to be estimated, and e is an error term.2 Different reasons can justify the inclusion of individual (X), and household and market (Z) factors as explanatory variables in labor market participation equations (1)-(2). An individual characteristic, such as the level of education can be treated as an explanatory variable that may indicate the poten.ial productivity of an individual at home and in market production. Holding market wages constant, an increase in the level of an individual's education can increase his or her probability of labor market participation if it increases the opportunity costs of staying at home. The household's constraints include such household asset variables as landholding, which may act as a proxy for productive household assets. The productive assets exert a price effect and an income effect on an individual's labor market participation. The price effect would raise the marginal product or "shadow price" of an individual's labor, while the income effect would encourage an individual to cons'