378.744 W55 141

RESEARCH MEMORANDUM SERIES

Library Waite Economics Dept. of Applied University of Minnesota ClaOff 1994 Buford Ave - 232 55108-6040 USA St Paul MN

WILLIAMS COLLEGE WILLIAMSTOWN MASSACHUSETTS 7im 378.744 W55 141

RESEARCH MEMORANDUM SERIES

ft5 Library Waite Economics Dept. of Applied University of Minnesota Buford Ave - 232 ClaOff 1994 USA St. Paul MN 55108-6040

THE RETURNS TO ENDOGENOUS IN 'S RURAL WAGE LABOR MARKET mm171.1' by Harold Alderman, Jere R. Behrman, t771 David R. Ross and Richard Sabot RM-14 November, 1994

1994-(Harold Alderman, Jere R. Behrman, David R and Richard Sabot)

, ,, , WILLIAMSTOWN ,, MASSACHUSETTS CA ii Research Memorandum No. 141 Center for Fernald House Williams College Williamstown, Massachusetts 01267

THE RETURNS TO ENDOGENOUS HUMAN CAPITAL IN PAKISTAN'S RURAL WAGE LABOR MARKET by Harold Alderman, Jere R. Behrman, David R. Ross and Richard Sabot RM-141 November, 1994

(c) 1994-(Harold Alderman, Jere R. Behrman, David R. Ross and Richard Sabot)

Note: This paper is intended for private circulation and should not be quoted or referred to in publication without the permission of the authors. THE RETURNS TO ENDOGENOUS HUMAN CAPITAL IN PAKISTAN'S RURAL WAGE LABOR MARKET

by

••• Harold Alderman, Jere R. Behrman, David R. Ross, and Richard Sabot*

November 1994

Abstract

This paper estimates the private returns to human capital in Pakistani rural labor markets using particularly rich data that permit inclusion of a number of dimensions of human capital and controlfor the endogeneity resulting from investment in human capital. The results suggest that without data on the determinants of investments in human capital it would not be possible to disentangle the separate effect of each dimension of human capital on wage differentials and, in particular, to distinguish human capital explanationsfor wage differentialsfrom signalling and credentialist models. With controlfor endogeneity of human capital and selectivity into wage employment, cognitive skills, but not schooling attainment separate from cognitive achievement nor long-run health status, affects wage differentials in rural Pakistan.

*Alderman, ; Behrman, University of Pennsylvania; Ross, Bryn Mawr College; Sabot, Williams College. The paper was prepared for the Human Capital Accumulation in Post Green Revolution Pakistan Project of the International Food Policy Research Institute. We are grateful to the World Bank and USAID for financial support; to Mary Bailey and Emily Mellott for able research assistance. The views presented here are those of the authors and should not be interpreted as reflecting the views of IFPRI, the World Bank or USAID. 1

Developing countries spend over $60 billion a year on , health, and other human capital investments. Thus it is important to determine whether resources devoted to human resources have a high payoff.. Aggregate cross-country estimates and micro studies suggest that the productivity effects of human capital investments in the developing world often are considerable.' Most of these studies focus on schooling, but some of them indicate that the returns to investment in health and nutrition are large, and possibly larger than for schooling investments, in poor populations.' Most of the literature on the returns to school, however, primarily or exclusively considers urban labor markets even though the majority of the population in developing countries lives in rural areas, in which in many cases the returns to human capital investments reputedly have increased substantially recently due to new technological and market developments.' Most studies that focus on education also fail to identify whether observed schooling associations with wages and other outcomes measure the contribution of cognitive achievement, credentialism, or ability signals. Almost all of the literature on the impact of schooling, labor force experience, and the longer-run indicators of health and nutrition (though not shorter-run indicators of nutrition) on wages and other

'Recent aggregate estimates are presented, for example, in Barro (1991), Birdsall and Sabot (1994), and Lau, Jamison and Louat (1991). Recent surveys of the micro evidence include Behrman (1990a,b 1993a) and Schultz (1988). We are aware of no studies that focus on wage determinants for post-green revolution rural Pakistan, which is the context of our empirical analysis. Studies of urban labor markets in Pakistan include Khan and Irfan (1985), Kozel and Alderman (1990), and Shabbir (1993). Previous studies of schooling in rural labor markets elsewhere in South Asia include Behrman, Birdsall and Deolalikar (1994), Behrman and Deolalikar (1989), Deolalikar (1988) and • Walker and Ryan (1990) on and Sahn and Alderman (1988) on Sri Lanka. We are not aware of any previous studies of the impact of health and nutrition on rural wages in Pakistan, though Behrman and Deolalikar (1989), Deolalikar (1988), and Sahn and Alderman (1988) present evidence for other parts of South Asia.

'See the survey in Behrman (1993a) for a review of micro studies and Behrman, Foster and Rosenzweig (1994) for a more recent study for Pakistan. Behrman (1993b) presents cross-country aggregate estimates that suggest that aggregate health and nutrition indicators for 1965 (controlling for initial per capita income) better predictaggregate per capita growth rates over the next quarter century than do aggregate school investments (also controlling for per capita income).

'With new technology and greater integration of markets, Schultz (1975), Sabot (1992) and others claim that better human resources have had advantages in permitting better adjustment to new and changing opportunities. Jamison and Lau (1982) and Pitt and Sumodiningrat (1991) present some of the more systematic cross-sectional micro studies on this topic and give references to other studies as well. Longitudinal studies would be preferable for the investigation of such a dynamic response, but we are aware only of the recent study of the Indian experience by Foster and Rosenzweig (1994b). 2 indicators of productivity in developing countries, moreover, ignores the fact that these human resources reflect choices that have been made in light of unobserved individual characteristics such as ability and inherent robustness, and thus may be subject to simultaneous or omitted variable biases that make interpretation very difficult.' That is, in terms of the human resources that are emphasized in such wage studies, the child truly may be "father to the man." We investigate the impact of different dimensions of human capital -- cognitive achievement produced partly through schooling, labor force experience, and health, as measured by nutritional status -- on wages for males in rural Pakistan. The rural Pakistan context is of interest because of the relatively low level of human resources and because of the apparent expansion of that labor market in part due to the widespread adoption of new agricultural technology and the increased demand for a number of supportive activities.' Because of special characteristics of our data -- particularly information on cognitive achievement, ability, wage work experience, anthropometrics, and childhood determinants of these human capital variables -- we are able to go beyond the previous literature on wage determinants by 1) including a wider range of human capital variables and 2) using the information on the determinants of childhood investments in human capital to control for their endogeneity in addition to controlling for selectivity regarding who reports wages. We address a number of questions about the private returns to human capita in Pakistani rural labor markets: How do human capital investments affect wages in the emerging rural markets? Urban wage labor markets have been extensively studied in low income countries. Do the models of the determinants of wage differentials used for studies of urban labor markets hold up in relatively sparse rural wage labor markets? Are the private returns to schooling primarily a function of gaining access to the market as in credentialist and signalling models? Or do the cognitive skills acquired through schooling directly affect wages as in human capital models? Are there productivity and wage returns to workers' health? If so, how do the returns to health compare with those to schooling? Are

'We are unaware of an exception to this statement regarding schooling and wage market experience in studies for developing countries. Haddad and Bouis (1991), Schultz and Tansel (1993), and Strauss and Thomas (1992) are exceptions with regard to some anthropometric or self-reported morbidity indicators of health and nutrition status in studies for developing countries.

'Behrman and Schneider (1993) present comparisons of human resources in Pakistan with the international experience. For a similar context in India Behrman, Rosenzweig and Vashishtha (1995) find that the new agricultural technologies induced expanded private and governmental rural labor market demands. 3 there important returns to work experience? If so, are the returns to actual wage work experience different from those to general maturity or total work experience? ,Our results suggest that models of wage differentials used for studies of urban labor markets hold up even in a relatively sparse rural wage labor market. However, unless researchers make the effort to gather data on the determinants of investments in human capital they run the risk of being unable to measure the separate contribution of each dimension of human capital to wage differentials and, in particular, to distinguish human capital explanations for wage differentials from signalling and • credentialist models. Following a careful effort to control for the endogeneity of human capital indicators and for selectivity into wage employment, we find that cognitive skills, but not schooling attainment separate from cognitive achievement, affects wage differentials in rural Pakistan. Section 1. Data Since 1986, the International Food Policy Research Institute (IFPRI), under the auspices of the Pakistan Ministry of Food and Agriculture, has been administering a multipurpose survey to a panel of 800+ rural households containing over 7,000 individuals drawn from villages in three relatively poor districts--Attock in the Punjab, Dir in the North West Frontier Province (NWFP), and Badin in the Sind--and one relatively prosperous district--Faisalabad in the Punjab.6 Human capital modules, on which we draw heavily for this study, were administered in the spring of 1989, the 10th round of the survey. These modules contain inter alia measures for individual respondents of the variables needed to estimate wage relations and to control for selectivity into the wage market. Table 1 shows the main and secondary activities of adults in our sample.' A quarter of the men hold 'wage jobs as their primary activity, and some others hold wage jobs as their secondary activity. About a tenth of the men have as their main activity nonagricultural self-employment, and almost half of the men's main activity is work on farms, primarily on own-family farms (though 6.5 percent work on other farms for non-wage compensation). Secondary activities, reported by 30 percent of the men, are concentrated around their homes and on their own farms. Few women hold wage jobs so in our analysis we consider only men.

6The only province not represented, Baluchistan, has only a small proportion of the rural population.

'More precisely, we include in the sample for this study individuals aged 15 years or older who are no longer in school. 4

What do wage earners do in rural Pakistan? Table 2 focuses on the 195 male respondents for whom we have complete data for the statistical analyses that follow. Forty-three percent are unskilled manual workers, and an additional 22 percent hold semiskilled jobs. The third largest category is those in supervisory positions, accounting for nearly 15 percent of the sample. There are substantial differences across regions in the occupational structure of our male wage labor sample. Semiskilled labor's share of Faisalabad's wage labor force is nearly three-times as great as that in Attock. Table 3 presents descriptive statistics for the full sample of adult males and for the subsample • of adult male wage workers. Wage workers have on average much more schooling, much higher cognitive skills, higher pre-school ability, much higher wage work experience (though lower total work experience), and higher body mass (BMI) indices. Wage workers live closer to wage employment opportunities and come from households with smaller holdings of total and irrigated land, with fewer adult males, and that receive smaller transfers. Such summary statistics for observed variables suggest that controlling for who selects to participate in the wage market may be important to avoid selectivity biases in the estimates. A special feature of our data set is a more expanded set of indicators of human capital than used in previous studies of rural wages in developing countries (as well as in wage studies in other developing and developed country contexts). Cognitive achievement: Our measure of cognitive skills was generated by administering (in the regional language) tests of literacy and numeracy specially designed by the Educational Testing Service8 to every person in our sample over nine years of age and with at least four years of schooling.9 The distribution of the cognitive skill test scores for those who took the tests exhibits

8These tests have been used successfully in research on human capital accumulation and labor markets in East and West Africa (see Boissiere, Knight and Sabot 1985, Knight and Sabot 1990, Glewwe 1994, Glewwe and Jacoby 1994). For examples of the test questions see the appendices in Knight and Sabot (1990). Literacy tests have been used to explore wage correlates in Morocco (Lavy, Spratt and Leboucher 1992). Cognitive skills so measured, perhaps several years after the completion of school, reflect the cognitive skills at the time of termination of school plus any subse- quent investment or depreciation in cognitive skills. Estimates in Behrman, Ross and Sabot (1991), however, indicate that time and experience subsequent to schooling do not affect cognitive achievement in our data.

9Since tests were administered only to those with at least four years of schooling, scores had to be imputed for those with less schooling or such individuals have to be dropped from the sample. Those with no education were assigned zero scores. (The scores of a subsample of the unschooled who were given the tests confirmed the appropriateness of this assignment). Respondents with one to 5 substantial variance and appears to be normal, which suggests that the tests were appropriate for the population sampled. We view cognitive achievement as being produced by individual characteristics such as ability and gender, family background characteristics such as parental schooling, school attainment, school quality, and regional and village effects. The availability of cognitive achievement scores for our sample has three important implications for the present study. First, school attainment is what is used in almost all studies to measure the effects of schooling investments on wages.' But school attainment is only one input into what schools produce. Individuals with identical school attainment may have much different cognitive achievement -- which we presume is the primary product of schools -- because of different abilities, different family backgrounds, different qualities of school and different community effects. Therefore school attainment may be a poor proxy for what schooling is producing that has returns in labor markets. Cognitive achievement seems a priori to be a much more satisfactory measure -- a possibility that we can explore. Second, as is well known in the human capital literature, a positive association between school attainment and wages may indicate either that schooling has a product that is rewarded in the labor market or that schooling signals certain attributes that are desired in the labor market (or, of course, some combination of these two possibilities). However, observations on cognitive achievement, buttressed by evidence that cognitive achievement increases with school attainment (see Behrman, Khan, Ross and Sabot 1994 and Behrman, Ross, Sabot and- Tropp 1994), permit us to choose between these explanations. This issue is central to our study. Third, cognitive achievement -- like years of schooling and other indicators of human capital investments -- arguably is correlated with unobserved individual characteristics that affect both cognitive achievement and wage market participation and wages (e.g., motivation). The use of human capital indicators in OLS estimates of wage functions may result in biased estimates since

••• these variables may in part represent such omitted variables in addition to the direct effect of the

three years of school and qualified respondents who failed to take the test are excluded from subsamples for estimates that use the cognitive achievement scores.

'Though there are a few recent exceptions in the economics literature that use cognitive achievement instead (e.g., see references in note 8). Also recent studies in other literatures, such as in epidemiology and education, have used cognitive achievement to measure the output of schools (e.g., Harbison and Hanushek 1992, Lockheed and Hanushek 1988, Nokes et al. 1992a,b, and the references therein). 6

human capital indicators. To avoid this problem we can use instrumental variables in which we replace the actual values of cognitive achievement (and the other human capital indicators) in the wage relation by their values estimated from first-stage regressions of cognitive achievement on predetermined variables as discussed in the Appendix. Schooling attainment: The usual measure of schooling that is included in wage functions is schooling attainment, measured in years of schooling (or sometimes schooling levels) completed. In rural Pakistan, schooling typically involves five primary grades and three middle school grades. The matric exam is taken at grade 10, the FA/FSc exam is taken at grade 12 and the BA/BSc exam is taken at grade 14. Few students have the opportunity to sit for exams beyond the matric. Pre-school ability: To obtain a measure of pre-school reasoning ability, we administered Raven's (1956) Coloured Progressive Matrices (CPM), a test of reasoning ability that involves the matching of patterns, to everybody in the sample over nine years of age." The test is designed so that formal schooling does not influence performance, though performance may reflect early childhood environment as well as innate capacity. The distribution of the CPM test scores is not truncated at either tail; it exhibits substantial variance and appears to be normally distributed. The disaggregated distributions for Dir, the Punjab and Badin are very similar. Since schooling levels differ substantially across regions, this similarity is consistent with the presumption that schooling attainment does not influence performance on Raven's CPM test. This test has been used to control for pre-school ability in estimates of the determination of cognitive achievement in other economics studies (e.g., Glewwe 1994, Knight and Sabot 1990) and in other literatures (e.g., Nokes et al. 1992b). We treat the Raven's score as predetermined in all of our analysis that is reported in this paper. Because of some controversy over what this test score means,' however, we also have

"Since the test was administered to people over nine years of age, it might be more accurate to refer to these it as measuring ability that is independent of schooling. As such phraseology is

•••• cumbersome, with this caveat we use the term pre-school ability. The appendices in Knight and Sabot (1990) provide some examples from this test.

12 Khan- (1993), for example, raises some questions about the Raven's test that we are using. We also find significant regional and gender differences in Raven's tests when we examine in detail the distributions of scores in our sample (Alderman, Behrman, Khan, Ross and Sabot 1993). Our exploration of the gender gap in Raven's scores tentatively suggests that it is due to early childhood acculturation that is not otherwise related to the variables in the present analysis (Alderman, Behrman, Ross and Sabot 1992). 7 undertaken parallel estimates in which we exclude the Raven's score as an explanatory variable. The estimates of relevance for the topic of this paper do not change importantly by doing so. Work experience: "Total work experience" is the difference between current age and the greater of age at the completion of schooling or 15. The average is 26.7 years for all workers, 22.0 years for current wage workers. This is the standard measure of potential post-schooling work experience used by Mincer (1974) and many others, except that we include only such experience obtained when individuals are at least 15 years old as in Behrman and Birdsall (1983) under the assumption that child labor performed by children younger than 15 does not have direct effects on these children's adult human capital stock that is rewarded in the wage market.' The total experience measure is important if general maturity or work of any type -- not just wage work -- enhances productivity that is rewarded in the wage market. Current wage workers have lower average total work experience than do others (Table 3). "Wage labor experience" is the total of all spells of wage employment based on recall data. This measure of work experience should be included in the analysis if actual wage work experience is rewarded in the wage market differently than total experience. Most studies of wage determination have measures akin to total work experience, but not to wage labor experience since most data sets do not have this information. There is substantial persistence over time regarding who works in wage-paying jobs; those earning wages at the time of the survey average over 13 years of wage experience while those not receiving wages at the time of the survey average less than five years of wage experience. The gap between the two experience measures for wage workers is consistent with shifts between employment in the wage sector and employment on the family farm. •Anthropometric measures of health status: We use the Body-Mass Index (BMI), defined as the ratio of weight in kilograms to the square of height in meters, and height in meters as our indicators of health status. Cole (1991) and Fogel (1991a,b) survey the use of BMI and argue that it is a particularly good indicator of health status. For populations in developed countries the relation between BMI and health status is nonlinear, with "best" health (at least as measured by subsequent mortality experience) at intermediate levels (see Fogel 1991a,b). For populations such as in rural Pakistan, however, almost everyone is within the range where the association between BMI and

"There may be indirect effects if such child labor is associated with schooling attainment; these are included in our schooling attainment variables. We note that very few children in the sample who are younger than 15 have wage market experience. 8 health status is increasing. Height is thought to be a longer-run measure of health, and has been found to be significantly associated with wages in studies of rural labor markets in India and the Philippines (Deolalikar 1988, Haddad and Bouis 1991). Section 2. Estimated Wage Functions We estimate semilog wage functions for adult males in rural Pakistan, on the assumption that an individual's marginal product of labor (and thus, in perfectly competitive wage markets, his wage) depends upon his human capital, though with possible variations across regional labor markets.' This assumption does not point towards any particular functional form, but the semilog form is supported by some well-known explorations of alternative functional forms (e.g., Heckman and Polachek 1974) and is widely used in the literature. This wage production function depends directly only on human resources that affect the marginal product of labor, and not on prices and nonhuman household assets. These predetermined prices and assets, however, affect the comparison between the returns to participating in the wage market and to other time uses, and thus identify the selectivity control for current wage market participation that is used later in this section and presented in the Appendix. Predetermined prices and assets, particularly those affecting childhood investments in human capital, also permit the identification of human capital variables in current wage and wage market participation relations, as is discussed later in this section and in the Appendix. OLS semilog wage estimates Table 4 presents OLS (ordinary least squares) -regressions of the logarithm of monthly wage income on our broad range of human capital variables with control for regional variations in labor markets. These estimates are similar to those usually presented in the literature in that the human capital variables are treated as if they are independent of the disturbance term in the wage relation and there is no control for selectivity .into the wage market. Each of the six alternative regressions in Table 4 includes quadratics in total experience and in wage experience and the regional controls. F statistics reject the null hypothesis that all four experience coefficients are zero at the conventional five percent significance level in all but the second and sixth regressions. The t statistics suggest that wage experience is a more relevant

14This is consistent with the interpretation of these functions as simply hedonic wage indices in which the coefficients of various human capital indicators reflect the value that wage markets place on those attributes. Under more restrictive assumptions the semilog functions also can be viewed as derived from equilibrium assumptions about schooling investments as in Mincer (1974). 9 measure than total experience, with the maximum impact of wage experience on wages after about 20 years. The first five of these regressions in addition include one of the five schooling-related or health-related indicators. The coefficient estimates are significantly positive at the five percent level for cognitive skills, years of schooling, and pre-school ability and at the ten percent level for BMI. The regressions that include years of schooling and cognitive achievement fit the data better than do the other three (i.e., with lower standard errors of regression and higher les). The point estimate for years of schooling, under the common Mincerian (1974) interpretation, implies a rate of return to schooling of 4.2 percent. The sixth regression includes all five of the schooling-related or health- related indicators. The point estimates of the three school-related variables are each about half or less than if these are included one at a time (as in the first three regressions) and all the point estimates are fairly imprecisely estimated (with none of the t statistics indicating significance even at the 10 percent level) though an F test indicates that the human capital variables are jointly significant. The results in Table 4 are suggestive of possibly important human capital effects in Pakistani rural labor markets, though with a number of ambiguities growing out of a substantial degree of multicollinearity as well as the limited variance of the height variable. In particular, we are unable to distinguish the separate effects of the five schooling-related and health-related indicators and the separate effects of the measures of total work and wage labor experience. Contributing to this ambiguity, but also casting doubt upon the validity of the estimates are two possibly major statistical problems: (1) human capital investments are behavioral ones in light of unobserved attributes such as ability and inherent healthiness, so that indicators of human capital may be correlated with the disturbance in the wage relation, and (2) behavioral decisions determine who elects to participate in the wage market -- which may result in selectivity bias. These are possible problems not only with our estimates in Table 4, but with most of the previous estimates in the literature on wage determinants in developing countries. For that matter, the first problem is widespread in the wage

I determination literature for developed counties (though selectivity controls are fairly common for estimates for developed economies). 10

Semi-log wage estimates with control for behavior relating to human capital investments and decisions regardingarticipation in wage labor markets We deal with the first problem, endogeneity of human capital variables, by using estimated instrumental variable values of our human cpaital variables.' These instrumental variable estimates effectively purge the human capital variables of components such as unobserved abilities and physical robustness that, if not eliminated, may cause biases. Common correlation of wage earnings and the human capital variables with unobserved abilities and physical robustness may also contribute to multicollinearity raising the standard errors of the estimates in Table 4. To identify human capital variables in the current wage and wage market participation relations, we must have among our first- stage instruments predetermined variables that do not enter directly into the current wage and wage market participation relations. As noted above, we argue that variables that affected the accumulation of these human capital stocks in the past (e.g., distance to schools when of school age, primary book costs, parental schooling, pre-school ability) serve this purpose. To construct these estimates we also use our knowledge that three of our human capital variables -- cognitive achievement, schooling attainment, and wage labor experience -- are truncated at zero for our sample, so they are related to the first-stage variables in a nonlinear manner. Height and BMI, in contrast, are not truncated. We do not instrument total experience: Most respondents in the sample concluded school before they were 15 years old so total experience primarily reflects age or maturity. The details of the estimation are discussed in the Appendix. Selective wage market participation has the effect of truncating our sample--we observe wages only for those currently employed. To the extent that the human capital variables are positively correlated with wage market participation, OLS estimates of the wage relation will bias the coefficients on the human capital variables toward zero. We deal with this second problem' by maximum likelihood estimates (together with the wage relations) of current wage participation based

"Using instrumental variables provides consistent, but still potentially biased, estimates of the coefficients. The precision of the estimates depends upon how well the instrumenting variables are correlated with the endogenous variables.

"The use of selectivity controls is designed to yield estimates that, while still biased, are consistent. In small samples, it is possible for efforts to control for selectivity to worsen the observed bias--particularly where the selectivity controls excluded from the relation of interest are wealdy related to the selection decision and/or highly correlated with variables in the relation of interest (e.g., Mroz 1987, Newey et al. 1990). Because our excluded selectivity controls are significantly related to wage market participation, we believe this is not a serious problem in this case. 11

on a comparison of the wages obtained versus the returns to alternatives. The returns to alternative the time use, in turn, are affected in. part by a set of variables (e.g., the current distance to work, current number of males over age 16 in the household, current net transfers received by the household, and current rainfed and irrigated land held by the household) that do not enter directly into the wage relation, so they permit identification of the selectivity control in the wage relations. The human capital variables themselves, of course, also enter into the wage participation determination since they affect the wages and possibly the returns to other uses of time. They are treated as endogenous for such estimates, in which case -- as noted above -- they are identified by their dependence on variables that are predetermined from the point of view of investments in children's human capital, such as schooling prices and parental schooling, that are assumed not to have direct effects on current wage labor force participation but only indirect effects through the human capital stocks)." The details of the estimation are discussed in the Appendix. Table 5 presents five estimates that permit exploration of the possible impact of these two problems on the wage relations. The first four columns all have the complete set of human capital variables that were used in Table 4. The first column reproduces the OLS estimates from column six of Table 4 for easy comparison with the other results. The next two columns indicate the effect of correcting for only one of the two statistical problems afflicting the OLS estimates: the second column controls for the endogeneity of the human capital investments with instrumental variable estimates, but not for selectivity in wage market participation. The third column controls for possible selectivity in wage labor participation with maximum likelihood estimates, but not for the endogeneity of human capital. The fourth and fifth columns are the a priori preferred maximum likelihood estimates that control for both the endogeneity of human capital and selective behavior regarding wage market participation. The fifth column differs from the fourth by using a parsimonious specification in which the schooling (other than cognitive achievement) and health variables have

• coefficients that are constrained a priori to be zero. (A Wald test on years of schooling, pre-school ability, and the two anthropometric measures fails to reject the null hypothesis that all four

'Schooling attainment, cognitive achievem?nt, and height are determined early in the life cycle, • prior to wage labor market participation and prior to adulthood (including the possibility of establishing a separate household), so the first-stage variables do not include the current variables that affect current wage labor participation. BMI and wage participation are determined by both longer- run (e.g., BMI through height) and current variables (e.g., BMI through weight), so the first-stage variables include both the earlier childhood variables and the current variables. 12 coefficients are zero in the preferred relation.) At the bottom of the table are given tests of joint-zero coefficient restrictions (F tests for the first two columns and Wald tests for the last three) with P values in parentheses. All of the estimates indicate that the set of three schooling-related indicators are significant at the five percent level. The preferred estimates indicate that cognitive skills are a significant determinant of wages with a point estimate about twice as large as in the OLS estimates and with much greater precision: a t-test of significance is passed at the five percent level. In contrast, years of schooling and pre-school ability have insignificant coefficient estimates in all of the alternatives in this table. Health does not have a significant impact on wages in our sample: in no case is the probability that the coefficient estimates are nonzero anywhere near the conventional critical level of 5 percent." The preferred estimates in columns four and five (and to a lesser extent the estimates controlling only for selectivity in column three) indicate that experience is important with diminishing effects, but do not permit confident identification of the importance of total versus wage experience. Section 3. Conclusions Inclusion of a rich array of human capital variables in a standard semilog wage function yields ambiguous results for a sample of wage workers in rural Pakistan. Correcting for statistical problems related to the endogeneity of the human capital variables -- made possible by the availability in our data of special information on the determinants of childhood investments in human capital -- and wage market participation selectivity resolves some of these ambiguities. Our most important result is that cognitive achievement, but not schooling attainment separate from cognitive achievement, has a significant impact on wages in rural Pakistan that is robust.. The impact of cognitive achievement on wages, moreover, is substantial, with a one standard deviation increase in cognitive achievement implying an increase in wages of over 20 percent. The importance of cognitive achievement and not of years of schooling supports a human capital interpretation of schooling-related investments rather than a credentialist or signaling one. Numeracy and literacy

"This does not demonstrate that health has no productivity impact in rural Pakistan. To the contrary, nutrition may have an impact only in cases in which the effect is easily monitored, such as on own-farm productivity and piece work (as suggested by the results for other Asian rural areas in Foster and Rosenzweig 1994a). Indeed, Behrman, Foster and Rosenzweig (1994) find a positive impact of calories consumed by family members during the planting season on harvest profits in rural Pakistan. Moreover, the point estimates for the coefficient on BMI, though imprecise, are comparable to those elsewhere in the literature. 13 apparently add to productivity in the wage market that is rewarded through higher wages. Therefore investments in schooling quantity and quality that increase cognitive achievement may be warranted on. efficiency/productivity grounds.' To assess the efficacy of such investments, of course, their costs must be incorporated into the analysis. Our preliminary estimates in other studies suggests that the rates of return to expanding primary schooling of low quality and to increasing the quality of current low-quality rural primary schools are both fairly high in comparison with the rates of returns on alternative investments (e.g., Behrman, Ross, Sabot, and Tropp 1994 and Alderman, Behrman, Khan, Ross and Sabot 1995).

'Since both school quality and quantity enter into the production of cognitive achievement, the result that cognitive achievement has significant positive effects implies that school quality may be important in wage determination (e.g., Behrman and Birdsall 1983, Harbison and Hanushek 1992). 14 Table 1 Percentage Distribution of Respondents 15 and Over Who Have Completed School

Main Activity Secondary Activity*

Men Women Total Men Women Total

Wage worker 25.1 1.0 12.5 6.9 0.6 3.7

, Work own farm 28.2 0.1 13.5 14.1 10.2 12.4

Work on family farm 12.0 0.1 5.8 5.5 24.8 13.6

Work on other farm** 6.5 0.3 3.3 4.2 7.6 5.6

Nonagriculture self employed 10.2 2.0 6.0 5.3 33.3 17.1

Household tasks 8.7 94.8 53.7 54.0 20.0 39.7

Looking for work 2.6 0.3 1.4 6.0 0.6 3.7

Other 6.6 1.4 3.9 3.9 2.8 3.5

1445 1582 3027 433 315 748

*Percentages are for the quarter of the sample reporting a secondary activity. -For in-kind or other non-wage compensation. 15 Table 2 Percentage Distribution of Wage Earners by Occupation and Sector

Occupation Full Attock Faisal Dir Badin Sector 5.8 Unskilled manual 43.0 43.3 34.8 37.8 52.6 Agriculture 7.2 Semiskilled manual 22.2 13.4 37.0 16.2 24.6 Manufacturing 9.7 Skilled manual 6.3 14.9 0.0 8.1 0.0 Electric, gas, water 18.4 Junior clerical 9.7 6.0 13.0 13.5 8.8 Construction 8.7 Senior clerical 0.5 0.0 0.0 2.7 0.0 Retail/wholesale trade 3.9 Supervisory 14.5 17.9 13.0 16.2 10.5 Transportation 37.7 Other 3.8 4.5 2.2 5.5 3.5 Services Other 8.7

195 16

Table 3 Sample Means and Standard Deviations for Wage Function

Adult Men All Wage Workers

Individual Characteristics Wage worker 21.9% Log(wage) 6.78 (0.65) Attock 20.3% 32.3% Faisalabad 18.5% 19.0% Dir 21.0% 19.5% Badin 40.1% 29.2% Cognitive skills 8.15 13.73 (13.92) (16.57) Years of schooling 2.54 4.41 (4.06) (4.98) Pre-school ability 19.51 21.40 (6.57) (6.41) Total experience 26.72 22.02 (16.38) (13.58) Wage labor experience 4.81 13.15 (9.10) (10.62) BMI 20.24 20.14 (2.84) (2.77) Height 1.67 1.68 (0.07) (0.06) Father was a wage worker 8.5% 15.9%

Household Characteristics Distance to work 19.16 17.27 (12.60) (20.61) Males over 16 3.37 2.89 (1.88) (1.70) Net transfers (000 rupees) 4.84 4.11 (15.11) (19.07) Rain fed acres 2.72 2.94 (9.49) (10.98) Irrigated acres 6.09 2.27 . (15.07) (6.16)

N 890 195 17

Table 4 OLS Wage Regressions

1 2 3 4 5 6

0.007 Cognitive skills 0.013 (4.49) (1.41) Years of schooling 0.046 0.023 (4.53) (1.32) 0.006 Pre-school ability 0.020 (2.50) (0.67) 0.025 BMI 0.031 (1.83) (1.54) Height -0.286 -0.597 (0.38) (0.81) Total work experience 0.007 0.008 -0.002 -0.006 -0.006 0.009 (0.58) (0.65) (0.16) (0.43) (0.46) 0.74 (Total experience)2 -0.0001 -0.0001 -0.00003 -0.00002 -0.00002 -0.0001 (0.62) (0.62) (0.14) (0.08) (0.09) (0.64) Wage experience 0.036 0.030 0.042 0.043 0.047 0.028 (2.73) 2.23 (3.10) (3.14) (3.42) (2.07) (Wage experience)2 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 (2.53) (2.13) (2.93) (2.84) (3.09) (1.97) Faisalabad -0.162 -0.119 -0.109 -0.273 -0.236 -0.172 (1.30) (0.94) (0.80) (2.06) (1.75) (1.27) Dir 0.003 -0.002 01.08 -0.062 -0.028 -0.010 (0.03) (0.02) (0.79) (0.48) (0.21) (0.08) Badin 0.191 0.202 0.121 0.043 0.054 0.179 (1.65) (1.74) (1.02) (0.36) (0.44) (1.51) Constant 6.286 6.273 6.132 6.078 7.147 6.659 SER 0.594 0.594 0.616 0.621 0.626 0.591

.156 .158 .095 .081 .065 0.166

F-statistic [P-value]: Restricting all experience zero 2.55 [0.040] 1.85 [0.122] 2.99 [0.020] 3.60 [0.008] 4.28 [0.002] 1.70 [0.151]

(t-statistics in parentheses) N = 195 18

Table 5 Wage Regressions by Type of Control

Simultaneity* & Selectivity- OLS Simultaneity* Selectivity- Full Model Parsimonious (1) (2) (3) (4) (5)

Cognitive skills 0.007 0.009 0.011 0.015 0.017 (1.41) (1.82) (1.06) (2.11) (3.30) Years of schooling 0.023 0.025 0.023 0.050 (1.32) (1.15) (0.85) (1.04) Pre-school ability 0.006 0.004 0.006 0.008 (0.67) (0.44) (0.45) (0.74) BMI 0.025 0.067 0.024 0.038 (1.54) (1.53) (0.98) (0.63) Height -0.597 -0.153 -0.497 0.227 (0.81) (0.16) (0.55) (0.18) Total work experience 0.009 0.017 0.007 0.024 0.024 (0.74) (1.35) (0.39) (1.39) (1.50) (Total experience)2 -0.001 -0.0004 -0.0001 -0.001 -0.001 (0.64) (1.61) (0.46) (1.67) (1.81) Wage experience 0.028 0.238 0.039 0.230 0.261 (2.07) (2.03) (0.94) (0.94) (0.97) (Wage experience)2 -0.001 -0.028 -0.001 -0.027 -0.031. (1.97) (1.76) (1.10) (0.52) (-0.54) Faisalabad -0.172 -0.134 -0.168 -0.140 -0.171 (1.27) (0.98) (1.09) (0.91) (1.19) Dir -0.010 0.035 -0.034 -0.081 -0.118 (0.08) (0.24) (0.19) (0.48) (0.76) Badin 0.179 0.199 0.173 0.089 0.089 (1.51) (1.59) (1.14) (0.59) (0.59) Constant 6.659 5.285 6.410 4.741 6.100 SER 0.591 0.609 0.575 0.608 0.605 Rho 0.157 0.516 0.482 (0.31) (2.08) (2.13)

F-statistic [P-value]:

All but Constant Zero 4.21[0.000] 3.11[0.000J 42.69[0.000] 27.46[0.007) 21.74[0.005] Schooling/Anthropometric Zero (5 df) 5.31[0.000] 3.81[0.003] 21.74[0.001] 15.24[0.009] Schooling Zero (3 df) 7.62[0.000] 4.44[0.005] 15.54[0.001] 11.40[0.010] Anthropometric Zero (2 df) 1.75[0.177] 1.18[0.309] 1.18[0.556] 0.54[0.763] Experience Zero (4 df) 1.70[0.151] 1.76[0.139] 8.30[0.081] 10.92[0.027] 11.87[0.018]

*Instrumental Variables for Cognitive Skills, Years of Schooling, BMI, Height and Wage Experience are described in the Appendix. -Selectivity controls appear in Table A.1.

N = 195 (t-statistics in parentheses) 19

APPENDIX

In Section 2 we discuss our general strategy for dealing with the two estimation problems of (1) endogeneity of human capital and (2) selectivity regarding current participation in the wage market and our identifying assumptions. In this appendix we describe the estimations in detail. (Copies of the first- stage estimates are available from the authors upon request.)

Endogeneity of human captital: We deal with endogeneity of human capital by using estimated instrumental variable values of our human capital indicators. These instrumental variable estimates effectively purge the human capital variables of components such as unobserved abilities and physical robustness that, if not eliminated, may cause biases because such attributes may affect wages and therefore be included in the disturbance term of the wage relation. To construct these estimates we use not only variables that are predetermined from the point of view of the household, but our knowledge that three of these variables -- cognitive achievement, schooling attainment, and wage labor experience - - are truncated at zero for our sample for a variety of reasons (e.g., in the case of schooling, we observe no schooling when no school was locally available). We adopt estimation strategies that are consistent with these truncations, and therefore more efficient than if we were not to take the truncations into account. Height and BMI, in contrast, are not truncated. As noted above, we do not instrument total experience since most respondents in the sample concluded school before they were 15 years old so total experience primarily reflects age or maturity. As also noted above, we treat pre-school ability as predetermined but find that alternative estimates in which this variable is constrained to have zero coefficient estimates do not differ significantly from those that we present in this paper.

Many (particularly older) Pakistanis had no school available when they were children, and thus had no schooling attainment and zero cognitive achievement. For those for whom a primary school was available,' we estimated a probit equation relating the probability of attending school to exogenous individual characteristics (ability, age, gender), parental household characteristics (parental income and schooling), and district and village characteristics (explicitly including distances to school and prices of books) at the time they were of school age.21 Village dummy variables (for villages with five or more respondents in the appropriate sample) proxy for school quality and other village level effects. We estimated separate probits for men younger than and older than 25 years of age. Significant determinants of the probability of starting school are age, family income (for the younger cohort), father's education (for the older cohort) distance to primary school, and primary book costs. Conditional on starting school, we estimated also corresponding ordered probits relating the level of schooling to exogenous

• 2°In Alderman, Behrman, Ross and Sabot (1993) we explore whether the availability of schools in our sample responds to observed village-level characteristics. We find that the allocation of schools across villages appears neither to favor higher income villages (which would seem to have more political power), nor poorer ones (which might be favored if equity considerations were weighed heavily). Therefore, in these estimates we consider the availability of local schools to be given from the point of view of individual households.

'These characteristics in principle are the same for all of the outcomes that were determined in childhood (though perhaps different for adult labor force experience and BMI). Regional dummy variables were dropped to the extent they were perfect linear combinations of the village level dummy variables. Similarly, we dropped the dummy variable for mother's education; every respondent in the sample whose mother completed primary school attended school. The same problem led us to drop this dummy variable from the starting age tobit. 20 characteristics. Age (for the younger cohort), family income, father's education, availability of middle school, distance to middle school, and middle school book costs are significant determinants.

From the ordered probits we derive estimates of schooling attainment for school leavers. For boys still in school, we estimated a tobit equation relating age at the start of school to the same exogenous characteristics. Although we can reject the null hypothesis that all the explanatory variable coefficients are zero, the equation has low explanatory power. Nevertheless, by subtracting this predicted age from the current age, we derive predicted current years of schooling for those still in school. For those currently in school, instrumented years of schooling is then the minimum of current years of schooling predicted in this fashion and years of schooling as predicted by the ordered probit.

To create an instrument for cognitive skills, we build upon our previous explorations (Behrman, Khan, Ross, and Sabot 1994; and Behrman, Ross, Sabot, and Tropp 1994) to specify that cognitive achievement depends upon individual characteristics (ability, age, gender), parental household characteristics (parental income and schooling), and district and village characteristics (including school quality). Because we have found in these previous studies that there are differences between the determinants of cognitive achievement in reading and in mathematics, we also estimate the first-stage relations separately for reading and mathematics.'

For most young wage workers, our instrument for cognitive skills incorporates school quality measures. We limit our estimates of these cognitive achievement production functions to respondents under 26 years of age for whom we could identify the locally-available ,school and thus use explicit Indicators of school quality that were collected in our school survey; such indicators are less likely to provide accurate information for older responders about school quality at the time they were of school age.' We estimate the relations separately for numeracy and literacy because statistical tests reject

'With regard to the impact on wages, however, we find no difference between the effect of numeracy and that of literacy (the Wald statistic for the null hypothesis that their coefficients are the same in a specification otherwise identical to the preferred relation in column four of Table 5 above is insignificant at the 5 percent level). Therefore in discussing the wage function we report only estimates with total cognitive achievement scores, which are the sum of those for numeracy and literacy.

We also include women in these estimates to increase the precision; in Behrman, Khan, Ross and Sabot (1994) we report explorations that indicate the only significant gender difference in these cognitive achievement functions is in the constant, not in the other coefficients. The school survey provides measures of a rich array of school characteristics--too many to include individually in the cognitive skills production function. Therefore, we use the measures to create indices summarizing the important dimensions of school quality. The teacher quality in reading index is a linear function of teacher literacy, schooling attainment, teaching experience, and experience squared; the teacher quality in math replaces teacher literacy with teacher numeracy. Coefficients for the variables in the index are estimated jointly with the cognitive skills production function. The ratio of students to teachers serves as an index of student-teacher contact. Counting the exponent, there are more coefficients than variables associated with the teacher quality indices. We arbitrarily normalize the coefficient on the average teacher reading (math) test score to 1. Attempts to create an index of physical characteristics of schools--using linear functions incorporating, for example, measures of the year in which the school was constructed, textbook availability, the number of classrooms with black- boards and chalk, and desk availability—did not add significant explanatory power to the production function. Weights for the component measures were estimated as part of the cognitive skills 21 pooling these two dimensions of total cognitive achievement. Cognitive skills and schooling attainment conditional on availability are estimated simultaneously by maximizing the joint likelihood functions for educational attainment and the production of cognitive skills, controlling for the sample truncation resulting from the fact that cognitive achievement at each level is observed only for respondents who did not remain in school beyond that level. Schooling attainment--measured as no schooling, less than fourth grade, fourth or fifth grades, and post primary--is a linear function of the explanatory variables. Selectivity thresholds indicate movements in the latent schooling attainment variable among levels. Because the level of schooling, the quality of schooling, family characteristics, and the ability of the individual all interact with one another, a multiplicative (Cobb-Douglas) functional form is used for the production function--with gender and regional dummies serving as shifters for the constant. Pre-school ability, schooling attainment (after controlling for endogeneity), the student-teacher ratio, and the teacher quality indices are significant determinants of reading and math scores.

For all other respondents for whom cognitive skills are observed, cognitive skills are instrumented using regressions of math and reading test scores, separately for those 25 years old and under (for whom the school survey did not cover the characteristics of the locally-available school) and those over 25 years of age, on the same set of exogenous characteristics used above. The inverse of the Mills' ratio controls for selectivity into grade 4--below which our tests of literacy and numeracy were not given. Gender specific village dummy variables account for quality differences. These village dummy variables account for most of the explained variation in cognitive skills. Other significant determinants are pre-school ability, household income (reading for the younger cohort), and father's education (for the .older cohort).

Health status arguably also (like our other human capital indicators) may not be independent of the disturbance term in the wage relation. Individuals who inherently are more robust, for example, may have better health indicators and command higher wage rates because they are more energetic. Haddad and Bouis (1991) treated shorter-run anthropometric indicators such as BMI as endogenously determined in wage relations, but not height. Yet investments in the health of children (as reflected in their adult height), as well as in their schooling, may respond to unobserved attributes (e.g., inherent robustness and energy) that are persistent and affect their adult wages. If so, then even such long-run indicators of human resources such as height should be treated as endogenous in wage estimates.

To obtain an instrumented value for height, the first stage relations include the same predetermined childhood variables as for the schooling-cognitive achievement system, but for height there is no need to be concerned about truncation. Beyond the village level dummy variables, family income and age cohort are significant determinants. Height, like schooling and cognitive achievement, is basically determined for this sample in childhood. However, an individual's body mass index and wage labor market experience are influenced not only by predetermined childhood variables, but also by predetermined variables to which the individual is exposed as an adult. BMI, for example, reflects not only the childhood determinants of height, but also the adult determinants of weight. Wage labor force participation, likewise, reflects both investments made while a child (e.g., schooling) and therefore the predetermined variables from childhood, but also the determinants of the relative returns to participating in the wage market versus other time uses as an adult. Therefore, the body mass index and wage labor market experience are instrumented by regressions on the a set of variables relating to current household characteristics (i.e. those variables other than the individual's human capital that enter into the wage selectivity relation discussed below) as well as childhood characteristics. BMI is not truncated, but wage labor market experience is, so a tobit is used to allow for the sample truncation for respondents who have

production function. 22 never held a wage job. Significant determinants of BMI are age, number of males over age 16 in the household, household irrigated and rain-fed acreage, father's education, and whether father had been a wage worker. Significant determinants of wage labor experience are total work experience, whether father was a wage worker, household irrigated and rain-fed acreage, father's education, and availability of middle school.

Selective current wage market participation: We deal with the second problem, selective wage market participation, by maximum likelihood estimates (estimated together with the wage relations) of current wage participation based on a comparison of the wages obtained versus the returns" to alternatives. The returns to alternative time use, in turn, are affected in part by a set of variables (e.g., the current distance to work, the current number of males over age 16 in the household, current net transfers received by the household, and current rainfed and irrigated land held by the household) that do not enter directly into the wage relation, so they permit identification of the selectivity control in the wage relations. The human capital variables themselves, of course, also enter into the wage participation determination since they affect the wages and possibly the returns to other uses of time. As was the wage relation, the selectivity equation is susceptible to bias resulting from the endogeneity of the human capital variables. Instruments for these variables are identified in the selectivity relation by their dependence on variables that affected the accumulation of these human capital stocks in the past (e.g., distance to schools when of school age, primary book costs, parental schooling, pre-school ability) but that are assumed not to have direct effects on current wage labor force participation (but only have indirect effects through the human capital stocks). Table A.1 presents coefficients for the selectivity controls estimated simultaneously with. the earnings equations in the last three columns of Table 5.

• 23

Table A.1 Selectivity Controls For Table 5

(3)* (4) (5)*

Cognitive Skills 0.010 -0.007 -0.002 (0.95) (0.51) (0.16) Years of Schooling -0.005 0.048 0.034 (0.12) (1.11) (0.74) Pm-School Ability -0.006 0.019 0.018 (0.42) (1.75) (1.73) Total Experience -0.043 0.010 0.010 (1.97) (0.60) (0.63) (Total Experience)2 0.0001 -0.0005 -0.0005 (0.40) (1.60) (1.64) Wage Labor Experience 0.224 -0.011 -0.009 (11.67) (0.08) (0.06) (Wage Labor Experience)2 -0.004 -0.003 -0.004 (8.57) (0.16) (0.18) BMI -0.014 -0.011 -0.026 (0.48) (0.20) (0.46) Height 1.214 1.416 1.364 (1.04) (1.29) (1.29) Father was a Wage Worker 0.296 0.645 0.637 (1.43) (3.61) (3.62) Distance to Work -0.014 -0.009 -0.009 (123) (3.13) (3.19) Males over 16 -0.072 -0.075 -0.075 (1.50) (2.19) (2.20) Net Transfers -0.003 0.003 0.003 (0.70) (0.94) (0.89) Faisalabad 0.381 0.266 0.261 (1.53) (1.33) (1.32) Dir -0.361 -0.024 -0.025 (1.58) (0.12) (0.13) Badin -0.076 -0.433 -0.435 (0.34) (2.43) (2.48) Rain Fed Acres-Attock -0.005 -0.003 -0.003 (0.58) (0.71) (0.67) . Irrigated Acres-Attock 0.081 0.022 0.025 (0.39) (0.14) (0.15) Irrigated Acres-Faisalabad 0.151 -0.173 -0.172 (2.16) (3.26) (3.34) Rain Fed Acres-Dir -0.050 -0.054 -0.055 (1.00) (1.95) (2.06) Irrigated Acres-Dir -0.101 -0.111 -0.108 (0.85) (1.19) (1.17) Irrigated Acres-Badin -0.008 -0.011 -0.011 (0.59) (1.30) (1.31) Constant -1.808 -2.573 -2.160 (0.89) (1.22) (1.04)

Wald Statistic (df) 472.8 (34) 749.4 (34) 754.6 (30)

890

(t-statistics in parentheses)

*Instrumental Variables for Cognitive Skills, Years of Schooling, BMI, Height and Wage Experience are used as described in the Appendix. 24

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A series of papers written by members of the Department of Economics on topics pertaining to the economics of less developed countries. A copy of any paper (and/or a reprint of the published version) will be mailed on request. See the order blank for further details.

1984-1995 Research Memorandum

RM-89 John Sheahan, "The Elusive Balance Between Stimulation and Constraint in Analysis of Development," July, 1984.

RM-90 Paul G. Clark, "The Policy Environment for Egyptian Industry," July, 1984.

RM-91 Brian Levy, "A Theory of Public Enterprise Behavior," September, 1984.

RM-92** Stephen R. Lewis, Jr., "The Impact of Shashe Project on Botswana's Economy" in C. Harvey, Ed., Papers on the Economy of Botswana, 1981.

RM-93* Stephen R. Lewis, Jr., Taxation for Development, Oxford University Press, 1984 (available only through the publisher).

RM-94 Gordon C. Winston, "The Utilization of Capital in Developing Countries: A Survey of Empirical Estimates," October, 1984.

RM-95** Brian Levy, "The Perils of Partnership: Dow in Korea," chapter 4 in Karl Moskowitz, ed., From Patron to Partner: The Development of U.S.-Korean Business and Trade Relations (Lexington: Lexington Books, 1984).

RM-96** S. R. Lewis,. Jr. and D. N. Mokgethi, "Fiscal Policy in Botswana, 1966-82" in Botswana's Economy since Independence (New Delhi: Tata McGraw-Hill Publishing Co. Limited, 1983).

RM-97 John Sheahan, "Economic Policies and the Prospects for Successful Transition From Authoritarianism in ", January, 1985.

RM-98 Morton Owen Schapiro, "Economic Development and Population Growth: Implica- tions from a Model of U.S. Demographic History," January, 1985.

RM-99 J. Armitage and R. H. Sabot, "Efficiency and Equity Implications of Subsidies of Secondary Education in Kenya," January, 1985.

RM-100 Brian Levy, "The State-owned Enterprise as an Entrepreneurial Substitute in Developing Countries: The Case of Nitrogen Fertilizer," June, 1985.

RM-101 Lee J. Alston and Joseph P. Ferrie, "The Use of In-Kind Benefits in Agricul- ture: A Synthesis and Test," July, 1985. Amartya Sen, "Property and Hunger," 1985. RM-102

RM-103 Stephen R. Lewis, Jr., "Africa's Development and the World Economy," September, 1985.

An RM-104 J.B. Knight and R.H. Sabot, "Educational Policy and Labor Productivity: Output Accounting Exercise," October, 1985.

RM-105 Brian Levy, "The Determinants of Manufacturing Ownership in Less Developed Countries: A Comparative Analysis," November 1986.

RM-106 Brian Levy, "Foreign Aid in the Making of Economic Policy in Sri Lanka, 1977-1983," March, 1987.

Hundred RM-107 Stephen R. Lewis, Jr., "Economic Realities in Southern Africa: One Million Futures," in C. Bryant, ed., Poverty, Policy, and Food Security in Southern Africa (Boulder, CO: Lynne Rienner Publishers, 1988), pp. 39-92.

RM-108 A. Gelb, J. B. Knight and R. H. Sabot, "Lewis Through a Looking Glass: Public Sector Employment, Rent-Seeking and Economic Growth," January, 1988.

RM-109 Humberto Barreto and Robert Whitesell, "Estimation of Output Loss from Allocative Inefficiency: Comparison of the Soviet Union and the U.S.", April, 1988.

RM-110** Brian Levy, "Korean and Taiwanese Firms as International Competitors: The Challenges Ahead," September, 1988.

RM-111 Henry J. Bruton, "The and the Less Developed Countries in the 1990s", November, 1988.

R1f-112 Brian Levy, "Transactions Costs, the Size of Firms and Industrial Policy: Lessons from A Comparative Case Study of the Footwear Industry in Korea and Taiwan," December, 1988.

RM-113 Robert S. Whitesell, "Why Does the Soviet Economy Appear to Be Allocatively Efficient?" December, 1988.

RM-114 Brian Levy, "Export Traders, Market Development, and Industrial Expansion," March, 1989.

RM-115 Brian Levy, "Industrial Interests and the Structure of Protection in Less Developed Countries: Some Evidence from Brazil," July, 1989.

R1'-116 Henry J. Bruton, "Protection and Development," July, 1989.

RM-117 Thomas C. Pinckney, "The Multiple Effects of Procurement Price on Production and Procurement of Wheat in Pakistan," February, 1990.

RM-118 Catherine B. Hill, "Commodity Booms in Botswana and the Permanent Income Hypothesis," February, 1990. John Sheahan, "Reducing Poverty inREettlft America: Markets, Democracy, and. Social Change," March, 1990.

RM-120 David M. Kemme and Robert S. Whitesell, "Industrial Growth and Efficiency in Eastern Europe," April, 1990.

RM-121** Henry J. Bruton, "Import Substitution," July, 1990.

RM-122** John Wakeman-Linn, "International Insurance for Developing Country Debt: A Natural Successor to the Brady Plan?" July, 1990.

RM-123 William K. Jaeger, "The Causes and Extent of Africa's Food Crisis," December, 1990.

RM-124 Jere R. Behrman and Mathana Phananiramai, "Thai Morbidity Determinants by Age Groups and Conditional Projections to the Year 2010," (original Decem- ber, 1990) Revised July, 1991.

RM-125 Catherine B. Hill, "A Precautionary Demand for Savings and Tests of the Permanent Income Hypothesis in Botswana," January, 1991.

RM-126 Henry Bruton and Catherine Hill, "The Development Impact of Counterpart Funds: A Review of the Literature," May, 1991.

RM-127 Jere R. Behrman and Ryan Schneider, "Bolivian Schooling Investments in an International Perspective: Where Does Bolivia Fit?" May, 1991.

RM-128 Jere Behrman, Nancy Birdsall and Anil Deolaikar, "Marriage Markets, Labor Markets and Unobserved Human Capital: An Empirical Exploration for South-Central India," May, 1991.

RM-129 Jere R. Behrman, "Investing in Female Education for Development: Women in Development Strategy for the 1990's in Asia and the Near East," May, 1991.

RM-130 Jere R. Behrman and Ryan Schneider, "Thai Schooling Investments in an Interna- tional Perspective," May, .1991.

RM-131 Jere R. Behrman and Robert Moffitt, "Framework for Analysis of Female Paid Labor Supply in Islamic Emena Countries," May, 1991.

RM-132 Jere R. Behrman and Chalongphob Sussangkarn, "Do The More Wealthy Save Less?" June, 1991.

RM-133 Jere R. Behrman, "Human Capital: An International Perspective on Bolivian Performance and Policy Options," July, 1991.

RM-134 Jere R. Behrman and Ryan Schneider, "Where Does Brazil Fit? Brazilian Schooling Investments in an International Perspective," July, 1991.

RM-135 John Sheahan, "Peru's Return Toward an Open Economy: Macroeconomic Complications and Structural Questions," June, 1993. Henry J. Bruton, "Asian Lessons for African Development," EMboBer, 1993.

RM-137 Sandeep H. Patel, Thomas C. Pinckney and William K. Jaeger, "Smallholder Wood Production and Population Pressures in East Africa: Evidence of an Environmental Kuznets Curve?" December, 1993.

RM-138 Nguyuru H.I. Lipumba, "Structural Adjustment Policies and Economic Performance of African Countries," October, 1994.

RM-139 Henry J. Bruton, "Total Factor Productivity Growth," February, 1995.

RM-140 Jere R. Behrman, David Ross, Richard Sabot., Matthew Tropp, "Improving The Quality Versus Increasing the Quantity of Schooling," May, 1994.

RM-141 Harold Alderman, Jere R. Behrman, David R. Ross, and Richard Sabot, "The Returns to Endogenous Human Capital in Pakistan's Rural Wage Labor Market," November, 1994.

RM-142 Nancy Birdsall, David Ross, and Richard Sabot, "Inequality and Growth Reconsidered," February 1995.

** Reprint only available Department of Economics Williams College RESEARCH PAPER SERIES

A series of papers written by members of the Department of Economics on topics that do not pertain directly to less developed countries. From time to time documentation for computer programs and other research tools developed by members of the department will be distributed in this series as Special Research Papers and denoted by the letters RPS. A copy of any paper (and/or a reprint of the published version) will be mailed on request. See the order blank for further details.

1984-1995 Research Papers

RP-63 Lee J. Alston, Samar K. Datta and Jeffrey B. Nugent, "Tenancy Choice in a Competitive Framework with Transactions Costs: A Theoretical and Empirical Analysis," February, 1984 (Journal of Political Economy, December, 1984).

RP-64 Lee J. Alston and Morton Owen Schapiro, "Inheritance Laws Across Colonies: Causes and Consequences," February, 1984 (Journal of Economic History, May, 1984).

RP-65 P. Srinagesh, "Public Utility Pricing Under Risk: Self-Rationing With Non-Linear Prices," March, 1984.

RP-66 David R. Ross, "A Measure of Conditional Bivariate Association," April, 1984.

.••-• RP-67 P. Srinagesh, "Nonlinear Prices and the Regulated Firm," April, 1984 (Quarterly Journal of Economics; Feb. 1986).

RP-68 Stefano Fenoaltea, "Public Works Construction in Italy, 1861-1913: A Progress Report," May, 1984.

RP-69 Stefano Fenoaltea, "The Industrialization of Italy, 1861-1913: A Progress Report," May, 1984.

RP-70 James Neumann, "Natural Resource Policy in New Jersey's Pinelands," July, 1984.

RP-71 David R. Ross, "Learning by a Monopolist," August, 1984.

RP-72 Gordon C. Winston, "Activities in Time: A New Theory of Work and Consumption," September, 1984.

RP-73 David R. Ross, "Learning to Dominate," September, 1984.

RP-74** Roger Bolton, "Multiregional Models: Introduction to a Symposium," Journal of Regional Science, Vol. 20, No. 2, 1980.

RP-75** Morton Owen Schapiro, "Land Availability and Fertility in the United States, 1760-1870" in Journal of Economic History, Vol. XLII, No. 3 (Sept. 1982). Morton Owen SchapirRPeid*Bennis A. Ahlburg, "Suicide: The Ultimate Cost of Unemployment" in Journal of Post-Keynesian Economics, Winter 1982-83, Vol. V, No. 2.

RP-77** Dennis A. Ahlburg and Morton Owen Schapiro, "Socioeconomic Ramifications Changing Cohort Size: An Analysis of U.S. Postwar Suicide Rates by Age and Sex" in Demography, Vol. 21, No. 1, February, 1984.

RP-78** Roger Bolton, "Regional Policy in the United States in The Canadian Journal of Regional Science, V, 2 (1982).

RP-79** Roger Bolton, "Industrial and Regional Policy in Multiregional Modeling," chapter 7 in Regional Dimensions of Industrial Policy (Lexington: Lexington Books, 1982).

RP-80** Brian Levy, "World Oil Marketing in Transition" in International Organization 36, 1, Winter 1982.

RP-81** Ralph M. Bradburd and Richard E. Caves, "A Closer Look at the Effect of Market Growth on Industries' Profits" in The Review of Economics and Statistics, Vol. LXIV, No. 4, November, 1982.

RP-82** S. R. Lewis, Jr. and D. N. Mokgethi, "Fiscal policy in Botswana, 1966-82" in Botswana's Economy since Independence (New Delhi: Tata McGraw-Hill Publishing Co. Limited, 1983).

RP-83 Lee J. Alston and Joseph P. Ferrie, "Resisting the Welfare State: Southern Opposition to the Farm Security Administration," September, 1984.

R2-84 Lee J. Alston, "True Tenancy' in the South: Estimates for 1900 and 1910," September, 1984.

RP-85** Michael S. McPherson, "Want Formation, Morality, and Some 'Interpretive' Aspects of Economic Inquiry" in Social Science as Moral Inquiry (New York: Columbia University Press, 1983).

RP-86** Michael McPherson, "Efficiency and Liberty in the Productive Enterprise: Recent Work in the Economics of Work Organization" in Philosophy & Public Affairs, Vol. 12, No. 4, Fall 1983.

RP-87** Michael S. McPherson and Morton Owen Schapiro, "Economic Public Policy Implications of an 'Outmoded' Work Force" in Educational Record, Fall 1983.

RP-88 Gordon C. Winston, "Three Problems with the Treitment of Time in Economics," January, 1985.

RP-89 David R. Ross, "The Significance of Learning in Reducing Costs," February, 1985.

RP-90 Richard Krouse and Michael McPherson, "A 'Mixed' Property Regime: Equality and Liberty in a Market Economy," February, 1985. David Fairris, "Unions and the Work Environment: VineelVersus Monopoly Power Effects," September, 1985.

RP-92 Richard Krouse and Michael McPherson, "On Rawlsian Justice in Political Economy: Capitalism, 'Property-Owning Democracy,' and the Welfare State," September, 1985.

RP-93 T.R. Lakshmanan and Roger Bolton, "Regional Energy and Environmental Analysis," October 1985.

RP-94 Randal R. Rucker and Lee J. Alston, "The Effectiveness of Government Policies to Alleviate Agricultural Distress, 1925-1939," March, 1986.

RP-95 Edward R. Morey and W. Douglass Shaw, "The Impact of Acid Rain: A Characteristics Approach to Estimating the Demand For and Benefits From Recreational Fishing," March, 1986.

RP-96 Gordon C. Winston, "Leisure," March, 1986.

RP-97 Richard M. Peck and Padmanabhan Srinagesh, "Uncertain Lifetime and Ordinal Impatience," April, 1986.

RP-98 David K. Howe and David Fairris, "Unions, Fringe Benefits, and Nonpositional Goods Consumption," November, 1986.

RP-99 John Wakeman-Linn, "Coverage Ceilings on Outpatient Mental Health Care: The Patient's Perspective," January, 1987.

RP-100 Lori Gladstein Kletzer, "Job Tenure and Earnings After Permanent Job Loss," March, 1987.

RP-101 Gerald Epstein, "The Illiquidity Trap," May, 1987.

RP-102 Charles P. Kindleberger, "Henry George's Protection or Free Trade," May, 1987.

RP-103** Michael S. McPherson and Mary S. Skinner, "Paying for College: A Lifetime Proposition," July, 1987 (The Brooking Review, Fall, 1986).

- RP-104 Michael S. McPherson, "Federal Student Aid Policy: Can We Learn from Experience?" July, 1987 (The Rockefeller Institute Conference Proceedings, Spring, 1986).

RP-105** Morton Owen Schapiro, "A General Dynamic Model of 19th Century US Population Change," July, 1987 (Economic Modelling, October, 1985).

RP-106 Ralph M. Bradburd and David R. Ross, "A General Measure of Multidimensional Inequality," July, 1987. (Oxford Bulletin of Economics and Statistics, November 1988)

RP-107 David R. Ross, "The Profitability-Concentration Relation: Applying a U.K. Test to U.S. Data," Sept., 1987. Gordon C. Winston, "Imperfectly RARio881 Choice: Rationality as the Result of a Costly Activity," October, 1987.

RP-109 Padmanabhan Srinagesh, "Random Demand, Capacity and Pricing Below Cost", November, 1987.

RP-110 Ralph M. Bradburd & David R. Ross, "Can Small Firms Find and Defend Strategic Niches? A Test of the Porter Hypothesis", November, 1987.

RP-111 David R. Ross & Ralph M. Bradburd, "The Effect of Industry Heterogeneity in Structure-Performance Studies," December, 1987.

RP-112 Michael S. McPherson, "Appearance and Reality in the Guaranteed Student Loan Program," December, 1987.

RP-113 Morton Owen Schapiro, "Socio-Economic Effects of Relative Income and Relative Cohort Size", January, 1988.

RP-114 Michael S. McPherson, "How Can We Tell if Federal Student Aid is Working?", January, 1988.

RP-115 Michael S. McPherson, "Family Ability to Pay: A Lifetime Perspective", February, 1988.

RP-116 P. Srinagesh, David R. Ross, and Gordon C. Winston, "On the Applicability of Shephard's Lemma when Capital is Optimally Idle", February, 1988.

RP-117 Padmanabhan Srinagesh and Ralph M. Bradburd, "Quality Distortion by a Discriminating Monopolist", February, 1988.

RP-118 David R. Ross, "Profits, Concentration and Raw Materials-Based Manufacturing", -February, 1988.

RP-119 John Wakeman-Linn, "Alternative Notions of Credit Market Equilibrium: Their Significance for Monetary Policy," March, 1988.

RP-120 Lori Gladstein Kletzer, "Industry Wage Differentials and Permanent Job Loss: Do Workers Displaced from High-Wage Industries Have Longer Durations of Joblessness?", May, 1988.

RP-121 John Wakeman-Linn, "Monetary Policy and Endogenous Credit Rationing," May, 1988.

RP-122 Gordon C. Winston and Richard G. Woodbury, "Myopic Discounting: Empirical Evidence," July, 1988.

RP-123 Catherine B. Hill, "Different Types of Commercial Policies, Employment, and the Current Account Assuming Optimizing Behavior in an Intertemporal Framework," August, 1988.

RP-124 Catherine B. Hill, "The Effects of Different Trade Policies in Open Economy Macroeconomic Models," August, 1988. Padmanabhan Srinagesh and Ralph Bradburd, RQuality Discrimination and Bidirectional Quality Distortion: Generating the Dupuit Outcome," October, 1988.

RP-126 Roger Bolton, "Integrating Economic and Environmental Models: Some Preliminary Considerations," October, 1988.

RP-127 Richard Sabot and John Wakeman-Linn, "Grade Inflation and Course Choice," November, 1988.

RP-128 David Fairris, "Shopfloor Relations in the Postwar Capital-Labor Accord," December, 1988.

RP-129 Lori Kletzer, "Returns to Seniority After Permanent Job Loss," December, 1988. (American Economic Review, June 1989.)

RP-130 Roger Bolton, "An Economic Interpretation of a "Sense Of Place," January, 1989.

RP-131 Lori Gladstein Kletzer, "Earnings After Job Displacement: Job Tenure, Industry, and Occupation," April, 1989.

RP-132** Duncan P. Mann and Jennifer P. Wissink, "Money-back Contracts with Double Moral Hazard," June, 1989. (RAND Journal of Economics, Summer 1988.)

RP-133 Duncan P. Mann and Jennifer P. Wissink, "Hidden Actions and Hidden Characteristics In Warranty Markets," June, 1989. (International Journal of Industrial Organization, forthcoming.)

RP-134 David Fairris, "Compensating Payments and Hazardous Work in Union and Nonunion Settings," July, 1989.

RP-135** Michael S. McPherson, Morton Owen Schapiro, and Gordon C. Winston, "Recent Trends in U.S. Higher Education Costs and Prices: The Role of Government Funding," July, 1989. (AEA Papers and Proceedings, Vol. 79 No. 2, May 1989.)

RP-136 Catherine B. Hill, "The Costs of Mismanaging Commodity Booms and The Optimal Policies Given Uncertainty," July, 1989.

RP-137 Michael S. McPherson and Morton Owen Schapiro, "Student Aid and Enrollment in Higher Education: Reconciling Econometric Findings and Historical Data," August, 1989.

RP-138 David Fairris and Lee J. Alston, "Wages and the Intensity of Labor Effort: Efficiency Wages Versus Compensating Payments," March, 1990.

RP-139 Richard Sabot and John Wakeman-Linn, "Performance in Introductory Courses: A Production Function Analysis," March, 1990. (to special Roger Bolton, Randall Jackson, and Guy West, "EdiRBrI4Mntroduction" issue of a journal, Socio-Economic Planning Sciences, vol. 23, No. 5, 1989, pp. 237-240, March, 1990).

RP-141** Duncan P. Mann and Jennifer P. Wissink, "Money-Back Warranties vs. Replacement Warranties: A Simple Comparison," July, 1990. (AEA Papers and Proceedings, Vol. 80, No. 2, May 1990.)

RP-142** D. J. Macunovich and R. A. Easterlin, "Application of Granger-Sims Causality Tests to Monthly Fertility Data, 1958-1984," August, 1990. (Journal of Population Economics, 1:71-88, 1988.)

RP-143** Richard H. Day, Kyoo-Hong Kim, and Diane Macunovich, "Complex Demoeconomic Dynamics," August, 1990. (Journal of Population Economics, 2:139-159, 1989.)

Fared RP-144 Lori G. Kletzer, "Job Displacement, 1979-1986: How Have Black Workers Relative to White Workers?" June, 1990.

RP-145 Jere R. Behrman, "Macroeconomic Policies and Rural Poverty: Issues and Research Strategies," September, 1990.

RP-146 Jere R. Behrman, "The Debt Crisis, Structural Adjustment and the Rural Poor," September, 1990.

RP-147 Jere R. Behrman, "Women's Schooling and Nonmarket Productivity: A Survey and a Reappraisal," September, 1990.

RP-148 Jere R. Behrman and Anil B. Deolaikar, "School Repetition, Dropouts and the Returns to School: The Case of Indonesia," October, 1990.

RP-149 Jere R. Behrman, "Nutrient Intake Demand Relations: Income, Prices, Schooling," September, 1990.

RP-150 Barbara L. Wolfe and Jere R. Behrman, "The Synthesis Economic Fertility Model: A Latent Variable Investigation of Some Critical Attributes," September, 1990.

RP-151 Jere R. Behrman and Anil B. Deolaikar, "Unobserved Household and Community Heterogeneity and the Labor Market Impact of Schooling: A Case Study for Indonesia," October, 1990.

RP-152 Jere R. Behrman and Anil B. Deolaikar, "The Poor and the Social Sectors During a Period of Macroeconomic Adjustment: Empirical Evidence for Jamaica," October, 1990.

RP-153 Jere R. Behrman and Santiago Levy, "The Drawbacks of Export Drawbacks," November, 1990.

RP-154 Jere R. Behrman, Robert A. Pollak, and Paul Taubman, "The Wealth Model: Efficiency in Education an Distribution in the Family," November, 1990. RP-155 Diane J. Macunovich, "An Evaluation of the Butz-Ward Hypothesis of Countercyclical Fertility," November, 1990.

RP-156 Diane J. Macunovich, "An Evaluation of the Butz-Ward Hypothesis of Countercyclical Fertility: Technical Documentation," November, 1990.

Old Issue RP-157 Roger Bolton, "'Place Prosperity vs. People Prosperity' Revisited: An With a New Angle," May, 1991.

RP-158 Craufurd D. Goodwin, "Political Economy and Civil Society," Henry George Lecture, Williams College, May, 1991.

May, .RP-159 Jere R. Behrman, "Gender Issues in Labor Markets and Household Behavior," 1991.

RP-160 Lori G. Kletzer, "Industrial Mobility Following Job Displacement: Evidence from the Displaced Worker Surveys," March, 1992.

RP-161 Lori G. Kletzer, "Industry Wage Differentials and Wait Unemployment: Evidence from the 1988 Displaced Worker Survey," March, 1992.

RP-162 Roger Bolton, "What Power on Earth? Arthur Latham Perry's Reaction to Henry George," June, 1993.

RP-163 William K. Jaeger, "The Welfare Cost of a Global Carbon Tax when Tax Revenues Are Recycled," May, 1993.

RP-164 Diane J. Macunovich, "A Review of Recent Developments In the Economics of Fertility," June, 1993.

RP-165 Diane J. Macunovich, "The Missing Factor: Variations in the Income Effect of the Female Wage on Fertility in the U.S.," June, 1993.

RP-166 Thomas C. Pinckney and Peter K. Kimuyu, "Land Tenure Reform in East Africa: Good, Bad, or Unimportant?" June, 1993.

RP-167 Lori G. Kletzer, "Sector-Specific Skills and the Returns to Seniority: Evidence from Reemployment Following Job Displacement," June, 1993.

RP-168 William K. Jaeger, "Is Sustainability Optimal?: Examining the Differences Between and Environmentalists," November, 1993.

RP-169 Roger Bolton and Rodney C. Jensen, "Regional Science and Regional Practice," October, 1994.

** Reprint only available Research Series Department of Economics Fernald House Williams College Williamstown, Massachusetts 01267

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