Human Resources Development and Operations Policy HRO Working Papers Evaluation of Integrated Human Resource Programs T. Paul Schultz January 1 995 HROWP 47 Papers in this series are not formal publications of the World Bank. They present preliminary and unpolished results of analysis that are circulated to encourage discussion and comment; citation and the use of such a paper should take account of its provisional character. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. Evaluation of Integrated Human Resource Programs by T. Paul Schultz Prepared for the World Bank as a background to the Human Resource Strategy Paper, "Investing in People," Human Resources Development and Operations Policy, Washington, D.C. The comments on a previous draft are appreciated from G. Psacharopoulos, H. Patrinos, R. Chase, A. Judd, and other participants at Bank workshops. The author's address: P.O. Box 208269, Yale Station, 27 Hillhouse Ave., New Haven, CT 06520-8269. Abstract Human resource programs are evaluated by comparing the productivity of individuals who have benefited from a specific program to the productivity of similar individuals who have not had this benefit. To implement such an evaluation may require a model of the demand of households and individuals for several distinct forms of human capital, of how public agencies and private firns work with households to produce these forms of human capital, and of how these investments increase the lifetime productivity of individuals and thereby contribute to economic growth and socio-demographic development. This paper proposes to estimate an integrated wage function that provides the policymaker with a basic tool for simulating the private and social returns to human resource stocks, specifically those in schooling, vocational experience, child nutrition, migration, adult health and nutrition and family planning. I Contents I. Individual Productivity Effects of Human Resources ....... . . . . . . . . . . . 1 II. Individual Productivity Function ........... . .. .. .. . .. .. .. . .. . . 4 III. Types of Human Capital ............... ... ... ... ... .. ... .. . 6 IV. Problems in Applying the Framework to Program Evaluation ...... . . . . . . 8 V. Critical Aspects of Programs to Evaluate ........................ 11 VI. Cross-program Effects and Returns to Scale ....................... 12 VII. Public and Private Program Interactions ......................... 14 VIII. Who Benefits from Human Resource Programs ..................... 15 IX. Conclusions for Program Evaluation ........................... 15 References ................................................ 17 I I. Individual Productivity Effects of Human Resources Human resource programs are evaluated by comparing the productivity of individuals who have benefited from a specific program to the productivity of similar individuals who have not had this benefit. To implement such an evaluation may require a model of the demand of households and individuals for several distinct forms of human capital, of how public agencies and private firms work with households to produce these forms of human capital, and of how these investments increase the lifetime productivity of individuals and thereby contribute to economic growth and socio-demographic development. This paper proposes to estimate an integrated wage function that provides the policy maker with a basic tool for simulating the private and social returns to human resource stocks, specifically those in schooling, vocational experience, child nutrition (proxied by adult height), migration, adult health and nutrition (proxied by adult weight given height), and family planning (proxied by avoided unwanted births). To complete the task of program evaluation, the policy maker must provide information on the value of public sector subsidies and private household resources used to produce the various forms of human capital. Many studies of education, nutrition, health, labor mobility, and training have sought to measure the contribution of these forms of human capital to the productivity of workers and to modern economic growth. The base of knowledge in this field is growing rapidly but is unavoidably qualified because returns to investments in human capital are only realized over a lifetime. In addition, without true social experiments designed to assess how randomized policy interventions work through the actions of the family and individuals, statistical estimation of causal relationships may be biased or conditional on hypothetical structural models. Microeconomists and other social and biological scientists have examined the economic and physical functioning of individual workers with different characteristics, while macroeconomists have analyzed aggregate output, measures of labor inputs, and the economic growth experience of nation states. A consensus has been forged that recent periods of sustained growth in total factor productivity are critically dependent on improvements in a population's nutrition, health, education, and mobility. Analysis of these forms of human capital must now be integrated to measure with greater precision their distinct effects on economic development, because stocks of human capital acquired by individuals may not be independent: they potentially interact with'each other in their impact on the productivity of the worker, and they are often subject to diminishing (or increasing) returns. Traditional semi-log-linear approximations for wage functions should, therefore, be extended to more flexible specifications that allow for returns to vary and interactions to exist among several forms of human capital in addition to education. Finally, in considering the effects of human capital on development, we are primarily interested in how state and family investments influence the formation of reproducible human capital and how it in turn impacts on labor earnings and growth. The exogenous skills and genetic endowments of workers that are largely unaffected by state and family actions are less 2 central to our objective, except as they mask or modify measured returns to the reproducible component of human capital.' Figure 1 illustrates the overall system that constrains parents whose resources and preferences govern, for the most part, investments in their children's human resources. These human resource stocks are then expected to influence their children's lifetime productivity, and other outcomes, given the aggregate economy's derived demand for the different types of labor services the children can offer. The objective of the integrated program evaluation approach is to measure two connections: (i) the impact of the human resource stocks on adult productivity (i.e., wages); and (ii) the opportunity cost of social resources expended on human resource programs (or perhaps infrastructure) to modify human capital investments. The social policy maker needs to assess the social internal rate of return earned by these human resource programs, or the discount rate that would reduce the present value of the extra wage returns associated with the human resource stocks to the present value of the marginal expansion of public subsidies needed to create these stocks. A number of conceptual and statistical issues arise in jointly estimating without bias these two behavioral and technical linkages in a specific setting. Do the endogenous components of the variation in human capital exert the same "effect" on labor productivity as do the exogenous endowments? If they differ, it is the productive effects of endogenous reproducible human capital that policymakers need to know. Specifically, much of the variation in height, for example, is due to genetics. It is not clear whether differences in this genetic endowment have the same productive effect on workers as do differences caused by variation in childhood health and nutritional investments. Specification tests can be implemented to assess whether different sources of variation in human capital stocks are exogenous or endogenous, and thus whether specific forms of human capital are justifiably treated as exogenous or endogenous variables when estimating extended wage functions (Schultz, 1994). 3 Flgure 1: Human Resource Programs and Their Consequences on Productivity Refional CTInrsrte/Rai c $ Ea u~~- -.- Derive Demnand for iiibor. '4 = loprdct1empIome > -Rebt±ivc umeo Traop orimieand dabi Geh & ~ ~ 4hurtsucm'znencBaA4 edrohb < 9i . Paent iDnos and Transfers-to ChiTdreiiin. v i Thves=f±ents in. Euman CapitaiT -.b.t.H =in A~~~~~~~A L~ es=ex -. .o .dBcucss I'-N Family ofO.~ -_~Numi nSnn~(Heighj'Q 4, *Pament-Educoa Scollgl'sie 2 (-c n.- 0 . s,xvAd°t.,*l_ ulilth @ M '§,s ................ 4fid ResidenLtb -14goeienerihId t; ^icLaResource ProoTams*. aP<.;t F c (Subsi&ecfSeuJjt: 1,~- .. 9 - : eh.(.:i..>-.e .) ;- 4 II. Individual Productivity Function Social resources are used to produce stocks of human capital that enhance the productive capacity of the individual over the long run. How individuals allocate their productive capacity - whether they make further investments in themselves, work in the home, work in the labor market, or engage in leisure activities - should not necessarily influence how we measure the private or social return to that human capital. Because of the difficulty of assigning a value to the output of labor in self-employment, home production, or leisure, it is a common practice to analyze (following a procedure Heckman (1979) first proposed) the market productivity of persons who work for a wage in the labor market, and then correct for any bias that may arise from analyzing only this selected sample. The critical requirement to correct for sample selection bias is that there is some observed variable known to affect the probability of working for a wage, but not to affect the market-offered wage. The potential correlation across individuals between the errors in the market wage equation and the wage participation equation is responsible for the sample selection bias (Schultz, 1993). A natural choice for an exclusion restriction that could identify the sample selection procedure is a variable that affects only the individual's marginal productivity in nonwage production, only the individual's utility from leisure, or both, but is uncorrelated with the market wage. Inherited land, business assets, or wealth raise the individual's productivity in nonwage activities and thereby decrease the probability she or he would work for others as a wage eamer. By assumption, these wealth variables have no effect on market productivity or the wage offer, nor should these wealth variables be determined by the market wage, or past labor supply and savings behavior.2 Individuals who are observed not to work for wages are assumed to value what they produce with their time more highly than the market wages they could receive. It is common to assume that the variables which are observed to influence market wage opportunities, such as education, also affect nonwage opportunities, roughly in the same proportion. Without more information on the structure of the nonwage production function and utility function, the market wage function is assumed to determine the marginal productivity effects in wage and nonwage activities of human resource investments.3 These productive effects are then independent of 2 Actual correlations do not inform us of the validity of an identification restriction. Nonetheless, the expected negative sign of the effect of the identifying wealth variables in the wage participation equation might be reversed (to positive) if the wealth variables were in fact endogenous to the household demand system and reflected the accumulation of wealth from having received a high wage in the past. In my experience, the land and business asset variables usually are negatively associated with wage participation in low-income countries, given the educational attainment and age (i.e., postschooling experience) of the individual (Schultz, 1993). A positive coefficient for the wealth variables in the wage-participation function might suggest that this choice of identifying restriction was invalid. The productivity of labor in nonwage activities for those who do not participate in such activities can be inferred only if the functional form of nonwage marginal product function is known, and its identifying argument known and observed. If the underlying nonwage (home) production function is subject to constant returns to scale, 5 how time is allocated between wage and nonwage activities. This assumption may be particularly important for evaluating how human resource programs enhance the productivity of women because only a small share of adult women in many low-income countries work for wages. More research should assess the adequacy of these methods for inferring the productive gains from human resources. The wage function is determined by several types of conditioning variables. First, there are initial abilities, which may be thought of as genetically determined or at least not affected by the input of family or public resources.4 Second, there are several dimensions of human capital, which are produced means of production, formed jointly by family and individual private inputs of time and goods and by public inputs of goods and services, typically provided in the region of residence. The production possibilities to create human resources are constrained by biology, technology, and perhaps, in the short run, by organizational limitations. The third type of variable entering the human resource production function is age or experience.5 Experience acquired after completing school is expected to enhance initially labor productivity. Mincer (1974) found a quadratic function in years of potential postschooling experience (i.e., age - years of schooling - seven) approximated reasonably well U.S. 1960 Census male earnings data. This quadratic specification could be derived from a model in which the share of a worker's time invested in on-the-job training declined linearly with postschooling experience. With sufficient data, higher-order polynomials in potential postschooling experience as postulated by Becker (1965), the inframarginal producer surplus in nonwage production accruing to labor would be zero, contrary to Gronau's (1977) suggestion. However, some investigations of wage participation find that it increases in education, nutritional/health status, and migration. This could be interpreted as evidence that the marginal productivity of human resources is greater in wage than in nonwage activities (Schultz, 1989; Schultz and Tansel, 1992; Schultz, 1994). In the initial exploration of variations in the Mincerian wage functions, much effort went into assessing the bias to the schooling coefficient caused by the omission from the wage function of the individual's initial ability, or IQ for short. It is clear that if ability increases the amount of schooling received, and ability increases productivity holding schooling constant, then the omission of ability will tend to bias upward the estimated schooling return (Griliches, 1977). However, subsequent studies have also indicated that using a variety of family variables in an effort to "pick up" ability and family effects can introduce an offsetting downward bias on the estimated schooling return (caused by errors in measurement of the true [quality adjusted] education) which is larger than the omitted variable upward bias (see e.g., Lam and Schoeni, 1993). In the situation being analyzed here, the ability must be exogenous to the family and public human resource investments. I Because education is assumed to be exogenous to the wage function in Mincer's specification, and age is exogenous, this measure of potential postschooling experience is exogenous. The implicit assumption is that all activities engaged in after leaving school contribute equally to later market productivity. When experience is adjusted for periods of nonparticipation, as would seem plausible in the case of intermittent female workers, this actual experience variable becomes endogenous because it depends on lagged labor supply. If such an actual market experience variable is added to the wage function, some lagged instrumental variables must be specified that are independent of the current period wage function error. 6 may also be added to improve the empirical fit of the wage function to wage data.6 For other human resource accumulation processes, such as gestation, birthweight, height, weight-for- height, but probably not migration, biological age may be more relevant than experience. When education is treated as endogenous, then postschooling experience also becomes endogenous, and experience may be replaced by an unrestricted polynomial in age. HII. Types of Human Capital In this section I propose five forms of human capital that may increase the lifetime productivity of a worker, although the relative returns to different types may vary by income and technological stage of the society, and each type may, after a certain point, be subject to diminishing returns. The first is net childhood nutritional status, which is measured by early physical growth and development, such as height-for-age and weight-for-height. It is believed that height at the fourth birthday is already a good predictor of an individual's final adult height (Martorell and Habicht, 1986). Adult height is, moreover, an important determinant of adult productivity, and reemerges as inversely correlated with chronic health problems among the middle-aged and elderly (Fogel, 1991). In addition to improving adult productivity and health, childhood nutritional status may also enhance the performance of children in school and in early training tasks, and thereby affect the return to other human capital investment activities (Behrman, 1993; Moock and Leslie, 1986). Alternatively, the covariation of nutrition and school achievement among children might be caused by unobserved heterogeneity of families and children, as would occur if credit constraints or preferences affected families in their investments in both nutrition and schooling. Nutritional status in childhood is the sum of several factors, of which nutritional intake is probably the most important. Another is the exposure to infection and disease, which places extra demands on nutritional inputs and reduces the efficiency with which the body can absorb nutrition, such as through the incidence of diarrhea, or develop immunities. The child's work load can also place different demands on diet. Poor nutritional status is revealed in adults who are stunted, or experience a deficit in their mature height. This adult measure of height appears to be relatively stable after maturity from about age 18 to 50. The second form of human capital is schooling. Children start school at very different ages, from age 5 to 10, and continue for different numbers of years, including repetitions. The length of school terms and the number of hours a day the school provides instruction varies across and within countries. The attendance of "enrolled" pupils during the regular school term 6 Murphy and Welch (1990) found that a quadratic function was justified in fitting male wage profiles from large Current Population Surveys from the United States in the last two decades. The choice of functional form to summarize wage and eaming profiles tends not to be critical, i.e., whether quadratic or cubic. But if postschooling experience is replaced by age, internal rates of return to education can no longer be inferred directly from the wage function coefficient on education, but must be adjusted for different amounts of age for the same individual. Also, experience tends to fit log wage data better than age, when both are expressed in quadratic form. 7 is also far from uniform. If there were a consensus on how to measure school quality per hour attended, this would also exhibit substantial variation. Consequently, describing investments in schooling by "years completed" is only a first-approximation that urgently needs to be extended and refined in many directions. The third form of human capital is migration, which may occur repeatedly over a lifetime and involve return migration. To simplify, I focus here on any movement of adults from their region of birth, which most often occurs in the first few years after completing school. Migration occurs more frequently for more educated individuals (Schultz, 1982), suggesting, again, two alternative hypotheses. Education and migration may be complementary forms of human capital, or families that invest more in the education of their children also are more likely to finance their migration, possibly because they face a lower cost of investment funds (Becker, 1967), or because they simply have a stronger taste for child human capital. Much of the return to education for a rural-born individual may be realized only through migration, suggesting a close linkage between these investments (Schultz, 1988a). Returns to a variety of human resource programs may therefore be measured more precisely by joint evaluation. If migration occurs first in order to continue secondary or higher education, because these forms of education are only provided in some urban areas, the chronological and causal order of these two human resource investments may be difficult to disentangle. Migration may not be as closely associated with increasing market-productive opportunities for women as for men because families often move together, and collectively families may assign greater weight in the choice of destination to the earnings opportunities of males rather than females (Mincer, 1978). The fourth form of human capital is the means to avoid unwanted fertility, which enhances women's market productivity by allowing them to continue their education, to migrate where their skills are most valuable, or to allocate time to their most rewarding work. To the extent that public resources that subsidize family planning and provide associated information about birth control can delay a woman's first birth or reduce the subsequent number of births, a woman's market human capital may increase. Because children satisfy other demands of parents, estimating the impact of family planning subsidies on unwanted childbearing must disentangle private demands for children from the enhanced market productivity of women, if they delay and limit their unwanted childbearing. It is not surprising that family planning is viewed as a means to "empower" women because it is likely to increase their economic opportunities. Little analytical effort has been directed to measuring family planning as a mechanism for enhancing women's human capital. The fifth and final form of human capital is an adult's current nutritional status, proxied by weight for height squared (BMI), which affects the current productivity of the individual, particularly at low levels of calories and for energy-demanding tasks. This indicator of nutritional status among adults must be treated as simultaneously determined with the choice of productive activity, because increased income can also support increased current expenditures on nutrition and the performance of more demanding jobs (Strauss, 1986; Pitt et al., 1990). Unbiased estimates of the one-directional effect of improved adult nutrition on wage productivity 8 requires valid instrumental variables that predict current nutrition or BMI and are uncorrelated with the error in the wage function. The prices of nutrients in the locality or the cost of health care are possible instruments for this purpose (Strauss, 1986; Deolalikar, 1988; Sahn and Alderman, 1988). Poor adult current nutritional status is known as wastin and is often measured by the BMI (Fogel, 1991). Because BMI and height are often nearly orthogonal, both can be used together to explain variation in individual productivity without extreme problems of multicollinearity. IV. Problems in Applying the Framework to Program Evaluation I have focused on only five human capital stocks to simplify exposition, to illustrate the potential for integrating human resource program evaluations, and to offer a concrete specification to guide empirical analysis. There are many limitations to such a scheme. Health is not separately distinguished as a human resource goal, but the indicators of childhood and adult nutrition are selected because of their high correlation with childhood and adult mortality.7 Although these nutritional indicators are less well documented as predictors of morbidity, they nonetheless appear to summarize parsimoniously the cumulative repercussions of disease and nutrition on adult well-being and the quality of life (Stewart and Ware, 1993; Broome, 1993). Human resource activities are defined here to encompass explicitly early nutrition/child survival interventions, current nutrition/preventative health measures, education, and family planning programs. Labor market mobility as a means for realizing the highest possible returns on a population's human capital warrants more explicit study as a significant factor facilitating development. Transportation and communication infrastructure and reduced regulations on job turnover and residential change are rarely considered as factors in low-income countries explaining their differential success in adjusting to exogenous shocks due to weather or terms of trade. Analysis of labor markets, and particularly the determinants of labor force participation, time allocation, and migration should be an integral part of human resource divisions within development agencies and governments. To perform timely assessments of human resource programs, periodic household labor force surveys will be needed. These surveys should include human resource stocks and wages and be in a form to merge with community data on human resource and welfare programs that influence households in their investments in human resources. There is some evidence that chronic health problems measured by clinical investigations among the middle aged may be predicted by adult height (Fogel, 1993). Physical limitations on daily activities are also associated with height (Stewart and Ware, 1993). The occurrence of acute illnesses among adults can also be related to anthropometric indicators of health status. Days ill and unable to work in the last month among male wage earners in C8te d'lvoire and Ghana are associated with lower wage rates when the individual is working and reduced likelihood of entering the wage labor force. These measures of acute illness are treated with caution, however, because of the subjective nature of these self-response indicators of health status (Schultz and Tansel, 1992). 9 In estimating a wage function that relates human resource stocks to adult productivity, several econometric specification issues are paramount. First, which forms of human capital are exogenous or endogenous, in other words, are these stocks uncorrelated or correlated with the unobserved wage determinants impounded in the error of the wage function? Characteristics of the region of residence have been used to identify forces that may plausibly influence the accumulation of human capital stocks independently of the individual's wage. The simultaneous determination of wages and adult weight (BMI) is noted as an instance where the effect of adult weight on wages could only be estimated by instrumenting weight by local prices of food, availability of health care, or food-for-work programs for unemployed (e.g., Strauss, 1986; Deolalikar, 1988; Deolalikar and Gaiha, 1992). Although fertility and migration are often viewed as choice variables in an economic household production framework (Schultz, 1974; 1992; Sabot, 1982), few studies attempt to endogenize migration and unwanted fertility in a wage function.8 Education is generally treated as exogenous, but intergenerational studies of the schooling of children could treat access to schools and their quality as public program inputs that may be expected to influence household investments in human resources but not directly impact on wages. Programmable policy variations that occur across local areas are thus a promising source of instrumental variables (IV) to explain the human resource stocks that in turn serve as arguments in the wage function. These IV estimates have two advantages and one shortcoming. First, if the human resource stock is measured with classical measurement error, these IV estimates of the wage function eliminate the resulting bias, which tends to understate the productivity effect of human capital (Griliches, 1977). Second, if the human resource stock is endogenous and correlated with the error term in the wage function, these IV estimates are consistent and unbiased in a large sample, whereas the ordinary least squares (OLS) estimates are not.9 A third standard problem is that of omitted variables, such as exogenous ability or other dimensions of human capital. If these omitted inputs that should be in the wage equation are uncorrelated with the included inputs, the estimates of the wage function are unbiased for evaluating the effect of measured policy interventions. However, if the omitted variables are correlated with the included variables, a standard mispecification problem may occur (Griliches, 1977; Lam and Schoeni, 1993). The shortcoming of using local variation in human resource programs to instrument human resource stocks in the wage function is that the local placement of programs may be correlated with omitted variables (e.g., healthiness of environment) or selective migration to regions with better programs. Either selective program placement or migration may bias the IV estimates based on regional programs (Rosenzweig and Schultz, 1983; Rosenzweig and Wolpin, 1986). A The effect of one unanticipated birth on the wage of Malaysian women is estimated to be a 9.6 percent reduction in this rate, whereas in the U.S. an unexpected birth is associated with married women receiving a 9.9 percent lower wage (Schultz, 1992: Table 5.1). 9 If the explanatory power of the instruments is relatively weak, the IV estimates mav not be reliable and specification tests will merely confirm exogeneity due to the poorly identified model (Staiger and Stock, 1994). 10 A central goal of human resource evaluation studies should be to estimate the wage returns to various human capital stocks that can be augmented by existing and readily implemented public programs. Depending on the purpose of the study and the nature of the program intervention, each form of human capital may be treated as exogenous or endogenous. Schooling is conventionally treated as exogenous following Becker (1964) and Mincer (1974). Schooling is formed during childhood when the family of origin exerts considerable control over the investment decision, whereas the school sets some standards for advancement, and the years completed appear to augment the productive capacity of a wide quality-range of students. Other forms of human capital are perhaps jointly determined over an adult's lifetime: their allocation of time to market labor supply; on-the-job training; job search and turnover; migration; and fertility (for at least females). These adult investment-related decisions are more plausibly viewed as endogenous to the wage determination process. Childhood nutrition and health conditions, summarized by adult height, and adult health and nutrition, summarized by weight-to- height, have traditionally been assumed exogenous by economic historians for the purposes of inferring their effect on labor productivity (Fogel, 1986; 1994; Floud et al., 1990). But with the recent reconsideration of weight and height as potentially endogenous variables in the. labor productivity function (Strauss, 1986; Deolalikar, 1988; Thomas and Strauss, 1993; Schultz, 1994), the question of how to deal with anthropometric indicators of stature/health/nutrition has taken on increasing importance. Would the reproducible health human capital captured by some of the variation in these indicators of stature have a distinctively different impact on productivity than would the genetically fixed endowment of stature that individuals are given at the time of their conception and then developmentally express? A slowly growing body of evidence suggests that at least the ratio of weight to height squared (i.e., BMI) and perhaps also height are appropriately modeled as endogenous human capital stocks, if the goal of the analysis is to evaluate what human resource programs can contribute to economic productivity at current levels of human capital development and technological opportunities. Moreover, if the research goals are to describe the longer term role of public policies on intergenerational economic mobility in a society and to quantify the effects of public policies and programs that affect both childhood and adult investments in human capital, then all forms of human capital formation should be endogenized (if suitable instruments exit) within the model and explained by programs and parent endowments. Another specification problem occurs when "fixed effects" are introduced to eliminate the bias of omitted variables at the individual, household, or community levels. It has been demonstrated that when special samples are analyzed to minimize the role of the omitted "ability" variable, such as samples of twins, or models that include family fixed-effects, community fixed-effects, individual fixed-effects, or when extensive family socioeconomic controls are added, errors in the measurement of human capital stocks (e.g., education) become exaggerated, leading to a downward bias on the coefficient estimated on education in a wage function (Griliches, 1977; Griliches and Hausman, 1986; Lam and Schoeni, 1993). A similar downward bias in the estimated effect of other human resource investments on wages may be expected when this class of fixed-effect methods is employed, but the source of measurement error could be partially or fully offset by other sources of bias. 11 Some recent studies of wage functions in the United States have relied on unusual legal provisions surrounding schools in different administrative areas as instrumental variables for explaining a small fraction of the variation in educational attainment of individuals (Angrist and Krueger, 1991). This administratively explained variation is then used to obtain IV estimates for education in the wage function. Although these instrumental variables may not be efficient, with sufficiently large samples they may imply statistically significant estimates of the private rate of return to education (Angrist and Krueger, 1994). These estimates in several U.S. studies are close to the simple regression (OLS) estimates of the form proposed by Mincer that treat education as exogenous, measured without error, and uncorrelated with ability. One conclusion that might be drawn from these recent wage studies is that the bias due to the omission of ability or family background in estimating wage functions is not as serious or substantial as originally believed. Moreover, adding further controls in the wage function for family background can seriously understate the returns to schooling due to exacerbating the problem of measurement error of education. These various econometric sources of bias in estimating wage functions have not yet been adequately appraised for the other forms of human capital emphasized in this paper. Based on recent Living Standards Measurement Surveys (LSMS) for Ghana and Cote d'Ivoire, education and migration appear to be exogenous in wage functions, whereas IV estimates are needed to estimate the endogenous effects of height and BMI on wages of men and women (Schultz, 1994). Interaction effects among the human resource variables are also anticipated from the literature, such as between child nutrition and education or between education and migration. Estimation of these interaction effects among endogenous human resource inputs may clarify whether these inputs are technically complements or substitutes for each other in their effect on labor productivity. V. Critical Aspects of Programs to Evaluate Three questions need to be answered in setting public sector priorities to help the family invest in human resources and encourage development. A cluster of human resource programs needs to be considered together in canceptually evaluating its benefits for families. Empirically, evaluations can build on standard data from household surveys matched to data on public resources provided to community schools, health, and family planning programs, etc. The first question is how to get the most benefit from a given expenditure on allied programs. This measure of efficiency should explicitly allow for the likely effects of one program on the outcomes of all other programs, or cross-program effects. The second question is how program benefits are distributed across types of individuals and families, such as the rich and poor, which may inform us about the equity of the program. The third question is how the cost effectiveness of programs would differ if they were in the private or public sectors. In many spheres it is difficult to achieve the same efficiency in the public sector as in the private sector, but the private sector may not be able to reach the same target groups that the public sector can because of their different organizational structures and need to collect fees for services. Benefits from different functionally-oriented programs are difficult to compare, such as health and family 12 planning. Much work remains to be done before it is possible to compare outcome measures, such as a prevented birth, a prevented death, or reduced morbidity, in comparable welfare units. The purpose of this paper is to explore cross evaluation of human resource programs in terms of their comparative impact on a common objective: increasing individual productivity. A framework within which cross-program effects might be measured is outlined in section 6, and public/private program substitution possibilities can then be explored in section 7. The personal distribution of benefits (or costs) are finally incorporated into this framework in section 8. It is useful to note at the outset that this general approach has two clear limitations. First, the distribution of benefits across groups can be readily evaluated only when the groups are defined in terms of exogenous variables, i.e., where group membership is not related to choices and allocational decisions made by the observed individual and family. Second, the spatial variation in programs and policies must be assumed random with respect to unobservables, notably the preferences of the population and productivity and healthiness of regional environments.'" VI. Cross-program Effects and Returns to Scale Many hypotheses are advanced to explain how and why particular programs help a family modify its behavior in a manner that is beneficial to the family and perhaps also to society. Two distinctive program designs with a shared objective may strengthen both programs in achieving their common goal, or one may weaken the independent impact of the other program. The former, complementarv effects, are most frequently documented across different types of human capital investment programs. An explanation for this pattern of reinforcement is that one form of human capital enhances the returns to another form. For example, improvements in child nutrition/health permit children to learn more at school (Moock and Leslie, 1986; Gomes-Neto et al., 1992), and healthier children can expect to live a longer, healthier life during which to earn market returns from schooling (Floud et al., 1990). The latter, substitution effects between two social programs, can be expected when the programs are directed to achieving the same end., but through alternative mechanisms or motivations or instruments, such as may be the case when public and private sector programs seek to satisfy the same demands. These potential synergies between social programs, either positive or negative, may change with the scale of interventions, possibly reinforcing each other at low levels and then substituting for each other at higher levels. For example, in a family planning program it may be useful to combine doctors and nurses into different types of programs, some stationary in hospitals and clinics, while others are mobile in outreach teams. Both types of program '° To deal with the migration of individuals to regions that provide preferred social programs, or program placement in regions with distinctive populations or welfare problems, raises identification problems that are discussed elsewhere (Rosenzweig and Schultz, 1983; Rosenzweig and Wolpin, 1986; Pitt, Rosenzweig, and Gibbons, 1993). 13 personnel may be motivated to improve health and reduce the number of unwanted births, but they use different mixes of trained manpower and different organizational delivery systems. In some contexts the clinics and outreach programs may reinforce each other, and in other cases they may substitute for each other, reducing the effectiveness of the other program. Thus, these cross-program effects must be empirically assessed to improve estimates of how effective public sector program efforts will be in different circumstances. One simple way to estimate the sign and magnitude of cross-program effects is to add interaction variables between allied human resource and social welfare programs to models of behavior or output determination, as, for example, in the wage function discussed here. Thus, the wage or fertility of a woman in a region is expected to be a function of her characteristics and the input of program efforts per woman in her residential region. But the program input may be channeled into either family planning clinic or outreach activities. The wage or fertility equation estimated by the program evaluator would then include a separate program input for clinics and outreach, plus an interaction term between the two regional program activities. According to the discussion above, the two segments of the program might be expected to exert positive effects on wages, and the cross-program effect would be negative if the programs were substitutes, and positive if they were complements. The opposite signs would be expected in an equation accounting for fertility. An example of this approach applied to the study of fertility is found in Schultz (1971; 1988b). Estimates of such program interaction effects are more reliable if quadratic terms are also included for the two program effort variables, in which case the specification can be generally interpreted as a second-order Taylor series approximation for any functional relationship between the program input variables and the measure of program output. With the inclusion of quadratic terms in each program activity, it is also possible to infer how the returns to each program varies with the scale of program effort, and hence how the marginal returns to program inputs may differ from the average returns. The objective of public policy should be to achieve the same marginal return from equal marginal outlays in various programs, given that both programs have the same objective. It is a common pattern for the marginal return to program inputs to decline after some program scale is reached and the demand for the good or service is gradually satisfied or, in other words, the market is saturated with the r,elevant skill or service (Schultz, 1971; 1988b; 1992). In those instances where the primary objectives of allied social welfare programs differ, such as with education, health, and family planning, the task of comparing all returns is not straightforward, but the magnitude of cross-program effects may still be substantial and readily assessed, if not compared in equivalent monetary terms. In the previous section I proposed evaluating human resource programs within a single wage function to facilitate such comparisons. A basic feature of many social welfare programs is that they may influence the costs and benefits of having children (or avoiding unwanted births) while changing the net benefit streams from investments in the education and training of those children (Rosenzweig and Wolpin, 1980; Foster and Roy, 1992). The most direct route by which public policy may influence fertility is through the provision of information and related services for evaluation and 14 use of modem birth control. If these programs help parents avoid more unwanted births, the evidence from a number of studies suggests that parents reallocate some of their gains to investing more in their children's schooling (Schultz, 1992). If the income effects associated with the benefits of these types of social programs were negligible, then household demand theory predicts that the estimated (uncompensated) cross-program effects should be symmetric, or of the same sign and equal in magnitude. In the earlier discussion of the wage equation, if the two programs were health subsidies and school subsidies, the direct program effects are expected to be positive, and the program interaction effect might be positive as well, if they are complementary. This would imply that health expenditures had a higher payoff in regions with greater school expenditures, other things being equal. VII. Public and Private Program Interactions Most public human resource programs provide a service that can also be obtained through private markets, although perhaps in a different quality or form. It should be expected that when public subsidies are provided for a good or service that is also available in the private market, some consumers will switch from private to public providers because of the public subsidy, without necessarily changing outcomes or behavior. Consequently, the supply of contraceptives distributed freely by a public program is likely to overstate the added contraceptive protection provided to the population by the program (Schultz, 1971). Some contraceptors will merely shift their source of supply without improving their contraceptive efficiency. This is, of course, only a special case where parallel programs may exist with approximately the same objective. The only way to assess accurately the effect on contraceptive use of a subsidy to either program is to analyze both programs together to avoid overstating public program effectiveness. Moreover, an analysis should focus on the final outcome of fertility or female wages, rather than the intermediate input of contraceptive behavior (prevalence), if a more precise measure of program effectiveness is sought. In a study of Thai birth rates in 1976-1981, the fertility effects of government subsidies to the public sector health and family planning program and to the private, nonprofit family planning program are estimated. The study finds that both the private and public sector family planning subsidies are associated with lower levels of fertility, holding constant for the age, education, and household income of women. The larger public sector subsidies are also associated with diminishing returns to program scale within province administrative units. In other words, the marginal returns in terms of preventing births are smaller than the average returns to government expenditures per woman. This form of diminishing returns to scale within provinces was evident as early as 1969 in the pioneering Taiwanese family planning program (Schultz, 1971; 1988b). In Thailand, the public and private family planning program subsidies are shown to be substitutes for each other, as might have been expected (Schultz 1989; 1992). 15 VIII. Who Benefits from Human Resource Programs To assess how social program effects are distributed, it is convenient to add additional interaction variables between program treatments and exogenous groups. In the illustrative fertility model, it has been hypothesized that family planning provides information and assistance on how to adopt more effective modem means of birth control. If it is more difficult for the less educated women to assess a new birth control technology, and program inputs are of only little value to the more educated women, public subsidies for local family planning should then have their greatest impact on the fertility and productivity of less educated women. An analogous problem arises where public sector extension activity promotes adoption by farmers of new technological inputs and management practices. These farm-extension activities have been shown to raise the profits of less educated farmers by a greater proportion than the profits of more educated farmers (Evenson, 1986; Birkhauser et al., 1991). An interaction variable is defined in this case as the product of the local region's program subsidy and the individual's education. It is clearly important also to hold constant for education and the local level of program subsidy in the fertility (or wage determining) equation. If the local input of family planning activity had a greater beneficial effect in helping women with lower levels of education avoid unwanted births, then the estimated coefficient on this interaction variable would be positive (negative), in the fertility (wage) equation, while the direct effects of the program and women's education would both be negative (positive). This pattern is observed for fertility in Colombia in 1973 (e.g., Rosenzweig and Schultz, 1982) and is generally consistent with the larger gaps recorded between desired and actual fertility among the least educated women in Latin America and South East and East Asia in the World Fertility Surveys (Schultz, 1989). The first phase of the In-Depth Fertility Surveys from three regions of China, collected in 1985, indicate that the partial effect of a local family planning worker in the community on the fertility of older women is larger for less educated women. As in the other studies, both the woman's education and the presence of a local family planning worker contribute to lower levels of fertility, and the family planning worker narrows the fertility differentials by women's education (Schultz and Zeng, 1991). In each of these studies it would be interesting to know if female wages were larger in those groups in which fertility was lower due to program activity, but many of the surveys used to study fertility have not collected information on wages or earnings. IX. Conclusions for Program Evaluation Household survey data from individuals on fertility, child health, child schooling, adult education, wage rates, and other sources of income, and household expenditures can be merged with regional data for the residential area on public expenditures on human resource programs. These merged data provide a flexible and representative basis for assessing the success of human resource programs to help individuals increase their productivity and families cope with the challenges of economic and demographic change in the low-income world. Putting modern technologies to effective use to control their reproduction, protect their family's health, and 16 educate their children are closely related achievements that do not proceed independently. If public objectives can be achieved by both private and public sector providers of schooling, health, or family planning, the simultaneous analysis of both public and private providers is mandatory. In some parts of the world, the public sector may not be the most cost effective or equitable provider of basic services, even those that are traditionally associated with the public sector, such as health care (Birdsall and James, 1993). The prices and quality of services in the public and private sectors should be analyzed together with the traditional household demand data on expenditures, time allocation, wages, prices, and nonearned income to isolate the payoff to expanding public sector subsidies. The personal distribution of the wage gains from social welfare programs is rarely estimated but should become an important ingredient in deciding what goods and services the public sector should provide and to what segments of the population they should be subsidized. Subsidies for some public sector services may benefit predominantly the poor and help them overcome their initial economic disadvantages. These subsidies should be associated with families achieving for themselves greater intergenerational mobility, through their improved control of unwanted births and the increased health, nutrition, and education of their children. Other public sector services, such as urban hospital care and university education, may benefit predominantly urban middle and upper classes. These public services may appear to be inequitable income transfers from taxpayers to the rich, without notably affecting average wages, mortality, schooling, or fertility. Identifying which public services should become self-financing by fees-for-service may help sustain and expand government assistance for human resource programs where they remain a cost effective and equitable human resource investment to increase individual productivity, alleviate poverty, and accelerate growth. References Angrist, J.D., and A.B. Krueger, 1991, "Does Compulsory Schooling Affect Schooling and Earnings?" Quarterly Journal of Economics 105:4 (November). , 1994, "Split Sample Instrumental Variables," NBER Technical Paper No. 150, Cambridge, MA. Becker, G.S., 1964, Human Capital. New York: Columbia University Press. , 1965, "A Theory of the Allocation of Time," Economic Journal 75(299):493-517. , 1967, "Human Capital and the Personal Distribution of Income," Woytinsky Lecture, University of Michigan, Ann Arbor, MI. Behrman, J.R., 1993, "The Economic Rationale for Investing in Nutrition in Developing Countries," World Development 21(11):1749-1771. Birdsall, N., and E. James, 1993, "Health, Government and the Poor. " In The Epidemiological Transition, eds. J.N. Gribble and S. Preston. Washington, DC: National Academy Press. Birkhauser, D., R.E. Evenson, and G. Feder, 1991, "The Economic Impact of Agricultural Extension: A Review," Economic Development and Cultural Change 39(3):607-650 (April). Broome, J., 1993, "Qalys", Journal of Public Economics 50(2):150-167. Deolalikar, A.B., 1988, "Nutrition and Labor Productivity in Agriculture." Review of Economics and Statistics 70(3):406-413. Deolalikar, A.B., and R. Gaiha, 1992, "Targeting of Rural Public Works," Intrahousehold Resource Allocation Conference of International Food Policy Research Institute, Washington, DC, (February 12-14). Evenson, R.E., 1986, "The CIGAR Centers: Measurement of Impact on National Research, Extension and Productivity," Consultative Group in International Research, The World Bank, Washington, DC. Floud, R., K. Wachter, and A. Gregory, 1990, Height. Health. and History. Cambridge: Cambridge University Press. Fogel, R., 1986, "Growth and Economic Well Being: 18th and 19th Centuries." In Human Growth, Vol. 3, eds. F. Falkner and J.M. Tanner. New York: Plenum Press. 18 _, 1991, "New Sources and New Techniques for the Study of Secular Trends in Nutritional Status, Health Mortality and the Process of Aging," NBER Working Paper No. 26. Cambridge, MA (May). _, 1993, "The Relevance of Malthus for the Study of Mortality Today," Working Paper, University of Chicago. , 1994, "Economic Growth, Population Theory and Physiology," American Economic Review 84(3):369-395. Foster, A.D., and N. Roy, 1992, "The Dynamics of Education and Fertility: Evidence from a Family Planning Experiment." Processed. University of Pennsylvania, Philadelphia, PA. Gomes-Neto, J.B., E.A. Hanushek, R.H. Leite, and R.C. Frota-Bezzera, 1992, "Health and Schooling: Evidence and Policy Implications for Developing Countries," Rochester Center for Economic Research, Working Paper No. 306, Rochester NY. Griliches, Z., 1977, "Estimating the Returns to Schooling, " Econometrica 45(13): 1-22 (January). Griliches, Z., and J.A. Hausman, 1986, "Errors in Variables in Panel Data," Journal of Econometrics 31:93-118. Gronau, R., 1977, "Leisure, Home Production and Work," Journal of Political Economy 85(6): 1099-1125 (December). Heckman, J.J., 1979, "Sample Bias a Specification Error," Econometrica 47(1):153-162 (January). Lam, D., and R.F. Schoeni, 1993, "Effects of Family Background on Earnings and Returns to Schooling," Journal of Political Economy 101(4):701-740 (August). Martorell, R., and J.P. Habicht, 1986, "Growth in Early Childhood in Developing Countries." In Human Growth, Vol. 3, eds. F. Falkner and J.M. Tanner. New York: Plenum Press. Mincer, J., 1974, "Pooling Experience and Earnings." New York: Columbia University Press. ______ 1978, "Family Migration Decisions," Journal of Political Economy 86(5):749-773. Moock, P.R., and J. Leslie, 1986, "Childhood Malnutrition and Schooling in the Terai Region of Nepal," Journal of Development Economics 120:33-52. 19 Murphy, K.M., and F. Welch, 1990, "Empirical Age Earnings Profiles," Journal of Labor Economics 8(2):202-229 (April). Pitt, M.M., M.R. Rosenzweig, and D.M. Gibbons, 1993, "The Determinants and Consequences of Placement of Government Programs in Indonesian," World Bank Economic Review 7(3):319-348 (September). Pitt, M.M., M.R. Rosenzweig, and M.D.N. Hassan, 1990, "Productivity, Health, and Inequality in the Intrahousehold Distribution of Food in Low Income Countries," American Economic Review 80(5):1139-1156 (December). Rosenzweig, M.R., and T.P. Schultz, 1982, "Child Mortality and Fertility in Colombia: Individual and Community Effects," Health Policy and Education 2:305-348. , 1983, "Estimating a Household Production Function," Journal of Political Economy 91(5):723-746 (October). Rosenzweig, M.R., and K. Wolpin, 1980, "Testing the Quantity-Quality Fertility Model: The Use of Twins as a Natural Experiment," Econometrica 48(1):227-240. , 1986, "Evaluating the Effects of Optimally Distributed Public Programs: Child Health and Family Planning Interventions," American Economic Review 86:470-482. Sabot, R.H. (ed.), 1982, Mieration and the Labor Market in Developing Countries. Boulder, CO: Westview Press. Sahn, D.E., and H. Alderman, 1988, "The Effects of Human Capital on Wages and the Determinants of Labor Supply in a Developing Country," Journal of Development Economics 29:157-183. Schultz, T.P., 1971, "A Framework for Analysis and Its Application to Taiwan's Family Planning Program," P-4674, The Rand Corporation, Santa Monica, CA. , 1974, "Birth Rate Changes over Space and Time." In Economics of the Family, ed. T.W. Schultz. Chicago: University of Chicago Press. _____,1982, "Notes on the Estimation of Migration Decision Functions," In Migration and the Labor Market in Developing Countries, ed. R. Sabot. Boulder, CO: Westview Press. _,___ 1988a, "Education Investments and Returns." In Handbook of Development Economics, Vol. 1, Chap. 13, eds. H. Chenery and T.N. Srinivasan. Amsterdam: North Holland Pub. Co. 20 _, 1988b, "Population Programs: Measuring Their Impact on Fertility and the Personal Distribution of Their Effects," Journal of Policy Modeling 10(1): 113-149. , 1989, "Women and Development: Objectives, Framework and Policy." Working Paper WPS-200, Women in Development Division, Population and Human Resources Department, The World Bank, Washington, DC (April). , 1992, "Assessing Family Planning Cost Effectiveness: Applicability of Individual Demand-Program Supply Framework." In Family Planning Programmes and Fertilitv, eds. J.F. Phillips and J.A. Ross. New York: Oxford University Press. , 1993, "Investments in the Schooling and Health of Women and Men: Quantities and Return," Journal of Human Resources 28(4):694-734. , 1994, "Human Capital and Development," paper for presentation at 22nd International Agricultural Economics Association meeting, Harare, Zimbabwe, August 28, 1994. Schultz, T.P., and A. Tansel, 1992, "Measurement of Returns to Adult Health," Discussion Paper No. 663, Economic Growth Center, Yale University, New Haven, CT. Schultz, T.P., and Zeng Yi, 1991, "The Association Between Community and Individual Characteristics and Fertility in Rural China." Processed. Economic Growth Center, Yale University, New Haven CT. Staiger, D., and J.H. Stock, 1994, "Instrumental Variables Regression with Weak Instruments," NBER Technical Paper No. 151, Cambridge, MA. Stewart, A.L., and J.E. Ware, Jr. (eds.), 1993, Measuring Functioning and Well Being. Durham, NC: Duke University Press. Strauss, J., 1986, "Does Better Nutrition Raise Farm Productivity?" Journal of Political Economy 94(2):297-320 (April). I Thomas, D., and J. Strauss, 1993, "Health, Wealth and Wages of Men and Women in Urban Brazil. " Paper presented at the conference on Women's Human Capital and Development, Bellagio, Italy (May). I I Human Resources Development and Operations Policy Working Paper Series Contact for Title Author Date paper HROWP1 5 Reproductive Tract Infections, May T.H. Post October 1 993 0. Shoffner HIV/AIDS and Women's 37023 Health HROWP1 6 Job Security and Labor Ricardo D. Paredes November 1 993 S. Khan Market Adjustment in 33651 Developing Countries HROWP1 7 The Effects of Wage Luis A. Riveros November 1 993 S. Khan Indexation on Adjustment, 33651 Inflation and Equity HROWP1 8 Popular Participation in Philip R. Gerson December 1 993 L. Malca Economic Theory and Practice 37720 HROWP1 9 Economic Returns from Edwin Mansfield January 1 994 I. Dione Investments in Research and 31447 Training HROWP20 Participation, Markets and Deepak Lal January 1 994 L. Malca Democracy 37720 HROWP21 Safe Motherhood in Patricia Daly January 1994 0. Shoffner Francophone Africa Michael Azefor 37023 Boniface Nasah HROWP22 Indigenous People and George Psacharopoulos February 1 994 I. Conachy Poverty in Latin America Harry Anthony Patrinos 33669 HROWP23 Is Grameen Bank Sustainable? Shahid Khandker February 1 994 S. David Baqui Khalily 33752 Zahed Khan HROWP24 Concepts of Educational Marlaine E. Lockheed March 1 994 M. Verbeeck Efficiency and Effectiveness Eric Hanushek 34821 HROWP25 Scientific Research for Erik W. Thulstrup March 1 994 L. Malca Development 37720 HROWP26 Issues in Education Finance Stephen P. Heyneman April 1 994 B. Cassorla and Management in ECA and 37172 OECD Countries HROWP27 Vocational Education and Julian Schweitzer April 1 994 A. Gonzalez Training: The Role of the ' 37799 Public Sector in a Market Economy HROWP28 Social Security Issues and Nguyen X. Nguyen May 1994 . M. Espinosa Elements of Reform 37599 HROWP29 Health Problems and Policies Mary Eming Young May 1994 0. Shoffner for Older Women: An 37023 Emerging Issue in Developing Countries HROWP30 Language and Education in S.M. Cummings May 1994 M. Espinosa Latin America: An Overview Stella Tamayo 37599 Human Resources Development and Operations Policy Working Paper Series Contact for Title Author Date paper HROWP31 Does Participation Cost the Jesko Hentschel June 1994 D. Jenkins World Bank More? Emerging 37890 Evidence HROWP32 Research as an Input into Harold Alderman June 1 994 P. Cook Nutrition Policy Formation 33902 HROWP33 The Role of the Public and Deepak Lal June 1 994 M. Espinosa Private Sectors in Health - 37599 Financing HROWP34 Social Funds: Guidelines for Soniya Carvalho July 1994 K. Labrie Design and Implementation 31001 HROWP35 Pharmaceutical Policies: Graham Dukes July 1 994 0. Shoffner Rationale and Design Denis Broun 37023 HROWP36 Poverty, Human Development Harsha Aturupane August 1 994 P. Cook and Growth: An Emerging Paul Glewwe 30864 Consensus? Paul Isenman HROWP37 Getting the Most out of Helen Saxenian September 1994 0. Shoffner Pharmaceutical Expenditures 37023 HROWP38 Procurement of Denis Broun September 1 994 0. Shoffner Pharmaceuticals in World 37023 Bank Projects HROWP39 Notes on Education and Harry Anthony Patrinos September 1994 1. Conachy Economic Growth: Theory 33669 and Evidence HROWP40 Integrated Early Child Mary Eming Young October 1 994 0. Shoffner Development: Challenges and 37023 Opportunities HROWP41 Labor Market Insurance and Deepak Lal October 1994 M. Espinosa Social Safety Nets 37599 HROWP42 Institutional Development in Alberto de Capitani October 1 994 S. Howard Third World Countries: The Douglass C. North 30877 Role of the World Bank HROWP43 Public and Private Secondary Marlaine E. Lockheed November 1 994, M. Verbeeck Schools in Developing Emmanuel Jimenez 34821 Countries HROWP44 Integrated Approaches to T. Paul Schultz November 1994 M. Espinosa Human Resource 37599 Development HROWP45 The Costs of Discrimination in Harry Anthony Patrinos November 1 994 I. Conachy Latin America 33669 HROWP46 Physician Behavioral Nguyen X. Nguyen December 1 994 M. Espinosa Response to Price Control 37599 HROWP47 Evaluation of Integrated T. Paul Schultz January 1995 M. Espinosa Human Resource Programs 37599