دانلود مقاله ISI انگلیسی شماره 18422
عنوان فارسی مقاله

سرمایه انسانی، آموزش و سلامت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
18422 2003 15 صفحه PDF سفارش دهید 7650 کلمه
خرید مقاله
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عنوان انگلیسی
Human capital, schooling and health
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Economics & Human Biology, Volume 1, Issue 2, June 2003, Pages 207–221

کلمات کلیدی
بهداشت - بهره وری - سرمایه انسانی - تحصیلات - بازده
پیش نمایش مقاله
پیش نمایش مقاله  سرمایه انسانی، آموزش و سلامت

چکیده انگلیسی

A consensus has been forged in the last decade that recent periods of sustained growth in total factor productivity and reduced poverty are closely associated with improvements in a population’s child nutrition, adult health, and schooling, particularly in low-income countries. Estimates of the productive returns from these three forms of human capital investment are nonetheless qualified by a number of limitations in our data and analytical methods. This paper reviews the problems that occupy researchers in this field and summarizes accumulating evidence of empirical regularities. Social experiments must be designed to assess how randomized policy interventions motivate families and individuals to invest in human capital, and then measure the changed wage opportunities of those who have been induced to make these investments. Statistical estimation of wage functions that seek to represent the relationship between wage rates and a variety of human capital stocks may yield biased estimates of private rates of return from these investments for a variety of reasons. The paper summarizes several of these problems and illustrates how data and statistical methods can be used to deal with some of them. The measures of labor productivity and the proxies specified for schooling and adult health are first discussed, and then the functional relationships between human capital and wages are described. Three types of estimation problem are discussed: (1) bias due to omitted variables, such as ability or frailty; (2) bias due to the measurement of an aggregation of multiple sources of human capital, e.g. genetic and socially reproducible variation, which may contribute to different gains in worker productivity; and (3) errors in measurement of the human capital stocks. Empirical examples and illustrative estimates are surveyed.

مقدمه انگلیسی

Child and adult survival and schooling have increased rapidly in the second half of the 20th century. According to some measures, health and education in the low-income countries are catching up to the levels in the high-income countries (Schultz, 1993). Does convergence in these forms of human capital between the world’s poorer and richer populations promise to narrow international differences in productivity and, if so, by how much? To answer such questions, the relationship between survival and schooling, on the one hand, and personal productivity, on the other hand, should be quantified in a variety of countries. Even then, difficulties remain in comparing the productive quality of schooling within and across countries, and in measuring health status as a human resource. The social return to human capital incorporate social subsidies in the production of the capital and benefits from the capital enjoyed by individuals other than the responsible family unit that is altruistic in valuing positively the enhanced productive capacities of other family members. Public investments in schooling and health should be guided by the distinct priorities implied by these social rates of return. An example where private and social returns might diverge would be the control of infectious diseases where external social benefits arise from reduced contagion. At the level of the nation, recent periods of sustained growth in total factor productivity (i.e. growth in economic output that is not explained by increases in inputs of physical capital, land, or labor hours) are closely associated with improvements in a population’s schooling, nutrition, and health (Schultz, 1961, Kuznets, 1966, Denison, 1967 and Barro and Sala-i-Martin, 1995). At the level of the individual, statistical studies of random sample surveys and censuses reveal significant positive partial correlations between wages, earnings or income and a worker’s schooling, nutrition, and health, stratified by sex and controlling for age or post-schooling experience (Strauss and Thomas, 1995). Macro- and micro-data organized according to these parallel conceptual frameworks strongly suggest that these relationships have a causal basis. Nonetheless, estimates of the magnitude of productive returns to investments in education and health are subject to considerable uncertainty and are qualified by limitations in data and analytical methods. This paper reviews the problems that occupy researchers in this field and draws attention to the accumulating evidence of empirical regularities. Establishing the magnitude of these returns to schooling and health is a first step to concluding how much the convergence in these forms of human capital across and within countries can contribute to narrowing inter- and intra-country inequalities. Investment of time and resources in the formation of human capital increases the productive potential of workers (and increases as well consumer benefits and leisure) that are realized over a lifetime. Measuring the internal rate of returns to human capital calls for an inter-temporal analysis of costs and benefits of birth cohorts over their lifetimes. Most data pertain to cross-sections, however, that describe inputs and outcomes in one period of time across different individuals grouped by age. Demographers recognize the limitations of such synthetic constructs from cross-sectional data designed to represent cohort experiences over time. Assumptions are necessary to translate cross-sectional evidence into human capital lifetime investment returns (Mincer, 1974). Whether these working assumptions are an innocuous simplification or a serious limitation on our knowledge remains to be determined. Growing examination of repeated cross-sections allow statistical samples of cohorts to be followed as they grow older and long prospective panels describe individuals over time, subject to attrition bias. Both of these approaches may reduce our reliance on cross-sectional data to infer within cohorts the determinants and consequences of human capital. Sample surveys that collect information on the life histories of respondents may also alleviate the memory error problem of recall, due to asking retrospectively about the lifetime of a cohort. Time-varying conditions which are exogenous to the individual remain scarce although they can be useful for identifying in panels dynamic models of behavior. In addition, without true social experiments designed to assess how randomized policy interventions change the motivations for families and individuals to invest in different amounts of human capital, and consequently to affect their potential earnings, statistical estimation of the relationship between wages and human capital investments may not approximate the effects that would follow from a properly designed randomized social experiment or the likely effect of a general change in policy. This paper considers several of these problems and illustrates how data and statistical methods are being used to deal with some of them.

نتیجه گیری انگلیسی

Extensive historical and contemporary studies in low- and high-income countries document that health and nutritional status, measured in terms of a long-run indicator such as height and as a shorter-run indicator such as BMI (weight for height), influence labor productivity per unit time worked, and labor supplied per adult year to market work, and longevity (Fogel, 1994, Strauss and Thomas, 1995 and Schultz and Tansel, 1997). At levels of real income when nutrition is very low, these effects of health and nutrition on productivity and survival are reported to be substantial, but there is not yet agreement on the precise magnitudes of the health productivity effects or how costly they are to achieve by private expenditures or public regulations or outlays. Consequently, internal rates of return to particular interventions, policies, or institutional investments are not yet known. There are suggestions from many studies that BMI and nutritional intake should be treated as endogenous. Even adult height that is molded during uterine development and early childhood is modified by household and community resource allocations, is subject to measurement error, and consequently is appropriately viewed as an endogenous human capital variable in the adult wage function over a lifecycle, even if most of the human capital investments reflected in adult height are undertaken by parents and not the adult worker. The statistical methods outlined in this paper promise in the next generation of health and economic studies to define with increasing precision the contribution of the health transition to modern economic growth in the low-income world since the second world war. This work will complement the extensive work on today’s high-income countries which has documented the evolution and cross-sectional differentials in anthropometric indicators of health during the early phases of industrialization and economic development (Komlos, 1994). Evidence on the wage returns to education has evolved much further than that on health, where analyses have been replicated from hundreds of labor force and integrated demographic household surveys in countries at all levels of development (Schultz, 1993). As in the cases cited of Ghana and Côte d’Ivoire, private wage returns to education are affected by both the relative supply of educated workers to the economy, and the derived demand for educated workers, which depends on the composition and growth of the aggregate economic output. Although most of the evidence of returns to education is potentially subject to multiple sources of statistical bias (e.g. omitted variables, errors in measurement, endogeneity, heterogeneity in productive response to treatment) these sources of bias are not all in one direction, and do not appear to distort seriously the simple pattern that emerges when one documents how workers wages increase proportionally with their schooling. There is a biological basis for expecting the economic productive returns to nutrition to exhibit diminishing returns as nutrition and health improve, but there is less reason to expect that the pattern of returns to schooling will be uniform in all settings, or even subject to a common pattern of diminishing returns. Indeed there are many reasons for different distributions of the supply of education in the population and aggregate economy-wide differences in the derived demand for skills to generate notably different rates of return to schooling at various levels of schooling. Moreover, even within a country, the pattern of returns can change abruptly, as in the US where returns to college education declined during the 1970s and increased sharply after 1980. Neither change in the supply of workers by age, education or sex, nor macroeconomic imbalances, nor increasing penetration of international trade, can adequately explain these far reaching changes in wage structures in the US that are now found in a growing number of high and low-income countries. Thus, wage structures for education may change unexpectedly and should be periodically monitored by surveys to provide guidance as to where to expand public educational systems. Evidence is accumulating that health and schooling contribute to higher labor productivity in most countries. Yet, it is not clear when education first became a critical factor enhancing labor productivity. There are few representative surveys that provide information on education and wages before the 1940 US Census. It seems unlikely that education was an important productive characteristic for most workers in the 19th century, when apprenticeships, on-the-job experience, and family training transmitted most productive skills. Why in the twentieth century did the opening up of the world economy to trade, capital mobility, and the diffusion of technology create extensive opportunities for better educated workers to outperform their peers in a widening range of jobs? With a better answer to this question, it may be possible to forecast the future evolution of returns to education, and begin to formulate testable hypotheses which might help to account for the growing evidence of substantial returns to health human capital, measured imperfectly along many diverse dimensions.

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