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The Changing Transitions to Adulthood in Developing Countries: Selected Studies 11 Assessing the Economic Returns to Investing in Youth in Developing Countries James C. Knowles and Jere R. Behrman Youth ages 10 to 24 constitute a large proportion of society and have many pressing health, education, economic, and social needs. Despite the critical value of youth to future well-being, countries may invest inappropriately in their healthy development. There may be gaps between current and socially desired levels of investment in youth such that social rates of returns to investments in youth are higher on the margin than for alternative uses of these resources. If so, the case may be strong for using public resources to close this gap. Some evidence suggests that youth-focused interventions may be cost-effective in improving health, reducing poverty, and providing overall benefits to society. Compared to investments in child health and development, investments in youth often offer a shorter time lag between costs and benefits, thereby having higher benefit-cost ratios, all else equal, if even a relatively modest discount rate is used. Also, in countries in which there has been underinvestment in children, investments in youth may offer an opportunity to “catch up” in the area of human capital investments. Yet full economic analyses of the benefits and costs of investments in youth in developing countries are rare. This chapter explores the economic case for investments in youth in developing countries by synthesizing the current knowledge of the economic costs and benefits of those investments, analyzing key gaps in the evidence, and identifying priority research needs.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies MAJOR THEMES The current cohort of youth in developing countries is the largest cohort ever, either in the past or predicted for the future, given the stage of the demographic transition which developing countries have experienced on average, though there are variations across countries and regions. This means that whatever investments are made in youth in developing countries have an important impact on a relatively large share of the population. It also means there may be large resource implications and large intergenerational transfers required to make substantial investments in youth. Major changes have been occurring in the context for youth in developing countries: The world has become more integrated due to economic, technological, and cultural globalization. Developing countries in which hundreds of millions of youth live—particularly in Asia but also elsewhere—have experienced historically unprecedented economic growth, while smaller but still large numbers of youth, particularly in sub-Saharan Africa, Latin America, the Middle East/North Africa, and Central Asia, live in countries with limited economic growth or stagnation, often with high rates of youth unemployment. Human capital investments in the form of formal schooling and training have expanded rapidly, particularly for females, and have facilitated the exploitation of new technologies and new markets by those in whom such investments have been made. At the same time, severe fiscal constraints faced by most developing countries together with reappraisals of governmental roles have led to a growing share of the investments in youth, particularly in health and schooling, being financed directly by households rather than through governments. The health and nutritional environments have changed radically, with rapid transitions in each of these, so that on average there have been substantial improvements as reflected in increased life expectancies, with a shift from contagious diseases and malnutrition that impinge particularly on infants and children to chronic diseases that affect adults and particularly the aging—while at the same time new health problems, most notably HIV/AIDS, have spread rapidly and in some areas have become major threats. Cultural norms and legal changes, often related to globalization, have shifted to more emphasis on gender equalities, individualism, and materialism.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Therefore, it is necessary to rethink and to reevaluate the range of investments in youth in developing countries, inter alia, schooling, training, reproductive health, and investments in other aspects of health, including behavioral changes related to food consumption, physical activity, and substance use. The large cohort size means that there are pressures on resources that are likely to be squeezed due to the large numbers, therefore strengthening the need for the best evaluations possible for any use of scarce resources for investments in youth. The changed context means that the economic returns to different investments in youth probably have altered substantially. This reappraisal of the economic returns to investing in youth in developing countries must incorporate certain critical features. These include: The inclusion of an appropriately wide range of such investments, including their costs and benefits, within a lifecycle context. The considerable lag in the effects and ultimate outcomes of many of these investments, implying that the choice of an appropriate discount rate may be of considerable importance. Consideration of these investments within the frameworks of standard policy concerns of efficiency and distribution and trade-offs between efficiency and distribution. Sensitivity to problems in making inferences from behavioral data given endogenous choices (selectivity), important unobserved variables, and other measurement and estimation problems. The likelihood that youth investments in one area impact investments and behavior in other areas. For example, reducing youth unemployment might strengthen the demand for schooling. Improving nutrition might improve school performance and reduce the health risks of a youthful pregnancy. Greater clarity regarding matters such as what are costs and what are transfers. The previous literature, for example, often confuses resource costs with transfers, such as welfare payments. ORGANIZATION OF CHAPTER Reassessing the economic benefits of investing in youth in developing countries requires frameworks for analysis to organize the existing fragmented and imperfect information. The next section presents such frameworks, then turns to problems of empirical inferences, and a basic framework for policy evaluation. Building on this foundation, the following sections turn to estimates of the rates of return to different investments, with an effort when possible (which is too infrequent) to distinguish between private and social rates of return and between females and males.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies The strategy is to identify the time pattern of costs and benefits (requiring the translation of impacts into economic terms if they are not presented in those terms) over the lifecycle from a range of piecemeal estimates, and then to estimate the ratio of the discounted benefits to the discounted costs. The methodology used to measure costs and benefits is sufficiently flexible to incorporate, in a simple way, a wide range of effects of different investments. The robustness of these estimates is explored by relaxing different assumptions related to critical aspects of costs, benefits, and the discount rate. Such estimates are presented for a number of alternative investments in youth, including formal and nonformal schooling, reproductive health, school-based health interventions, and investments to reduce the consumption of tobacco. The final section presents a synthesis and conclusions, with emphasis on what are the highest return investments in youth, how these compare with other investments, and what are the highest priority research areas. To implement this strategy, we combine the piecemeal information we have been able to find on the effects and costs of investments in youth in developing countries, together with information that permits translating the effects into benefits measured in monetary terms, in order to estimate benefit/cost ratios and internal rates of return. We start with a lifecycle perspective and consider the estimated costs at the time the investments are made and the subsequent effects over the lifecycle, based on the best estimates we have been able to find. For the benefits, we need the effects of investments in youth in areas such as schooling, unemployment, mortality and morbidity, teen pregnancies, and HIV/AIDS, and a way of associating a monetary value to each effect. We then calculate the present discounted values of the benefits and the costs, conditional on assumptions about the discount rate, and the internal rates of return to these investments. Because of the great uncertainties that underlie many of the estimates that we use, we present some alternative estimates based on alternative assumptions for key variables, such as the discount rate. In light of the considerations discussed below about policy motives, we would like to be able to make separate estimates of total benefits and costs and private benefits and costs in order to identify the extent to which there are efficiency reasons for using public resources to increase certain investments. Unfortunately, in most cases this distinction is difficult to make. However, we try to identify cases where the private and social benefits are likely to diverge. THREE MAJOR CONCLUSIONS First, there are large gaps in what we know with confidence about many aspects of rates of return to investments in youth in developing countries. Most studies are not sensitive to major estimation problems in
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies assessing the determinants or the impacts of such investments. Most studies focus only on the impacts and do not consider the costs (including, possible distortionary costs), which also must be understood to assess the economic returns to such investments. In a number of cases, they further confuse resource costs with transfers. Often the impacts are in terms of some objective, such as improved health and nutrition, but not translated into economic benefits by assessing productivity effects or by using the resource cost of alternative means to attain the same effects. The majority of studies, moreover, do not consider whether the policies examined are likely to be the preferred policies for attaining the policy objective. For such reasons there is a considerable research agenda in order to inform policy makers and other interested parties about the economic returns to various investments in youth in different contexts in developing countries. Second, nevertheless, the available evidence suggests there are some high-return investments in youth in developing countries and there are efficiency reasons for using public as well as private resources for such investments due to inadequacies in markets such as for capital, insurance, and information. Examples of such high-return investments include both supply-side and demand-side investments in formal schooling, investments in adult basic education and literacy targeted to adolescents, investments in some types of school health services (e.g., micronutrient supplementation), investments designed to reduce the consumption of tobacco, and possibly some types of reproductive health investments. Third, what are relatively high rates of return for different investments in youth depends importantly on the context of such investments. Rates of return to schooling, for example, are likely to be much higher in dynamic contexts in which there are rapid changes in technologies and markets through greater integration into world markets. Many health and nutrition investments tend to yield higher returns in settings in which health and nutrition conditions are poor. The economic returns to reproductive health investments designed to reduce rates of HIV infection increase substantially with HIV prevalence in the targeted age groups. FRAMEWORKS FOR ANALYSIS Why Frameworks for Analysis Are Necessary Good analysis of impacts of investments in youth has tripartite foundations: data, modeling, and estimation. These three dimensions are critically interrelated. Data, of course, are essential for empirical analysis, limit the extent to which analyses can be undertaken, and shape most of the estimation problems. If there were available data from well-designed and well-
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies implemented experiments,1 associations between observed investments in youth and observed outcomes would reveal the underlying causality directly. But for numerous reasons, including costs and ethical concerns, such experimental data are rarely available.2 Therefore, although there are likely to be high returns for some aspects of policy analysis to increase experimental data, most analysis will continue to be based on behavioral data. Such behavioral data can “speak for themselves” regarding associations between investments in youth and various outcomes. But they generally cannot “speak for themselves” with regard to what observed determinants—policies or otherwise—cause differences in investments in youth or to what extent observed investments in youth cause different outcomes. The problem is that most data result from a number of behavioral decisions taken by households, individuals, bureaucrats, policy makers, and others in light of a number of factors unobserved by analysts.3 Good analysis of what causes household and individual investments in youth or of what effects such investments have is difficult, and requires a much more systematic approach than simply looking at associations among observed variables. Analytical frameworks permit exploring systematically investments in youth, point to what data are needed for such explorations, facilitate the interpretation of empirical findings, and help to identify some of the probable estimation issues that should be addressed given the data used. The analytical frameworks provided by models are essential if the empirical estimates are based on behavioral data generated in the presence of unobservables such as innate ability and family connections. The problem, for example, is that youth with greater ability and motivation and better innate health may be more productive directly and may also benefit from higher levels of investments. Therefore, it may be difficult to sort out the effect of investments in youth per se as opposed to the fact that such investments are correlated with unobserved abilities, motivation, and innate health. 1 Good experiments have random assignment between treatment and control groups, no attrition problems, and double-blind treatment. 2 Data may be available from “natural experiments” in which, due to some fortuitous happenstance, all unobserved (by analysts) variables are the same in two groups. But though such natural experiments are a conceptual possibility, it is difficult to find two situations in which all unobserved variables are likely to be identical. 3 Throughout this chapter “unobserved” means unobserved by analysts and policy makers—which, of course, depends on the data set, though there are some widely unobserved factors (e.g., innate ability, innate health, family connections, preferences). Such factors, although not observed by analysts, are observed (perhaps imperfectly with learning) by the individuals whose behaviors are being studied, and these individuals make decisions in part based on these factors. Many recent studies emphasize these unobserved factors and their importance in analysis of behavioral data.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies For such reasons, empirical effects of investments in youth can be analyzed satisfactorily with nonexperimental data only within frameworks that incorporate well behaviors related to the phenomena of interest. To be interpretable, estimates based on behavioral data require some model of the underlying behaviors, though far too often in the literature the models used are not explicit. Those who are not clear about their framework of analysis may think they are revealing underlying truths unconstrained by such frameworks, but instead they are usually making implicit assumptions that may not be plausible upon examination. Analytical Frameworks for the Determinants of Investments in Youth Households and the individuals in them are the proximate sources of demands for many investments in youth (e.g., schooling, health, social capital, behaviors that lead to productive lives), given their predetermined assets (i.e., physical, financial, and human, including endowments4), production functions related to human resources, public and private services related to investments in youth (i.e., schools, health clinics), and current and expected prices for inputs used in investments in youth and for outcomes of the investments. Policies, of course, may enter directly or indirectly into this process through a number of channels, ranging from the accessibility and quality of public and private services to the functioning of capital markets for financing investments in youth to the functioning of markets in which these investments are expected to have returns. Becker’s (1967) Woytinsky Lecture provides a simple framework for investments in human resources that captures many of the critical aspects of investments more broadly in youth and which has been widely appealed to in rationalizing empirical studies of the determinants of investments in youth. In Becker’s framework, human resource investment demands, under risk neutrality, reflect the equating of expected marginal private benefits and expected marginal private costs (both in present discounted terms) for investments in a given individual. The marginal private benefit curve depends importantly, inter alia, on expected private gains in productivity in all of the ways in which the human resource investment may have impacts. The marginal private benefit curve is downward sloping because of diminishing returns to investments in youth (given genetic and other endowments) and because, to the extent that investments in youth take time (e.g., schooling, training, and most other forms of education and social capital, as well as time and other resources devoted to search for better options in labor and other markets), greater investments imply greater lags in obtaining the returns and 4 “Endowments” means characteristics that are given independent of behavioral decisions. Genetically determined innate ability and innate health robustness are examples.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies a shorter postinvestment period in which to reap those returns. The marginal private cost may increase with investments in youth because of higher opportunity costs of more time devoted to such investments (especially for schooling and training) and because of increasing marginal private costs of borrowing on financial markets. The equilibrium human resource investment for an individual is where the marginal private benefits and the marginal private costs are equalized. This equilibrium human resource investment is associated with an equilibrium rate of return that equates the present discounted value of expected marginal private benefits with the present discounted value of expected marginal private costs. This simple stylized representation of human resource determinants is based on a dynamic perspective, with both benefits and costs not only in the present but also those that are expected in the future and with current period options conditional on past decisions. Thus it is consistent with placing investments in youth in a lifespan perspective, as has been emphasized from a number of perspectives. The marginal private benefit and marginal private cost curves are likely to vary across youth because of variations in observed and unobserved individual, family, household, and community characteristics, the latter in part related to policies and to markets. Changes in any of these factors can shift these curves and thus the equilibrium investment levels. This simple framework systematizes six critical points for investigating dimensions of the determinants and the effects of investments in youth—and how these relate to policy choices. First, the impacts of changes in policies may be hard to predict by policy makers and analysts. If households or other entities face a policy or a market change, they can adjust all of their behaviors in response, with cross-effects on other outcomes, not only on the outcome to which the policy is directed. Second, aggregation to obtain macro-outcomes will average out random stochastic terms across individuals or households. But such aggregation does not average out systematic behavioral responses at the microlevel. Therefore associations among macro-variables can reveal, conditional on the overall context, what those associations are—but not causal effects of processes occurring at the micro-level. Third, the marginal benefits and marginal costs of investments in a particular individual differ depending on the point of view from which they are evaluated: (1) There may be externalities or capital/insurance market imperfections so that the social returns differ from the private returns, and (2) there may be a difference between who makes the investment decision (e.g., parents) and in whom the investment is made (e.g., youth). The effectiveness of policies are likely to depend crucially on the perceived private effects by private decision makers, and these may differ from the social effects of interest to policy makers.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Fourth, investments in youth are determined by a number of individual, family, community, (actual or potential) employer, market, and policy characteristics, only a subset of which are observed in available data sets. To identify the impact of the observed characteristics on investments in youth, it is important to control for the correlated unobserved characteristics.5 Fifth, to identify the impact of investments in youth, it also is important to control for individual, family, community, market, and policy characteristics that determine the investments in youth and also have direct effects on outcomes of interest. Sixth, empirically estimated determinants of, and effects of, investments in youth are relevant only for a given macroeconomic, market, policy, schooling, and regulatory environment in which there may be feedback both at the local level and at a broader level. Empirical Issues: Measurement To assess the rates of return to investments in youth, we need to (1) measure what we mean by investments in youth and by various outcomes that might be affected by investments, (2) estimate the impact of investments in youth on the latter measures, (3) assign a monetary value to these effects, and (4) measure the costs of these investments. These are not trivial tasks. This section considers some of the measurement difficulties. Investments in Youth In the case of schooling, which is an important example, most empirical studies represent human resource investments empirically by years of schooling or highest grade (level) of schooling completed. Though “years” and “grades” of schooling are often used as synonyms, they need not be the same if there is grade repetition, as is widespread in many parts of the developing countries (e.g., in much of Latin America). One of the major costs of schooling is the opportunity cost of time in school, which is greater if there is more grade repetition for a given schooling grade attainment. Putting aside the question of the time spent in school, there are other limitations of grades (years) of schooling as a measure of human resource investments. Probably most important is the implicit assumption that school quality is constant. But empirical measures indicate that school quality 5 For example, suppose that schools with higher quality tend to be in areas in which expected rates of return from investments in youth tend to be greater, but only indicators of school quality and not expected rates of return are observed in the data. In this case, if there is not control for the unobserved expected rates of return in the analysis, the impact of school quality on such investments is likely to be overestimated because in the estimates school quality proxies in part for unobserved expected rates of return to these investments.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies varies substantially, so it would be desirable in assessment of the impact of human resource investments in youth to represent not only the time (grades, years) that they spend in school, but also the quality of that schooling. If both the quantity and the quality of schooling should be included, but only the quantity is included, the likely result is to overstate the impact of time in school and to miss that there is likely to be an important quality-quantity trade-off. Besides schooling (or education more broadly defined), there are many other investments in the human resources of youth. Such investments may be directed, for example, at improving health, nutrition, information, social capital, and habitual behaviors that lead to desirable outcomes. Similar problems exist in empirical measurement of these variables, as for education. For example, health is often measured by anthropometric indicators, respondent reports, or clinical reports on disease histories; respondent reports on capabilities for undertaking certain activities or tests for doing so; or respondents’ self-assessment of health. Some of these indicators may be good measures of particular disease conditions, but that does not make them (or their inverse) necessarily good measures of what people mean by good health. For another example, social capital is often measured by participation in group activities, but this is at best an imperfect and endogenous indicator of whether one has social capital in the sense of being able to obtain information or resources at times of need. Outcome Variables Unfortunately there are many problems in measuring these outcomes.6 For some outcomes that may be affected importantly by investments in youth, data usually used in the social science literature do not include direct measures—self-esteem and learning capacities are two examples. This may mean that important outcomes are missed when assessing the impact of investments in youth. For some other outcomes there are, at best, imperfect indicators—representing health by health-related inputs (e.g., nutrients), reported disease conditions, curative health care, and preventative health measures (e.g., vaccinations). Some of these measurement problems may be systematic, moreover, resulting in biases in estimated impacts of investments in youth. If, for example, those who have less schooling report less sickness for the same health conditions than those with more schooling (perhaps because the degree of sickness viewed as normal is less with more schooling), impacts of schooling on actual sickness are likely to be underestimated. 6 We limit our discussion here to microdata because the problems with the aggregate data are so severe (see Knowles and Behrman, 2005).
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies For some other measures of outcomes, conventions are used in the literature that may not be sensible. For example, economic gains from reducing mortality are represented at times by the present discounted value of foregone earnings of an individual (e.g., CGCED, 2002). For young individuals, such gains can be considerable. For measuring the purely economic benefits of survivors, however, this seems an overstatement of the economic costs of mortality because such individuals also would have consumed perhaps most or all of their earnings over their lifetime.7 For another example, the economic gains from improving the earnings capacities of individuals are at times measured by the reduction of their demands on governmental social welfare systems for transfers (e.g., CGCED, 2002). But governmental transfers, though perhaps appropriately viewed as costs from a budgetary perspective, are not resource costs in the sense desirable to evaluate policies. There are likely to be some resource savings if social welfare programs are reduced because some resources are consumed in running and financing such programs. But such possible resource savings should not be confused with the amount of transfers involved (and the latter probably greatly exaggerate the true resource costs). For one last example, the gains from reducing crime are at times equated with the amount of losses that crime victims suffer due to crime. But, again, a significant component of the costs so calculated is the transfer from the victim to the criminal, particularly in thefts. The amount of such transfers, once again, is not likely to reflect well the true resource costs of crime. All three of these examples point to substantial difficulties in evaluating benefits and to questionable practices that have been used in some previous studies. We intend to deal with these difficult questions by evaluating the benefits in terms of the least cost alternative way of obtaining the same objective, along lines implemented by Summers (1992, 1994) in a well-known study of the economic benefits of female schooling. This procedure gives, for hard-to-evaluate outcomes, what society is willing to pay for alternative ways of attaining the same gain—and thus, if the prices that are used in the evaluation reflect the true social marginal costs of resources, the true resource costs of such gains. Note that this method in principle includes both the direct resource cost gains and the indirect resource cost gains. To illustrate the latter, investments in youth that reduce crime may not only have gains from directly improving the safety of citizens, but from indirectly encouraging international tourism and international investments. This procedure accounts for what resources society would be willing to pay for alternative ways of reducing crime in light of all these gains. 7 This measure of the economic value of life also implies that there is no value to the life of an individual who is not productive because, for example, of age or disability.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Components Units Enhanced labor productivity % Reduced underutilization of labor % Increased adult work effort % Increased social capital Crime rate Expanded access to risk-pooling services 1 insured person Reduced age at which children achieve a given level of schooling 1 year Reduced cost of medical care % Averted infertility 1 woman Increased tax revenue $ Increased education 1 year of schooling completed Averted youth unemployment 1 youth Reduced child labor 1 hour Averted teen pregnancy 1 pregnancy Averted HIV infection 1 infection Averted STIs 1 infection Averted TB infections 1 infection Improved health 1 DALY Improved nutritional status (height) 1 cm Improved nutritional status (body mass) % change in body mass index Improved nutritional status (anemia) 1 anemic person Improved nutritional status (iodine deficiency) 1 iodine-deficient person Improved nutritional status (Vitamin A deficiency) 1 Vitamin A-deficient person Improved nutritional status (birthweight) 1 kilo Reduced obesity 1 obese person Improved mental health 1 depressed person Delayed marriage 1 year Averted drug/alcohol abuse 1 person Averted physical and/or sexual abuse 1 victim Averted crime 1 criminal Improved self-esteem 1 youth Averted female genital cutting 1 victim Reduced fertility 1 birth Averted abortion 1 abortion Reduced tobacco use 1 tobacco user Reduced violence and civil conflict 1 death Averted social exclusion 1 excluded person Averted orphans 1 orphan SOURCE: Knowles and Behrman (2005).
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Broad Effects (20) Improved Self-Esteem (21) Averted Female Genital Cutting (22) Reduced Fertility (23) Averted Abortion (24) Reduced Tobacco Use (25) Reduced Violence and Civil Conflict (26) Averted Social Exclusion (27) Averted Orphans + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies a procedure previously used, for example, by Summers (1992, 1994). This procedure simplifies the process of doing the simulation experiments referred to above. The next section develops the methodology using matrix algebra notation so that interested readers can understand the details of the procedures. Some readers may wish to skim this section and proceed directly to the following section, which presents an illustration. Methodology for Cost/Benefit Analysis The objective is to estimate an s × 1 vector of discounted benefits (B) corresponding to an s × 1 vector of alternative investments38 in youth (H), expressed in thousands of US$ (for concreteness). Then this vector can be used together with an s × 1 vector (C) of the discounted costs, again corresponding to the s × 1 vector of alternative investments in youth (H), in order to calculate an s × 1 vector of cost/benefit ratios or an s × 1 vector of internal rates of return, once again with one element for each of the corresponding alternative investments in youth (H). The procedure we use begins with a matrix (spreadsheet) of discounted effects (E) with m + n rows and s columns.39 The first m rows refer to directly monetizable effects of a $1,000 investment in youth. The last n rows refer to the broad effects of the investment (i.e., effects that cannot be directly monetized). The s columns refer to s alternative investments that is, H1, H2, H3,…, Hs. We partition the matrix of effects (E) into submatrices E1, containing the first m rows of E, and E2, containing the last n rows of E.40 We next define the broad effects translation matrix (T) with m + n rows and n columns, each (m + n) × 1 column of which lists first the m directly monetizable components of one of the n broad effects and then the n remaining components of one of the n broad effects. The elements in each column of matrix (T), that is, the components of each broad effect, are in fact discounted effects, while the last n rows of each column in matrix (T) are themselves discounted broad effects. We next partition the matrix (T) 38 Alternatively, the procedures described here might be used to estimate the benefit/cost ratios of a set of related investments that would extend the coverage of a program to successively larger percentages of the program’s target population. In this case, the cost-effectiveness of each successive investment might decline to reflect the increasing marginal cost of providing services to successively harder-to-reach subgroups of the target population. 39 Because the effects of a given investment in youth are assumed to occur over the person’s lifetime, the effects are discounted to a single age (e.g., 18 years). 40 E and T (see next paragraph) are assumed to be fixed matrices for the alternatives that we explore. This requires the assumption that, for the range of investments considered, the marginal effects are constant. In the interest of simplification, our procedure also assumes there are no interactions among the various investments (although there are clearly cases where this is not true, i.e., where synergies exist).
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies conformably into submatrices T1, containing the first m rows of (T) (corresponding to directly monetizable effects), and T2, containing the last n rows of (T) (corresponding to broad effects). The matrix (T) embodies an important simplifying assumption, that is, that the components of each broad effect do not vary by intervention. Otherwise, the matrix (T) would be much larger (i.e., (m + n) × n × s instead of only (m + n) × n). Lastly, we define an (m + n) × 1 vector of benefits (Z) corresponding to one unit of each of the m + n directly monetizable and broad effects.41 We partition Z into Z1, containing the first m elements of Z, and Z2, containing the last n elements of Z. The first m elements of vector Z (Z1)—the benefits associated with directly monetizable effects—are estimated directly. The last n elements of vector Z (Z2)—the benefits associated with the broad effects—are calculated by transposing the rows and columns of the two submatrices in matrix T to obtain the n × m submatrix T1' and the n × n submatrix T2', and then postmultiplying T1' by the m × 1 vector Z1 and adding it to the expression obtained by postmultiplying T2' by the n × 1 vector Z2: Which implies: Where I is the n × n identity matrix (i.e., matrix with 1s on the diagonal and zeroes elsewhere). In some cases, we may want to use indirect estimates of the benefits of broad effects rather than estimating them directly (i.e., use indirect estimates for some elements of Z2, rather than basing them on estimates of the corresponding elements of (I − T2')−1T1' Z1). Under these assumptions, the s × 1 vector of discounted benefits (B) corresponding to s alternative investments in youth (H) is given by: The vector of discounted benefits (B) is compared to the vector of discounted costs (C), both of which are discounted to the same age (e.g., age 18, or the age at which a given investment is assumed to occur). The benefit/cost ratio (BCR) for a given investment s is defined as: 41 The benefits are discounted back to the actual average age at which the investment is assumed to occur, which we assume to be age 18 in the absence of any reason to make an alternative assumption.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Where bs refers to element s of B and cs refers to element s of C. The internal rate of return (IRR) for a given investment s (IRRs) is calculated as the discount rate that makes bs = cs. A separate set of matrices (spreadsheets) in principle should be used for different categories of countries (e.g., grouped according to their per capita income), for different genders, and for other groups (e.g., ethnic minorities) for which disaggregated analysis is needed. The limited availability of the necessary disaggregated estimates, however, limits the extent to which such disaggregation currently is possible. A Hypothetical Illustration of the Methodology We illustrate the methodology with a (relatively) simple, purely hypothetical example. For our example, we take the case of an investment (e.g., a scholarship program for girls) designed to increase the amount of schooling a girl has by one year. We assume that the target group of girls would have quit school at age 13 after completing six grades, in the absence of the investment, and that with an investment of $1,000, we are able to keep 10 girls age 13 in school until they complete seven years of schooling, at which point they will be age 14. The $1,000 investment consists of the following costs: $250 in distortionary costs to raise the revenues to finance this investment, $100 for the program’s administrative costs, $300 in costs to accommodate the additional 10 girls in school (i.e., $30 per enrolled pupil), $250 of household investments, largely in the form of foregone earnings of the girls, and a discounted cost of $100 for the girls’ children to continue their schooling beyond the grade at which they otherwise would have left school. We assume that the effect of the hypothetical investment is to increase schooling among the 10 girls by a total of 10 school years (one completed school year per scholarship beneficiary) and that this increase in years of schooling completed results in a proportionate increase in education (as measured by cognitive achievement). However, increased education is a broad effect assumed to have the following components: Each girl’s labor productivity increases by 10 percent from age 16 until retirement (assumed to occur at age 60). Completed fertility is reduced by one child (i.e., the average woman decides not to have a fifth child at age 36). Each girl’s health improves by 0.05 DALYs per year, beginning at age 14 and continuing throughout her lifetime (which is assumed to be 70 years). The health of each of the girls’ four children is assumed to improve by 0.05 DALYs beginning at her age 36 and continuing for the 18 years that her children are assumed to spend with her.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Each of the girls’ four children completes 0.5 years more schooling than they otherwise would have completed by the time each girl (woman) reaches age 40. The probability that the girls will be infected by HIV decreases by 0.01 from age 15 to 24. Most of the above components of increased education are directly monetizable effects. However, the increased education that the girls’ children are assumed to receive is a broad effect assumed to have the same components as the girls’ own increased education (but with substantially longer lags). But, in the case of the girls’ children, it is assumed that there is no effect of the investment on their own children’s schooling or on their children’s risk of contracting HIV. In addition, each HIV infection averted is assumed to be a broad effect with the following components (reflecting the additional assumptions that a girl, if infected with HIV, would be infected on average at age 20, that the infection would turn into AIDS at age 25, and that she would die as soon as she turns 27): The additional medical care necessary to treat a person with AIDS is averted at ages 25 and 26 (i.e., during the last two years of life). There is an improvement in health equivalent to 34.6 DALYs at age 27. Annex Table B11-1 presents the assumed effects (including the components of broad effects) of this hypothetical investment over each girl’s lifecycle. There is only one effect in Annex Table B11-2, the broad effect of 10 girls receiving one additional year of schooling at age 14. Because the benefits are discounted to age 18, the effect of the investment is shifted forward to age 18, using the discount rate. The last three effects (i.e., the last three rows of the table) are broad (i.e., not directly monetizable) effects. Annex Table B11-3 presents the broad effects translation matrix (T) for this hypothetical example. The numbers presented in this table are the cumulative sums of the annual effects presented in Annex Table B11-1 discounted to age 18. The unit benefits associated with each of the assumed effects are assumed to be as follows: Enhanced labor productivity. Each woman’s full earnings are assumed to be $100 annually. Reduced fertility. The value to society of averting each woman’s fifth birth at age 36 is assumed to be $50 (based on the least cost alternative means of reducing fertility by one birth). Improved health. The value to society of each DALY gained is assumed to be $10 (based on the assumed cost per DALY gained from the least cost alternative investment to improve health).
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies ANNEX TABLE B11-1 Assumed Timing of Lifecycle Effects for One Girl in Hypothetical Girls’ Scholarship Example Age Increased Education Enhanced Labor Productivity Reduced Fertility Improved Health Improved Health of Children Discounted (to age 18) cumulative sum of effect 1.2155 2.0576 0.4155 1.1972 1.0200 Age 13 0 0 0 0 0 14 1 0 0 0.05 0 15 0 0 0 0.05 0 16 0 0.1 0 0.05 0 17 0 0.1 0 0.05 0 18 0 0.1 0 0.05 0 19 0 0.1 0 0.05 0 20 0 0.1 0 0.05 0 21 0 0.1 0 0.05 0 22 0 0.1 0 0.05 0 23 0 0.1 0 0.05 0 24 0 0.1 0 0.05 0 25 0 0.1 0 0.05 0 26 0 0.1 0 0.05 0 27 0 0.1 0 0.05 0 28-35 0 0.1 0 0.05 0 36 0 0.1 1 0.05 0.2 37 0 0.1 0 0.05 0.2 38 0 0.1 0 0.05 0.2 39 0 0.1 0 0.05 0.2 40 0 0.1 0 0.05 0.2 41-53 0 0.1 0 0.05 0.2 54-60 0 0.1 0 0.05 0 61-69 0 0 0 0.05 0 70 0 0 0 0.05 0
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies Increased Education of Children Reduced Risk of HIV Infection Improved Health (HIV) Decreased Medical Care (HIV) Discount Factor 0.6837 0.0939 22.3035 1.3875 0 0 0 0 1.2763 0 0 0 0 1.2155 0 0.01 0 0 1.1576 0 0.01 0 0 1.1025 0 0.01 0 0 1.0500 0 0.01 0 0 1.0000 0 0.01 0 0 0.9524 0 0.01 0 0 0.9070 0 0.01 0 0 0.8638 0 0.01 0 0 0.8227 0 0.01 0 0 0.7835 0 0.01 0 0 0.7462 0 0 0 1.0 0.7107 0 0 0 1.0 0.6768 0 0 34.6 0 0.6446 0 0 0 0 — 0 0 0 0 0.4155 0 0 0 0 0.3957 0 0 0 0 0.3769 0 0 0 0 0.3589 2 0 0 0 0.3418 0 0 0 0 — 0 0 0 0 — 0 0 0 0 — 0 0 0 0 0.0791
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies ANNEX TABLE B11-2 Effects Matrix (E) for Hypothetical Scholarship Program for 10 Girls Effects of Hypothetical Investment Scholarship Program Enhanced productivity 0.0000 Reduced fertility 0.0000 Improved health 0.0000 Improved health of children 0.0000 Decreased medical care expenditure 0.0000 Increased education 12.1551 Increased education of children 0.0000 Averted HIV infections 0.0000 SOURCE: See text. ANNEX TABLE B11-3 Broad Effects Translation Matrix (T) of Hypothetical Scholarship Program for Girls Components of Broad Effects Broad Effects Increased Schooling Increased Schooling of Children Averted HIV Infections Enhanced productivity 2.0576 2.0576 0 Reduced fertility 0.4155 0.4155 0 Improved health 1.1972 1.1972 22.3035 Improved health of children 1.0200 1.0200 0 Decreased medical care expenditure 0 0 1.3875 Increased schooling 0 0 0 Increased schooling of children 0.6837 0 0 Averted HIV infections 0.0939 0 0 SOURCE: See text. Decreased medical care expenditure. The medical care expenditure for HIV-infected persons is assumed to be $270 annually during their last two years of life. The full unit benefit vector (Z) for this hypothetical example is presented in Annex Table B11-4.
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The Changing Transitions to Adulthood in Developing Countries: Selected Studies ANNEX TABLE B11-4 Benefit Vector (Z) for Hypothetical Example of Scholarship Program for Girls Benefits Elements of Vector Z Enhanced productivity 100 Reduced fertility 50 Improved health 10 Improved health of children 10 Decreased medical care expenditure 270 Increased schoolinga 475 Increased schooling of childrena 249 Averted HIV infectionsa 597 aElements obtained by solving the equation above for Z2, i.e., Z2 = (I − T2')−1 T1' Z1. SOURCE: See text. REFERENCES Alderman, H., Behrman, J.R., Ross, D., and Sabot, R. (1996). The returns to endogenous human capital in Pakistan’s rural wage labor market. Oxford Bulletin of Economics and Statistics, 58(1), 29-55. Angrist, J.D., Bettinger, E., Bloom, E., King, E., and Kremer, M. (2002). Vouchers for private schooling in Colombia: Evidence from a randomized natural experiment. American Economic Review, 92(5), 1535-1559. Ballard, C., Shoven, J., and Whalley, J. (1985). General equilibrium computations of the marginal welfare costs of taxes in the United States. American Economic Review, 75(1), 128-138. Becker, G.S. (1967). Human capital and the personal distribution of income: An analytical approach (Woytinsky Lecture). Ann Arbor, MI: University of Michigan. (Republished in Human capital [2nd ed.] pp. 97-117) 1975, New York: National Bureau of Economic Research. Behrman, J.R., and Knowles, J.C. (1998a). The distributional implications of government family planning and reproductive health services in Vietnam. (Prepared for the Rockefeller Foundation). Philadelphia: University of Pennsylvania. Behrman, J.R., and Knowles, J.C. (1998b). Population and reproductive health: An economic framework for policy evaluation. Population and Development Review, 24(4), 697-738. Behrman, J.R., and Knowles, J.C. (1999). Household income and child schooling in Vietnam. World Bank Economic Review, 13(2), 211-256. Behrman, J.R., Ross, D., and Sabot, R. (2005). Improving the quality versus increasing the quantity of schooling: Evidence from rural Pakistan. Pennsylvania Institute for Economic Research. (PIER Working Paper 02-022.) Available: http://www.econ.upenn.edu/Centers/pier/Archive/02-022.pdf [accessed September 2005]. Caribbean Group for Cooperation in Economic Development (CGCED). (2002, June). Youth development in the Carribbean. (Draft Report No. 24163-LAC.) Washington, DC: The World Bank. Commission on Macroeconomics and Health. (2001). Macroeconomics and health: Investing in health for economic development. Geneva, Switzerland: World Health Organization. Devarajan, S., Squire, L., and Suthiwart-Narueput, S. (1997). Beyond rate of return: Reorienting project appraisal. World Bank Research Observer, 12(1), 35-46.
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Representative terms from entire chapter: