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Microeconomic Approaches to Development: Schooling, Learning, and Growth
Mark R. Rosenzweig

I illustrate the variety of approaches to development issues microeconomists employ, focusing on studies that illuminate and quantify the major mechanisms posited by growth theorists who highlight the role of education in fostering growth. I begin with a basic issue: what are the returns to schooling? I discuss microeconomic studies that estimate schooling returns using alternative approaches to estimating wage equations, which require assumptions that are unlikely to be met in low-income countries, looking at inferences based on how education interacts with policy and technological changes in the labor and marriage markets. I then review research addressing whether schooling facilitates learning, or merely imparts knowledge, and whether there is social learning that gives rise to educational externalities. I next examine studies quantifying the responsiveness of educational investments to changes in schooling returns and assess whether and where there exist important barriers to such investments when returns justify their increase.

Within the field of economic development over the past 15 years or so, particularly significant advances have been made in what can be loosely called micro-development, an area defined principally by the units that are examined, not by a particular methodological approach.

The units may be individuals, households, networks, banks, government agencies and so on, as opposed to countries. Within this area, economists use a wide variety of empirical methods informed to different degrees by economic models, use data from developed and developing countries, and some use no data at all, to shed light on development questions.1 The best of this work speaks to the major questions of development and even informs, if not provides the
foundation for, macro models of development and growth.

I will illustrate the variety of approaches to development issues that microeconomists have employed by focusing on studies that illuminate and quantify the major mechanisms posited by growth theorists who highlight the role of education in fostering growth. For example, Lucas (1988) emphasizes the role of education by stressing the importance of learning externalities. But what evidence do we have for these? Theories of long-run growth that span thepre- and post-Industrial Revolution focus on the interaction between technical change and schooling. Lucas (2002) and Galor and Weil (2000) explain the shift from a stagnant world in a Malthusian equilibrium to one of sustained growth as the result of technical change inducing investments in schooling which leads to a shift to smaller families.
Nelson and Phelps (1966) suggest that a major mechanism by which poor countries develop is through technological transfer, and hypothesize that schooling facilitates such transfers because it improves the ability to master technology. Some key questions suggested by these models are: (i) what is the contribution of schooling to productivity in low-income countries? (ii) does technical change raise schooling returns, and for whom? (iii) does learning play an important role in adopting and adapting to technical change? (iv) how important are learning and thus schooling externalities?and, (v) does schooling investment respond to variation in returns, whatever their source, or are there important barriers to human capital investment when returns are high?Studies in micro-development have provided credible answers to these questions using new data and a variety of empirical approaches.

The methods used encompass structural estimation, exploitation of natural policy experiments and exogenous advances in technology, difference-in-difference evaluation of programs, the examination of a large variety of implications of a single model (a preponderance of circumstantial evidence), and randomized field experiments, among others. Many of these studies have made important contributions to knowledge that satisfy high standards of evidence. Indeed, it is precisely the similarity in findings arising from different methods, data, and contexts that contributes to building confidence in conclusions.

I begin with a basic issue: what are the returns to schooling? A standard approach in economics has been to use regressions with wages as the dependent variable and a measure of education as a regressor to estimate the returns from schooling. I will argue that this approach is problematical for identifying the contribution of schooling to productivity in a development context, and is particularly inadequa
te for exploring the ways in which education might affect conditions for economic growth.
I then discuss microeconomic studies that estimate returns from schooling using alternative approaches, looking at inferences based on how education interacts with policy changes, with technology change, and with the marriage market. I then turn to the questions of whether schooling merely imparts knowledge or whether it also facilitates learning particularly in setting undergoing technical change and whether there is social learning that gives rise to educational externalities. I next examine studies that address the question of the responsiveness of educational investments to changes in schooling returns and whether and where there exist important barriers to such investments when returns appear to justify their increase. I end with brief reflection.

The Rate of Return to Schooling and the Productivity of Schooling
A key question in development concerns the contribution of schooling to productivity.Estimates of how schooling augments productivity are informative about the existence of barriers to schooling investment, which have been hypothesized to be a key reason for the low levels of schooling observed in most low-income countries Growth theorists also suggest that a key issue is under what conditions schooling contributes more and under what conditions less to productivity. The measurement of schooling returns is not easily obtained from randomized experiments, as schooling attainment cannot be randomly assigned, and even if the cost of schooling were randomized across parents of children (as in the initial stages of the Mexican Progresa program), one would need a long time frame to assess how adult productivity was affected. Nevertheless, there have been important contributions made in micro development in identifying productivity effects of schooling.

However, the main problem is that the model justifying the Mincer earnings function suggests that βj by itself is of little value in understanding the productivity of schooling. And the model also implies that if one adheres to the Mincer model, the argument that the absence of school quality variables (or even controls for ability) induces bias is misplaced.
The original specification of the wage function derived by Jacob Mincer (1958) was based on a general-equilibrium “equalizing differences” model incorporating the assumption that individuals discount future income and that there are no nonmarket barriers to schooling or occupations—that is, the amount of schooling chosen by individual workers is not constrained by school availability or by access to finance.

Under these assumptions, lifetime wages must be equal for all workers no matter what their schooling level. For example, if college graduates had higher lifetime earnings, then more persons would go to college, driving down the wages of college graduates until lifetime incomes were the same. Moreover, since agents deciding on human capital investments would compare the returns to schooling with the returns to capital, the discount rate would be equated to the cost of capital. In the Mincer earnings function given earlier, therefore, the parameters have a structural interpretation in terms of the model: the intercept is the wage a worker would earn in country j who had no schooling, wj = W(0)j ––the “base wage” for country j—and the rate of return to schooling is actually the rate of return to capital in the economy.

Thus, in the Mincer model, differences in the “rate of return” to schooling across countries reveal little about cross-country differences in the productivity of schooling, and may equally reflect capital market conditions.2 Differences in the productivity of human capital or in the schooling production function (school quality) across economies (in equilibrium) will be reflected not in the Mincer βj, but in the quantities of schooling. The reason is that people will invest in schooling until the marginal product falls to equal the interest rate. Thus, in equilibrium,variables reflecting the quality of schooling will be unrelated to workers’ earnings across
countries, given their quantity of schooling.

Knowledge of the rate of return to schooling βj is also insufficient to characterize either the marginal contribution of a worker to output or to predict the aggregate quantity of schooling in an economy, as both also depend on the level of the “base wage” wj.3 And the Mincer model is silent about the determination of an economy’s base wage. Indeed, if as is assumed in the Mincer model factors are mobile, the principal effect of growth lies in raising the base wage.
An empirical question is whether cross-country variation in the base wage or in the Mincerian rate of return to schooling accounts for more of the variation across earnings for workers around the Within a developed country, where the assumptions of the Mincer model are more credible, an increase in the productive value of high-skill workers might be reflected in a temporary rise in the returns to schooling coefficient, and thus changes in β over time might suggest changes in the relative value of schooling.

The addition to output of a worker in country j who obtains an additional year of schooling,
given the Mincer wage specification, is βwjeβS. world who have the same schooling. In Rosenzweig (forthcoming), I use data on earnings for workers in 120 countries and new information on cross-country school quality (Bartik, 2008) to estimate the Mincer earnings model. I find that the variation in the base wage wj across countries is substantial, and accounts for most of the variation earnings across workers of the world (as opposed to variation in βj ).4 Moreover, I reject the Mincer model, as I find that school quality variables also are significant determinants of earnings, net of school years.

Thus, in a development economics context, thinking about returns to education or skill by using a cross-country or within-country regression of wages and years of education seems unlikely to yield insights about the determination of schooling and its returns. Instead, the key issues are what determines the amount of and productivity of skill and why does it vary (so uch) across countries, as is evidenced not by variation in β but by variation in the base wage.

That is, why does giving a U.S. resident a college education increase earnings by orders of agnitude more than a similar investment in schooling in a country like Nigeria? The micro development research agenda goes beyond the Mincer earnings function approach to measuring the returns to schooling in a number of ways:by accounting for the endogenous variation in schooling, by dealing with the selection of workers into sectors, by measuring more directly the productivity of schooling, and/or by assessing directly the productivity of schooling in different contexts so that a better understanding of where and when schooling investments pay off can be obtained. Here, I give the flavor of that research with three examples.

Esther Duflo’s (2001) study of the impact of the INPRES program in Indonesia on schooling attainment uses a difference-in-difference technique applied to different birth cohorts across multiple consecutive censuses to show how a school building program applied differentially across areas of the country increased the schooling attainment of the affected cohorts. The empirical challenge to identifying the impact of the program is that the increase in school building was purposively more intensive in low-enrollment areas. Duflo obtains an estimate of the impact of the program by comparing the change in schooling across birth cohorts affected and unaffected by the program in high-intensity and low-intensity program areas.

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Because the methodology takes into account endogenous program placement, the variation in the program provides a plausible instrument for schooling variation across birth cohorts and places of birth. Although the focus of the paper is on the effectiveness of the program in improving schooling attainment, Duflo also examines how the earnings of the relevant cohorts were affected. By comparing the earnings gain with the (very modest) schooling gain induced by the program, Duflo is able to quantify the marginal effect of schooling on earnings. She includes in her estimates those workers who were self-employed, so as to avoid the selectivity bias associated with excluding such workers (the majority). Interestingly, Duflo’sCross-country variations in the base wage have been shown to strongly affect the selectivity of the international migration of skilled workers and of students seeking a university education outside of their country of birth (Jasso and Rosenzweig, 2009; Rosenzweig, 2007, 2008).

The coefficients on the schooling quality variables may reflect more than school quality effects on earnings, as such variables may be correlated with imperfections in the capital market. calculated return to schooling for the most inclusive sample ranges from 7.45 to 3.6 percent, while the Psacharopoulos (1994) reported estimate of the Mincer βj for Indonesia is 9.4 percent. Foster and Rosenzweig (1996) used data on agricultural profits to estimate the contribution of schooling to profits. They went beyond measurement of schooling returns,however, to test an implication of the Nelson-Phelps hypothesis that the returns to schooling rise when there are opportunities to adopt new technologies by examining data characterizing the early stages of the Indian “green revolution,” when new high productivity seeds were first made available to farmers.
Foster and Rosenzweig employed a structural approach, estimating how the parameters of the conditional agricultural profit function for Indian farmers changed as a result of the introduction of new seed varieties. Using panel data at the household level on profits, input prices, capital assets and schooling for Indian farmers, they simultaneously estimated the amounts of agricultural technical change across Indian districts due to spatial variation in agroclimatic conditions, the contribution of primary schooling (additional schooling beyond primary was not important) to profits (i) before the new technologies were introduced and (ii) as a function of the subsequent rise in technologically-induced farm productivity. Their estimates, which allowed for the endogenous accumulation of assets and schooling, suggested that prior to the green revolution, farmers with a primary education exhibited about 10 percent higher profits than farmers without schooling, conditional on assets. However, in states with high technical change at the end of the eleven-year period of their panel, profits for the same farmers with primary schooling were 40 percent higher than those for illiterate farmers, while the profit differential by schooling remained the same in areas with little or new suitability to the new seeds.

What about payoffs to schooling outside of the labor market? In many low-income countries, women do not participate significantly in the paid labor market, yet women’s schooling attainment can be higher than that of men. One common hypothesis is that the schooling of women is complementary to the production of human capital of children; indeed, almost all data sets show a positive correlation between maternal schooling and child schooling.

There are many alternative interpretations of this relationship; for example, the intergenerational correlation in schooling might reflect a genetic link in ability, or women who are more educated may have more bargaining power in the household and tend to prefer to allocate resources to children. Behrman, Foster, Rosenzweig and Vashishtha (1999) took up the challenge of identifying the effects of a mother’s schooling on the efficiency of children’s human capital accumulation.

Their structural and reduced-form estimates of the determinants of profit trajectories and adoption behavior, which employ the same game-theoretic equilibria as in the Besley and Case non-cooperative model, show not only clear patterns of learning exhibited in the conditional (on adoption) profit functions but also of free riding— specifically, farmers near other farmers with large landholdings (who are therefore more likely to Conley and Udry (forthcoming), using panel data on fertilizer use by Ghanaian farmers, make inferences about social learning not by how the adoption of a farmer is related in any particular way to the adoption of another farmer, but by how the adoption of one farmer depends on the new information provided by neighbors on profitability.

However, their study contains no estimates of how profits or fertilizer returns varied by farmer schooling. adopt), for given own landholdings, are less likely to initially adopt. Simulations of the model also indicated that farmers with more-educated neighbors also postponed adoption initially but their learning was faster overall. This evidence thus provides direct support for the role of positive schooling externalities via social learning mechanisms.

Bandiera and Rasul (2006) employ the same target input model to look at the adoption of new sunflower seeds in Mozambique. They also find evidence consistent with learning based on the fact that when a large number of adopters exist in a network, own adoption is diminished (free riding), a finding inconsistent with mimicking or social pressures. They also find that literate farmers are more likely to adopt first. Munshi (2004), using the same data as Foster and Rosenzweig (2006), exploited another feature of the Indian green revolution to demonstrate the importance of learning in adoption.

The fact-based premise of his study is that compared to wheat seeds, rice seeds were much more sensitive to agro-climatic conditions and were also more suitable in those areas where such conditions were more variable. He shows theoretically that, because information about own productivity from neighbors’ adoption is less informative for rice growers than for wheat growers, the adoption behavior of the former should be substantially less related to neighbor’s adoption behavior. His empirical results are consistent with this hypothesis.

As noted, in all three of the settings in these four studies in which learning is evidently important, more educated farmers adopted the new-technology crops at greater rates, at least initially. This is consistent with two hypotheses: that such farmers initially have more information about a new technology (perhaps because they read more) or such farmers learn more from the same experience. Foster and Rosenzweig (1996) and Rosenzweig (1995) estimate directly how the returns to new seed adoption are affected by schooling over time, and find that new seed profitability is higher for primary-educated farmers. More interestingly, more-educated farmers in the first period when there was no prior experience earned no higher profits than others; the schooling advantage was only observed after some use, and then diminished with experience, consistent with enhanced learning.

One issue in inferring the contribution of schooling to the profitability of new seeds is that the choice of new seed use may reflect factors unknown to the econometrician that are correlated with anticipated profitability. While the studies employ models that explicitly depict purposive and forward-looking adoption behavior, and use instrumental variables and other identification strategies based on the models to take into account this issue, an experiment in which farmers are randomly allocated (perhaps by varying the price) new technology seeds may yield more conclusive evidence on the role of schooling in enhancing the profitability of adoption. Duflo et al. (2008) carried out a field experiment in Kenya randomly subsidizing a prespecified dosage of fertilizer, and then estimated returns from fertilizer variation. They found that neither more educated farmers nor farmers with previous experience with fertilizer obtained higher profits from increased fertilizer use compared with their less educated or experienced

This null result for schooling returns in the Kenyan experiment, combined with the findings from the other adoption studies from agriculture that examine education effects, together provide a consistent explanation for where and when schooling may have high productivity— at least in the farming sector. At least one of two conditions must be met: (i) A novel technology with potentially high payoffs about which there is some uncertainty that can only be resolved by use; and/or (ii) scope for costly misuse of the technology.


In the case of the new Indian cotton, wheat and rice seed varieties and for Mozambique sunflower seeds, this criterion is met, and schooling has positive effects on both the early adoption and profitability of the new seeds. For the Kenya case, as pointed out by Suri (2007), the seeds being used were not new, so there was little new to learn with respect to adoption. Moreover, the principal input that affects crop yields for new seed varieties is fertilizer; getting fertilizer amounts correct is the main challenge with most high yield seeds. In the Kenya experiment, fertilizer dosage was under experimental, not farmer, control and thus the additional scope for allocating inputs to achieve maximum profits, and thus for schooling to be productive, was limited by design and context.

Of course, not all learning effects take place within the agricultural sector. Consistent with both the experimental and non-experimental findings within agriculture, a recent field experiment (Dupas, 2009) that randomly assigned different prices for new, improved bed nets obtained findings showing the combined presence of social learning and schooling effects on adoption. In particular, take-up rates were greater among the more educated and respondents
who had neighbors receiving the lowest prices were more likely to adopt and keep the new nets,given the prices they faced.

Schooling Investment Responses to Changes in the Returns to Schooling A key building block of macro-growth models that seek to explain the transition to sustained growth is that schooling investment rises in response to increases in its return. This is also a key assumption of the Mincer model. However, a major hypothesis in development
economics is that due to absent or imperfect credit markets, many households in low-income countries will be unable to augment human capital investment even in a setting where such returns are high. Morever, pre-school human capital, a complement to school-produced human capital, may be low in such households so that their return to schooling investment will also be relatively low. Finally, households may not be aware of the payoffs to schooling, particularly if they occur outside of their immediate environment, especially if mobility costs are high.

Micro-development research has shed light on the responsive of schooling to changing returns. In general, it is difficult to change the return to schooling experimentally. Two non experimental studies examine periods in which exogenous changes in the returns to schooling occurred, brought about by either technical change or by changes in policies that induced relative shifts in occupational demand. A third study, however, carries out a field experiment that induces a change in perceptions of schooling returns among students. The picture that emerges from all device (menstrual cup) among groups of school age girls, is notable in showing that those girls with more adopters among their friends were more likely not only to adopt the new device but also to use it more effectively. Their results thus more directly demonstrate that the observed associations between own and neighbor usage reflect at least in part learning rather than only non-cognitive peer effects.

Three studies is that household schooling investment seems to be responsive to local changes in the returns to schooling, but barriers to mobility associated with informal institutions and perhaps credit or income constraints may impede the full realization of the gains for some households. Foster and Rosenzweig (1996) used their district-specific structural estimates of agricultural technical change associated with the Indian green revolution to assess if school enrollments were responsive to local technology advances, which they showed had raised schooling returns.

Using data on enrollment rates of children 10-14 in a panel of households in 1971 and 1982, they regressed the change in enrollment rates over the period on the estimated district-level changes in technology, again exploiting the fact that the advances of the green revolution were spread unevenly over India because of the differing agro-climatic conditions Note that this estimation approach assumes that mobility across districts is low, as is consistent with migration data in India. The results indicated that cultivating households did significantly increase school enrollment rates in high technical change areas, net of school presence and wealth effects. However, landless households did not.

The differential response to technical change from cultivator and non-cultivators in schooling investment is consistent with the learning-based idea that decision-makers – farm owners in this context – benefit from augmented schooling in a setting of high technical change.

Manual workers do not face the task of applying the new technologies and thus reap no benefit from additional schooling. In this setting, where children from landless households are unlikely to become cultivators, the relevant returns to schooling for non-cultivators are thus unaffected by agricultural technical progress. Occupational immobility could then explain this result.

Agricultural technical change did increase the wages of the landless, and thus the incomes in landless households, so that there was an increase in school attendance rates for both landless and cultivating households over the period. However, rural landless households remained poorer than landed households, and so the inability to finance human capital investment could still be part of the explanation for the differential schooling response.

Munshi and Rosenzweig (2006) studied the schooling investment responses to the substantial changes in the returns to English schooling that occurred in Mumbai in the last two decades of the twentieth century. Their study helps to distinguish between mobility barriers and family resources as constraints on schooling investments when the returns to those investments increase. In the period of their study (1982-2002) there was a substantial shift in demand toward white-collar occupations and industries brought about by policy reforms opening India’s economy to trade and international finance.

Post-reform earnings of those workers who had attended an English medium school rose substantially compared with those who had gone to local-language schools: specifically, for given years of schooling, among men, the earnings differential between English-medium and local-language school alumni rose from 17 to 27 percent at the end of the period; for women, the rise was from 3 to 27 percent.8 Munshi and Rosenzweig (2006) then looked at the enrollments rates over the 20-year period in the two types of schools stratified by low-, medium-, and high-caste families, which The earnings differential by years of schooling remained constant over the entire period, at about 10 percent per year of schooling for both men and women.

closely correspond to the rankings of parental incomes and schooling attainment. In the decade prior to the reforms, upper-caste children predominantly attended the private, and more expensive, English medium schools, with the rest concentrated in public local-language schools.

The high- versus lower-caste enrollment gap was 35 percentage points for boys and 25 percentage points for girls. However, in the second decade, corresponding to the period in which returns to English rose, there was a massive shift of all groups to English medium schools, and rates of enrollment for girls across the caste groups converged to less than a 10 percentage point differential between high-caste girls and the lower-castes across the two types of schools. That is, the response to the new returns to schooling type was even greater among the poorer, less educated households. The findings for girls seem to suggest that credit and human capital constraints were not insurmountable barriers to schooling investments when payoffs to schooling increase.

However, enrollment rates by school type across caste groups did not converge for boys,and at the end of the second decade there was still a 20-25 percentage point gap in rates of enrollment in the two types of schools. Because boys and girls come from the same households, the lack of convergence for boys cannot be explained by credit constraints or household human capital. Munshi and Rosenzweig’s (2006) examine the hypothesis that network externalities associated with sub-caste networks that dominated the blue-collar jobs held by low- and medium-caste men (but not women) make it optimal under some conditions to restrict occupational mobility, at least for lower levels of return differentials across white- and bluecollar occupations.

In both the rural and urban Indian studies, parents were evidently aware of the changes in the returns to schooling.. Jensen (forthcoming) carried out a survey to assess how well students in the Dominican Republic were aware of the level of income differentials by schooling and an experiment to assess the responsiveness of schooling choices to changes in perceptions about the returns. Jensen first obtained econometric estimates of earnings differentials for primary and secondary school graduates by estimating the Mincer wage specification to data from a survey of prime-age workers (excluding the approximately one-third of workers residing in rural areas to minimize the problem of accounting for the sources of earnings among the self-employed).

He then carried out a survey of students in urban primary schools of students’ own estimates of earnings by schooling level. The students’ perceptions of schooling returns were generally lower than those implied by the Mincer regressions.

It is not clear whether the discrepancy between perceptions and the estimates can be generalized to conclude that underestimation of returns is a factor contributing to low schooling investment in low-income countries, as it is not clear whose estimates are more accurate and relevant. The regression-based estimates are subject to the usual ability-bias issues, and in addition, the regression sample includes selective rural-to-urban migrants (not representative of the urban-born children) but excludes a not insignificant number of urban-born citizens who outmigrated from the Dominican Republic, and tend to be better-educated than the population as a whole.9 The important contribution of this study, however, does not rest on the correctness of the schooling return estimates, but rather on the findings from the experiment carried out. In 2000, over 20 percent of secondary school graduates born in the Dominican Republic resided in OECD countries (Docquier and Marfouk, 2005).

Jensen (forthcoming) selected a random subset of the students to be provided the results from the econometric-based estimates. Since in most cases these were higher than those believed initially by the students, the question was whether this new information would affect schooling decisions. Jensen found that the new information, whatever its veracity, did increase perceived returns to schooling in the treatment group and, for students from families of above-median incomes, actual schooling attainment did significantly increase in the treatment relative to the control group. However, students from the poorer households were not responsive to the change in perceived returns, consistent with the hypothesis that credit-constraints or pre-school human capital deficits are barriers to schooling investments in some, but not all, households in lowincome countries.

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