Average and Marginal Returns to Upper Secondary Schooling in Indonesia

Abstract

This paper estimates average and marginal returns to schooling in Indonesia using a semiparametric selection model. Identification of the model is given by geographic variation in access to upper secondary schools. We find that the return to upper secondary schooling varies widely across individuals: it can be as high as 50% per year of schooling for those very likely to enroll in upper secondary schooling, or as low as À10% for those very unlikely to do so. Average returns for the student at the margin are substantial, but they are also well below those for the average student attending upper secondary schooling. 1 We would have liked to use instead the distance between the community of residence in childhood and the nearest school in childhood. It is, in theory, possible to construct such a variable because there should be information about the opening date for all schools in the sampled communities. Unfortunately, many of these dates are missing, and using this information would result in a drop in our sample size of more than 50%, and in hopelessly imprecise estimates. Our assumption is that current residence and current school availability are good approximations to the variables we need (as in Card, 1995). We show below that this measure of distance to school is a good predictor of upper secondary school attendance. We discuss in detail how likely it is that our assumption holds. P. CARNEIRO, M. LOKSHIN AND N. UMAPATHI 18 Note: This table presents estimates of various returns to upper secondary school attendance for the semiparametric and normal selection models: average treatment on the treated (ATT), average treatment effect (ATE), treatment on the untreated (ATU), marginal policy relevant treatment effect (MPRTE) and policy-relevant treatment effect (PRTE) corresponding to a 10% reduction in distance to upper secondary school. Returns to upper school are annualized to show returns for each additional year. Bootstrapped highest posterior density 95% intervals are reported in parentheses. AVERAGE AND MARGINAL RETURNS TO SCHOOLING IN INDONESIA 29

Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Improved Cassava Varieties in Nigeria

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“…4 However, the productivity gain from adoption (h d and h) is more likely to be heterogeneous conditional on adopters' observed and unobserved characteristics. To capture this heterogeneity, we extend the above average treatment effect model and estimate marginal treatment effects (MTEs) (Heckman and Vytlacil, 2005;Carneiro et al, 2017). As explained above, let the observed productivity level (Y i ) of adopters and non-adopters be Y i and Y 0 , respectively.…”

The Impact of a Senior High School Tuition Relief Program on Poor Junior High School Students in Rural China

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Tracking and Specialization of High Schools: Does School Choice Matter?

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