variables, height and schooling, for each one of the three exposure groups. In all cases the matching improves the balance.

In the results section we report average treatment effects (ATE), which reflect the average of the differences in outcomes between individuals in the treatment group, and those in the control group. Results are similar when estimated using kernel estimators (results not presented; available upon request).

In line with the current literature, the variance of the matching estimator was bias-corrected to render it consistent and asymptotically normal (Abadie and Imbens, 2009).

In addition to the general effects, variables were included to control for the effects of belonging to a population that reported speaking an indigenous language and for the migrant population, defined as individuals who in 2017 reside in villages different from their households of origin in 1997-2000.

Considering that the model seeks to identify the effect of PROSPERA on mobility and health indicators based on the differences in time of exposure to the program during critical periods of the development of human capital, additional estimations were performed to explore to what extent the results of the model actually identify differential exposure or, result from other factors (such as the age difference).

First, the same matching scheme was implemented using provider outcome variables. Here we use variables where there should not be an observed effect of PROSPERA, either because these characteristics of the providers are predetermined with respect to the program (maximum height, basic education, results in the cognitive and memory test), and/or are characteristics we do not expect to be affected by the program (such as whether the provider speaks an indigenous language). In addition, an analysis considering the design of the original 1997-2000 experiment was performed. For this analysis, individuals who came from treatment villages of the original experiment were classified as part of the intervention group, and individuals who came from the original control villages in the comparison group.

# _C. Estimation of the health returns_

To estimate the health returns we used a Mincer model (Mincer, 1974) with a Heckman selection correction (Heckman, 1979). This model identifies the returns to the human capital investment by measuring the increase in income from labor associated with higher levels of human capital, in this case, health and education: where W� is the income of individual _i_ , as a function of their health level (S�), education (E�) and other individual characteristics (X�). This general model has previously been used to estimate the returns to health and education, measured by height and years of schooling, respectively, and has generally shown positive returns (Croppenstedt and Muller, 2000; Dinda _et al_ ., 2006; Thomas and Strauss, 1997; Mayer-Foulkes, 2003).

The above model corrects for two difficulties presented by estimation with OLS: censorship of the labor income variable (due to non-randomized labor participation), and the potential endogeneity between income, on one side, and height and education on the other, due to omitted variable bias.

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