on income is greater for the population with lower height, which is also the population living in greater conditions of poverty. Furthermore, the authors state that this coefficient captures the effect of the parent’s variables of origin and, specifically, Case and Paxon note that the magnitude is associated with a higher correlation with the increase in cognitive abilities accompanied by the additional growth in height, so that to the extent that these factors are controlled, the coefficient may be substantially reduced in magnitude.

Considering the gains in intergenerational mobility of height and schooling of PROSPERA that we reported on the previous section, a male individual with greater exposure to the program (Group 1) would receive a 9.4% higher average income due to the mobility in height and 12.0% increase in schooling in comparison with those who were not exposed to PROSPERA in critical periods. For females, the increases would be 17.1% and 17.8%, respectively.<sup>17</sup> Thus, the total returns on labor income are composed of both the accumulation of health and of educational capital.

This result translated into pesos implies a base hourly wage of 14.73 pesos for the males in the comparison group, which would increase to 18.84 pesos for the group with the highest exposure to PROSPERA. For women, the estimated base hourly wage is 12.68 in the comparison group, which would increase to 20.18 pesos in the group with the highest exposure to the program.<sup>18</sup> The association between BMI and income results negative and significant: each additional point of BMI is associated with a reduction in labor income of 2.1% for males and 3.1% for females. The sign of the coefficient is as expected because, as previously mentioned, overweight and obesity consume health capital which is in this case reflected in the expected returns.

Controlling for accumulated capital, measured by height and years of schooling, and for the context variables that determine labor participation, no statistically significant differences are observed in the expected income for indigenous persons.

Other results, consistent with the results of the mid-term evaluation, indicate that individuals who migrated from their villages of origin have on average higher incomes: 47.3% higher for males and 20.3% for females, in comparison to those who remained in their villages of origin. We have no data on the population that migrated to the United States or to parts of Mexico other than major cities. Thus, this estimate reflects only the migration to places captured in the ENCEL 2017 data. We thus expect this estimate to underestimate the effect of migrating on income, as those who migrate to the United States likely have the highest income and are excluded from this analysis.

Despite this, the estimator reflects that the return to accumulated capital in health and education can be enhanced in presumably more favorable contexts, that is, those with less _marginalization_ (poverty) and isolation than the villages of origin. This same logic is reflected in the estimations of the selection model (columns 2 and 4), where having migrated from one’s place of origin reflects a greater probability of entering the labor market, especially for females.

> 17 The mobility in additional height for male individuals of the group with greater exposure in critical periods is of 2.4 cm. In an equivalent manner, the mobility in additional years of schooling is 3.53 years. For females the calculation is equivalent.

> 18This considers the average income of the population and adds the average gains by height and schooling.  Pesos reported in the collection period of ENCEL 2017.

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