Using the original PROSPERA experimental design, an impact of 1.5 years in education for the inhabitants of the treatment villages was found and this result is statistically significant at the 10% level, consistent with the literature. When the sample is separated by age groups, this result loses statistical power, but is positive in all cases. Results for BMI and cognitive and memory scores are not robust to the different specifications used in this exercise and, in general, are not statically significant.

As a whole, these results indicate that PROSPERA improved the accumulation of human capital among the rural population that received the program.

Table 9. Estimation of the effect of PROSPERA considering the original evaluation design (1997-2000)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|Mobility height|Mobility<br>education|BMI|Cognitive<br>score|<br>Memory<br>score|
|Treatment vs Control|1.452*|1.570*|0.481|-0.898*|-0.340*|
||(0.771)|(0.862)|(0.612)|(0.501)|(0.176)|
|Observations|1,337|2,850|2,741|2,756|2,852|
|Treatment vs Control,|-0.182|0.359|1.318**|-0.074|0.052|
|group 0|(0.851)|(0.696)|(0.599)|(0.333)|(0.226)|
|Observations|116|238|228|224|239|
|Treatment vs Control,|2.938**|0.683|-0.866|-0.447|0.025|
|group 1, 2 and 3|(1.387)|(1.029)|(1.152)|(0.410)|(0.217)|
|Observations|1,205|2,580|2,482|2,500|2,581|

Notes: The estimations are contrasts between the matching differences of the indicator between the group of exposure and the comparison group. Variables for matching: in the provider’s height, eligibility score of PROSPERA in 1997 (linear and square), classification as poor household in 1997, gender, provider’s gender, provider’s age (linear and square), provider’s education, in 1997, two-months period when the transfers of PROSPERA were first received in the household of origin, number of persons in the household in 1997, and labor income of the household in 1997. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations by propensity score matching based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

_C. Returns to human capital: Intergenerational mobility and what is the effect of improvements in health and education on labor income?_ The translation of the estimated intergenerational mobility in height and schooling into social mobility is approximated by the returns in height and schooling on individuals’ labor income. Table 10 shows the returns model with estimations separated by gender based on the Heckman model. Columns 1 and 3 show that an increase of 1% in height is associated with higher labor income: 10.73% for males and 8.76% for females. We also find that a one-year increase in schooling is associated with 3.4% more labor income for males and 4.8% more labor income for females. This analysis does not consider potential non-linear returns to schooling.

In comparison to the literature on height and income, the height coefficient we estimate is higher than expected for the complete national income distribution in Mexico (see, for example, Vogl 2014). Our estimate is similar, however, to the estimates for the population in the lower part of the distribution, with levels of 8% and 11%, in studies like those from Lundborg _et al._ 2009, Case and Paxon 2008 and Persico _et al_ . 2004. In addition to finding coefficients of similar magnitude to ours for the lower part of the income distribution, these authors find that the effect 31