preserves sample size and improves statistical power when performing subgroup analysis, and it maintains comparability in the health and education indicators, as both then use the same sample.

Table 2 reports the estimations of mobility in terms of schooling. As expected, the level of persistence estimated here is lower than the level of persistence estimated for health (height), due to the latter including the genetic component. In line with this is our finding that the estimation for the sample where the providers are the father or mother is not different from when they are other types of providers.

In this case we find a small difference in coefficients on persistence when we correct for censorship (e.g. comparing OLS to Tobit models). Similar to the height model, the persistence coefficient is reduced from 0.34 to 0.29 when the village level covariates are included as controls, which suggests a geographical effect on the potential mobility, of the magnitude of 71% (Table 2, column 1, 3, Tobit estimations).

As presented in the program’s theory of change, we expect that the accumulation of capital in the health and education dimensions follows a pattern of joint determination. To model this correlation, Table 4 presents the estimation of intergenerational mobility simultaneously for height and schooling using rank-rank models. The results are largely consistent with the results reported in the individual position models (Table 3), with a slight increase in the mobility potential in health when the effect of educational mobility is jointly considered in its determination. Here, we estimate that the potential mobility in health is 68%, while in education it is estimated as 78%.

Table 2. Intergenerational mobility in years of schooling among the rural population of PROSPERA

||(1)||(2)||(3)||
|---|---|---|---|---|---|---|
|VARIABLES|OLS|Tobit|OLS|Tobit|OLS|Tobit|
|Provider’s years of|0.332***|0.343***|0.287***|0.297***|0.282***|0.292***|
|schooling|(0.024)|(0.025)|(0.023)|(0.024)|(0.023)|(0.023)|
|Migrant|||||0.666***|0.679***|
||||||(0.113)|(0.117)|
|Speaks indigenous language|||||0.332*<br>(0.177)|0.338*<br>(0.184)|
|Constant|10.108***|10.294***|9.547***|9.746***|9.403***|9.599***|
||(0.419)|(0.437)|(0.536)|(0.559)|(0.524)|(0.546)|
|Adjusted R square|0.15||0.186||0.194||
|Observations|4,425||4,423||4,423||

Notes: _(1)_ = Estimations controlling by the youth’s age and gender variables; provider’s age, gender and schooling. _(2)_ = additional to the variables of (1), entities of the sample of ENCEL, _marginalization_ (poverty) index in 1995 of the villages in the sample, and identification of the control or treatment village of the experimental evaluation of 1997-2000. _(3)_ = additional variables of (2), speaking an indigenous language and migrant. OLS= Ordinary least squares. Clustered Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

For both the health and education indicators, the rank-rank models provide larger estimations of mobility potential than the estimations that result from directly using the level of the indicator (58% and 71%, respectively). As indicated in the methods section, the estimation of mobility with the rank-rank models are more robust because they reduce biases stemming from extreme values.

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