# **VI.C DiD Estimates of Program Roll-out**

Our second identification strategy, based on the sequential roll-out of the policy, provides additional evidence and on larger differences in exposure to the program than the experimental variation discussed so far. At the same time, the data employed in this part contain direct measures of food security and vulnerability which add another layer to our welfare analysis. Further to that, we can also test for the effect of PROSPERA on later dependency on governmental programs, this would be a test of a successful policy able to graduate its beneficiaries out of social programs. Our approach here is to produce DiD estimates similarly to Parker and Vogl (2018), but with INEGI’s 2015 intercensus data.<sup>19</sup> _Figure 2_ contains the main estimates of this strategy. Each sub-graph shows the _τ_ estimates from equation (5), which correspond to difference-in-difference estimates. As one moves to the right through the X-axis you increase the exposure to PROSPERA (calculated by comparing 1998 to different delays to enroll) for a given cohort with respect to the cohort that should not be benefited by the program (17-22 year old individuals in 2001).

The results show positive effects on education, particularly for the cohort with the youngest members, which increases their schooling attainment between 0.5 and 1.5 additional years. For the other cohorts, the program involves an increase around 0.5 years of schooling. Regarding income, positive and significant effects are found: household per capita income increases between 2% and 8%, with largest effects for the cohort of those aged 6-10 in 2001. Positive effects are also found for a composite index of house characteristics and durable assets. When analyzing asset by asset, the largest effects are found for type of floor, washer, and cellphone. The largest effects are again found for the youngest cohort. To test for dependency on government aid, or graduation from social programs, we use, as dependent variable, a dummy equal to 1 if the household receives transfers from the government. In this case, overall the evidence suggests that longer exposure to PROSPERA decreases the dependency on government aid. Unfortunately, this measure does not distinguish PROSPERA from other type of government aid. Finally, we look at proxies for vulnerability and food security: two different measures directly addressing food consumption. One question asks whether any adult, in the household, went hungry at least once during the last month. The second question asks whether not enough money was available to purchase enough food variety. We take these two questions as proxies for food security, vulnerability, and quality of the diet. For the younger cohorts we have positive effects on this measure of food security, as those exposed longer are less likely to go hungry (although the estimates are not precise). As for food variety, larger effects are found for the youngest cohort. For them, the likelihood of not being able to buy enough variety decreases between 1 and 2 percentage points for those with longer exposure.<sup>20</sup> These latter two effects on

> 19As explained in section V.B, these estimates are part of our current research where we seek to use an RDD design with the original rule that established that participating localities would be enrolled to the program based on a marginality index.

> 20The food scarcity variables are separately asked for children and adults in the household. Similar estimates

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