geographical coverage, individuals were not found in the address given to surveyors or they did not respond. In addition, a sub-sample of 1,994 individuals outside the relevant age groups was mistakenly surveyed.<sup>10</sup> In our analysis we use all the individuals sampled.

Table 3 gives further detail about the different possibilities of sample attrition. In this table we use a logit model to relate household baseline characteristics with the probability of attrition. Even columns add the interaction of the baseline characteristics with the treatment dummy to further explore the possibility of selective attrition. In columns (1) to (4) we present evidence about the attrition of the original households, which are the foundation of the sampling design. Columns (1) and (2) give evidence of the locality selection (similarly as in Table 1 Panel A) while columns (3) and (4) give evidence of the possibility to find respondents in the selected 334 localities. As these columns show, older head members of smaller households are more likely to be missing. This is consistent with the evidence presented in Table 1, where we found no evidence of selective attrition. Finally, columns (5) and (6) include newly formed household attrition. Given that these households did not exist in 1997, its attrition is based on the possibility to find members in the relevant ages that moved out of the original household. In this sense, the attrition results from the 4,207 members who were searched for and not found. Section VI.A.3 does a robustness exercise where inverse probability weights are employed to adjust for attrition.

**Available information.** Both for _original_ and _new_ households, dwelling’s characteristics and information on durable assets were gathered at the household level, we use such information to explore PROSPERA’s long-term impacts on welfare. Individuals in the critical age groups described were asked several additional questions on income and work-related topics. An important limitation of the ENCEL 2017 data is that non-durable consumption (crucially including food consumption) is not available. This somewhat constrains our welfare analysis, especially for a poor population context, where food expenditure represents a high proportion of total expenditures. However, as an additional exercise, we propose an imputed measure on non-durable consumption expenditure, which includes food, personal products, and clothing, adapting the methodology of Blundell et al. (2005) and Attanasio and Pistaferri (2016) to the available data and context (see Appendix Section C). It is important to remind the reader that while durable consumption items are typically bigger ticket items, their purchase is infrequent, and for poor households the total expenditure on durables is typically about 20% of total expenditure, so that providing evidence on non-durable expenditure seems rather important.

_Table 4_ gives descriptive statistics for the original and new households employed in our analysis using 1997 and 2017 data. For new households, the 1997 information corresponds to that of their respective household of origin. The comparison between 1997 and 2017 shows an

> 10These individuals added _by mistake_ are not a random sub-sample of all the individuals out of the group of interest, but correspond to those for whom age was not available in 1997-2000 and were mistaken for newborns.

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