modifications to all of the current components (Yaschine, forthcoming). This dynamic, typical of long-term interventions, hinders our ability to estimate the effect from PROSPERA’s intervention with the required precision.

Even if only the more stable components in the history of the program (education, health and nutrition) are considered, it is not easy to estimate the effects of PROSPERA on occupational mobility and occupational attainment of the youths for various reasons. First, in the educational component, even if we focus attention exclusively on the accumulated amount of scholarships granted as a proxy for treatment intensity, in this specific case, self-selection would be possible, in so much that the transferred amounts reflect individual behavior. Ultimately, households with youths who are better adapted to school life, possess greater abilities or higher levels development, receive greater transfers due to showing lower rate of lagging behind in school or dropout.<sup>34</sup> Second, the scholarships, although “labeled” as such, pass entirely straight into the transfer bag of the household as a whole, which translates into different levels of economic advantages depending on the size and composition of the household. Thus, in some respects, the whole household benefits from the educational component of the program’s transfers, and the degree of economic advantage provided by the component depends on its structure. Third, and closely related to the previous issue, since the program’s transfers are consumed by the household, and given the structure of households, it is practically impossible to separate the effects of the different components of the transfers.

What is clear is that the manner in which the program intervenes in the lives of the youths depends on the number of individuals that make up the household and their respective ages, as well as the length of time they remain beneficiaries of the program.

To overcome the difficulties associated with the evolution of the program’s intervention and the selectivity of its support package due to conditionalities of that package, instead of using a reasonably stable component or the volume of the transfers as a proxy for the intervention, in this study we propose to statistically evaluate the intensity of exposure of the households to the program, exploiting the longitudinal design of ENCEL. Specifically, our approach assumes, based on the operational rules of the program and a review of its administrative records, that the demographic trajectory of the household, and the temporal distribution of the transfers, are good indicators of the intensity of the program’s treatment to which the households have been exposed.

In order to exploit this approach, in practice, we used Structural Equation Models (SEM),<sup>35</sup> to estimate the treatment variable as a latent class variable (unobserved), indicated (measured) by

> 34 This issue has been discussed for the case of Oportunidades by Attanasio, Meghir and Schady (2010). As an outlet for this endogeneity of transfers, Kugler and Rojas (2018) choose to use the number of years that each individual was eligible to receive the scholarships granted by PROSPERA.

> 35 For such purpose, we propose an approach that uses a latent class analysis (LCA, a specific case of SEM) (Goodman, 1974) for the estimation of the counterfactual distributions. The latent classes approach for causal analysis in experiments with random assignment was used by Frangakis and Rubin (2002) to control selectivity caused by noncompliance after assignment.

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