RD estimator, the population affected is located just in the neighborhood of the discontinuity. However, since we observe many different discontinuities across municipalities, this problem is much less serious. The group of families we are probably missing are those located at the very bottom of the income distribution, who are never in the neighborhood of any threshold, even in municipalities where this threshold is relatively low. On the other end, there are municipalities where the observed discontinuity is large, and others where it is not. Only the first ones contribute information to our estimates. We show that there are not large differences between the two types of municipalities except in one dimension: municipalities with large discontinuities are more likely to be urban. Therefore, our estimated impacts are more likely to be relevant for the urban than for the rural poor.

Finally, previous work has overlooked unique aspects of the program, in particular, the effectiveness of the program with respect to the level of social exclusion of families relative to the social services. We also show that the demand side component of CS is only productive when the corresponding supply of social services is enhanced and reorganized.

That said, it is useful to briefly review the existing literature on this topic.<sup>4</sup> Galasso (2006) conducts the a short term evaluation of this program focusing on the first two years of program rollout. She uses a household survey that was specifically collected for the evaluation of this program, and two empirical methods, namely matching on the propensity score, and a regression discontinuity procedure analogous to the one used in our paper. She finds that the program induced significant impacts on the education and health of households, and the take-up of social benefits. This last result is consistent with the findings in this paper, whereas the administrative data we use do not allow us to analyze either health or education outcomes in detail.<sup>5</sup> Our ability to use administrative records for the whole population of welfare recipients in Chile is a substantial improvement over the data used in her paper if the goal is to rely on an RD type estimator. The cost of using this data is that the set of outcomes that we can possibly observe is much smaller. Larra ̃naga, Contreras and Ruiz Tagle (2009) and Hoces, Hojman and Larra ̃naga (2011) were developed contemporaneously with our paper. They use the same administrative dataset as us, although in the latter paper they also complement it with the household survey originally used in Galasso (2006). They rely on a different method, differences-in-differences and matching, and focus only the two first cohorts of

> 4Colombia introduced in 2007 a program similar to CS ( _Juntos_ ), but which unfortunately suffered from a number of implementation problems, limiting the comparison we can make with our study (see Econometria, 2011). This is especially unfortunate because, unlike CS (for which we must rely on quasi-experimental of evaluation), the evaluation of _Juntos_ had originally an experimental design. However, during the evaluation period there was incomplete treatment with most families receiving at most 6 home visits (instead of the 20 originally expected), so only a very weak version of the program could be studied. In addition, social workers had a caseload that was much heavier than that of CS. Finally, although households were encouraged to take-up social services, in many cases such services were not available to them.

> 5The survey used in Galasso (2006) was designed for the use of a matching estimator, and it is neither particularly suitable nor large enough for a credible implementation of her RD estimator.

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