**Subsidies** The first dimension of heterogeneity looks at whether the conditions were met at the onset of the program, before 2002. In particular, we examine the impacts of CS on SUF separately for families who took-up SUF in either 2000 or 2001 and for those who did not. The reason for doing this is that the take-up of SUF before the existence of CS shows that family members have knowledge of the existence of the subsidy and its availability to the family. Furthermore, they are able to access it. It is possible that CS does not substantially impact SUF take-up for these families, while at the same time it has a larger impact for those families for whom we have no past record of SUF take-up. The latter are more likely to be in a situation of isolation and exclusion from the welfare system.

Table 5 examines the impact of CS on the take-up of SUF for the two groups of families described above and it presents estimates from equations (4) and (5). The table includes two panels each with two sets of rows with the estimates for the short and medium run impacts: panel A presents estimates for those not receiving SUF before 2002, and panel B includes estimates for families who received it before 2002. Columns (1) and (2) show that CS has very high and sustained impacts on the take-up of SUF for those without prior take-up of this subsidy, but a much smaller impact on families who have taken up this subsidy before. The difference between the two groups is large and statistically significant. The p-value for the null hypothesis that the effect for those without SUF prior 2002 is equal to the effect for those that did not receive SUF prior to 2002 is 0.16 and 0 for 2 and 4 years of exposure, respectively. The take-up of SUF increases for the former group of families by 18 to 22% 2 and 4 years after program enrolment, respectively. These coefficients are statistically different from zero even after accounting for multiple hypothesis testing.<sup>41</sup> We also examine whether there are any differences in program impacts between cohorts entering the program before and after 2004. In order to study this issue, we split the sample by cohorts that were differentially exposed to the increase in the supply side: the 2002-2004 cohorts and the 2005-2006 cohorts.

the local of residence. For example, families would incur in a higher transportation cost to access services. Thus, when we allow the effects to vary by urban and rural areas, we cannot reject the null of similar effects across areas in the take-up of subsidies (SUF), employment programs and labor market outcomes. We also allow the effect to vary by whether families live in municipalities where social workers are allocated more or less families (in particular, municipalities where on average social workers are allocated more or less than 50 families). Such sample split can be problematic, since municipalities that allocated more families to each social worker might also be those municipalities with more experienced and able social workers. Also, higher load of social worker correlate strongly with geographic remoteness, so a disaggregation by caseload is similar to a disaggregation by urban and rural areas. When we try such sample split we fail to detect any correlation between the effects we find and the caseload of social workers.

> 41There are virtually no impacts of CS on the take-up of SAP, regardless of whether families have taken up this subsidy before or not (see table A.7). We find a negative effect of CS on participation in SAP for those families that received it before 2002. This is a crowd out effect to which we cannot attribute a conclusive cause, but a strong possibility is measurement error in the determination of which families are eligible to SAP in the administrative records.

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