person loses her job, the changes in her consumption may be proportionally little if compared to changes in her income. Finally, the presence of measurement error may be an issue for both income and expenditure explanatory variables (see Banks et al. [1997] and Attanasio et al. [2011]).

To deal with these potential endogeneity issues we follow Thomas [1997] and Thomas [1990]<sup>70</sup> who used non-labor household income per capita as an instrument for total income per capita as a robustness check. We use an even narrower definition of non-earned income which is transferred non-labor income. This variable is composed of by private and public pension, social transfers (except BFP), allowances and other transfers. In this case, we used a subsample of households which did not participate in BFP but received another type of monetary transfer over this period. We assume that transferred income is not correlated with taste shifters that may impact expenditure share, but is positive correlated to total income. In addition, it is a source of income that is less vulnerable to transitory shocks than wages, for example. Our decision of using a subsample of households who receive transfers may generate a selection issue due to the fact that pensions are more likely to be transferred for elderly persons. For this reason, we run a similar test using only allowances.<sup>71</sup> Figure 4 shows the graphical representation of the baseline results.<sup>72</sup> To illustrate the income effect due to the money transfer, we consider a typical household. The mean income per capita (before transfer) for households which are just below the eligibility threshold of the program (R$ 100 _<_ income p.c. _<_ R$ 120) and which participate in Bolsa Famila is at 109.8 R$ per capita.<sup>73</sup> The mean transfer for this income bracket constitutes R$ 19.72 per capita or 18 percent of the household income; it is hence a considerable increase in purchasing power for the households. The mean transfer moves the household from R$ 109.8 to R$ 129.5. When considering the below graphs, we can see how the household expenditure share would change for the typical household in the absence of other effects. The income before and after the transfer for the typical household corresponds to a move from log income 4.70 to 4.87 in the graph; the two levels of income are indicated by the two grey vertical lines.

From the graphs, we can see that the linear and quadratic models in income do not differ considerably around the threshold. For this reason, we confine our analysis to the linear model.<sup>74</sup>

> 70Thomas [1997] uses a previous version of the Brazilian POF dataset, called ENDEF, covering the period of 1975-76.

> 71The results are available upon request.

> 72The ECs, estimated based on log expenditure instrumented by log income, are available in section 8.7 of the appendix.

> 73We chose this income bracket because it is informative of income effects around the eligibility threshold. The results can be compared to the Local Average Treatment Effect which will be estimated using a Regression Discontinuity subsequently.

> 74This result seems in line with previous research. Attanasio et al. [2011] also found the linear specification to provide the best fit for the expenditure share on food which they analyzed in the context of a Colombian CCT

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