where _Yiτt_ represents our outcomes of interest: A dummy variable which takes the value of 1 (i) if family _i_ in municipality _τ_ has a family member in formal dependent employment,<sup>18</sup> and (ii) our main outcome of interest: if a family _i_ in municipality _τ_ left BF.<sup>19</sup> Given the nature of the dependent variable, we use a linear probability model.<sup>20</sup> Employment Growth _τt−_ 1 is defined as the change in the total number of formal private sector jobs in the municipality _τ_ and semester _t −_ 1.<sup>21,22,23</sup> _Xit_ is a vector of time-varying co-variates (age groups of the household head and the number of children living in a household).<sup>24</sup> _αi_ are family fixed effects that also capture in which trimester the family left, _pt_ are time-fixed effects. We use clustered standard errors at the municipality level since our treatment (employment growth) is happening at the municipality level.

# **4.2 Endogeneity Problem and Instrumental Variable Approach**

To answer our research question we need to address several methodological challenges and potential sources of endogeneity.

**Municipality-level population growth** The first challenge is the identification of exogenous positive changes in local employment opportunities. While we can observe the total formal job growth by municipality per year/semester/month, we cannot control precisely for municipalitylevel population growth since the last census in Brazil was in 2010 (as of 2019). Yet, the population effect appears to be negligible (IBGE’s annual estimates of population growth between 2011 and 2019 averaged 1% (IBGE, 2020)) compared to the role of local economic cycles. Formal employment has been closely following economic cycles, with a variation of about 12% between the lowest and highest levels in the same period. In fact, the Brazilian economy had more formal wage jobs in 2012 than in 2019 (MTE, 2019). In addition, large parts of the employed population in Brazil work informally (IBGE, 2019), so excess population growth, in the absence

> 18The dummy dependent variable _having a family member in formal dependent employment_ takes the value of 1 if a family member is in formal dependent employment and 0 if no family member is in formal dependent employment.

> 19The dummy dependent variable (family _i_ leaves BF) takes the value of 1 if the family leaves the program – based on the official payroll information of BF containing information on monthly benefit payouts of the benefit –, 0 if it stays, and is missing if the family is not in BF in the time-point.

> 20A conditional fixed-effect model could be used as well, but the linear probability model makes an IV approach and the interpretation of the results more straightforward.

> 21To obtain this information, we use RAIS data, which reports the number of job links, which slightly differs from the number of workers since a small share has more than one formal job.

> 22In the case that families moved between municipalities we consider the employment growth of the origin municipality as common in the migration literature (Jaeger _et al._ , 2018).

> 23In order to not have our results driven by extreme outliers, we winsorize employment growth at the 99.5 percentile. By doing so we change less than 1% of the municipalities’ employment growth.

> 24The CadUnico data provides only a few variables that vary over time.

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