Table 2: Family characteristics

||All families|
|---|---|
|Number of Families in BF|6,582,657|
|Number of Families that left during the observation period|4,602,881|
|Number of families that came back to the program|630,549|
|Number of families that moved to diferent municipalities|457,079|
|Number of families that moved to a diferent municipality prior to exit|80,629|
|Average number of years in the program|3.8|
|% of families having one member in formal dependent employment at entry|28.02|
|% of families having one member in formal dependent employment at exit|35.48|
|% of families having head in formal dependent employment at entry|15.12|
|% of families having head in formal dependent employment at exit|21.98|

**Notes:** The table presents summary statistics for families registered in CadUnico in December 2012 for the first time and being in BF at the beginning of the first semester of 2013. Information on RAIS are obtained through a merge of CadUnico data, BF payroll information and administrative matched employer-employee data (RAIS).

characteristics of adults, as reported at the time of registration, and their probabilities to exit BF. This serves as useful descriptive evidence in support of the identification strategy for the effect of local employment changes on the probability of exit, which will be described in the next section. Following prior literature and Morgandi _et al._ (2023), we employ the Kaplan–Meier (KM) estimator and Cox regressions.<sup>15</sup> Table 3 shows that exit probabilities are correlated with some of the beneficiaries’ observable characteristics at entry.<sup>16</sup> The speed and probability of exit from the program increase with age and education levels of the adults, particularly those with tertiary diplomas. Exit is also higher in urban areas. Families with 1 or 2 children have a higher probability of leaving the program while families having more than 3 children are less likely to leave compared to families without children.

# **4 Empirical Strategy**

## **4.1 The Basic Model**

To identify the effect of local employment growth on our two outcomes of interest – the probability of having at least one family member in formal dependent employment and on families’

> 15The KM estimator is a non-parametric statistic, which is used to estimate the survival function (in our case time in BF). The estimator can be written as _S_<sup>�</sup> ( _t_ ) = _i_ :� _ti≤t_ �1 _− n_<sup>_di_</sup> _i_ �, where _ti_ represents a time when at least one event (exit) happened, _di_ the number of events (exits), which happened at time _ti_ , and _ni_ individuals known to have survived (have stayed in the program) up to time _ti_ . Cox regression is a method of investigating the effect of one or more variables upon an event takes to happen and can be written as _λ_ 0( _t_ ) = _λ_ 0( _t_ ) _e_<sup>_Xβ_</sup> , where _λ_ 0( _t_ ) is a non-parametric baseline hazard function and _X_ are matrices for the independent variables.

> 16A positive coefficient is associated with an increased likelihood of leaving the program. A hazard ratio (shown in column (2)) greater than 1 corresponds to a shorter time in the BF program, whereas a hazard ratio smaller than 1 indicates being for a longer time in the BF program.

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