wage gap between Venezuelans and Brazilians and how much of it can it be explained by individual, firm and municipal level characteristics?

To answer the first question, the following firm selection model is estimated as a linear probability model and as a Probit model.<sup>24</sup> where _Pf,ijz_ is a dummy taking the value 1 if the firm _i_ in municipality _j_ and province _z_ has at least one Venezuelan employed and zero otherwise. _Firmijz_ is vector including dummies for the size and industry (agriculture, manufacturing and service) of the firm, gender ratio, average age, proportion of workers who are white, proportions of workers with high school education, proportion of workers with college degrees, proportion of workers in the firms working with temporary contracts and the proportion of Brazilian workers who work in the firm in an occupation that requires education below their highest education. _Xjz_ is log of natural number of the Venezuelan refugees and migrants living in municipality _j_ and province _z_ and _yz_ is the province fixed e↵ects.

To answer the second question on how firm characteristics a↵ect integration at the firm level, the high number of firms (80%) with no Venezuelan employees may lead to selection bias as a result, we estimate the model using both ordinary least squares regression and the two-stage Heckman selection model (Heckman, 1979). This paper estimates the two-stage Heckman selection model with and without using any exclusion restriction. Without restriction, the model is identification by the non-linearity of the functional form. It then uses proportion of Brazilian who work in the firm in a position that requires education level below the individual’s education level as the exclusion restriction and re-estimate the Heckman selection model using the exclusion restriction. This paper argues that if Venezuelans see that many Brazilians are downgraded in the firm, they might not be too willing to work there but once Venezuelans are present in the firm, they care about the relative Venezuelans’ performance and not directly the Brazilians’.<sup>25</sup> The model below shows the second stage estimated in the Heckman model and for the OLS model, we include the same variables except the _λ_ s (Inverse Mills ratio).

where _Ff,ijz_ is the F-index of integration at the firm and all the other covariates are as described before except _λijz_ , which is the inverse mills ratio, which is estimated using the first stage selection model of the Heckman model and _Firmijz_ includes a dummy which is 1 if the Venezuelans in the firm are more likely to be downgraded than Brazilians .

To answer the third question, a Mincer wage regression is estimated using both ordinary least square estimation and a two-stage Heckman selection model. The selection problem in the Mincer regression arises from the fact that we only observe the wages of workers, who are in wage employment as of December 2019, consequently in the RAIS dataset, this paper only observes wages of about 80% of the workers in the formal sector. In line with previous estimation strategies, this paper estimates the Heckman model with and without using exclusion restrictions. The exclusion restriction that

> 24The Probit model is used as a robustness check.

> 25The paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian downgraded a↵ecting the selection model but not the extent of integration.

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