To estimate the association between school and municipal characteristics and extent of integration, the high number of schools (97%) with zero Venezuelan enrollment becomes a problem. Cameron and Trivedi (2010) points out that ordinary least squares regressions will not yield consistent estimates because the censored sample is not representative of the population and suggests the use of Heckman (1979) model as one of the ways to control for the bias. This paper first estimates the relationship between the above covariates and the F-Index in an ordinary least-square linear model (OLS) and then in a two-stage Heckman selection model ((Cameron and Trivedi, 2010) and (Heckman, 1979)). This paper estimates the two-stage Heckman selection model both with and without using any exclusion restriction. Without exclusion restriction, the model identification is based solely on the non-linearity of the functional form. This paper uses the proportion of Brazilians who attend classes lower than their age equivalents as the exclusion restriction and re-estimate the Heckman selection model using the exclusion restriction. An exclusion restriction is a variable or variables that explains variation in the selection variable but does not a↵ect the outcome variable directly. This paper argues that if Venezuelans see that many Brazilians are overaged in a school, they might not be too willing to enroll but once Venezuelans are present in the school, they care about the Venezuelans’ performance and not the Brazilians.<sup>22</sup> The model below shows the second stage regression estimated in the Heckman model and for the OLS model, we include the same variables except the _λ_ s (Inverse Mills ratio).

where _Fe,ijz_ is the F-index of integration and all the other covariates are as described before except that _Sijz_ now includes a dummy if the Venezuelan on average are more likely to be mismatched to grade than the Brazilians at the school and _λijz_ is the inverse mills ratio or Heckman correction term (Heckman, 1979), which controls for the fact that not all schools has Venezuelan students. The inverse mills ratio is estimated using the Probit model estimation of our selection model previously discussed.<sup>23</sup>

# **3.2.2 Formal Labor Market**

Like most other Latin American countries, Brazil’s labor market is segmented into informal and formal sectors, with about 40% of the country’s employed workforce working in the informal sector, that is in a job unregulated by the government. This paper concentrates only on the formal labor market as data on the informal sector is not available. Although in most countries, refugees’s access to the labor market is limited to the informal sector (Clemens et al., 2018), in Brazil, refugees also have access to the formal labor market, which in theory should allow Venezuelans to be more integrated and contribute positively to the economy. However, 12% of the total employment in Brazil is public sector employment (OECD, 2017), which bars foreigners from participating.

For the formal labor market, this study asks three questions: 1) What are the characteristics of the firms that hire Venezuelans? 2) How do firm characteristics and municipality level Venezuelan refugees and migrants a↵ect integration of Venezuelans in the formal labor market? 3) Is there a

> 22This paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian over age a↵ecting the selection model but not the extent of integration in the second stage.

> 23This paper shows the results of these models using 2020 education census in the main body. It also re-estimates the model using fixed e↵ect models and the 2019 education census. The results are given in the Appendix (7.2).

13