Table 10: The effect on gross income in Roraima by education and industry

||||||**Col**<br>|**lege**<br>||||
|---|---|---|---|---|---|---|---|---|---|
||Agriculture|Industry|Construction|Trade|Transport|Hospitality|IT& Real Estate|Public|Education & Health|
|Conventional|0.943<sup>_∗_</sup>|-0.470<sup>+</sup>|0.461<sup>_∗_</sup>|-0.146<sup>_∗_</sup>|-0.0841|-0.0431|0.218<sup>_∗_</sup>|0.168<sup>_∗_</sup>|-0.117<sup>_∗_</sup>|
||(0.0852)|(0.259)|(4.11e-09)|(0.0176)|(0.101)|(0.0715)|(0.0106)|(0.0448)|(0.0397)|
|Bias-corrected|0.992<sup>_∗_</sup>|-0.302|0.594<sup>_∗_</sup>|-0.210<sup>_∗_</sup>|-0.231<sup>_∗_</sup>|-0.0928|0.289<sup>_∗_</sup>|0.244<sup>_∗_</sup>|-0.192<sup>_∗_</sup>|
||(0.0852)|(0.259)|(4.11e-09)|(0.0176)|(0.101)|(0.0715)|(0.0106)|(0.0448)|(0.0397)|
|Robust|0.992<sup>_∗_</sup>|-0.302|0.594<sup>_∗_</sup>|-0.210<sup>_∗_</sup>|-0.231|-0.0928|0.289<sup>_∗_</sup>|0.244<sup>_∗_</sup>|-0.192<sup>_∗_</sup>|
||(0.142)|(0.277)|(0.0856)|(0.0569)|(0.161)|(0.0665)|(0.0369)|(0.106)|(0.0232)|
||||||**Below **<br>|**College**<br>||||
|Conventional|0.0137|0.000639|-0.0299<sup>_∗_</sup>|-0.00774|0.0677<sup>_∗_</sup>|0.0148|-0.0134|-0.0637<sup>_∗_</sup>|0.0856<sup>_∗_</sup>|
||(0.0134)|(0.0234)|(4.22e-10)|(0.00900)|(1.16e-09)|(0.0111)|(0.0171)|(0.0173)|(0.00985)|
|Bias-corrected|0.0194|0.0176|-0.0344<sup>_∗_</sup>|-0.00197|0.0664<sup>_∗_</sup>|0.0183<sup>+</sup>|-0.00612|-0.0479<sup>_∗_</sup>|0.116<sup>_∗_</sup>|
||(0.0134)|(0.0234)|(4.22e-10)|(0.00900)|(1.16e-09)|(0.0111)|(0.0171)|(0.0173)|(0.00985)|
|Robust|0.0194|0.0176|-0.0344<sup>_∗_</sup>|-0.00197|0.0664<sup>_∗_</sup>|0.0183|-0.00612|-0.0479<sup>_∗_</sup>|0.116<sup>_∗_</sup>|
||(0.0194)|(0.0367)|(0.00705)|(0.0106)|(0.0325)|(0.0130)|(0.0192)|(0.0168)|(0.0110)|

> + _p <_ 0 _._ 10, _∗ p <_ 0 _._ 05. The standard errors are given in parentheses. + _p <_ 0 _._ 10, _∗ p <_ 0 _._ 05. The bandwidths and the estimates are calculated using the regression discontinuity model proposed by Calonico et al. (2020) and Calonico et al. (2014). All standard errors are clustered at score variable. The data is segregated by education and industry. College refers to those with some higher education and Below college refers to those with high school, fundamental level and basic level education. The monthly income is in Brazilian reais adjusted for inflation by IPCA. The covariates included are quarterly dummies and previous quarter’s value of the outcome variables.

# **6.2.2 Tests**

Since this paper focuses on the sharp RD design, this section tries to provide some empirical evidence supporting the validity of the design. This section also provides further evidence in support of the results discussed above using an alternate methodology, namely the synthetic control matching advocated by Abadie et al. (2010).

The continuity of the score density at the cutoff is assumed in estimating the sharp RD effects and so this section starts by examining whether in a local neighborhood near the cutoff, the number of observations below the cutoff is significantly different from the number of observations above it. By the structure of the setup of the estimation method, there is only one observation per quarter or per score variable and consequently units do not have the ability to precisely manipulate the value of the score that they receive. The McCrary density test is conducted and the discontinuity takes the value of 0.28 with a standard error of 1.56 and is non-significant, suggesting no evidence of sorting around the cutoff.

Since the choice of cutoff at the first quarter of 2018 is by observing the population jump in Roraima and some Venezuelan refugees and migrants have entered Roraima in 2017, albeit in smaller number, the next validity test tries to examine whether there was any significant increase in population in Roraima in the four quarters of 2017. The cutoff at the first quarter of 2018 is replaced by each of the quarters of 2017 and the rest of the quarters in 2018. Figure 11 shows that the positive population shock only happens in the first quarter of 2018. The changes in the working age population in the artificial cutoff are mostly negative and non-significant, which provides evidence for the appropriateness of using the first quarter of 2018 as the cutoff to estimate the population shock and its subsequent impact on fiscal and labor market variables. All the estimations use uniform kernel and the optimum bandwidth and covariates include quarter fixed effects and previous quarter’s working age population.

Next, this paper tries to validate that the population shock that is observed in Roraima in the 28