# **Earnings are modeled using sociodemographic characteristics and the selection correction terms from**

**the first stage (Mills ratios).** The results of the OLS estimation of the log-earnings equation, with and without the selection correction terms, are shown in Table 3 and Table 4. Table 3 displays the log-earnings gains from internal migration regardless of the reason for migration, and Table 4 exhibits the log-earnings gains from internal migration due to economic reasons. The first and fifth columns in Tables 3 and 4 show the unconditional log-earnings gains from internal migration for women and men, respectively, five years after the migration decision. The second and sixth columns show the log-earnings gains after including fixed effects for the states of origin. The third and seventh columns show the log-earnings gains after controlling for sociodemographic characteristics. Finally, the fourth and eighth columns show the logearnings gains after including the selection correction terms. For more details see Annex 4, Tables A4.1 and A4.2.

**As expected, the higher the level of educational attainment, the higher the expected earnings.** Tertiary education is statistically significant, at a 1% level for both women and men. The number of hours worked is also statistically significant and increases the expected earnings for both men and women.

**Both selection correction terms are statistically significant.** The two selection correction terms (the one for the propensity to migrate and the one for the probability to work) are highly correlated with earnings and are statistically significant for women and men. The latter suggests that the sub-sample of people who migrate and the subsample of people who work are not randomly selected from the population, and consequently, the parameters estimated via OLS without controlling for selection are biased and inconsistent.

**The results from the log-earnings equation suggest a positive impact of internal migration on labor income.** Table 3 shows the parameter estimates associated with the migration choice for different specifications of the log-earnings equation. Similarly, Table 4 displays the parameter estimates related to the migration choice due to economic reasons for different specifications of the log-earnings equation. The coefficient in the log-earnings equation that accounts for self-selection bias, either for migration regardless of the reasons or economic-related migration, is statistically significant at a 1% level and positively correlated with earnings for both women and men. The latter suggests positive income gains from the migration decision after controlling for selection bias. For women and men, the coefficients are higher when the migration is due to economic reasons.

Table 3: Results from the log-earnings equation. Regression coefficients for the dummy variable identifying internal migrants.

|||W|omen|||M|en||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Internal migration|0.171***|0.189***|0.0936***|0.0903***|0.251***|0.262***|0.136***|0.135***|
||(0.0309)|(0.0313)|(0.0300)|(0.0291)|(0.0247)|(0.0248)|(0.0245)|(0.0252)|
|Sociodemographics|||YES|YES|||YES|YES|
|Fixed effects for the|||||||||
|states of origin||YES|YES|YES||YES|YES|YES|
|Self-selection bias||||YES||||YES|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 34