to account for possible composition differences across cohorts that arise because our data is from household surveys that include samples of individuals –which implies a cohort-age cell error that deviates from the true cohort-age means, and which could vary, among other things due to changes in sample size across surveys. The central result is the high stability of the coefficient estimates. The only exception is the relation between USE dropout and HE returns, which in this case are also positive but highly statistically significant.

# **4.2 Estimations for different samples**

We explore differences across males and females by using regression (5) as a main point of reference (Table 5). For both genders, respectively, we estimate the base regression by using the same independent variables as in Table 3, while the independent variable refers to only one gender at a time. In a separate regression, we use gender specific dependent variables and also gender specific independent variables for wages, employment, and the returns to schooling.

The most notable result is that most of the conclusions obtained from regression (5) remain for both genders, with two main differences arising between the estimations. The first is that the coefficient for wages is not significant for female USE age enrollment, while in the case of males these remain as statistically significant influences. Employment levels however, have a positive and statistically significant effect throughout, which suggests that it is labor opportunities rather than labor market remuneration levels which lead females to stay longer in the schooling system.

The second difference has to do with the returns to HE, where the coefficients in the case of females are highly significant when using the general returns as reference, but not when the gender specific HE rewards are considered. In contrast in both regressions the returns to USE are highly associated with female school enrollment. Another element of interest is that for males, most variables become of higher significance when using gender (male) specific indicators, which suggests that for males, specific gender related conditions are of more significance than changes in the labor market at large, as opposed to females.

Finally, we estimate our base regression separating countries into early, transition and late dropout as per the classification suggested in Section 2 (Table 6). We find an interesting difference across estimations in the relation with average wages and employment shares. Specifically, the coefficients for these variables are negative and statistically significant for countries with early dropout, which suggests for this category –which mostly includes low income countries with the exception of Colombia- substitution effects related to the opportunity cost of attending school can supersede the size of the income effects in terms of the incentives provided by the labor market for remaining in school. This in turn is consistent with generally having higher marginal propensities to consume at lower incomes, which lead to preferring current rather than future consumption. Other less significant differences are that the coefficients for changes in the returns to HE and for the share of poor individuals accessing LS are only marginally significant in the cases of late drop out and dropout in transition, respectively.

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