Indicators database (2013 edition) for identifying country-level variables that cannot be computed directly from the surveys.

Since the estimation is performed in a first difference setting with Huber-corrected robust standard errors and year dummies, our results control for cohort time-invariant characteristics. All regressions are performed separately for males and females. We test with a series of specifications and report the model that shows more stability and consistency in the coefficient estimates.

Table 1 presents the relationship between the changes in the share of Osow youth in each cohort of females at the stage when they shift between ages 12-14 to ages 15-17, from ages 15-17 to 1820, from 18-20 to 21-23, and from 21-23 to 24-26 years of age, respectively (all independent variables refer to changes during the respective years of observation). In this way, we are able to trace the dynamics of the association between the change in the share of Osow youth over the life cycle of each specific cohort, and the set of variables characterizing changes in community and macro factors that occur in the respective country exactly during the same time that the cohort age shift occurs.

The first variable included is the share of the population that resides in rural areas at each point of observation (from the World Development Indicators database). Since upper Secondary services are generally more easily available in urban areas, we would expect that the larger the share of the population in rural settings, the larger the proportion of adolescents that do not have direct access to the education services when transiting from Lower to Upper Secondary school, and the group more likely to drop out. Since employability is normally low at these ages, this would presumably be associated with an increase in the proportion of individuals in the Osow youth group.

According to our results, this variable is in fact positive and highly significant for the transition at the younger ages from 12-14 to the 15-17 range. However, for age shifts thereafter, although the coefficients are still positive, they lose their statistical significance. This is consistent with the interpretation that access to educational services is critical for taking the time allocation decision when individuals are at the age of transiting from Lower to Upper Secondary schooling.

A second community level variable is the proportion of children classified as malnourished in each country and year, which is taken, on the one hand, as an indicator of the proportion of the population with low incomes –and thus, low capacity for financing schooling- and also of the relative number of children with adverse conditions for achieving a normal level of academic performance, which presumably affects their transition through the education cycle. Our results show that the association with the change in the share in Osow youth is of the expected positive sign –increases in malnutrition correspond to greater shares of Osow youth- but the coefficient is not statistically significant in any of the age transitions observed for females.

The third set of variables we consider are the returns to Higher and Upper Secondary schooling, which are calculated directly from our household survey database. The returns are estimated only for the population of prime age between 35 and 55 years of age and therefore do not include the 24