By following each year-of-birth cohort, differences in the proportion of Osow youth across cohorts can be controlled for with context variables, while aggregate conditions across countries can be captured through standard macro variables, including variables contemplated by Behrman et.al. (2014). Furthermore, cohorts can be followed into the future to verify whether the proportion of Osow youth early on in the life cycle is associated with different patterns of labor market outcomes in the future for the same cohort.<sup>7</sup> Household surveys are adequate for this kind of analysis since they incorporate information on the time uses of all individuals, including school attendance, labor market participation, both of these, or remaining Osow. Additionally, they allow for statistical and econometric analysis of the underlying causes of exclusion, since it is also possible to estimate from them variables such as the returns to education, type of employment, and many other variables directly related to the Osow condition, including the characteristics and time use of all members of the household. One issue worth noting, however, is that each country produces these data in different years, formats, computing codes, questionnaires and definitions.

For this work we build on the household survey data bank constructed by Cardenas et.al. (2011), which includes 215 household surveys for 18 Latin American countries covering three decades since the 1980s. We include 19 additional recent surveys and standardize them using the same procedure, which leaves us with 234 cross sections. The country with the largest number of surveys is the República Bolivariana de Venezuela with 24, followed by Brazil with 17; Argentina, Honduras, Panama, Peru, and Paraguay with 15; Colombia, Costa Rica, El Salvador and Uruguay with 14; Mexico with 13; the Dominican Republic with 12; Chile and Ecuador with 10; Bolivia, Guatemala with 7; and Nicaragua with 5. Nine percent of the surveys are for 1980-1989, 31 the average behavior of the variables of interest for these groups rather than for individuals. Deaton (1997) and Attanasio and Banks (1998), are some of the first to use this technique.

7Since the surveys are nationally representative in a given period of time averaging across cohorts likely reduces idiosyncratic measurement error and heterogeneity (Attanasio & Banks, 1998). An additional feature is that this approach avoids to a large extent the attrition bias common to panel analysis. It should be mentioned, however, that cohort analysis of the type used for our exploration is not free from some potential problems. Two of the most common are mortality and migration which may alter the composition of some cohorts over time. In the case of mortality, if there is a correlation with the Osow youth status, it could be possible that changes in the proportion of Osow youth as estimated from household surveys are downward biased –say, if mortality among Osow youth is greater than among other youth in the same cohort due to the exposure to greater risks. Deaton and Paxson (1999) and Attanasio and Hoynes (1998) attempt some corrections to similar data and argue that the effects of mortality can be significant. Since the window of the life cycle of our interest is relatively short, we would expect these effects to be small in our case. Differential immigration or migration associated with schooling can also potentially introduce biases depending on the composition of migrants. If a country receives large flows of Osow individuals in young cohorts, the composition effect exaggerates the proportion of Osow youth, and vice versa. Additionally, and perhaps more relevant for this study, if migration is positively correlated with schooling, the composition effect will be positive, and can be considerable depending on the age pattern of migration. Unfortunately, historical data on migration flows to assess the magnitude of this bias are not available for all the countries considered in the database used here. Furthermore, migration flows can be abrupt and change drastically depending on the conditions of countries at particular points in time. In section 3 we address this issue to take it into consideration for our econometric estimates.

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