surveys include those who have left the education system. Finally, having enough cross sections (as is our case), permits statistical and econometric analysis of the relation between a series of factors and the variables of interest, as in Section 4.

We construct our data set using micro data from 234 cross sections available for 18 LA countries spanning from 1980 to 2012, from which schooling indicators representative of more than 96 percent of the population of the region can be constructed. Nine percent of the surveys are for 1980-1989, 31 percent for 1990-1999, and 60 percent for the 2000-2011 period. Appendix Table A.1 identifies the number of surveys and years available for each country. The surveys are representative of the total population of each country, with the exception of Argentina and Bolivia for surveys prior to the 2000s and Uruguay, where the samples are for urban areas only.

Since each country produces data in different years, formats, computing codes, questionnaires and definitions, we used the micro data to standardize relevant indicators and produce a comparable data set within countries, and across countries and years. Our homogenization process considers each country’s different age requirements for attending each education level. We follow UNESCO’s ICSED 1997 classification when available, which identifies the official age at which youth should be in the schooling system, by education level, and official local data elsewhere.<sup>12</sup> Most countries organize their education system in four different levels: pre-primary, primary, lower secondary, upper secondary, and higher education –some countries like Peru do not distinguish between lower and upper secondary.

We find that the greatest variety in arrangements is precisely for USE (Appendix Table A.2). Official USE attendance ages are from 15 to 17 in 9 of the 18 countries analyzed (Argentina, Costa Rica, Ecuador, Honduras, Mexico, Panama, Paraguay, Uruguay, and República Bolivariana de Venezuela), with a typical duration of 3 years and early graduation options in some cases. Entry is also at age 15 in Colombia and Peru, but with a shorter duration of 2 years. In Bolivia, Chile, and the Dominican Republic USE lasts 3 years, but has an earlier entry (age 14). The oldest entry ages are at 16, (in Guatemala, Nicaragua, and El Salvador), with duration of 2-3 years. Uniquely, in Brazil USE starts at age 15 but lasts 4 years. So, through our standardization process, we adjust the information to define the age at which adolescents are expected to be enrolled in USE.

The micro data also permits us to address how enrollment and graduation rates are defined. As illustrated by Heckman and Laffontaine (2007) and Murane (2013) levels and changes in these

> can be significant. However, since our life cycle window of interest is relatively short and focused at early ages, we expect these effects to be small in our analysis. (ii) Differential immigration or migration associated with schooling can also introduce biases. Large inflows of low educated individuals in young cohorts can affect composition by underestimating schooling levels, and vice versa. Additionally, if migration and schooling are positively correlated, a positive composition effect will lead to over estimation of attendance and graduation. We lack historical data on migration flows to assess the magnitude of this bias, but do not expect these potential changes to affect our results significantly since our analysis focuses on relatively young ages that are less prone to migration, as well as a relatively short period of the life cycle.

> 12ICSED definitions can be found at http://www.uis.unesco.org/Education/ISCEDMappings/Pages/default.aspx.

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