level reached between 45 and 48 percent of USE age youth.<sup>7</sup> These USE completion rates are well below the average for OECD countries, of around 84 percent in 2009 (see OECD (2011)). While USE attendance rates are clearly related to household and individual decisions made at USE ages, they are also a consequence of early life events that influence both the probabilities of LS completion and of accessing and completing USE.<sup>8</sup> This implies that in order to better understand the dynamics of USE, it is necessary to go beyond a snapshot –or even series of snapshots taken at a certain age -to more comprehensively view earlier schooling trajectories. To do so empirically, having panel data following specific individuals at stages of their life cycle during which they take USE attendance decisions, and also during previous stages would be ideal. Unfortunately, these kinds of data are not widely available in LA.<sup>9</sup> An alternative approach in the literature of life cycle choices has been to use repeated cross sections of data—typically from household surveys— to track the life cycle path of representative groups of individuals belonging to a birth cohort.<sup>10</sup> As noted by Verbeek (2008), the main limitation of these types of data is that they follow groups of representative individuals rather than the same individual over time. When the within cohort variance of the variable under analysis is low this may be of less concern since the average will be close to what is observed in individual observations. However, when high variability is observed, the cohort average will reflect individual cases to a much lower extent. Two advantages of using repeated cross sections, however, are that they minimize attrition biases and problems of non-response that are common in true panels, and when surveys are nationally representative averaging across cohorts likely reduces idiosyncratic measurement error and heterogeneity (Attanasio & Banks, 1998).

Repeated cross sections were selected as an adequate empirical option for the present study, since in LA enough household surveys covering long time spans are available to allow us to follow a cohort from its entry to primary, at ages 5-6, until its exit from USE age (around age 18). These surveys provide information on each generation’s schooling history as well as the extent of dropout specifically at USE.<sup>11</sup> Furthermore, as opposed to official education administrative records, such

> 7 See Alfonso, et.al. (2012), Bassi, et.al. (2013), and Székely (2014)

> 8 See Grantham-McGregor et al. (1999), Schady (2012), Case and Paxon (2008), Paxon and Schady (2007), Cunha and Heckman (2007), Cunha, etal. (2006), Heckman (2006a, 2006b, 2006c), Heckman (2007) and Heckman (2008), Vegas and Santibañez (2010), and Kundsen et al. (2004), which measure the effects of events occurring early  in life on future education and other outcomes.

> 9 Several countries collect panel data for labor market surveys, but typically follow individuals for a reduced time, which does not allow for the longer term perspective we require. Administrative records from education sectors are not suited for a dynamic approach since they only register information on individuals in the schooling system.

> 10 This approach was proposed initially by Browning, Deaton, and Irish (1985) for analyzing other outcomes that are expected to vary throughout the life cycle, such as savings, but has been used in other contexts due to its richness. Shorrocks (1975), Moffit (1993), Deaton (1997), and Attanasio and Banks (1998) are some of the first to use this technique in the context of the analysis of savings. Recent applications to other areas include Dang, et.al. (2012), Cruces, Fields and Viollaz (2013), and Székely and Karver (2014).

> 11 Two problems of this type of cohort analysis are differential mortality and  migration. (i) If mortality and schooling are correlated,  estimated changes in attendance rates could be upward biased – (e.g. if mortality among  less educated youth is greater than among more educated individuals in the same cohort due to greater risk exposure). Deaton and Paxson (1998) and Attanasio and Hoynes (1998) attempt some corrections to similar data and argue that such effects

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