compute the number of schools per number of individuals of the relevant school age in each municipality, and the result is that the marginal effect is statistically significant and quite relevant in magnitude.<sup>21</sup> According to our estimates, there is a negative marginal effect of almost 14 percent from having fewer schools per school age individuals, and it is notable that the magnitude of the coefficient for rural location falls significantly –to about one half of its value- when incorporating this variable. In this estimation, the marginal effect of the supply of education services actually shows the largest influence on school attendance as compared to the rest of the variables considered. This suggests that the availability of services is still an important determinant of enrollment, at least in countries like El Salvador and Honduras.

As discussed in the conceptual framework, shocks at the household, community, or macro level can also play an important and lasting role in schooling decisions. In an assessment of global evidence, Baez and Santos (2010) find that natural disasters impact educational attainment through multiple channels, and have negative and durable net effects. Disasters can reduce household income and assets, destroy productive infrastructure, and change local labor markets, all of which directly impact on educational choices. In Central America, the 2001 earthquakes in El Salvador, Hurricane Mitch in Nicaragua, and Tropical Storm Agatha have been associated with higher rates and intensity of child labor following the disasters, and, in the case of Agatha, removal of children from school to facilitate this work and/or cut back on schooling-related expenditures (Santos 2007; Baez et al 2007; Baez and Santos 2008; Baez et al 2015). Depending on the context, effects may be heterogeneous across children within a family: Bustelo (2015) finds that young adolescents (ages 13-15) increased their workload while younger children did not, and that only boys’ school participation was affected after Tropical Storm Stan hit Guatemala. Importantly, Baez and Santos (2010) find that the largest impacts are often on the poor.

# **3.2 Empirical correlations: The role of macro variables**

The paper by Bentaouet-Kattan and Székely (2015), referred to earlier, implements the conceptual framework in Behrman, de Hoyos, and Szekely (2015) by focusing on macro variables, which complements the picture presented above.<sup>22</sup> Specifically, the authors estimate a model where changes in school enrollment across each cohort for which they have data are correlated with a set of variables characterizing the macro environment in which human capital accumulation decisions are taken. The authors present a base regression estimated through differences in differences which accounts for potential problems of omitted variables bias and endogeneity, among other issues, that can be used here for further exploration. The general specification used is: Where changes in enrollment for cohort _i_ between periods _t_ and _t-1_ for country _c_ are a function of a set of independent variables observed for each cohort for each period of time, for each country. Four groups of variables are considered. The first group (D) includes the population fertility rate

> 21 The population data is taken from the National Population Census of 2007 and extrapolated to 2012.

> 22 For a larger list of references on analysis of the causes of school dropout see Bentaouet-Kattan and Székely (2015). Other related analysis focusing on specific aspects of early dropout are Cardoso and Vermer (2007), South, et-al. (2005), Ingrum, (2006), Marcotte (2013), Dearden, et.al. (2008), Mo, et.al. (2014), Bentaouet-Kattan and Székely (2015b), Chioda (2013), Ibañez, et.al. (2013), and Munyo (2013). Useful references that are specific to CA countries are Jimenez and Gaete (2010) for Costa Rica, Marshall, et.al. (2014) for Honduras, and PREAL (2014) for Nicaragua.

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