We use a fixed effects model to assess whether greater exposure to violence has had an impact on human capital accumulation at the municipality level. The fixed effects models that we estimate are of the form: where yit represents the educational variable of interest, αi a set of municipality fixed effects, βt a set of year fixed effects and homicidesit the homicide rate per 100,000 inhabitants for municipality (or a normalized transformation of the homicide rate) _i_ at time _t_ . The municipality-year specific error term is given by εit. The coefficient of interest is γ. All standard errors are clustered at the municipality level.

The education variables in our first set of specifications are the log of the total number of students at different education levels for each municipality over the period 1994-2010. They are reported separately for the different school levels, which allows us to run our regressions separately for primary, lower secondary, and upper secondary. This approach is useful as it allows us to explore whether violence is affecting education in different ways according to school level. Running the specification presented in Equation (1) separately for each school level would allow us to get some preliminary suggestive evidence about the effect of violence on education.

We also run the fixed effects model for the education variables from our second data set which was constructed using the information from ENLACE. When using this data set, we can aggregate the student information to the (i) municipality level, and (ii) school level. When using the data aggregated at the school level we include school fixed effects as opposed to municipality fixed effects. The homicide variable corresponds to the annualized homicide rate per 100,000 inhabitants, normalized according to the national homicide rate per 100,000 inhabitants in 2007 (general homicide rate of 8.3 and drug related homicide rate of 2.6). All regressions are run with clustered standard errors at the municipality level.<sup>4</sup> Including municipality fixed effects removes all observed and unobserved municipality characteristics that are constant, thus removing the bias in the estimation that is caused by municipality-invariant characteristics. In particular, if certain municipality characteristics are correlated with both an increase in violence and a decrease in schooling, a model without fixed effects would yield a downward biased estimator of the impact of homicides on education. By including municipality fixed effects --and assuming that the trends of these characteristics are time invariant-- the bias would be eliminated. The yearly fixed effects eliminate biases that may be occurring at the national level for any given year.

> 4 Oaxaca is excluded from the analysis using ENLACE data. The education data for the state of Oaxaca is known to be unreliable and incomplete. Oaxaca is a highly rural state, and although it only accounts for less than 3.4% of the national population (Census, 2010) it is divided into 571 counties (23.2% of the total number of counties). Oaxaca has not experienced a particularly high increase in violence as of 2006. The state is excluded from our econometric analysis, although the regressions were also run including the state of Oaxaca and results are robust.

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