The first four columns present the results when we do not include fixed effects for municipality and year. Thus, these may not be interpreted as difference-in-difference results and are driven by both the cross-sectional and longitudinal changes in homicide-rates. The first column shows the impact on students of basic school (grades 1 to 9 of formal schooling). The coefficient of -0.01 would be interpreted as showing that a municipality that experienced a typical increase in homicide rates (i.e. equal to the average of the country) would observe a 1% reduction in the number of children enrolled in basic education. This number arises from similar reductions in primary school students (0.95% -column 2-) as secondary school students (1% -column 3). Column 4 shows the effect on the number of upper secondary school students (grades 10 to 12). The effect on that group is somewhat larger at 1.5%.

These effects, though may be seen as small in economic magnitude, are statistically significant. Some may even argue that these impacts are of substantial economic significance given the large importance of education on labor market earnings (i.e. Card 1999).

A different picture emerges once we control for year and municipality fixed effects, which is our preferred empirical strategy. In this manner, we are effectively comparing the changes in the number of students enrolled in the municipality against the changes in the homicide rate. Columns 5 to 8 present these results. The effect for basic school disappears. The coefficient is not only small (less than 0.001 percent) but also quite accurately estimated. We can reject, for example, the hypothesis that the effect is larger than 0.06%. Zooming in to the impacts on primary (grades 1 to 6) or secondary (7 to 9 grades) we find the same null or miniscule effects.

The results for upper secondary school, however, are different. The coefficient for that regression (column 8) is not insignificant in a statistical sense (at least not at the 10% level). The 0.3% negative effect of the homicide rate is still much smaller than what we obtain when we do not control for state and fixed effects, and we can reject a negative impact larger than 0.5%.

The results presented in columns 9 to 16 rely solely on the period after 2007. We present these because one may hypothesize that the effects are different in the “War on Drugs” period since the issue became much more prominent. In addition, it allows us to use the “Drug Related Homicide Rate” variable in columns 13 to 16. The results are qualitatively similar as before. Although coefficients are generally of the expected sign, (i.e. consistent with a reduction of enrollments when violence is higher), the magnitudes are very small and statistically insignificant. In these cases, in addition, even when the dependent variable is the number of upper secondary school students, the coefficients are statistically undistinguishable from zero.

We present a graphical analysis of this in the figures below, to allow the reader to visualize the magnitude of these effects. In Figure 4 Panel A below, we present the evolution of upper secondary school total enrollments (using an index where the value for 2007 is set to 100), for municipalities where violence has greatly increased and for municipalities where violence has stayed relatively constant. For this purpose, municipalities were divided into “high violence increase” and “low change in violence” according to their homicide rate trend from 2007 to 2011. Counties classified as those 16