predicting violent crime.<sup>1</sup> Moreover, Pridemore (2011) criticizes the large cross-country literature that studies the link between inequality and homicide rates as most of those fail to control for poverty rates, which is the most consistent predictor of area homicide rates in the US empirical literature. Pridemore replicated previous cross-country studies that found a statistical significant relation between inequality and homicides, finding that when the models controlled for poverty rates, such relationship was not significant anymore.

Brush (2006) finds mixed results in terms of the effect of income inequality on crime rates using county level data for the United States. Using cross-sectional analysis he does find that income inequality promotes crime, although when centering his attention on a time series analysis he finds that income inequality reduces crime. Poveda (2011) finds that poverty and inequality both have positive impacts on the rate of homicide in seven major Colombian cities. Similarly, using a sample of OECD, Central and South American countries, Nadanovsky and Cunha-Cruz (2009), find that low inequality leads to a reduction in homicide rates. Demombynes and Ozler (2005) find that higher inequality in South Africa is associated with higher rates of property and violent crimes at the neighborhood level. Finally, in a recent study using inequality data for the United States at the state level, Chintrakarn and Herzer (2012) find that inequality has a negative effect on crime, i.e., the more inequality there is, the less crime. Their explanation for this counterintuitive result is that the higher the inequality within a state, the larger the demand for security services, which leads to a reduction in crime.

As this succinct literature review shows, empirical evidence on the effects of inequality over crime is mixed. In order to further analyze this question, this paper focuses on within-country variation in income inequality and crime rates using a unique data set of Mexican municipalities from 1990 to 2010. Additionally, the paper uses differentiated homicide rates, thus distinguishing whether the impact of inequality over crime rates is more pronounced for common crime, organized crime, or both. In particular, we expect that the effect of inequality on organized crime would be exacerbated in the context of Mexico’s Drug War. The literature has shown that the proliferation of gangs tends to increase the propensity to commit crimes as they facilitate access to knowledge and logistics associated with criminal activities (Thornberry et al. 1993; Zhang et al. 1999; Gatti et al. 2005). In other words, gangs tend to lower the marginal cost of criminal behavior. Proliferation of gangs would thus have a greater impact on crime levels in cities with a high degree of poverty and inequality, since higher costs are more likely to be a binding constraint for crime activity among individuals with less economic resources. The splintering of drug-trafficking gangs and their geographic diffusion during Mexico’s Drug War might have facilitated criminal behavior disproportionally among cities that became poorer and more unequal during this period. At the same time, increasing levels of inequality associated with rich individuals becoming richer would tend to exacerbate these effects, by increasing the expected pay-off of criminal activity. In other words, if the cost of crime decreases and the income differences between the poor and the rich

> 1 Neumayer (2005) used 59 countries in his sample. Fajnzylber, Lederman, and Loayza’s (2002b) have 45 countries in their sample.

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