The influence of femicide on criminal behavior: An empirical approach using economic complexity for crime prevention in Mexico
DOI:
https://doi.org/10.24201/ee.v39i1.449Keywords:
economic complexity, machine learning, femicide, crime preventionAbstract
Following the economic complexity methodology introduced by Hausmann et al. (2013), this study establishes an order of crime evolution in Mexico. This ordering is based on the complexity of crimes, as determined by the capabilities required to commit them. According to the study, the least complex crimes in Mexico include robbery in collective public transport, robbery in individual transport, counterfeiting, and auto parts theft. On the other hand, the crimes characterized by the highest complexity involve organized crime, intentional and unintentional homicide, and the trafficking of minors. Furthermore, the analysis reveals that femicide and rape exhibit the most significant centrality or influence within the criminal network. According to the model, crimes associated with violence against women are those that most increase the probability of committing another crime. Therefore, targeted prevention efforts aimed at reducing femicide and rape may substantially impact overall levels of criminality in Mexico. This study also highlights the importance of addressing violence against women in designing crime prevention policies in Mexico. Moreover, the methodology adopted in this study can be reinterpreted as a spectral clustering algorithm and thus also contributes to the literature on machine learning applications in public policy design.
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