Femicides, anti-violence centers, and policy targeting

This paper investigates the socio-economic and geographic patterns associated with femicides and examines the role of local policies in combating gender-based violence. First, we construct a novel, granular dataset of femicide cases in Italy spanning 2006–2022 and analyze it with machine learning techniques. This empirical analysis identifies areas at highest risk for women and pinpoints the main territorial predictors of the phenomenon. Second, we collect data on all local anti-violence centers (AVCs) and show that our femicide risk map only partially aligns with the local deployment of existing public support, suggesting that predictive analytics could enhance targeting strategies. Third, using detailed information on the timing of AVC openings in each province and a staggered non-parametric difference-in-differences approach, we find that, on average, AVC openings did not significantly reduce the number of femicides, although they led to a notable decline in sexual violence. These findings suggest ample room to improve the targeting and effectiveness of public policies aimed at combating violence against women.