(joint with Augusto Cerqua, Costanza Giannantoni and Marco Letta)
Abstract
This paper conducts a comprehensive study on the geographic and socioeconomic patterns associated with femicides and examines the role of local public policies and saliency in combating gender-based violence. First, we compile a novel, granular dataset of femicide cases for Italy covering the period 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 does not fully align with the local deployment of existing public support, suggesting that machine predictions could be employed to refine targeting criteria. Third, using detailed information on the timing of AVC opening in each province and a staggered non-parametric matching difference-indifferences approach, we show that the establishment of AVCs did not significantly decrease the occurrence of femicides, while it lowered the number of reported sexual violence cases. Lastly, we present evidence that a widespread and exogenous increase in public and media salience of the phenomenon triggers large-scale short-term effects on helpseeking behavior that extend far beyond the local level. These findings suggest ample room for improving the targeting and effectiveness of policy interventions aimed at combating violence against women.
Working paper here
