Femicides, Anti-Violence Centers,
and Policy Targeting
Augusto Cerqua, Costanza Giannantoni, Marco Letta, Gabriele Pinto
Department of Social Sciences and Economics, Sapienza Università di Roma
Augusto Cerqua, Costanza Giannantoni, Marco Letta, Gabriele Pinto
Department of Social Sciences and Economics, Sapienza Università di Roma
This website offers the first open-access datasets on femicides and Anti-Violence Centers (AVCs) in Italy at the municipal level. It is a companion to the scientific paper Femicides, Anti-Violence Centers, and Policy Targeting (Cerqua, Giannantoni, Letta, and Pinto, 2025), conditionally accepted in the European Economic Review.
Among developed countries, Italy ranks as one of the safest in terms of overall violence. According to OECD data, the country’s homicide rate—defined as the number of murders per 100,000 inhabitants—is 0.5, significantly lower than the OECD average of 2.6 (OECD, 2020).
Furthermore, homicides in Italy have been declining over time. As illustrated in the figure above, the total number of homicides has halved in recent years.
However, this downward trend is markedly gendered: the reduction has occurred exclusively among male victims. In contrast, homicides involving female victims—specifically those killed because of their gender, i.e., femicides—have remained stable over time.
In our paper, we construct two novel datasets on femicides and AVCs in Italy, forecast and map local femicide risk using machine learning techniques, and evaluate the impact of AVCs to help improve both the targeting and effectiveness of policies aimed at combating gender-based violence.
To support evidence-based research and inform more effective policymaking on this urgent and persistent issue, we have made both datasets freely available for download.
This project has received financial support from the Italian PRIN 2022 grant for the project “Strengthening TARgeting and Guidance with Actionable and Timely Evidence (STARGATE) for the implementation of the Italian National Recovery and Resilience Plan” (CUP: F53D23003220006).