Searcharxiv⌕ Search

arXiv subjects

Gabriele Pinto

Publications and source records attributed to Gabriele Pinto.

2 recordsLinked to original sources

Public salience mobilizes nationwide responses to violence against women

Does public salience around femicides mobilize societal responses or trigger further harm through imitation? Using newly assembled case-level data on femicides in Italy from 2017 to 2024, we study the nationwide consequences of the most salient cases for help-seeking, police reporting, and lethal violence. We find that highly salient intimate partner femicides (IPFs) generate nationwide increases in help-seeking, and that an exceptionally salient IPF also leads to a marked rise in police reports of violence against women. Yet we find no evidence of an increase in femicides-a pattern inconsistent with copycat theories. Responses are strongest during periods of peak salience. No comparable mobilization follows a lower-salience IPF, a similarly salient femicide committed by family members, a highly salient killing of a woman not classified as a femicide, or a lower-salience falsification case. Together, these comparisons indicate that mobilization depends on salience intensity, the victim-perpetrator relationship, and the gendered nature of the violence, rather than reflecting a generic response to salient violent crime. These findings suggest that public salience can contribute to prevention strategies against gender-based violence without amplifying lethal violence.

econ.GN↗

On the (Mis)Use of Machine Learning with Panel Data

We provide the first systematic assessment of data leakage issues in the use of machine learning on panel data. Our organizing framework clarifies why neglecting the cross-sectional and longitudinal structure of these data leads to hard-to-detect data leakage, inflated out-of-sample performance, and an inadvertent overestimation of the real-world usefulness and applicability of machine learning models. We then offer empirical guidelines for practitioners to ensure the correct implementation of supervised machine learning in panel data environments. An empirical application, using data from over 3,000 U.S. counties spanning 2000-2019 and focused on income prediction, illustrates the practical relevance of these points across nearly 500 models for both classification and regression tasks.

econ.EM↗