Quantifying Domestic Violence in Times of Crisis : An Internet Search Activity-Based Measure for the COVID-19 Pandemic. / Anderberg, Dan; Rainer, Helmut; Siuda, Fabian.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 10.12.2021.

Research output: Contribution to journalArticlepeer-review

E-pub ahead of print

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    Embargo ends: 10/12/22

Abstract

In contrast to widespread concerns that COVID-19 lockdowns have substantially increased the incidence of domestic violence, research based on police-recorded crimes or calls-for-service has typically found small and often even negligible effects. One explanation for this discrepancy is that lockdowns have left victims of domestic violence trapped in-home with their perpetrators, limiting their ability to safely report incidents to the police. To overcome this measurement problem, we propose a model-based algorithm for measuring temporal variation in domestic violence incidence using internet search activity and make precise
the conditions under which this measure yields less biased estimates of domestic violence problem during periods of crisis than commonly-used
police-recorded crime measures. Analyzing the COVID-19 lockdown in Greater
London, we find a 40 percent increase in our internet search-based domestic
violence index at the peak occurring 3-6 weeks into the lockdown, 7-8 times larger than the increase in police-recorded crimes and much closer to the increase in helpline calls reported by victim support charities. Applying the same methodology to Los Angeles, we find strikingly similar results. We conclude that evidence based solely on police-recorded domestic violence incidents cannot reliably inform us about the scale of the domestic violence problem during
crises like COVID-19.
Original languageEnglish
JournalJournal of the Royal Statistical Society: Series A (Statistics in Society)
Early online date10 Dec 2021
DOIs
Publication statusE-pub ahead of print - 10 Dec 2021
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43499011