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Recent contributions using police recorded calls-for-service and/or crime data to estimate impacts of COVID-19 lockdowns on the incidence of domestic violence (DV) have reported relatively modest effects. This may reflect a low reporting-propensity, exacerbated by the lockdown measures. Combining five years of daily Google Trends data for a set of DV-related search terms with daily data on DV crimes recorded by the London Metropolitan Police Service (MPS), we propose a method for generating a search-based DV-index, exploiting that both sets of data reflect the same inter-temporal variation in the (unobserved) DV incidence. Estimating the same model for the impact of lockdown on police-reported DV crimes and our search-based DV-index, we find a similar timing, but a substantially larger impact on the latter.
Authors
![Dan Anderberg](/sites/default/files/styles/square_desktop/public/2022-07/Dan%20Anderberg.jpg?itok=IQ6nE8RF)
Research Fellow Royal Holloway University of London
Dan is a Research Fellow of the IFS and a Professor of Economics at Royal Holloway University of London and received his PhD from Lund University.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Helmut Rainer
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Fabian Siuda
Working Paper details
- DOI
- 10.1920/wp.ifs.2020.2920
- Publisher
- The IFS
Suggested citation
D, Anderberg and H, Rainer and F, Siuda. (2020). Quantifying domestic violence in times of crisis. London: The IFS. Available at: https://ifs.org.uk/publications/quantifying-domestic-violence-times-crisis (accessed: 30 June 2024).
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