Effects of the 2019 Novel Coronavirus on Domestic Violence in the US

  • Funded by IZA - Institute of Labor Economics
  • Total publications:0 publications

Grant number: unknown

Grant search

Key facts

  • Disease

    COVID-19
  • Funder

    IZA - Institute of Labor Economics
  • Principal Investigator

    Unspecified Amalia Miller
  • Research Location

    United States of America
  • Lead Research Institution

    University of Virginia, IZA - Institute of Labor Economics
  • Research Priority Alignment

    N/A
  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Social impacts

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Around the world, policymakers and news reports have warned that domestic violence (DV) could increase as a result of the COVID-19 pandemic and the attendant restrictions on individual mobility and commercial activity. However, both anecdotal accounts and academic research have found inconsistent effects of the pandemic on DV across measures and cities. We use high-frequency, real-time data from Los Angeles on 911 calls, crime incidents, arrests, and calls to a DV hotline to study the effects of COVID-19 shutdowns on DV. We find conflicting effects within that single city and even across measures from the same source. We also find varying effects between the initial shutdown period and the one following the initial re-opening. DV calls to police and to the hotline increased during the initial shutdown, but DV crimes decreased, as did arrests for those crimes. The period following re-opening showed a continued decrease in DV crimes and arrests, as well as decreases in calls to the police and to the hotline. Our results highlight the heterogeneous effects of the pandemic across DV measures and caution against relying on a single data type or source