Imputing Unreported Hate Crimes Using Google Search Data - Dhammika Dharmapala; Aziz Huq (CESifo GmbH, 2024)
U.S. law requires the Attorney General to collect data on hate crime victimization from states and municipalities, but states and localities are under no obligation to cooperate by gathering or sharing information. Data production hence varies considerably across jurisdictions. This paper addresses the ensuing "missing data" problem by imputing unreported hate crimes using Google search rates for a racial epithet. As a benchmark of accurate hate crime data, it uses two alternative definitions of which jurisdictions more effectively collect hate crime data: all states that were not part of the erstwhile Confederacy, and those states with statutory provisions relating to hate crime reporting. We regress rates of racially-motivated hate crimes with African-American victims on Google searches and other relevant variables over 2004-2015 at the state-year level for each group of benchmark states. Adding the Google search rate for the epithet substantially enhances the capacity of such models to predict hate crime rates among benchmark states. We use the results of these regressions to impute hate crime rates, out-of-sample, to non-benchmark jurisdictions that do not robustly report hate crimes. The results imply a substantial number of unreported hate crimes, concentrated in particular jurisdictions. It also illustrates how internet search rates can be a source of data on attitudes that are otherwise hard to measure.
Keywords: K42; 330; hate crimes; victimization; internet search; crime reporting