Empirical Essays on Bias-Motivated Behaviour

Indulekha Guha, Purdue University

Abstract

This dissertation is a collection of three papers. Each paper constitutes a chapter. Each chapter empirically examines an aspect of bias-motivated behavior in the United States.The first chapter studies the impact of penalty enhancement statutes by state legislatures on the incidence of hate crimes in the United States. Penalty enhancements may deter crime, however, the passing of such laws may also increase awareness among law enforcement officials and increase arrests. Using administrative data on hate crimes and a difference-in-differences method that leverages state-level variation in the introduction of legislation, this paper does not find a significant effect of the state enactment of penalty enhancement statutes on hate-crime incidence rates.The second chapter examines whether election timing and election outcomes affect the incidence of crimes motivated by hate and intolerance. Using administrative data and a difference-in-differences design that compares election with non-election years, I show that hate crimes increase by an average of 28 percent in the three weeks around a US presidential election. This effect is larger in recent presidential elections and when there is no incumbent candidate. Second, using a similar design and cross-state variation in the timing of gubernatorial elections, I find no evidence that these state-level elections affect hate-crime incidence. Third, using regression-discontinuity designs based on vote counts, I find that the number of hate crimes is not affected by presidential or gubernatorial election outcomes.The third chapter studies the impact of presidential and gubernatorial election timing on the level of toxicity present on social media platforms such as Twitter. Together with Sameer Borwankar, I empirically determine the extent to which the toxicity of Twitter content changes during election times as compared to non-election times. We randomly sample Twitter users and collect all tweets made by this sample around election time. We use a difference-in-differences identification leveraging election and non-election years. We further focus on toxic content that is motivated by political polarization and examine various biasmotivation categories that come up in this content as well as the variation in the intensity of toxicity between national and local election times.

Degree

Ph.D.

Advisors

Carr, Purdue University.

Subject Area

Law enforcement|Criminology|Ethnic studies|Gender studies|Internet and social media studies|LGBTQ studies|Political science|Sexuality|Statistics

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