Words that Kill: An Economic Perspective on Hate Speech and Hate Crimes

Abstract

In recent years, a number of mass shooting incidents have been perpetrated by offenders motivated by neo-Nazi or other racist ideology. Conventional economic analysis of crime appears to shed little light on such acts. We propose a model in which potential offenders care not only about the intrinsic benefits from the crime and the expected costs of punishment, but also about the esteem conferred by those who share the potential offender's ideology. The number of such individuals is not known to the potential offender with certainty; it is a random variable, the distribution of which enters into the potential offender's expected utility from committing the crime. We argue that, assuming that the potential offender is risk-averse in esteem, increasing the variance of the distribution of this random variable lowers the expected utility from the crime, and thus potentially 'deters' it. This result holds even when (as we assume) the potential offender has available an unbiased estimator of number of racists (so that the increased variance simply represents a mean-preserving spread). Furthermore, we argue that this variance depends on the (legal and nonlegal) regime governing hate speech. We also develop an asymmetric information model of speech regulation, in which individuals trade off their 'expressive utility' from voicing their opinions against the costs imposed by formal and/or informal sanctions on hate speech. If the sanctions are sufficiently large, there exist pooling equilibria, which create uncertainty about the number of racists and/or the intensity of their hatred. This uncertainty reduces the precision of the potential offender's estimate, and lowers the expected utility from hate crimes. We consider a number of possible caveats and extensions, and discuss the implications for hate speech regulation.