Penalty Enhancement for Hate Crimes: An Economic Analysis

The issue of bias-motivated crimes has attracted consderable attention in recent years. In this paper, we develop an economic framework to analyze penalty enhancements for bias-motivated crimes. We extend the standard model by introducing two different groups of potential victims of crime, and assume that a potential offender's benefits from a crime depend on the group to which the victim belongs. We begin with the assumption that the harm to an individual victim from a bias-motivated crime is identical to that from an equivalent non-hate crime. Nonetheless, we derive the result that a pattern of crimes disproportionately targeting an identifiable group leads to greater social harm. This conclusion follows both from a model where disparities in groups' victimization probabilities lead to social losses due to fairness concerns, as well as a model where potential victims have the opportunity to undertake socially costly victimization avoidance activities. In particular, penalty enhancements can reduce the incentives for avoidance activity, and thereby protect the networks of profitable interactions that link members of different groups. We also argue that those groups that are covered by hate crime statutes tend to be those whose characteristics make it especially likely that penalty enhancement is socially optimal. Finally, we consider a number of other issues related to hate crimes, including teh choice of sanctions from behind a Rawlsian 'veil of ignorance' concerning group identity.

Optimal Test for Parameter Instability When the Unstable Process and the Error Distribution are Unknown

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does not provide asymptotic power gains. I first derive a test under known error distribution, which is asymptotically equivalent to LR tests for correctly identified unstable parameter processes under suitable conditions. The conditions are weak enough to cover a wide range of unstable processes such as various types of structural breaks and time varying parameter processes. The test is then extended to semiparametric models in which the underlying distribution in unknown but treated as unknown infinite dimensional nuisance parameter. The semiparametric test is adaptive in the sense that its asymptotic power function is equivalent to the power envelope under known error distribution.