This goal of this dissertation is to identify predictors of bias criminality in the United States at the county level from 2000 - 2009. There is relatively little known about bias crime occurrence in the United States. In addition, increased public attention to bias criminality requires additional social science research examining the predictors of bias crime in American communities. By examining traditional indicators of social disorganization theory, this dissertation seeks to explore the likelihood of bias crime occurrence at the macro-level. As such, the unit of analysis is United States counties. The N is 3,141. The data upon which this dissertation is based come from the Federal Bureau of Investigation (FBI), the United States Census Bureau (USCB), the Association of Religious Data Archives (ARDA), and Congressional Quarterly's Voting and Elections Collection. From the data, measures of economic deprivation, social heterogeneity (diversity), social cohesion, and residential mobility were created. These measures represent traditional indicators of social disorganization theory. Four models are introduced in this dissertation in order to answer several research questions that explore the differences between how these predictors affect various types of bias crime. Negative binomial regression and OLS regression are used to analyze the data and address the research questions. Specifically, anti-race motivated bias crime, anti-sexual orientation motivated bias crime, and anti-religion motivated bias crime types are considered. Although the findings were not resoundingly supportive of the application of social disorganization theory to the understanding of bias criminality, there are remarkable conclusions nonetheless. Measures of social heterogeneity - or diversity - seem to yield the most conclusive evidence toward predicting the risk of bias crime occurrence. Specifically, a county's (higher) percentage of Muslim residents was the strongest predictor of bias criminality across the United States from 2000 - 2009. Similarly, the percentage of Jewish residents, the percentage of non-White residents, and the percentage of foreign-born persons were positively related. Individual measures of residential mobility and social cohesion were also helpful to predicting bias crimes. In addition, county population was useful in predicting bias criminality. Urban areas and urbanized clusters were more likely to experience bias crime occurrence than were rural areas. In addition, results are inconclusive on whether hate crime legislation decreases the risk of bias crime occurrence. The findings indicate that more research is needed. Specifically, understanding a community's level of diversity seems to be important to the prediction of bias criminality in American counties.
Read
