Analysis of Hate Crime Rates in the United States: Statistical Modeling of Public Safety Issues Based on Socioeconomic Factors

In recent years, there has been a surge in awareness on hate crimes, in the United States of America and globally. News outlets have been publishing increasingly more coverages on distressing incidents of hate crimes across the country. More attention is needed to decipher the sources influencing the rise in hate crimes. This paper seeks to explore the relationship between United States hate crime rates and several socioeconomic factors. The data used throughout the study contains hate crimes rates, as well as information on varying factors including income, unemployment, race and citizenship distribution, urbanization and education attainment by state, for the year 2019. Four models are built for variable selection and model performance is compared to derive the best model to explain the factors influencing hate crime rate. The multiple linear regression, lasso regression, ridge regression and elastic net regressions all provide evidence to support the proposal that median household income, income inequality, and unemployment rates are significant factors in determining the appearance of hate crimes. The best-performing ridge regression model also implies the proportion of white people living in poverty is associated with hate crimes, but other models with similar performance do not have sufficient evidence to support this. Separating the states by their political inclination shows that the proportion of white people and the share of white people living in poverty have positive influences on hate crime rates in Republican states, whereas in Democratic states, median household income and unemployment rates are the two most significant factors influencing hate crime. However, further research is needed to establish relationships between hate crime rate and factors such as race and residency status for the United States as a whole.