Effects of County-Level Characteristics on Forcible Rape Arrest Rates

Background: The majority of sexual assault cases (60%) are not reported to the police and among cases that are reported, very few offenders will be arrested, prosecuted, and convicted. Out of 100 rape cases, only 40 will be reported to law enforcement, only 10 will lead to an arrest, 8 will be prosecuted, 4 will lead to a felony conviction, and only 3 rape offenders will be imprisoned. Research suggests that community-level characteristics (e.g. high poverty), contribute to drug-related offenses and violent crime such as burglary, homicide, and aggravated assault; however, few studies have explored the effects of similar community-level characteristics on forcible rape rates and subsequent arrest rates. Understanding contextual factors that may impact arrest rates can inform local criminal justice responses and policies on rape and sexual assault.

Methods: Two secondary data sources on county-level data were used for this study (n = 3,178). Crime data were used from the 2012 Uniform Crime Reports (UCR) and health data were linked using the 2012 County Health Rankings Data (CHRD). Linear regression analyses were conducted to determine whether violent crime arrest rate (i.e. murder, aggravated assault, and burglary), drug abuse crime arrest rate (i.e. possession, manufacturing, and/or sale of controlled substances), median income, unemployment, poor or fair health, and inadequate social support are predictive of respective forcible rape arrest rates.

Results: Findings suggest that violent crime arrest rates, drug abuse crime arrest rates, median income, unemployment, and poor or fair health are significant predictors of forcible rape arrest rates (R²= .09; F _{(6, 1690)} = 27.05, p<.001). Specifically, increased arrest rates for both violent crimes (b = .02; t = 9.59; p<.001) and drug abuse crimes (b = .001; t = 2.21; p<.05) are predictive of higher rape arrest rates, after controlling for other variables in the model. Higher levels of median household income (b = -.05; t = -2.67; p<.01), increased percentages of unemployment (b = -.19; t = -3.37; p<.01) and higher levels of poor or fair health (b = -.12; t = -2.92; p<.01) are predictive of lower rape arrest rates, after controlling for other variables. Inadequate social support was not a significant predictor of rape arrest rates. 

Conclusion: Findings support feminist theories on violence against women and suggest that county-level predictors of sexual violence may be, to some extent, different than predictors of other types of violent crime. Specifically, prior research suggests that high unemployment rates are positively linked to other types of violent crimes, yet lower percentages of unemployment were related to higher rape arrest rates in the current study. Additionally, lower levels of poor or fair health were related to higher levels of rape arrest rates and inadequate social support did not have an effect on arrest rates, suggesting that these factors may contribute to other types of violent crime, but not necessarily to rape arrest rates. Finally, data suggest that perpetrators of sexual violence in lower income counties experience higher rates of arrests, yet perpetrators of sexual violence are from all socioeconomic backgrounds.