Property Crime Arrest Gap
About
The Property Crime Arrest Gap measures the ratio of property crime-related arrests among Black Americans to property crime-related arrests among White Americans relative to their respective populations in a county. Individuals from both Hispanic and non-Hispanic ethnicities are included. According to the Federal Bureau of Investigation’s (FBI) Uniform Crime Reporting (UCR) program, property crimes include four offenses: burglary, larceny-theft, motor vehicle theft, and arson 1. In 2023, data on property crime arrests in the U.S. showed that White individuals were arrested more often for property crimes than Black individuals, although Black individuals were overrepresented in arrests relative to their proportion of the population, according to The Sentencing Project 2.
Why is the Property Crime Arrest Gap important to the Structural Racism and Discrimination (SRD) Index?
The Property Crime Arrest Gap between the dominant White race and marginalized races, such as the Black race, is an important outcome of systemic discrimination and disparities in the criminal justice system. Disparities in arrest rates for property crimes often stem from systemic biases in policing, which disproportionately target communities of color. These disparities are rooted in historical patterns of racial segregation, economic disadvantage, and over-policing of minority neighborhoods, leading to higher arrest rates for Black and Hispanic individuals compared to White individuals for similar offenses 3. Policies such as stop-and-frisk and broken windows policing have further perpetuated these gaps, resulting in racial profiling and unequal treatment within the justice system 4,5. Arrest disparities for property crimes contribute to broader cycles of inequality by limiting economic mobility, increasing the likelihood of incarceration, and reducing access to housing, employment, and educational opportunities 6,7. Addressing this gap is essential for reducing structural racism within the criminal justice system and promoting equitable outcomes across communities.
What is the expected relation to Structural Racism and Discrimination?
A higher value of the Property Crime Arrest Gap between Black and White populations contributes to a higher value or score of the SRD Index.
How is the Property Crime Arrest Gap calculated?
Data Source
We obtained the number of arrests for Property Crime reported to the FBI’s Uniform Crime Reporting (UCR) Program each year by police agencies in the U.S., summarized yearly from the Inter-university Consortium for Political and Social Research (ICPSR) of the University of Michigan. We obtained the population data disaggregated by race from the IPUMS National Historical Geographic Information System (NHGIS) 8. All data are publicly available. Below are the specifics of the data files used from ICPSR.
2020: Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, Summarized Yearly, United States, 2020 (ICPSR 38788).
2010: 1) Uniform Crime Reporting Program Data: Arrests by Age, Sex, and Race, Summarized Yearly, United States, 2010 (ICPSR 33522); 2) Uniform Crime Reporting Program Data: County-Level Detailed Arrest and Offense Data, United States, 2010 (ICPSR 33523).
2000: 1) Uniform Crime Reporting Program Data [United States]: Arrests by Age, Sex, and Race, Summarized Yearly, 2000 (ICPSR 3997); 2) Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest and Offense Data, 2000 (ICPSR 3451).
1990: 1) Uniform Crime Reporting Program Data [United States]: Arrests by Age, Sex, and Race, Summarized Yearly, 1990 (ICPSR 23341); 2) Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest and Offense Data, 1990 (ICPSR 9785).
For all years: Jacob Kaplan’s Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Arrests by Age, Sex, and Race, 1974-2021. https://www.openicpsr.org/openicpsr/project/102263/version/V15/view
Data
We used the following two variables at the county level.
| Variables* | Year | Unit |
|---|---|---|
| Rate of Arrests for Property Crime per 100,000 Black Population | 1990 | 2000 | 2010 | 2020 | Number |
| Rate of Arrests for Property Crime per 100,000 White Population | 1990 | 2000 | 2010 | 2020 | Number |
* Individuals from both Hispanic and non-Hispanic ethnicities are included.
Methodology
We calculated the Property Crime Arrest Gap using a ratio formula:
$$
RPrBlWhProperty = \frac{PrBlProperty}{PrWhProperty}
$$
Where:
RPrBlWhProperty: Ratio of proportions of property crime arrests of Black to White populations
PrBlProperty = BlProperty / BlPop: Proportion of arrests of Black individuals for property crime (BlProperty) to the total Black population (BlPop)
PrWhProperty = WhProperty / WhPop : Proportion of arrests of White individuals for property crime (WhProperty) to the total Black population (WhPop)
Data on four violent crime offenses, murder, rape, robbery, and aggravated assault for Black and White populations were filtered from the yearly crime data reported by agencies. The counts for these four offenses were added to get the count for violent crimes by each race (Black and White). The yearly summarized data does not have the county FIPS codes for all agencies. So, the filtered dataset was joined to the monthly dataset (https://www.openicpsr.org/openicpsr/project/102263/version/V15/view) to get the county FIPS code for all agencies using the originating agency identifier code. The count for violent crimes by race was aggregated by county using the county FIPS code.
Missing Data
The missing data were imputed in the following steps.
- For 2020, the states of Illinois and Florida did not have any data.
- We used the agency-level yearly summarized violent crime arrest data from ICPSR. To minimize missing or NULL values, we used the county-level aggregated (all races combined) data to fill the counties with a 0 count of arrests in our dataset for the years 2010, 2000, and 1990. The logic behind this is that if the total count of arrests for all races is 0, the count of arrests for Black and White populations should also be 0. Using this logic, 238 counties in 2010, 303 in 2000, and 338 in 1990 were filled with 0 in place of NULL values. We did not find the county-level aggregated crime data for 2020.
- After the imputation from the county-level datasets, the remaining missing values were filled using the median value of the 8 nearest neighbors in the same state. The eight nearest neighbors were identified using the KDTree algorithm in Python’s scipy.spatial module 9.
- After imputing missing data form steps 1-3, we have 260 counties (Illinois, Florida, and Islands) with no data in 2020, 78 counties (Islands) with no data in 2010, 24 counties (Kentucky) with no data in 2000, and 22 counties (Georgia) with no data in 1990.
Limitations
A large amount of missing data in the year 2020, 260 counties, including all the counties in the states of Illinois and Florida.
References
1. Federal Bureau of Investigation. (2019). Crime in the United States, 2019: Property crime. U.S. Department of Justice.
2. Ghandnoosh, N., & Barry, C. (2023, November 2). One in Five: Disparities in crime and policing. The Sentencing Project. https://www.sentencingproject.org/reports/one-in-five-disparities-in-crime-and-policing/
3. Epp, C. R., Maynard-Moody, S., & Haider-Markel, D. (2014). Pulled over: How police stops define race and citizenship. University of Chicago Press.
4. Harris, D. A. (1999). Driving while Black: Racial profiling on our nation’s highways. Washington, DC: American Civil Liberties Union.
5. Gelman, A., Fagan, J., & Kiss, A. (2007). An analysis of the New York City police department’s “stop-and-frisk” policy in the context of claims of racial bias. Journal of the American statistical association, 102(479), 813-823.
6. Wacquant, L. (2001). Deadly symbiosis: When ghetto and prison meet and mesh. Punishment & society, 3(1), 95-133.
7. Western, B. (2006). Punishment and Inequality in America. Russell Sage Found.
8. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [Dataset]. IPUMS.
9. Virtanen, P., Gommers, R., Burovski, E., Oliphant, T. E., Weckesser, W., Cournapeau, D., … & Feng, Y. (2021). scipy/scipy: SciPy 1.6. 0. Zenodo.