Race, excess suspicion, and larceny in Upstate NY

ABSTRACT

Archival crime data collected by a police agency in Upstate New York from 2008 to 2015; outcome, sentencing, and incarceration data collected by the New York State’s Department of Criminal Justice Statistics; and demographic data collected by the U.S. Census were analyzed to explore how a suspect’s race and sex affect the investigation, prosecution, conviction, and sentencing in larceny cases. Results suggest that Black men were more likely to be the targets of excess suspicion, less likely to be granted leniency by prosecutors, no more likely to be convicted, but, if convicted, more likely to be incarcerated than White men.

KEYWORDS:

• Consumer Racial Profiling
• Larceny
• Law Enforcement
• Racial Profiling
• Sentencing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1478601X.2022.2081966

Notes

1. Combining records after 2015 was not possible due to major changes in the crime records management software.

2. An alternative approach was also explored that looked only at the arrests for those incidents where at least one person was suspected of either Grand or Petit Larceny. All arrests stemming from those cases were included regardless of whether the eventual arrest code was classified as a larceny. However, only one record per arrestee was counted. A second Poisson regression model was fitted using this second set of arrest data, for the number of suspects as dependent variable and race, sex, and Grand/Petit Larceny as exploratory variables.

3. Because multiple suspects may be linked with the same case, the number of suspects could be higher than the total number of cases.

4. For a small number of suspects race was designated as ‘other’. Because of their small number they were also excluded from analysis.

5. The categorization into 4 groups is arbitrary, and a range of 3 to 10 categories have been explored. All analyses show similar results, and only results from 4 groups categorization are presented here.

6. The general mathematical expression of the baseline-category logistic regression model is specified in Appendix III.

Additional information

Notes on contributors

Sean G. Massey

Sean G. Massey is Associate Professor of Women’s, Gender, and Sexuality Studies and Psychology at Binghamton University, SUNY. He received his PhD in Social Personality Psychology from the Graduate Center of the City University of New York. His research focuses on the study of sexuality and gender, attitudes toward LGBTQ+ people, racial bias in educational and law enforcement contexts, and the relationship between social science and social change.

Richard A. Kauffman

Richard A. Kauffman, Jr. is Assistant Professor of Psychology at SUNY Oneonta. An evolutionary theorist, he received his PhD in Ecology, Evolution & Behavior from the Biological Sciences Department at Binghamton University. His research focuses on extending the explanatory scope of evolutionary theory to all aspects of humanity in addition to the rest of the biological world; emphasizing the utility of evolutionary theory as an applied science to inform evidence-based policy and practice at the individual, local and global scales.

Mei-Hsiu Chen

Mei-Hsiu Chen is the Director of Statistical Consulting Services in the Department of Mathematics and Statistics at Binghamton University, SUNY. She received her PhD in Biostatistics from Brown University. Her research ranges from designing and developing statistical methods in evaluating the accuracies of imaging modalities for screening and diagnosing cancers to applying appropriate methods in uncovering and understanding the contributing factors in basic sciences, educational disciplines, and law enforcement.

Wangshu Tu

Wangshu Tu is a postdoctoral fellow in the School of Mathematics and Statistics at Carleton University. He received his PhD in Mathematical Sciences from Binghamton University. His research interests are machine learning, mixture models, computational statistics and methodological development for microbiome data analysis.