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Researchers increasingly explore the consequences of policing for the educational outcomes of minority youth. This study contributes to this literature by asking: First, what are racial/ethnic disparities in long-term exposure to neighborhood policing? Second, how does this exposure affect high school graduation? Third, how much of the ethnoracial gap in high school graduation would remain if neighborhood policing was equalized? To address these questions, we use data from the New York City Department of Education and follow five cohorts of NYC public school students from middle to high school. Our findings reveal starkly different experiences with neighborhood policing across racial/ethnic groups. Using novel methods for time-varying treatment effects, we find that long-term exposure to neighborhood policing has negative effects on high school graduation with important differences across racial/ethnic groups. Using gap- closing estimands, we show that assigning a sample of Black and Latino students to the same level of neighborhood policing as white students would close the Black-white gap in high school graduation by more than one quarter and the Latino-white gap by almost one fifth. Alternatively, we explore interventions where policing is solely a function of violent crime, which close the Black-white gap by as much as one-tenth. Our study advances previous research by focusing on cumulative, long-term exposure to neighborhood policing and by assessing various counterfactual scenarios that inform research and policy. Keywords: Policing, Education, Inequality, Neighborhoods, Racial Disparities
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Detecting disparities in police deployments using dashcam data
Large-scale policing data is vital for detecting inequity in police behavior and policing algorithms. However, one important type of policing data remains largely unavailable within the United States: aggregated police deployment data capturing which neighborhoods have the heaviest police presences. Here we show that disparities in police deployment levels can be quantified by detecting police vehicles in dashcam images of public street scenes. Using a dataset of 24,803,854 dashcam images from rideshare drivers in New York City, we find that police vehicles can be detected with high accuracy (average precision 0.82, AUC 0.99) and identify 233,596 images which contain police vehicles. There is substantial inequality across neighborhoods in police vehicle deployment levels. The neighborhood with the highest deployment levels has almost 20 times higher levels than the neighborhood with the lowest. Two strikingly different types of areas experience high police vehicle deployments — 1) dense, higher-income, commercial areas and 2) lower-income neighborhoods with higher proportions of Black and Hispanic residents. We discuss the implications of these disparities for policing equity and for algorithms trained on policing data.
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- Award ID(s):
- 2142419
- PAR ID:
- 10425455
- Date Published:
- Journal Name:
- Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
- Page Range / eLocation ID:
- 534 to 544
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3593013.3594020
