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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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The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories
Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.
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- Award ID(s):
- 2104607
- PAR ID:
- 10361545
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Sociological Methods & Research
- Volume:
- 53
- Issue:
- 2
- ISSN:
- 0049-1241
- Format(s):
- Medium: X Size: p. 507-570
- Size(s):
- p. 507-570
- Sponsoring Org:
- National Science Foundation
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