The Material of Policing: Budgets, Personnel and the United States’ Misdemeanour Arrest Decline

Abstract

What accounts for the steady decline in misdemeanour arrest rates in the United States following their peak in the mid-1990s? This article links the fluctuation in low-level law enforcement to changes in the budget and staffing resources cities devoted to policing. This materialist explanation contrasts with accounts that emphasize policy changes like the adoption of community policing. Dynamic panel regression analyses of 940 municipalities indicate low-level arrest rates declined most in places that reduced their police expenditure and personnel, net of crime and other controls. The adoption of community policing was unrelated to misdemeanour arrests. Findings suggest lawmakers should consider how increasing police budgets or police force sizes will likely be accompanied by increases in misdemeanour arrests and their attendant harms.

Proximity and Police Departments: Spatial Dynamics of the Misdemeanor Arrest Decline

“…In line with resource dependency theory, research has shown that police agencies with more funding make more low-level arrests (Beck et al 2022). To capture this dynamic, we control for cities’ police expenditures using data from the U.S. Census Bureau’s Annual Survey of State and Local Government Finances.…”

“…This past research has measured the spread of policing policies. Sensitive to how policies and practices are often divorced (Beck et al 2022;Cordner and Biebel 2005;Manning 2008), in the present study we examine a police practice, misdemeanor arrests.…”

“…At higher scales, evidence of the relationship between arrest and crime rates is mixed. Although some city-level studies have documented an association between crime and arrests (e.g., Beck et al 2022; Chappell, MacDonald, and Manz 2006), others have not (e.g., Lum and Vovak 2018).…”

The effect of real-world proactive policing on crime

“…All crimes were aggregated annually, measured at the beat level, and converted into rates per 10,000 residents in Seattle police beats (see Figure 2). The crime rates were transformed into logs due to their positively skewed distributions within beats, which is a common procedure in longitudinal analyses of crime (e.g., Beck et al, 2022). Moreover, measuring rates of proactivity and crime in logs is a popular approach within this line of research given that it allows for the interpretation of potential deterrent effects in the form of elasticities (e.g., Chalfin & McCrary, 2017;Evans & Owens, 2007;Mello, 2019).…”

“…First, we estimated the effect of proactive policing on crime within police beats by up to two years. Police behavior in one year can influence crime up to two years later across multiple levels of spatial analysis (i.e., blocks, cities, states) (Marvell & Moody, 1996;8 We employed CLPMs over the more popular general methods of moments (GMM) estimators used to identify deterrent effects of proactivity on crime for two important reasons (e.g., Beck et al, 2022;Wu et al, 2021). First, empirical simulation evidence suggests that CLPMs exhibit less bias than the GMM estimators in small sample settings (N ≤ 100), which is advantageous given the current sample of police beats (N = 51) (Allison et al, 2017).…”