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Redirecting revenues from law enforcement fines, forfeitures, and related fees to fund local nonprofits: a policy design proposal

Published online by Cambridge University Press:  20 February 2025

Inkyu Kang*
Affiliation:
Department of Public Administration and Policy, School of Public and International Affairs, University of Georgia. 355 S Jackson St., Athens, GA 30602, USA
Su Young Choi
Affiliation:
Department of Public Administration and Policy, School of Public and International Affairs, University of Georgia. 355 S Jackson St., Athens, GA 30602, USA
*
Corresponding author: Inkyu Kang; Email: inkyu.kang@uga.edu
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Abstract

Monetary sanctions in law enforcement, including fines, forfeitures, and related fees, are susceptible to exploitation by agencies for self-serving profit motives. However, a key challenge in addressing this issue is disentangling the agencies’ profit-driven motives from their genuine commitment to upholding law and order. Against this backdrop, this study examines a novel policy design proposal: redirecting revenues from law enforcement to fund local nonprofits. This approach seeks to eliminate conflicts of interest without restricting the use of monetary sanctions as a tool for law enforcement, while simultaneously channeling revenues toward community benefits. Experimental evidence based on a representative sample of US adults (n = 1,030) further highlights this approach’s potential to improve public perceptions of, and attitudes toward, law enforcement agencies. The study concludes by discussing the broader implications of this proposal for the political economy of law enforcement, as well as key considerations and potential challenges for its implementation.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press
Figure 0

Table 1. Descriptive statistics

Figure 1

Figure 1. Vignette and random assignment.Note: Treatments that are highlighted in bold were not highlighted in the actual vignettes. The two survey questions were displayed in random order to prevent item-order bias in the measurements.

Figure 2

Table 2. Linear regression results summary (main findings)

Figure 3

Figure 2. Visualization of treatment effects.

Figure 4

Table A1. Generalized ordered logistic regression summary (baseline models)

Figure 5

Table A2. Analysis of variance (ANOVA) results summary

Supplementary material: Link

Kang and Choi Dataset

Link