Police Incentives, Policy Spillovers, and the Enforcement of Drug Crimes
-
Gregory J. DeAngelo
,
R. Kaj Gittings
Abstract
We consider the impact of a low priority initiative adopted in specific jurisdictions within Los Angeles (LA) County on police behavior. Low priority initiatives instruct police to make the enforcement of low level marijuana possession offenses their “lowest priority.” Using detailed data from the LA County Sheriff’s Department, a difference-in-differences strategy suggests that the mandate resulted in fewer arrests for misdemeanor marijuana possession in adopting areas relative to non-adopting. However, the lower relative reduction in marijuana arrests appears to be driven by an increase in misdemeanor marijuana arrests in nearby areas not affected by the mandate rather than a reduction in adopting areas. We interpret this result as suggestive evidence of policy spillovers from the low priority initiative.
Appendix
Count data models with poisson specification.
| Panel A: Misdemeanor Arrests | |||
| (1) | (2) | (3) | |
| Post | 0.5721*** | 0.7961*** | −1.0144 |
| (0.0305) | (0.0902) | (1.9350) | |
| LP*Post | −0.5646*** | −0.5610*** | −0.2998** |
| (0.2149) | (0.2135) | (0.1516) | |
| Reporting District FE | Yes | Yes | Yes |
| Period FE | No | Yes | Yes |
| RD-specific linear time trends | No | No | Yes |
| Observations | 66,686 | 66,686 | 66,686 |
| Panel B: Felony Arrests | |||
| (1) | (2) | (3) | |
| Post | 0.3794*** | 0.7846*** | 3.0387*** |
| (0.0373) | (0.1926) | (0.2205) | |
| LP*Post | −0.2248 | −0.2224 | −0.3873 |
| (0.2867) | (0.2852) | (0.3762) | |
| Reporting District FE | Yes | Yes | Yes |
| Period FE | No | Yes | Yes |
| RD-specific linear time trends | No | No | Yes |
| Observations | 66686 | 66686 | 66686 |
Note: The unit of observation is a reporting district-month. The dependent variable is count of misdemeanor or felony marijuana arrests in each reporting district-month. Models are estimated by Poisson regression with errors clustered by reporting district. Clustered standard errors are in parentheses. LP=1 if the reporting district is subject to the Low Priority Initiative. Post=1 after the law took effect. FE stands for fixed effects and RD stands for reporting district.
*p<0.1, ** p<0.05, ***p<0.01
Count data models with poisson specification for misdemeanor marijuana arrests by distance from West Hollywood.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Post | −0.3124 | 0.5235 | −0.2858 | −1.8554* |
| (1.0793) | (0.6616) | (0.3652) | (0.9552) | |
| LP*Post | −0.4439*** | −0.4219*** | −0.2705* | −0.1474 |
| (0.1596) | (0.1549) | (0.1506) | (0.1562) | |
| Reporting District FE | Yes | Yes | Yes | Yes |
| Period FE | Yes | Yes | Yes | Yes |
| RD-specific linear time trends | Yes | Yes | Yes | Yes |
| Distance from West Hollywood for Control RDs | <18.3 Miles | <26.9 Miles | Between 26.9 and 46.7 Miles | >46.7 Miles |
| Observations | 4451 | 16867 | 33428 | 16391 |
Note: The unit of observation is a reporting district-month. The dependent variable is count of misdemeanor or felony marijuana arrests in each reporting district-month. Models are estimated by Poisson regression with errors clustered by reporting district. Each column restricts the sample to a subset of control reporting districts based on a reporting district’s distance from West Hollywood. Using reporting districts as the unit of observation, the 5th percentile of the distance distribution is 18.3 miles from West Hollywood, the 25th percentile is 26.9, and the 75th percentile is 46.7 miles from West Hollywood. Models are estimated by least squares with errors clustered by reporting district. Clustered standard errors are in parentheses. LP=1 if the reporting district is subject to the Low Priority Initiative. Post=1 after the law took effect. FE stands for fixed effects and RD stands for reporting district.
* p<0.1, ** p<0.05, *** p<0.01
References
Adda, J., B. McConnell, and I. Rasul. 2014. “Crime and the Depenalization of Cannabis Possession: Evidence from a Policing Experiment,” 122 5 Journal of Political Economy 1130–1202.10.1086/676932Suche in Google Scholar
Benson, B.L., I. Kim, and D.W. Rasmussen. 1994. “Estimating Deterrence Effects: A Public Choice Perspective on the Economics of Crime Literature,” 61 1 Southern Economic Journal 161–168.10.2307/1060137Suche in Google Scholar
Benson, B.L., I. Kim, D.W. Rasmussen, and T.W. Zuehlke. 1992. “Is Property Crime Caused by Drug Use or by Drug Enforcement Policy?” 24 7 Applied Economics 679.10.1080/00036849200000036Suche in Google Scholar
Benson, B.L., and D.W. Rasmussen. 1991. “Relationship between Illicit Drug Enforcement Policy and Property Crimes,” 9 4 Contemporary Economic Policy 106–115.10.1111/j.1465-7287.1991.tb00354.xSuche in Google Scholar
Benson, B.L., D.W. Rasmussen, and I. Kim. 1998. “Deterrence and Public Policy: Trade-Offs in the Allocation of Police Resources,” 18 1 International Review of Law and Economics 77–100.10.1016/S0144-8188(97)00059-8Suche in Google Scholar
Benson, B.L., D.W. Rasmussen, and D.L. Sollars. 1995. “Police Bureaucracies, Their Incentives, and the War on Drugs,” 83 1–2 Public Choice 21–45.10.1007/BF01047681Suche in Google Scholar
DeAngelo, G., and E. Owens, 2017. Learning the Ropes: General Experience, Task-Specific Experience, and the Output of Police Officers. Working Paper.10.1016/j.jebo.2017.08.008Suche in Google Scholar
DeAngelo, G., and B. Hansen. 2014. “Life and Death in the Fast Lane: Police Enforcement and Traffic Fatalities,” 6 2 American Economic Journal: Economic Policy 231–257.10.1257/pol.6.2.231Suche in Google Scholar
DeAngelo, G., and B.C. McCannon. 2015. “Public Outcry and Police Behavior,” 16 2 The BE Journal of Economic Analysis & Policy.10.1515/bejeap-2015-0101Suche in Google Scholar
Gil, R., and J. Marion. 2012. “Self-Enforcing Agreements and Relational Contracting: Evidence from California Highway Procurement,” 29 2 Journal of Law, Economics, and Organization 239–277.10.1093/jleo/ewr026Suche in Google Scholar
Lazzarini, S.G., G.J. Miller, and T.R. Zenger. 2004. “Order with Some Law: Complementarity versus Substitution of Formal and Informal Arrangements,” 20 2 Journal of Law, Economics, and Organization 261–298.10.1093/jleo/ewh034Suche in Google Scholar
Leaver, C. 2009. “Bureaucratic Minimal Squawk Behavior: Theory and Evidence from Regulatory Agencies,” 99 3 American Economic Review 572–607.10.1257/aer.99.3.572Suche in Google Scholar
Lum, C., and D.S. Nagin. 2015. “Reinventing American Policing,” 35 2 The Crime Report 239–252.10.1086/688462Suche in Google Scholar
Makowsky, M.D., and T. Stratmann. 2009. “Political Economy at Any Speed: What Determines Traffic Citations?,” 99 1 The American Economic Review 509–527.10.1257/aer.99.1.509Suche in Google Scholar
Makowsky, M.D., and T. Stratmann. 2011. “More Tickets, Fewer Accidents: How Cash-Strapped Towns Make for Safer Roads.”,” 54 4 Journal of Law and Economics 863–888.10.1086/659260Suche in Google Scholar
Ross, A., and A. Walker. 2017. “The Impact of Low Priority Laws on Criminal Activity: Evidence from California,” Forthcoming at Contemporary Economic Policy.10.2139/ssrn.2648342Suche in Google Scholar
Sollars, D.L., B.L. Benson, and D.W. Rasmussen. 1994. “Drug Enforcement and the Deterrence of Property Crime among Local Jurisdictions,” 22 1 Public Finance Quarterly 22–45.10.1177/109114219402200102Suche in Google Scholar
Tonry, M. 1996. Sentencing Matters. Oxford, England: Oxford University Press.Suche in Google Scholar
Tsebelis, G. 1989. “The Abuse of Probability in Political Analysis: The Robinson Crusoe Fallacy,” 83 1 The American Political Science Review 77–91.10.2307/1956435Suche in Google Scholar
© 2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Empirical Legal Studies: CELS and CELSE
- Missing Rich Offenders: Traffic Accidents and the Impartiality of Justice
- Are Advocates General Political? An Empirical Analysis of the Voting Behavior of the Advocates General at the European Court of Justice
- The More Med-Mals, the Shorter the Litigation: Evidence from Florida
- Police Incentives, Policy Spillovers, and the Enforcement of Drug Crimes
- Determinants of Judicial Efficiency Change: Evidence from Brazil
- Cartels as Defensive Devices: Evidence from Decisions of the European Commission 2001–2010
Artikel in diesem Heft
- Empirical Legal Studies: CELS and CELSE
- Missing Rich Offenders: Traffic Accidents and the Impartiality of Justice
- Are Advocates General Political? An Empirical Analysis of the Voting Behavior of the Advocates General at the European Court of Justice
- The More Med-Mals, the Shorter the Litigation: Evidence from Florida
- Police Incentives, Policy Spillovers, and the Enforcement of Drug Crimes
- Determinants of Judicial Efficiency Change: Evidence from Brazil
- Cartels as Defensive Devices: Evidence from Decisions of the European Commission 2001–2010
