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Cannabis Control and Crime: Medicinal Use, Depenalization and the War on Drugs

Arthur Huber III , Rebecca Newman and Daniel LaFave EMAIL logo

Abstract

To date, 27 states and the District of Columbia have passed laws easing marijuana control. This paper examines the relationship between the legalization of medical marijuana, depenalization of possession, and the incidence of non-drug crime. Using state panel data from 1970 to 2012, results show evidence of 4–12 % reductions in robberies, larcenies, and burglaries due to the legalization of medical marijuana, but that depenalization has little effect and may instead increase crime rates. These effects are supported by null results for crimes unrelated to the cannabis market and are consistent with the supply-side effects of medicinal use that are absent from depenalization laws as well as existing evidence on the substitution between marijuana and alcohol. The findings contribute new evidence to the complex debate surrounding marijuana policy and the war on drugs.

Appendix

Table A1:

Data sources.

VariableSourceDescription
Law passage datesMarijuana Policy Project (2014), NORML (2014), Pacula et al. (2015)Year in which medical marijuana or depenalization law passed
Crime ratesBureau of Justice Statistics – Uniform Crime Reporting Statistics (UCR) (1970–2012)Per 100,000 population
Unemployment ratesStatistical Abstracts of the United States; Bureau of Labor Statistics/US Census BureauStatistical Abstract figures for 1970–1979; Census bureau figures for 1980–2012
Poverty rateStatistical Abstracts of the United States; Bureau of Labor Statistics/US Census BureauCensus Bureau figures for 1970, 1980–2012; Statistical abstract figures for 1971–1979
Personal per capita IncomeBureau of Economic AnalysisAdjusted to 2012 dollars
Incarceration ratesBureau of Justice Statistics; Sourcebook of Criminal Justice StatisticsPrisoners per 100,000 population. Historical Statistics on Prisoners for 1970–1971. Sourcebook figures for 1972–2012
Policing countsStatistical Abstracts of the United States; Uniform Crime Reporting Statistics (UCR) (1970–2012)Police per 100,000 population. Statistical abstracts for 1970–1992, UCR statistics for 1995–2012
Abortion lawsDonohue and Levitt (2001)Binary indicator for legalized abortion lagged 18 years. Following Donohue and Levitt, 5 states passed in 1970 and the remainder in 1973
Castle doctrine lawsCheng and Hoekstra (2013), Currier (2012)Binary indicator of having castle doctrine law. Cheng and Hoekstra through 2010. Individual state records 2011–2012
Shall-issue gun lawsAyres and Donohue (2003), National Rifle Association – Institute for Legal Action (2012)Binary indicator of state having a right to carry/shall-issue law
Alcohol policy lawsBeer Institute (2015), USDOT (1997), Carpenter (2007), Grant (2010)Minimum legal drinking age, state-level beer tax (cents/gallon), binary indicators for BAC 0.08 and zero-tolerance DUI laws
State population and demographicsUS Census BureauDemographics converted to percentage of total state population
Table A2:

Heterogeneity in state medicinal use laws.

Dependent variable: log of [...] crime rate
Violent crimeProperty crimeRobberyMurderAggravated assaultForcible rapeLarcenyBurglaryMotor vehicle theft
(1)(2)(3)(4)(5)(6)(7)(8)(9)
Medicinal use
Home cultivation, no dispensaries−0.122**−0.105*−0.176**−0.109**−0.104**−0.028−0.115**−0.111**0.016
(0.035)(0.042)(0.049)(0.032)(0.040)(0.016)(0.037)(0.042)(0.069)
Home cultivation and dispensaries−0.232**−0.108*−0.172**−0.018−0.282**−0.104**−0.165**−0.097**0.231**
(0.035)(0.043)(0.031)(0.038)(0.042)(0.031)(0.038)(0.037)(0.069)
Depenalization−0.0220.034*0.115**0.058−0.0740.0250.028*0.067**−0.023
(0.033)(0.015)(0.019)(0.034)(0.047)(0.027)(0.014)(0.014)(0.033)
State and year FEYYYYYYYYY
Additional controlsYYYYYYYYY
Observations2,1932,1932,1932,1932,1932,1932,1932,1932,193

Notes: Regressions include state and year fixed effects, additional time-varying controls, and population weights. State medicinal use laws are classified as including home cultivation and dispensaries following Pacula et al. (2015) and a reading of individual state statutes. Standard errors are calculated allowing for heteroskedasticity, correlation across states, and autocorrelation using a fixed-b approach appropriate for state panel data (see Vogelsang 2012). See Table 3C for included control variables.

  1. *Significant at the 5 % level.

    ** Significant at the 1 % level.

Acknowledgements

Thanks are due to Christopher Carpenter, Patrick Coate, Darren Grant, Samara Gunter, Tim Hubbard, Kyle Mangum, Ralph Mastromonaco, Randy Nelson, Michael Uhrich, seminar participants at the Issues in Political Economy Research Conference, and Peggy Menchen.

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Published in Print: 2016-10-1

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