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The impact of recreational marijuana dispensaries on crime: evidence from a lottery experiment

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Many North American jurisdictions have legalized the operation of recreational marijuana dispensaries. A common concern is that dispensaries may contribute to local crime. Identifying the effect of dispensaries on crime is confounded by the spatial endogeneity of dispensary locations. Washington State allocated dispensary licenses through a lottery, providing a natural experiment to estimate the causal effect of dispensaries on neighborhood-level crime. Combining lottery data with detailed geocoded crime data, we estimate that the presence of a dispensary has no significant impact on local crime in the average neighborhood. We estimate a small rise in property crime in low-income neighborhoods specifically.

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  1. The federal government regulates drugs through the Controlled Substances Act (21 U.S.C. §811), which does not recognize the difference between the medical and recreational use of marijuana.

  2. “Marijuana producer” means a person licensed by the WSLCB to produce and sell marijuana at wholesale to marijuana processors and other marijuana producers. “Marijuana processor” means a person licensed by the WSLCB to process marijuana into usable marijuana and marijuana-infused products, package and label usable marijuana and marijuana-infused products for sale in retail outlets, and sell usable marijuana and marijuana-infused products at wholesale to marijuana retailers. “Marijuana retailer” means a person licensed by the WSLCB to sell usable marijuana and marijuana-infused products in a retail outlet.

  3. Rape incidents constitute only 0.2% of reported crimes within jurisdictions in our sample where data is available.

  4. To further clarify the calculation of \(D_{it}^d\) and \(W_{it}^d\) in cases where treatment circles overlap, we provide an example in Fig. 8.

  5. We allow \(D_{it}^d\) and \(W_{it}^d\) to exceed one in order to capture treatment intensity. The choice implies that the partial effect of being treated by two dispensaries is twice that of being treated by a single dispensary. We test robustness to alternate constructions of \(D_{it}^d\) and \(W_{it}^d\) in Table 14 and find results are consistent.

  6. We test for finite sample bias by estimating the Cragg–Donald Wald F statistic. We estimate the F statistic at over 500 for all treatment radii, strongly rejecting the presence of significant bias.

  7. For example: Surveillance video shows pot shop owner use bear spray to thwart armed robbery, Fox 13, Seattle, 02/17/2020; Violent pot shop robbers wanted in Seattle, Fox 13, Seattle, 02/07/2020; ‘They held a gun to my head’: Armed robbers hit S. Seattle pot shop, KOMO News, 11/19/2019.


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We thank Gary Engelhardt, Simona Fabrizi, Alfonso Flores-lagunes, Ryan Greenaway-McGrevy, Ross Jestrab, Je.rey Kubik, Ste.en Lippert, David Neumark, Peter Phillips, Alex Rothenberg, Perry Singleton, Asha Sundaram, James Tremewan, Emily Wiemers, and seminar participants at the University of Auckland, Midwest Economics Association Conference, and Southern Economic Conference for their helpful comments and valuable suggestions. We appreciate the Pierce County Sheri. Department, the City of Tacoma Police Department, the Seattle Police Department, and the Washington State Liquor and Cannabis Board for their data support. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations of interest: none. All errors are our own.

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Appendix tables

See Tables 9, 10, 11, 12, 13, 14, 15.

Table 9 Effect of dispensaries on overall number of local crimes, combined areal unit approach
Table 10 Effect of dispensaries on overall number of local crimes, outcome in crimes per 10,000 residents
Table 11 Effect of dispensaries on local crime by disaggregated crime type
Table 12 Effect of dispensaries on local crime by crime type, using log-transformed crime measure
Table 13 Effect of winning the lottery on local crime, Poisson regression
Table 14 Effect of dispensaries on overall number of local crimes, alternative definitions of D and W
Table 15 Effect of winning the lottery on local crime by crime type and neighborhood characteristics, alternative neighborhood type definitions

Appendix figures

See Figures 6, 7, and 8.

Fig. 6
figure 6

Mean Monthly Crime Counts by Lottery Status. Mean monthly crime counts with a 95% confidence interval at 200 ms around dispensaries for a all crimes, b property crimes, c violent crimes, and d drug crimes by lottery result from 2010 to 2016. The vertical red line indicates the time of the lottery drawing. Month 0 is equal to January 2010. A significant threat to the identification would be the presence of differential crime trends between treatment and control areas. Visual inspection suggests the pre-treatment trends are parallel

Fig. 7
figure 7

Crime Heat Maps. The figure displays the frequency of crime occurring in a particular square unit, within a month, relative to the location of a dispensary applicant, averaged across applicants. Panel a shows the average crime activity before the lottery event, across all applicant locations. We compare lottery compliers (those winners who opened a dispensary at their application address) before (b) and after (c) the lottery event. We do not find a strong spatial correlation between applicant sites and pre-lottery crime rates when examining the immediate areas

Fig. 8
figure 8

Calculating W and D When Treatment Areas Overlap. The figure and table illustrate a hypothetical case of calculating values of W and D in a case where the treatment areas of three applicants overlap. In this example, at a specific time (t) lottery applicants X and Y have won the dispensary lottery but only applicant X is operating a dispensary. a-d represent areas as a share of a unit circle. For example, if a covers 20% of a circle, a=0.2

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Dong, X., Tyndall, J. The impact of recreational marijuana dispensaries on crime: evidence from a lottery experiment. Ann Reg Sci (2023).

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