How Medical Marijuana Laws Affect Crime Rates

Transcription

1 How Medical Marijuana Laws Affect Crime Rates Catherine Alford Department of Economics University of Virginia 237 Monroe Hall Charlottesville, VA September 2014 Abstract Since 1996, 23 states and the District of Columbia have passed medical marijuana laws. Opponents of medical and recreational marijuana laws argue that increasing access to marijuana will increase crime, while proponents argue the opposite. Using difference-in-differences, I take advantage of the legal changes across states and over time to estimate the effects of medical marijuana laws on crime rates. I find that allowing for marijuana retail stores (called dispensaries) increases overall state property crime rates by 8.12 percent and increases robbery rates by percent. On the other hand, allowing for home cultivation of marijuana has no statistically significant effect on property crime rates and decreases the robbery rate by percent. These results demonstrate the importance of market design in influencing the nature of marijuana transactions and the resulting incentives for crime following the legalization of marijuana. JEL Codes: H75, K42, I18 Keywords: Marijuana; Medical Marijuana Laws; Crime Corresponding author. Ph.D. candidate in the Economics Department at the University of Virginia. cea9e@virginia.edu. Phone: I would like to thank Leora Friedberg and Daniel Rees for their helpful comments. I would also like to thank the participants at the University of Virginia workshop for their helpful suggestions. All remaining errors are my own.

2 1 Introduction Over the past two decades, the legal status of marijuana has undergone dramatic changes in the United States. While possessing marijuana is still illegal under federal law, many states have passed mandates that either legalize the sale of marijuana to certain individuals or relax the punishment associated with possessing it. Since 1996, 23 states and the District of Columbia have passed medical marijuana laws (MMLs), which make it legal for individuals with certain medical conditions to possess and use marijuana. More recently, Colorado and Washington have legalized the possession and recreational use of marijuana by all individuals over 21 years of age and several other states are considering similar mandates. The recent passage of these laws has spurred debates over the costs and benefits of legalizing marijuana use. Understanding the effects of legalizing marijuana, whether for recreational or medical use, is becoming increasingly important as more states are considering and developing marijuana mandates. While it is too early to evaluate the impact of recreational marijuana laws on crime, the legal changes across states and over time allow me to to estimate the effects of medical marijuana laws on crime rates. Support for legalizing marijuana is typically based on several key arguments. Proponents often cite medical research that provides clinical evidence that, among other things, marijuana can help ease neuropathic pain; can alleviate some of the negative medical outcomes associated with multiple sclerosis; and can help ease nausea associated with HIV (Wilsey et al. (2012), Corey-Bloom et al. (2012), Riggs et al. (2012)). Proponents also argue that legalizing marijuana could save tax payers money by reducing costs associated with incarcerating non-violent individuals involved in the marijuana trade. According to Gettman (2007), the government spends $10.7 billion annually on marijuana related arrests. Additionally, legalizing marijuana allows states to tax the commodity. During the first month of sales of recreational marijuana, Colorado received $2 million in taxes related to recreational marijuana sales. Combined with taxes related to sales of medical marijuana, the state received around $3.5 million in taxes. 1 This money will be allocated towards public schools, infrastructure, and youth campaigns about substance use. Opponents of legalizing access to marijuana often argue that marijuana use leads to worse schooling and work outcomes and increases crime. For example, DeSimone (2002) finds that the use of marijuana substantially reduces the likelihood of employment and Yamada et al. (1996) find significant adverse effects of marijuana use on high school graduation rates. Pacula and Kilmer (2003) find a positive 1 2

3 relationship between self-reported marijuana use and non-drug related violent, property, and incomeproducing crime among recently arrested individuals. However, they also find evidence suggesting that marijuana use may only increase the likelihood of getting caught committing crimes. Research also shows that marijuana use in adolescence is significantly associated with aggressive behavior later in life (White, Hansell (1998)). Additionally, there is public concern that marijuana retail stores, which are often forced to operate as cash-only businesses will become the target of crime. 2 In states that have enacted laws that allow for marijuana retail stores, law enforcement officials have suggested that dispensaries have brought with them an increase in the crime rate. Earnie Martinez, a Denver police detective told the Denver post that [a]cross the state, we re seeing an increase in crime related to dispensaries. 3 In the past, it has been difficult to study the effects of marijuana legalization. Several states decriminalized marijuana during the 1970s. However, studying marijuana decriminalization laws is complicated because the laws can be unwritten policies by law enforcement agencies and hence difficult to measure. Additionally, marijuana decriminalization laws only directly affect the demand of marijuana; supplying marijuana remains illegal under these laws. As states like Colorado and Washington are developing legal marijuana markets, it is important to know the impact of different market design choices. Medical marijuana laws directly affect both the demand and the supply of marijuana by providing ways for patients to legally obtain marijuana and for suppliers to legally provide it. Studying the effects of medical marijuana laws can provide a better understanding of how different market design choices of recreational marijuana laws affect outcomes. 4 Research has also been limited, because it is difficult to observe and collect data about marijuana markets (such as who is purchasing marijuana, who is providing it, how much it costs). However, examining changes in violent and property crime can give an early indication of the criminality induced by different market design choices. While Morris et al. (2014) also study how the enactment of medical marijuana laws affect crime rates, my research differs in two important ways. First, I demonstrate the critical importance of including state-specific time trends in a difference-in-differences analysis with a long panel. As I show, pre-existing trends in crime rates differ across states, and notably, differ in states that legalized medical marijuana versus states that did not. Inclusion of state-specific time trends dramatically impacts the estimated effects of MMLs on crime rates. In several cases, particularly when considering the effects on property crime, including trends changes the sign of the estimated effects The Denver Post 4 See for a discussion about Mark Kleiman s recommendations for Washington s recreational marijuana market. 3

4 Second, I examine the effects of different dimensions of these laws. Aspects of the laws considered include whether or not the laws allow for marijuana dispensaries, or retail stores, and whether the laws allow for home cultivation. I find that allowing for dispensaries increases overall property crime rates by 8.12 percent. More specifically, MMLs increase burglary rates and larceny and theft rates. Enacting an MML that allows for retail stores does not statistically significantly increase overall violent crime rates. However, allowing for retail stores increases the robbery rate by percent. Enacting an MML that allows for home cultivation, on the other hand, does not increase crime and in some cases may decrease it. These results suggest that legalizing medical marijuana, but not allowing for dispensaries, may decrease robbery rates. The results presented in this paper illustrate that, when formulating marijuana legalization mandates, particular attention needs to be paid not only to the effects of the overall legalization but also to particular policy dimensions. 2 Background The current debate surrounding the legalization of marijuana has its roots in laws passed in the 1970s, when 11 states decriminalized marijuana. Generally, these decriminalization laws mandate that firsttime possession for a small amount of marijuana will not result in prison time or a criminal record. Decriminalization laws may affect crime rates by increasing the number of individuals using marijuana. Without providing a legal market for individuals to purchase marijuana, this may result in an increase in the size of the illegal marijuana market and the violence associated with illegal drug trafficking. Single (1989) and Thies and Register (1993) examine whether decriminalization laws affect the use of marijuana. Neither paper finds evidence that these laws increase marijuana use. Decriminalization laws may also affect crime by changing policing practices. A study evaluating the impact of California s decriminalization law finds that marijuana possession offenses decreased by approximately 50 percent while arrests of other types of drug offenders increased during the first 6 months after the enactment of the law. Furthermore, there was a reduction of approximately 75 percent in law enforcement and judicial system costs during the same period, suggesting that law enforcement may have reallocated resources after marijuana was decriminalized. 5 In this paper I focus on the effects of medical marijuana laws on crime rates for two main reasons

5 First, studying the effects of marijuana decriminalization laws is complicated because the laws are often unwritten policies by law enforcement agencies and hence difficult to measure. For example, New York decriminalized marijuana in 1977 by making possession of 25 grams or less of marijuana a noncriminal violation if the marijuana is not in plain view. However, possession of a small amount is considered a crime if the marijuana is in open view, leaving the law open to interpretation by law enforcement officers and state prosecutors. 6 Second, research on the effects of marijuana decriminalization laws may not be very informative about the effects of marijuana legalization. Decriminalization laws only directly affect the demand of marijuana; supplying marijuana remains illegal under these laws. As states like Colorado and Washington are developing legal marijuana markets, it is important to know the impact of different market design choices. Medical marijuana laws, on the other hand, directly affect both the demand and the supply of marijuana by creating a legal market for marijuana. Studying the effects of medical marijuana laws can provide a better understanding of how different market design choices of recreational marijuana laws affect outcomes. Over the past two decades, 23 states and the District of Columbia have passed laws legalizing the medical use of marijuana. MMLs may affect crime rates through several channels. First, they may increase the medical and recreational use of marijuana by lowering the perceived health and legal risks associated with using marijuana. An increased number of marijuana users may in and of itself be a source of increased crime. For example, using marijuana may act as a gateway into other illegal activities. Several papers investigate whether MMLs affect the use of marijuana, although a clear consensus has not been reached. 7 Results vary based on the data set used and the time period considered. Harper et al. (2012) and Anderson et al. (2012) focus on marijuana use among youth. Using the National Survey on Drug Use and Health, Harper et al. (2012) find that medical marijuana laws decreased past-month use among 12 to 17 year olds. On the other hand, using data from Youth Risk Behavior Surveys, the National Longitudinal Survey of Youth 1997, and the Treatment Episode Data Set, Anderson et al. (2012) find no evidence of medical marijuana laws affecting use among youth. Gorman and Huber (2007) study whether the level of cannabis use among recent arrestees is affected by medical marijuana laws. They find that these laws do not significantly affect use. Chu (2012) and Pacula et al. (2013), on the other hand, find evidence of medical marijuana laws increasing the use of marijuana. Chu (2012) finds that these laws increased marijuana arrests among males and increased the number of people entering into marijuana rehabilitation programs. Rather than using a dichotomous measure of whether or not a state 6 See for more information about New York s decriminalization law. 7 Pacula and Sevigny (2014) provide a brief overview of these studies. 5

6 has a medical marijuana law, Pacula et al. (2013) examine the effect of certain characteristics of the laws on use. They find there is no increase in use if a dichotomous measure of medical marijuana laws is used. However, they find that use increases in states that have medical marijuana laws that allow for home cultivation and legal dispensaries. Using marijuana may also make individuals more susceptible to being a victim of crime. For example, the majority of marijuana transactions are done using cash, so it is likely that an individual walking into a marijuana dispensary is carrying cash, thus increasing the chance of being robbed. Both of these effects would result in more crime. Additionally, MMLs may change policing practices. For example, police departments may reallocate their resources away from arresting individuals who are making small marijuana purchases and spend more resources pursuing more serious crimes, reducing the number of those crimes. One important difference in MMLs across states is in how they allow individuals to obtain marijuana. One way in which laws allow individuals to obtain marijuana is through home cultivation, so individuals grow their own marijuana plants in their homes. Other laws allow for dispensaries, which are essentially marijuana retail stores. All states allow for one of these methods and several states allow for both. How individuals are able to legally obtain marijuana may affect crime differently. When home cultivation is permitted, individuals may no longer need to participate in traditional cash marijuana transactions in order to obtain marijuana. This may limit his or her exposure to illegal activity. Allowing for dispensaries may, counter-intuitively, be associated with an increase in illegal marijuana trade. Often, there is a lag between the passing of the MML and the opening of a dispensary. In such cases, there is an increase in the number of individuals who can legally possess marijuana, but have no way of legally obtaining it. This may increase the illegal supply of marijuana, which could increase the number of crimes related to the illegal sale of marijuana, including robbery and assault. Once dispensaries are open, due to the fact that marijuana is federally illegal, most can only accept cash as payment. This results in stockpiles of cash at dispensary locations and with the owners and employees of the dispensaries, making them more susceptible to robbery as suggested by Sherman (1995). Though, Attorney General Eric H. Holder Jr. announced in January 2014 that the Department of Justice would soon offer rules that will encourage prosecutors not to prioritize cases against legal marijuana businesses, which may help to alleviate this problem. 8 There is limited research on the effects of marijuana legalization on crime. Some medical research suggests a positive association between marijuana use and violent crime, for example Niveau and Dang 8 6

7 (2003). Pacula and Kilmer (2003) find a positive relationship between marijuana use and non-drug related violent, property and income-producing crime. Anderson, Hansen, and Rees (2012) find that MMLs decrease traffic fatalities involving alcohol, suggesting that individuals are less likely to drink and drive when medical marijuana is legal in their state of residence. Desimone (1998) finds evidence that marijuana is a gateway to cocaine use, suggesting that marijuana use may be a gateway into other illegal activity. Most closely related to my work is Morris et al. (2014). Using the same data on crime rates that I do, from the Federal Bureau of Investigation s (FBI s) Uniform Crime Reporting (UCR) Program, Morris et al. explore the association between medical marijuana laws and crime rates. They find that the effect of MMLs is either not statistically significant or negative. This suggests that the passage of MMLs does not increase crime and may actually decrease it in some cases. My paper differs from Morris et al. (2014) in two important ways. First, one concern with using a difference-in-differences estimation strategy with a long panel is that the effects of state-specific time trends on crime may be incorrectly attributed to the policy change. That is, it may not be that MMLs decrease crime rates. It may be that in states that passed MMLs, crime happens to be decreasing at a faster rate than in states that do not pass MMLs. To test for this, I include state-specific time trends in my regressions. I find that the inclusion of state-specific time trends greatly impacts the estimated effects of MMLs on crime. In several cases, particularly when considering the effects on property crime, including trends changes the sign of the estimated effects. Secondly, I examine whether laws that allow for dispensaries have different effects on crime than states that allow for home cultivation and find that they do. I estimate that allowing for marijuana dispensaries increases state robbery, burglary, and larceny and theft rates, whereas allowing for home cultivation does not increase crime rates, and in some cases decreases it. This demonstrates the importance of examining the effects of different dimensions of MMLs on crime rather than treating all MMLs as the same. 3 Data In order to estimate the effects of MMLs on crime, I utilize crime rate data come from the Federal Bureau of Investigation s (FBI s) Uniform Crime Reporting (UCR) Program. I combine it with self-collected data on the enactment dates of MMLs and their dimensions. Additionally, I control for time-varying state-specific demographic characteristics that are likely correlated with crime rates and may confound the estimated effect of MMLs on crime rates. 7

8 3.1 Crime Data Since 1930, the FBI has collected crime statistics from law enforcement agencies across the United States. The UCR Program collects data on both violent crimes and property crimes. Violent crimes include murder and non-negligent manslaughter, forcible rape, robbery, and aggravated assault. Property crimes include burglary, larceny and theft, and motor vehicle theft. I estimate the effects of MMLs on the rate of each of these seven specific offenses as well as overall violent and property crime rates, where the rate is the number of offenses per 100,000 residents. The first MML was enacted in November of 1996 in California. Therefore, I use data from , so that I have pre-mml enactment data for all states with MMLs. Unweighted summary statistics are presented in Table 1. Rates of all types of offenses drastically decrease over this time period. For example, the average violent crime rate across states decreased from crimes per 100,000 residents in 1995 to in 2012, or 36.1 percent and the average property crime rate decreased by 37.3 percent over the same time period. Figure 1 shows how crime rates differ among states that pass MMLs prior to 2013 and those that do not. States that eventually pass MMLs have higher crime rates on average than states that do not; this level difference will be accounted for by state fixed effects. Additionally, rates for several types of crimes decrease at a faster rate in states that eventually pass MMLs. Among the violent crimes, murder and robbery rates decrease at a faster rate until year Overall property crime rates, burglary rates, and larceny and theft rates decrease at a faster rate over my entire sample period. This suggests that controlling for the differences in overall crime rate levels as well as differences in how the crime rates change over time will be important when estimating the effects of MMLs on crime rates. 3.2 Marijuana Laws Medical marijuana laws allow for the medical use of marijuana for individuals with certain illnesses or symptoms. 18 states (including the District of Columbia) legalized medical marijuana prior to Table 2 shows data I collected indicating the dates on which medical marijuana mandates become effective up until 2012, and whether the law allows for dispensaries or home cultivation. 9 I will identify the causal effect of medical marijuana laws on crime rates from these changes in medical marijuana laws. 9 It is important to note that I am estimating the effect of whether or not a state s law allows for dispensaries, not whether or not a dispensary is open in a state. As discussed in Anderson and Rees (2014), several states, such as Montana, tolerated dispensaries even though the mandates do not explicitly allow for them. Other states, such as New Jersey, have a several year lag time between passing a law that allows for dispensaries and the opening of dispensaries. In this paper, I am only focusing on whether or not a law explicitly allows for dispensaries. 8

9 Table 1: Crime Rates Summary Statistics, by Year Year Violent Crime Rates Property Crime Rates Overall Murder Rape Robbery Assault Overall Burglarly Larceny Vehicle Theft (399.16) (8.90) (14.10) (181.63) (222.80) ( ) (299.97) (847.56) (288.26) (371.27) (9.97) (11.96) (171.97) (206.29) ( ) (293.41) (806.10) (271.89) (321.98) (7.86) (12.02) (130.59) (196.69) ( ) (277.64) (755.08) (236.85) (283.66) (6.84) (11.41) (107.56) (184.07) ( ) (259.48) (676.56) (207.45) (261.83) (6.28) (12.74) (98.95) (168.77) (920.35) (231.32) (599.76) (199.67) (241.14) (5.77) (11.19) (94.51) (154.05) (868.12) (218.40) (573.08) (189.45) (251.71) (5.58) (11.35) (98.23) (161.69) (903.65) (225.60) (581.51) (220.60) (251.98) (6.40) (11.40) (98.88) (160.07) (965.23) (230.04) (601.08) (263.01) (244.87) (6.13) (12.55) (102.32) (150.74) (943.56) (233.95) (576.35) (277.01) (221.02) (4.95) (11.57) (86.40) (145.14) (867.32) (234.56) (533.59) (252.28) (221.37) (4.65) (11.28) (93.72) (142.17) (833.94) (233.23) (509.06) (245.66) (234.44) (4.19) (11.50) (99.08) (149.82) (761.98) (233.02) (445.73) (229.91) (226.01) (4.47) (11.15) (105.87) (139.53) (765.40) (237.57) (451.04) (209.72) (222.21) (4.40) (11.37) (108.66) (132.72) (727.65) (249.48) (424.17) (167.66) (205.62) (3.59) (11.21) (103.48) (122.94) (668.75) (246.85) (399.72) (134.30) (195.11) (3.19) (10.31) (99.97) (115.38) (639.21) (232.29) (393.70) (121.33) (175.31) (2.71) (9.90) (91.89) (106.96) (635.06) (237.82) (403.82) (105.16) (178.66) (2.37) (11.79) (88.62) (107.12) (606.76) (212.21) (413.43) (95.97) Above are the means of state-level crime rates, standard deviations are in parentheses. The standard deviations represent the variation in crime rates across states in each year. Data come from the Uniform Crime Reporting Program. Rates are the number of offenses per 100,000 residents. 9

10 Figure 1: Crime Rates, by Year Graphed above are the means of state-level crime rates among those states that enacted Medical Marijuana Laws by December 2012 and those that did not. Data on crime rates come from the Uniform Crime Reporting Program. Rates are the number of offenses per 100,000 residents. 10

11 Table 2: State Medical Marijuana Laws, State Effective Date Allows for Dispensaries Allows for Home Cultivation Alaska 03/04/99 03/04/99 Arizona 11/02/10 11/02/10 11/02/10 California 11/06/96 01/01/04 11/06/96 Colorado 06/01/01 06/07/10 06/01/01 Connecticut 05/04/12 05/04/12 Delaware 07/01/11 07/01/11 District of Columbia 07/27/10 07/27/10 Hawaii 12/28/00 12/28/00 Maine 12/22/99 11/03/09 12/22/99 Michigan 12/04/08 12/04/08 Montana 11/02/04 11/02/04 Nevada 10/01/01 10/01/01 New Jersey 10/01/10 10/01/10 New Mexico 07/01/07 07/01/07 07/01/07 Oregon 12/03/98 12/03/98 Rhode Island 01/03/06 06/16/09 01/03/06 Vermont 07/01/04 06/02/11 07/01/04 Washington 11/03/98 11/03/98 Of the 18 states that legalized medical marijuana, seven states only allow for home cultivation, but not for dispensaries; four only allow for dispensaries; and seven allow for both. Of the seven that allow for both home cultivation and dispensaries as of 2013, the original mandates in California, Maine, Vermont, and Rhode Island only allowed for home cultivation. The laws were later amended to also allow for dispensaries. The variation in the types of laws across states and over time will allow me to identify the effect of the types of laws on crime rates. 3.3 Time-Varying State Characteristics I control for several time-varying state-specific demographic characteristics in my estimation. These variables are likely correlated with crime rates. Ignoring these potentially confounding variables might result in incorrect estimates of the effects of MMLs on crime rates. I include the average age of the residents of a state, per capita income, the unemployment rate, and the number of law enforcement officers per 100,000 residents. I collected data on the average age of the residents from the U.S. Census Bureau, on per capita income from the Bureau of Economic Analysis, on the unemployment rate comes from the Bureau of Labor Statistics, and on the number of officers per capita from the UCR Program. While my main focus is on MMLs, I also control for marijuana decriminalization laws. Decriminalization laws generally mandate that first-time possession for a small amount of marijuana will not results in prison time or a criminal record. 12 states decriminalized marijuana prior to 2013, but 11 did so before my 11

12 sample begins in Therefore, any results of the effect of decriminalization on crime should be interpreted cautiously. Summary statistics of these demographic characteristics can be found in Table 3. Table 3: State Demographic Characteristics, Variable Mean Standard Deviation Medical Marijuana Law (MML) MML that Allows Home Cultivation MML that Allows Dispensaries Decriminalization Law Average Age Log of Per Capita Income Unemployment Rate Officers Per 100,000 Residents Above are the means of state-level characteristics, standard deviations are in parentheses. The standard deviations represent the variation in characteristics across states and over time. Medical marijuana and decriminilazation variables take on fractional values during the years in which the law changed. 4 Empirical Model and Results In this section, I examine the effects of MMLs on crime rates. The date of enactment and characteristics of these laws vary over time and across states. I use a difference-in-differences (DD) specification that takes advantage of this variation. First, I show that the inclusion of state-specific time trends is important in the estimation of the effects of MMLs on crime rates. Second, in addition to using a dichotomous measure of MMLs (enacted or not enacted), I examine whether different dimensions of the laws (allowing for home cultivation or allowing for dispensaries) affect crime differently. 4.1 Estimated Effects of MMLs In order to estimate the effect of enacting any type of MML, I estimate the following equation using a DD strategy. log (CrimeRate st ) = β 1 + β 2 MML st + αx st + µ s + λ t + ɛ st, (1) 12

13 where MML st is an indicator for whether medical marijuana is legal in state s and year t. 10 X st is a vector of state demographic characteristics in state s and year t including whether the state has decriminalized marijuana, the average age, the per capita income, the unemployment rate, and the number of law enforcement officers per person. These variables are likely correlated with crime rates. Ignoring these potentially confounding variables might result in incorrect estimates of the effects of MMLs on crime rates. Lastly, µ s and λ t are state and year fixed effects, respectively. State fixed effects control for unmeasured factors affecting crime that are constant across time, but vary by state, such as overall lenience towards crime. Year fixed effects control for unmeasured factors that vary over time, but are constant across states, such as how well the economy is doing. Results are presented in Tables 4 and 5. Table 4: Effect of MMLs on Violent Crime Rates, Violent Crime Murder Forcible Rape Robbery Assault b/se b/se b/se b/se b/se Medical Marijuana Law * (0.0530) (0.0653) (0.0492) (0.0473) (0.0747) Marijuana Decriminalized *** *** ** (0.0388) (0.0416) (0.0278) (0.0326) (0.0473) Average Age ** ** (0.0519) (0.0742) (0.0526) (0.0594) (0.0735) Per Capita Income * (0.7686) (0.9855) (0.5344) (0.5031) (0.9892) Unemployment Rate (0.0119) (0.0172) (0.0125) (0.0118) (0.0159) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Constant (8.0293) (8.8658) (5.7949) (5.9925) ( ) N R State Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes State-Specific Linear Time Trends No No No No No The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<0.01 Consistent with the previous research in Morris et al. (2014), I estimate that MMLs apparently decrease murder rates and most property crime. One concern about difference-in-differences estimation strategies is that it relies on the assumption that the enactment of medical marijuana laws is independent of crime rates. However, MMLs are passed by state legislators or state residents and so may be driven by state-specific factors, like how quickly crime rates are decreasing. Figure 1, as described earlier, shows that states that eventually enact MMLs have different crime trends than states who do not. Therefore, it may be the case that MMLs do not 10 Following convention in the literature, I use the natural logarithm of the crime rates in my analysis. 13

14 Table 5: Effect of MMLs on Property Crime Rates, Property Crime Burglary Larceny and Theft Vehicle Theft b/se b/se b/se b/se Medical Marijuana Law ** ** * (0.0217) (0.0272) (0.0275) (0.0656) Marijuana Decriminalized *** *** *** *** (0.0168) (0.0241) (0.0152) (0.0328) Average Age (0.0288) (0.0348) (0.0322) (0.0532) Per Capita Income (0.1956) (0.3770) (0.1911) (0.4191) Unemployment Rate (0.0100) (0.0109) (0.0100) (0.0158) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) Constant *** * ** (2.3624) (3.8242) (2.4368) (4.8717) N R State Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes State-Specific Time Trends No No No No The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<0.01 actually decrease crime. Rather, crime rates may simply be decreasing at a faster rate in states that passed MMLs in comparison to states that did not, perhaps independent of the passage of the MML or even because a downward trend in crime makes states more likely to consider MMLs. Therefore, I estimate the following equation with linear state-specific time trends, γ s Y ear t : log (CrimeRate st ) = β 1 + β 2 MML st + αx st + µ s + λ t + γ s Y ear t + ɛ st. (2) Results are presented in Tables 6 and 7. The estimated effects of MMLs on violent crime rates do not change much after controlling for state-specific time trends. I find that MMLs may decrease murders, with an estimated decline of percent. This estimate is only significant at the 0.10 significance level, however. Controlling for state-specific time trends leads to a major shift in the estimated effects of MMLs on property crimes, revealing positive and statistically significant effects. I estimate that MMLs increase overall property crime rates by 6.48 percent, with an 8.02 percent increase in burglary rates and a 6.18 percent increase in larceny and theft rates Larceny and theft are crimes in which an individual takes something of value from another person, such as shoplifting. In addition to taking something of value, burglary also includes unlawful entry (or breaking and entering) in order to take something. 14

15 Table 6: Effect of MMLs on Violent Crime Rates with State-Specific Time Trends, Violent Crime Murder Forcible Rape Robbery Assault b/se b/se b/se b/se b/se Medical Marijuana Law * (0.0370) (0.0621) (0.0445) (0.0397) (0.0454) Marijuana Decriminalized *** *** (0.0201) (0.0255) (0.0158) (0.0182) (0.0271) Average Age * (0.0795) (0.0812) (0.0413) (0.0766) (0.0973) Per Capita Income * * (0.2594) (0.4312) (0.2922) (0.3590) (0.3218) Unemployment Rate (0.0066) (0.0119) (0.0078) (0.0080) (0.0104) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Constant ** (4.6483) (5.3802) (3.8931) (4.9502) (5.8096) N R State Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes State-Specific Time Trends Yes Yes Yes Yes Yes The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<0.01 Table 7: Effect of MMLs on Property Crime Rates with State-Specific Time Trends, Property Crime Burglary Larceny and Theft Vehicle Theft b/se b/se b/se b/se Medical Marijuana Law ** ** ** (0.0296) (0.0322) (0.0290) (0.0693) Marijuana Decriminalized *** *** *** *** (0.0154) (0.0156) (0.0174) (0.0244) Average Age (0.0558) (0.0567) (0.0822) (0.0844) Per Capita Income * (0.2309) (0.2765) (0.2683) (0.4270) Unemployment Rate * * (0.0082) (0.0084) (0.0073) (0.0158) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) Constant *** ** (3.4633) (3.6619) (4.9439) (5.2204) N R State Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes State-Specific Time Trends Yes Yes Yes Yes The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<

16 4.2 Effects of Dispensaries and Home Cultivation MMLs vary across states in several ways, particularly in how they allow individuals to obtain legal marijuana. As seen in Table 2, MMLs either allow for home cultivation, dispensaries, or both. To see how these different types of MMLs affect crime, I estimate the following equation: log (CrimeRate st ) = β 1 + β 2 HomeCultivation st + β 3 Dispensary st (3) +αx st + µ s + λ t + γ s Y ear t + ɛ st, where HomeCultivation st takes the value one if a state has an enacted MML that allows for home cultivation in state s and year t and is zero otherwise and Dispensary st takes the value one if the MML allows for dispensaries. Results are presented in Tables 8 and 9. Table 8: Effect of Dispensaries and Home Cultivation on Violent Crime Rates, Violent Crime Murder Forcible Rape Robbery Assault b/se b/se b/se b/se b/se Home Cultivation ** (0.0451) (0.0693) (0.0517) (0.0434) (0.0575) Dispensaries *** (0.0400) (0.0617) (0.0427) (0.0315) (0.0550) Marijuana Decriminalized *** *** (0.0218) (0.0239) (0.0164) (0.0195) (0.0296) Average Age * (0.0767) (0.0892) (0.0427) (0.0691) (0.0970) Per Capita Income * * (0.2635) (0.4295) (0.2968) (0.3659) (0.3255) Unemployment Rate (0.0065) (0.0120) (0.0083) (0.0080) (0.0103) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Constant ** (4.5490) (5.6038) (3.9828) (4.7074) (5.8126) N R State Fixed Effects Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes State-Specific Time Trends Yes Yes Yes Yes Yes The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<0.01 The results show that allowing for home cultivation reduces robberies (a violent crimes), while allowing for dispensaries increases robberies, burglary, and larceny and theft. Table 8 shows that allowing for dispensaries increases robberies by percent. Allowing for home cultivation, on the other hand, 16

17 Table 9: Effect of Dispensaries and Home Cultivation on Property Crime Rates, Property Crime Burglary Larceny and Theft Vehicle Theft b/se b/se b/se b/se Home Cultivation (0.0329) (0.0360) (0.0254) (0.0756) Dispensaries *** *** ** (0.0291) (0.0472) (0.0361) (0.0740) Marijuana Decriminalized *** *** *** *** (0.0157) (0.0150) (0.0174) (0.0232) Average Age (0.0562) (0.0542) (0.0819) (0.0828) Per Capita Income * (0.2318) (0.2746) (0.2646) (0.4187) Unemployment Rate * * (0.0083) (0.0082) (0.0075) (0.0157) Officers Per 100,000 Residents (0.0000) (0.0000) (0.0000) (0.0000) Constant *** ** (3.3898) (3.4080) (4.8157) (5.0510) N R State Fixed Effects Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes State-Specific Time Trends Yes Yes Yes Yes The dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. * p<0.10, ** p<0.05, *** p<0.01 decreases robberies by percent. Forcible rape, murder, and assault rates are not affected by either type of law. This suggests that policy makers may be able to minimize negative impacts on robbery crime rates by only allowing legal marijuana to be supplied via home cultivation. These results also suggests that in states that only allow for dispensaries, policy makers may be able to offset the increase in robberies by amending the law to additionally allow for home cultivation. Table 9 shows the effects on property crimes. Overall property crime increases by 8.12 percent in states that allow for dispensaries. This increase comes from a percent increase in burglaries and an 8.02 percent increase in larceny and theft. Neither form of MML affects vehicle theft. Unlike the results for violent crime, allowing for home cultivation has no affect on property crime rates. This suggests that states considering legalizing marijuana may want to only allow for home cultivation if they are trying to minimize the adverse affects on crime. 17

18 4.3 Robustness Checks In this sub-section I present several modifications to the regressions presented above to determine whether the findings are robust to alternative model specifications. These results are presented for each specific crime category in Tables 10 through 18. The columns labeled Model 1 present the results from my main specification in Equation 3, which are also presented in Tables 8 and 9. An assumption of the main specification presented in Equation 3 is that had a state not passed a medical marijuana law, it would have behaved similarly to the states that never passed them. However, that may not be the case here, as suggested by the differing trends presented in Figure 1. It may be better to only compare states that enact MMLs with each other, using the variation in the timing of enactment for identification. Therefore, I limit my sample to states that pass an MML prior to 2013 and re-estimate Equation 3 with the limited sample. Estimates are presented as Model 2. Overall the estimates are similar. Another assumption of the main model is that the effect of enacting a law that allows for both home cultivation and dispensaries equals β 2 + β 3. I test whether there is an interaction effect of allowing both home cultivation and dispensaries in Model 3 by including β 4 Both st in the regression presented in Equation 3. Both st equals one if state s allows both home cultivation and dispensaries in year t. Overall, the results are quite similar. The estimated effects of allowing dispensaries on burglary rates and larceny and theft rates increase from to percent and from 8.02 to percent, respectively. I estimate that allowing for both dispensaries and home cultivation decreases larceny and theft by percent, but does not have a statistically significant effect on burglary rates. This implies that laws that only allow for dispensaries increase burglary and larceny and theft more than those that allow for both dispensaries and home cultivation. Equation 3 additionally assumes that the effect of MMLs is constant over time. However, the shortterm effects of the laws may differ from the long-term effects. In Model 4 I test whether the short-term effects of allowing dispensaries differs from the long-term effects by running regressions that include dummies for each of the first five years an MML has been enacted. The results should be interpreted cautiously, however, because only California and New Mexico have had laws allowing dispensaries that have been enacted for five or more years as of The effect of allowing dispensaries on property crimes does not change much over time. On the other hand, the overall violent crime rate increases the longer a law that allows for dispensaries has been enacted. The effect on overall violent crime increases from 8.53 percent the year the law was enacted to percent four years after and the effect 18

19 on robberies increases from percent the year the law was enacted to percent four years after. While allowing for dispensaries has no overall effect on assault rates, there is an increase in assaults in states that have allowed for dispensaries for 2 or more years. As discussed above, one concern with my identification strategy is that the enactment of the MML may be endogenous. Including state-specific time trends should help alleviate any concerns that differences in crime rate trends is causing states to enact laws. I have also run specifications with a dummy for the year before a laws was enacted, which are not shown here. The coefficient on the dummy is never statistically significant, except for forcible rape, which is unaffected by MMLs. If I do not include state-specific time trends, then the estimated coefficient on the dummy is statistically significant for burglary rates, larceny and theft rates, and overall property crime rates. This suggests that states may be passing MMLs due to faster declines in crime rates and emphasizes the importance for controlling for state-specific time trends in my estimation. Lastly, one may be concerned that the error terms, ɛ st, are heteroskedastic across states, suggesting that I need to weight my regressions by population size in order to adjust for this. Let the individuallevel, rather than state-year-level, error term be denoted as ɛ ist for i 1,..., N st, where N st is population size of state s in year t. As discussed in Dickens (1990) and Solon et al. (2013), rather than assuming that the individual-level error terms are independent, my base specification assumes that they are correlated with one another at the state-year level. That is, I assume that individuals in each state-year group share some sort of unobserved determinant that affects whether or not he or she commits a crime. I denote that common unobserved determinant as γ st. I can break out the individual-level shock into ɛ ist = γ st + η ist, where γ st and η ist are independently and identically distributed with variances σ 2 γ and σ 2 η, respectively. Under this assumption, the variance of the state-year-level error term is V ar (ɛ st ) = σ 2 γ + σ2 η N st. Since N st is very large for all states (Wyoming is the smallest state with a population approximately 540,000), the variance of ɛ st can be approximated by σ 2 γ, which is homoskedastic. Therefore, OLS is still the best linear unbiased estimator. 5 Discussion and Conclusion In this paper I estimate the effects of MMLs on violent and property crime rates. I first demonstrate the importance of including state-specific time trends when estimating a DD model with a long panel. With out controlling for time trends, I estimate that MMLs decrease overall property crime rates by

20 Table 10: Robustness Checks of Estimated Effects on Violent Crime Rates Model 1 Model 2 Model 3 Model 4 b/se b/se b/se b/se Home Cultivation Allowed (0.0451) (0.0627) (0.0451) (0.0452) Dispensaries Allowed (0.0400) (0.0681) (0.0689) Both Allowed (0.0734) Year Dispensaries Allowed (0.0507) 1 Year After Dispensaries Allowed (0.0366) 2 Years After Dispensaries Allowed * (0.0443) 3 Years After Dispensaries Allowed ** (0.0475) 4 Years After Dispensaries Allowed ** (0.0527) 5+ Years After Dispensaries Allowed ** (0.0717) N R For the following tables, the dependent variable is equal to the natural log of the crime rate in state s and year t. Standard errors are in parentheses and are clustered at the state level. In each model I control for whether or not marijuana is decriminalized in a state, the average age, per capita income, unemployment rate, the number of police officers per 100,000 residents in each state, state fixed effects, year fixed effects, and state-specific linear time trends. Models 1, 3, and 4 include all 50 states, while Model 2 limits the sample to only states that passed a medical marijuana law prior to * p<0.10, ** p<0.05, *** p<0.01 Table 11: Robustness Checks of Estimated Effects on Murder Rates Model 1 Model 2 Model 3 Model 4 b/se b/se b/se b/se Home Cultivation Allowed (0.0693) (0.0753) (0.0682) (0.0705) Dispensaries Allowed (0.0617) (0.0708) (0.1216) Both Allowed (0.1322) Year Dispensaries Allowed (0.0814) 1 Year After Dispensaries Allowed (0.0604) 2 Years After Dispensaries Allowed (0.1001) 3 Years After Dispensaries Allowed (0.0768) 4 Years After Dispensaries Allowed (0.0831) 5+ Years After Dispensaries Allowed ** (0.0731) N R

21 Table 12: Robustness Checks of Estimated Effects on Forcible Rape Rates Model 1 Model 2 Model 3 Model 4 b/se b/se b/se b/se Home Cultivation Allowed (0.0517) (0.0531) (0.0504) (0.0517) Dispensaries Allowed (0.0427) (0.0421) (0.1083) Both Allowed (0.1097) Year Dispensaries Allowed (0.0974) 1 Year After Dispensaries Allowed (0.0405) 2 Years After Dispensaries Allowed (0.0597) 3 Years After Dispensaries Allowed (0.0508) 4 Years After Dispensaries Allowed (0.0912) 5+ Years After Dispensaries Allowed (0.0778) N R Table 13: Robustness Checks of Estimated Effects on Robbery Rates Model 1 Model 2 Model 3 Model 4 b/se b/se b/se b/se Home Cultivation Allowed ** * ** ** (0.0434) (0.0505) (0.0433) (0.0433) Dispensaries Allowed *** * * (0.0315) (0.0442) (0.0685) Both Allowed (0.0662) Year Dispensaries Allowed ** (0.0485) 1 Year After Dispensaries Allowed *** (0.0362) 2 Years After Dispensaries Allowed *** (0.0298) 3 Years After Dispensaries Allowed * (0.0533) 4 Years After Dispensaries Allowed *** (0.0546) 5+ Years After Dispensaries Allowed *** (0.0615) N R