As shown in Appendix Table 2, the estimates from both models are quite similar to our main Table 2 estimates. In Appendix Table 3, we explore the impact of extending the study window. Specifically, we present results that lengthen the study period to 60 days but include 3 separate indicators for closure days 1-10 , 11-20 , and 21-30 . We break up the extended post-period into three parts because lengthening the post-closure period likely introduces control days to the treatment period since, as documented in McDonald and Pelisek , some dispensaries reopened within a couple of weeks of closure.30 Col shows that increasing the pre-period generates results similar to our main specification with slightly tighter confidence intervals: the estimated effect of the first 10 days of closure on total Part I crime at 1/8 of a mile is almost 30% and is significant at the 1% level. At 1/4 and 1/3 of a mile, the first 10-day estimates are 12 and 9%, respectively, consistent with a decreasing monotonic relationship between the distance around dispensaries and the change in crime. Col shows the effects of dispensary closures 11-20 days after the event. We find effects that are both smaller in magnitude and only significantly different from zero at 1/3 of a mile. Estimates for the 21-30 day closure period in col are much less precise and are inconsistent in sign. This analysis confirms that pre-period trends are not driving our findings and that temporary dispensary closures had an immediate and temporary impact on crime. In Appendix Table 4, we test the sensitivity of the results to confusion over the closure date and potential lags in crime reporting. Specifically, cannabis grow equipment we drop June 6-8, 2010 from the analysis. Because this significantly limits our sample, we show results using 9, 19 and 29 days on either side of the June 7, 2010 but excluding June 6-8.
Those results are quite similar and, in many cases, more precisely estimated than our main Table 2 results. Finally we examine the effect of the multiple counting of crimes due to geographic overlap in dispensary neighborhoods. Because closure status is not geographically clustered, the main effect of this overlap is to mechanically bias our estimates towards zero, leading to an underestimate of the magnitude of the closure effect. To see this, we would ideally analyze dispensaries that have no neighbors within a wide radius, e.g., 1 mile. In practice, less than 5% of dispensaries are so geographically isolated. Consequently, in Appendix Table 5, we show sensitivity checks using the less restrictive requirements that dispensaries have a nearest neighbor more than 1/3 mile or more than 1/2 mile away. Using these restrictions leaves us with 158 dispensaries with a nearest neighbor more than 1/3 mile away and 79 dispensaries with a nearest neighbor more than 1/2 mile away. Across both restricted samples, the magnitude of the change in Part I crime is consistently larger than in the sample as a whole. The results for crime at 1/3 and 1/4 of a mile are statistically significant, despite the greatly reduced sample size. Restricting to dispensaries with a nearest neighbor more than 1/3 mile away, the estimates imply that Part I crime within a radius of 1/4 mile was about 47% higher around dispensaries ordered to close compared to those allowed to remain open, more than triple the main estimate in Table 2. When we restrict to the 79 dispensaries with a nearest neighbor more than 1/2 mile away, the estimates imply that Part I crime within 1/4 mile is 93 percent higher around dispensaries ordered to close compared to those allowed to remain open. While the results in Appendix Table 5 follow the expected pattern of increasing in magnitude as we reduce catchment overlap, the set of geographically isolated dispensaries may differ on other unaccounted for dimensions. As such, we cannot use the difference in these coefficients relative to the full sample to measure the average downward bias. Rather, these results provide suggestive evidence that our main results underestimate the true effect sizes.
We next analyze categories of Part I crimes, which are divided by the FBI into property and violent crimes. We estimate separate models for the following property crimes: burglary, grand theft auto, and larceny theft. Larceny theft is separately broken out as thefts from vehicles and other theft. Arson, a sub-category of Part I property crime is too rare to analyze separately. For violent Part I crime, we analyze aggravated assault and robbery. Murder and rape, which are included in total Part I violent crimes, are also too rare to analyze separately . Table 4 shows the impact of dispensary closures on crime by type using the preferred ITT approach that codes closures according to order status. These results show that the effect of dispensary closures loads on property crimes, specifically larceny, and, breaking that out further, theft from vehicles. As with total crime, the effects are very local and monotonically decrease with catchment area radii. This monotonic decrease in the closure estimates and confidence intervals can be seen clearly in Figures 3 and 4, which plot the implied percent change in Part I crimes and theft from vehicles, respectively, along with 95 percent confidence intervals at distances from 1/8 to 2 miles. At distances of 1/2 mile or greater we find no effect of closures on crime, and the small coefficients with relatively tight confidence intervals means we can explicitly rule out even small increases in crime at these larger distances. At 1/3 of a mile the models imply that property crimes increase by 12%, largely driven by increases in larceny and, specifically, theft from vehicles. Even more locally, the estimated effects imply that thefts from vehicles increase by almost 30% at 1/4 of a mile and by 100% at 1/8 of a mile around dispensaries ordered to close relative to those allowed to remain open. While the percent increase in crime near closed dispensaries is large, proper interpretation of these effects must take into account the low number of crimes around each dispensary on any given day. For example, combining the results of Tables 1 and 2, we see that closing a dispensary leads to just 0.0512 additional crimes per day within a third of a mile of the closed dispensary.
Burglary is the one exception to the general monotonic pattern. Here we find a large, negative and marginally significant coefficient for closures at 1/8th of a mile, positive and statistically insignificant coefficients at 1/4th, 1/3rd and 1/2 of a mile, a small negative and statistically insignificant coefficient at 1 mile, and a small negative statistically significant coefficient at 2 miles. While intriguing, this non-monotonic pattern does not admit to an obvious explanation. In addition, unlike the results for total crime or larceny, the burglary results do not hold up in robustness checks and are based on a very small number of events, vertical grow rack with an average of 0.0245 burglary per day at 1/8 of a mile. As such, this result should be interpreted with caution. As with our main results, we find that results for crime by type are insensitive to the treatment of defiers or the inclusion of the closure date .A crucial question in determining the social costs of crime associated with dispensary closures is whether the changes represent an increase in total crime or a shift of crime across either space or time. If crime is spatially displaced, then the increase in crime near a closed dispensary may be offset by decreases in crime further away. Since our main results show that closures lead to significant crime increases at distances of 1/4 to 1/3 of a mile around a dispensary, spatial displacement would imply corresponding decreases in crime at distances of greater than 1/4 to 1/3 mile. To check for this type of displacement, we examine the impact of closures on crime in concentric rings around each dispensary.32 Specifically, in Table 5 we analyze crime occurring between 1/4 and 1/3 of a mile, 1/3 and 1/2 of a mile, 1/2 to 1, 1/2 to 2 and 1 to 2 miles around dispensaries. At distances of 1/4 to 1/3 of a mile the coefficient on closure is, with the exception of violent crimes, positive. The increase within this band is not statistically distinguishable from zero, however. At 1/3 to 1/2 of a mile, the property crime estimate is negative but close to zero, albeit with a wide confidence interval. Since the overlap issue discussed previously should be exacerbated at larger radii, the magnitude of the estimates within the larger rings could be more downward biased than those at smaller distances. But given that these coefficients are never significant, these results do not provide strong evidence for spatial displacement. Analogous to spatial displacement, temporal displacement of crime would mean that the changes in crime associated with closures are offset by changes in crime either before or after the closure period. While the dispensary closure date was well known in advance, there are no clear “re-opening” dates. As such if criminal activity exhibited a significant ex-ante temporal elasticity, we would expect a decrease in crime around dispensaries scheduled to close but prior to actual closures as criminals waited until June 7 to commit crimes. We find little evidence of pre-closure differences in either the level or trend in daily crime around dispensaries ordered to close relative to those allowed to remain open. Most directly, since extending the pre-period window around June 7, 2010 yields similar results , it is unlikely that a pre-period decline in crime in anticipation of future crime commission can explain our results. In other words, criminals do not appear to postpone crimes in anticipation of the mass closure of dispensaries. Given the variation in pre-closure crime levels, we can generally rule out economically significant temporal displacement in the period just prior to the June 7, 2010 closures.In Los Angeles County, the Department of Public Health is charged, under the California Uniform Retail Food Facilities Law , with enforcing uniform statewide health and sanitation standards for retail food facilities according to the“science-based standards.” DPH inspects all facilities that provide food to the public . Based on the guidelines outlined in the California Retail Food Code , DPH environmental health specialists grade restaurants on various health and sanitation measures including improper holding temperatures, poor personal hygiene of food employees, contaminated equipment and the presence of vermin and, depending on the outcome, may order a temporary shutdown for remediation. Based on a Food Official Inspection Report , restaurants receive a numerical score between 0-100. Restaurants that score 70 and above are given a grade card that must be posted in an easily visible location . Restaurants that score less than 70 receive a numerical score card rather than a grade. Restaurants that score less than 70 twice in any twelve month period are subject to closure and the filing of a court case. Such closures are rare. More commonly, if the inspection turns up a “major violation,” meaning a violation, such as vermin harborage or infestation, sewage disposal problems or food temperature problems, that poses an imminent health hazard, the restaurant is subject to immediate closure without a permit suspension hearing. Restaurants closed for major violations remain closed until a subsequent follow-up inspection confirms that the situation has been satisfactorily resolved. Restaurants are inspected twice a year, although those that handle large quantities of “risky foods” or consistently score low may be inspected three times a year. The DPH may conduct an additional inspection in response to consumer complaints. Individual inspectors work specific geographic areas determined by the local environmental health office. They work with supervisors to set a schedule for restaurant inspections in increments of one or more months. While inspection scheduling is not standardized, inspections are, depending on the specific supervisor, scheduled weeks to months ahead of time. As such, although the timing of inspections are not explicitly randomized, the process makes it highly unlikely that the exact timing of inspections are correlated with trends in crime in the immediate area around each restaurant. In addition, DPH officials have stated that local conditions have no bearing on the timing of inspections.Most closures are caused by “major violations,” with roughly two-thirds of the closures in our sample due to vermin harborage or infestation.