The next most common offense is a lack of potable or hot water, which accounts for 12 percent of closures. Of the 888 closures, 766 or 86% of them have valid reopen dates. In all the cases we investigated, restaurants with no-reopen dates were in fact open and operational. In multiple conversations with EHA, we were unable to obtain any official reason for missing reopen dates. As described below, we take three approaches to dealing with restaurants with missing restaurant reopen dates – assigning the median closure period of 2 days, treating them as permanently closed or dropping them from the sample. Our primary approach uses the median closure period but, as shown below, the results are not sensitive to this choice.We focus on the universe of Los Angeles County restaurants that were closed for heath code violations between February 1, 2010 to October 31, 2010. Using the same basic specification as in equation , we define 1 as the period between a restaurant’s closure and reopen date. Because we restrict the sample to restaurants with health code violations, the identifying assumption for this analysis is that the timing of closures is uncorrelated with crime in the area immediately around the affected restaurant. Paralleling our dispensary analysis, we drop each first closure day in the analysis. In addition to the concern that crimes reported on closure dates may have occurred prior to that date, many restaurants will be closed for only part of the first closure day. In other words, cannabis grow racks some restaurants ordered to close temporarily remain open for part of the first closure day – both before and during the inspection. However, as with dispensaries, the results are similar when we include the first closure day in the analysis .

Appendix Table 10 shows summary statistics for restaurants in the 10 days prior to closure. Since all restaurants in our sample were subject to closure, there are no separate time-invariant restaurant characteristics for closed and open restaurants. Rather, these summary statistics show pre-closure characteristics of neighborhoods around restaurants subject to closure during our sample period. In general, the neighborhoods around restaurants do not look dramatically different from that around dispensaries . The most noteworthy differences are that these neighborhoods are slightly more populous, with larger families and lower family incomes. And, consistent with the fact that restaurant closures occur across the county, not just in the city of Los Angeles, the average Walk Score is slightly lower around restaurants than either dispensaries ordered to close or allowed to remain open . While the inspection scheduling process makes it unlikely that inspections are correlated with crime , a related concern is that the probability of closure conditional on an inspection is correlated with local crime conditions. If the probability of closure is affected when crime in the immediate vicinity of a restaurant is rising – because, for example, the inspector does a less rigorous review in order to minimize his exposure to crime – it could bias our results. To assess these concerns, we run placebo regressions to test for differences in crime within 1/4, 1/3, 1/2, 1 or 2 miles around restaurants in the days leading up to a closure. In other words, we estimate a regression of the form in but define a placebo closed dummy equal to 1 for the same length of time as the actual closure for the days prior to the closure event . As an alternate test, we define a placebo closed indicator for the day prior to, or the 2 days prior to the closure date . In all cases, we find no statistically significant relationship between the placebo closures and crime.

The point estimates are also small in magnitude, with the exception of the 1 day dummy , which, representing the shortest placebo time period, also has the largest standard errors. In short, we find no evidence of systematic changes in crime in the days leading up to these restaurant closures.In Table 6 we show restaurant results that recode those with missing reopen dates as having been closed for the median number of days closed across the sample, 2 days , treat those with missing reopen dates as closed through the entire post-period, or drop those restaurants with missing reopen dates. As with the dispensary analysis, we limit this analysis to the 10 days prior to and 10 days after any restaurant’s closure. Since results at 1/8 of a mile generally do not converge, we show results for crime at 2, 1, 1/2, 1/3 and 1/4 mile around restaurants. Pre-closure means for Part I crime at each of these distances are provided in col . Table 6 indicates that total Part I crime increases during temporary restaurant closures. At 1/3 of a mile, total Part I crime increases by about 9 to 12% around closed restaurants relative to open restaurants that were temporarily shut down within plus or minus 10 days. The results are similar irrespective of the treatment of restaurants without re-open dates. In addition, the results show a monotonic increase in the effect size as distance narrows up until 1/4 of a mile, at which point the coefficient is small and statistically insignificant. Table 7 presents results for the breakdown of crime by type, where restaurants with missing reopen dates are coded as closed for the median length of time in the data. As with dispensaries, we find that the effects of closures are concentrated on property crimes, specifically thefts from vehicles. The estimates imply an almost 30% increase in thefts from vehicles at 1/4 of a mile – generally the smallest radii we can analyze for restaurants. Again as with the dispensaries results, the effects quickly diminish with distance, becoming not just insignificant but also small in magnitude at distances of 1 mile and greater. As detailed in the appendix, these results are robust to several additional sensitivity checks: lengthening the window of time around restaurant closures , including closure days , coding restaurants with missing re-open dates as closed for the full post-closure period and dropping restaurants with missing reopen dates .

We next check for the displacement of crime either spatially or temporally in response to temporary restaurant closures. As with dispensaries, we check for spatial displacement by examining changes in crime in rings of various sizes around closed restaurants. Table 8 shows the crime changes occurring between 1/4 and 1/3 of a mile, 1/3 and 1/2 of a mile, 1/2 to 1 mile, 1/2 to 2 miles and 1 to 2 miles around closed restaurants. At 1/4 to 1/3 of a mile, which is fully contained within the radii where we find increases in crime around closed restaurants, the coefficient on closure is positive. The increase within this band is significant only for total crimes. The point estimates then drop and are both small in magnitude and not distinguishable from zero at 1/3 to 1/2 of a mile, suggesting that the increase in crime is localized to distances of less than 1/3 of a mile. To test for temporal displacement, we re-run our standard regression but supplement the restaurant closure period indicator with dummies for both the re-open date and the re-open date plus 1. We focus on the reopening period since restaurant closures are unexpected and thus could not have caused pre-closure shifts in criminality. Rather, the temporary restaurant closures could have led criminals to shift crime earlier in time to the closure period. Such a shift would decrease crime after a reopening. Instead, as shown in Table 9, we find significant increases in crime at 1/3 of a mile around restaurants during the closure period but no compensating decrease in crime on either the re-open day or the day after. The similarity in the broad pattern of results for restaurants and dispensaries despite the differences in the nature of these businesses, the reason for and timing of their closures, and the identifying assumptions of the analyses, cannabis drying racks provides additional evidence that the increase in crime following dispensary closures is not spurious. Furthermore, it suggests that the mechanism behind the decrease in crime is not dispensary-specific but indicative of a more general effect of business closures on crime.One potential common factor affecting crime may be a reduction in foot traffic. If dispensary and restaurant closures reduce foot traffic, informal policing or “eyes upon the street” may also be diminished and crime could increase. This hypothesis requires that the impact of business closures on crime be mediated through customer foot traffic. Such a connection seems intuitive since a closed business necessarily has fewer customers than an open one. The ideal data to test this would include measures of foot traffic by location. Given that such measures are unavailable, we use neighborhood characteristics to proxy for the relative impact of business closures on foot traffic in an area. Scores range from 0 to 100, and are based on walking paths to amenities.

Amenities within a 5 minute walk are given maximum points. More distant amenities receive points based on a decay function, with zero points after a 30 min walk. Pedestrian friendliness is comported into the measure based on population density, block length and intersection density. While Walk Scores do not capture the presence of sidewalks, street lights or speed limits, which likely improve the walking experience, they have been shown to be a useful measure of walkability .Walk Score identifies four categories of addresses based on their scoring system: CarDependent , Somewhat walkable , Very walkable and Walker’s paradise . Walkability is determined by the number and proximity of restaurants, bars, coffee shops, grocery stores, and so on. An address with a high Walk Score has many businesses and other features that generate foot traffic nearby whereas one with a low Walk Score has few businesses nearby and relatively little foot-traffic. How should the Walk Score interact with business closures to affect crime? Since a business with a high Walk Score is located near many other businesses, its customers likely represent a small share of local foot traffic. On the other hand, the closure of a business in a low Walk Score area should have a proportionally large impact on total foot traffic. As such, the eyes upon the street hypothesis would predict that, all else equal, the impact of business closures on crime should be negatively related to Walk Scores A more complete consideration of foot traffic must acknowledge that people are both crime deterrents and crime targets. For very isolated, car dependent areas with little foot traffic, a business closure could reduce crime in the area by removing the few existing crime targets. As an extreme example, consider a business that is the only feature for 1/3 of a mile and that its closure decreases the number of people in the area from N to zero. Such a closure would substantially decrease foot traffic. But, since there are virtually no remaining crime targets in the immediate area, crime would likely decline despite the loss of crime-deterring eyes upon the street. In this way, EUS predicts a non-monotonic relationship between business closures and Walk Scores: business closures will have smaller effects on crime in the most and least walkable areas and larger, positive effects in moderately walkable areas. In Table 10 we explore the interaction of business closures and walkability on crime. We find a significant positive closure effect on crime for both dispensaries and restaurants with low Walk Scores, with effect sizes approximately double that found in the full sample . When we examine crime by type , we see that, as in the full sample, the interaction effect is driven by increases in property crime, specifically larceny and theft from vehicles. In low Walk Score areas, dispensary or restaurant closures have more than double the impact on property crime than they do in high Walk Score areas. In column 2, we further divide up businesses using separate closure dummies for the Car-dependent, Somewhat walkable, Very walkable, and Walker’s paradise categories. Here again we find that the closure effect is smaller in highly walkable areas and larger and positive in the “somewhat walkable” areas. For “Car-dependent” areas, the sign of the coefficient flips and becomes negative; it is also both small in magnitude and statistically indistinguishable from zero.