reports the overall BID effect for the incidence of robbery and violent crimes in terms of the percentage reductions (posterior mean) associated with the adoption of BID and the 95% posterior probability intervals. also reports the posterior probability of observing an overall BID effect on reducing the incidence of robbery and violent crimes (P(μK<1), where μK represents the overall effect across the 30 BID areas. We find that the posterior probability of a BID effect is 0.96. In other words, there is strong evidence that BID reduced the robbery rate. The estimated percentage reduction: obtained as one minus the posterior mean (μK) indicates a 12% average reduction (95% posterior probability interval −2 to 24) in the incidence of robbery associated with the implementation of BID. For the total incidence of violence BID effects are in the same direction, but the statistical evidence is not as strong (P(μK <1)=0.91), and indicates an 8% average reduction (95% posterior probability interval −5 to 21) in the total incidence of violence associated with the adoption of BID.
Estimated percentage reduction in reported violent crimes from BID
Given that the observed probability of an overall BID effect was strongest for robbery, reports the BID area-specific effects for official reports of robbery in terms of percentage reductions in robberies (1–Ki). The individual BID area results for robbery show that for 14 of the 30 areas the posterior probability of observing a BID effect is 0.90 or higher. In terms of effect sizes for these 14 areas, the robbery rate is reduced by a high of 27% in the Century Corridor BID to a low of 14% in the Downtown Industrial BID. For another two BID areas, the observed posterior probability is more than 0.80, which still provides evidence for the presence of a BID effect in the expected direction. The BID effects appear to be most pronounced for the incidence of robbery, which one would expect is likely to be affected by environmental features of the neighbourhoods, such as hiring private security officers, which are the focus of BID efforts to control public space areas. Overall, there seems to be evidence in the data that the BID in Los Angeles had an effect in reducing the incidence of reported robberies.
Area-specific estimates of BID effects on robbery
Given the size of the BID effects appear to be most pronounced for robbery, this raises the question of how much BID spending occurs in relation to social costs saved by reducing robberies. Multiplying our estimated 12% (annual) reduction to the average incidence of robberies (M
=160.7) associated with BID implementation to the estimated social costs (US$39 287 in 2005 dollars)18
of an average robbery shows a marginal cost saving of approximately US$757 611 (in 2005 dollars) (annually). Given that the average annual budget of the 30 BID in Los Angeles was approximately US$736 670 (in 2005 dollars),12
this suggests that a sizeable social cost–benefit of BID implementation can be attributed to the reductions in robbery alone.
We also conducted several additional tests on our measures of violent crime and the methods for approximating BID effects. Homicides, for example, are the most accurately reported violent crimes, but we did not discuss the yearly trends in homicide because the counts are so low. The average number of homicides per year in neighbourhoods associated with BID is less than one, and the median (50th percentile) is zero. The point estimates from replicating our model for homicide counts varies widely, and the probability of detecting a BID effect is low (P(μK<1)=0.43)), suggesting that BID have no appreciable effect on homicide. In particular, the homicide model indicates a 5% increase associated with BID adoption but with a large CI (95% posterior probability interval −50 to 29), resulting from low counts and imprecision in our estimate. A combined estimate of the count of robbery and homicide together was statistically identical to that of robbery alone, suggesting that the BID adoption effect observed is driven by the rate of robberies.
We also constructed alternative model specifications in line with previous work that Brooks5
used on an analysis of the effects of BID on reported serious crimes in Los Angeles during earlier years. We compared the estimated effect of BID implementation on robberies and all reported violent crimes using all police reporting districts in Los Angeles as the unit of analysis, including those that do not intersect BID areas. We then included a dummy variable denoting the timing of BID, adjacent neighbourhoods to BID as control variables, and fixed-effect terms (dummy variables) for each individual reporting district, year, and their interactions (reporting district*year). Our results from these specifications were sensitive to the parameterisation of the outcomes. If we relied on ordinary least squares regression we found a statistically significant BID effect in reducing the mean incidence of robberies (b=−4.63; p<0.001) and all violent crimes (b=−7.33; p<0.001) by approximately 16% and 11%, respectively. However, if we relied on a Poisson regression model the results were substantially lower and were only marginally significant for robberies (b=−0.02; p=0.07) and total violence (b=−0.01; p=0.09), reducing the respective incidence by 3% and 2%. We think this sensitivity test provides further justification for our use of a simpler model that estimates only the BID effects for those areas that eventually adopted BID, rather than assigning BID effects to the entire city of Los Angeles.
The Bayesian hierarchical model provides an estimate of the effect of BID on the incidence of robbery and violent crimes in areas that were exposed to BID. Like all models this approach has several limitations. First, the model assumes that the population at risk of violence does not change once a BID starts. It is, however, possible that the establishment of a BID could change the population at risk of violent crimes in a number of ways. If, for example, there is a substantial increase in the number of shoppers or new residents because a BID was implemented then even a substantial decline in the incidence of violence would be offset by an increase in the denominator for the population at risk. Such an increase in the population at risk of violence would lead one to conclude that the adoption of the BID did not have an effect in reducing violence. Assuming that the population at risk coincides with the residential population in an area would be incorrect, as it is almost certain that successfully implemented BID attract a larger number of people for commerce to areas.
Second, the model assumes that the level to which violent crimes are reported to the police does not change systematically with the adoption of a BID. If, however, the adoption of a BID implies an increase in local merchants' and residents' willingness to report crimes to the police and an increased response from the police to combat crime, then the violent crime reports may actually increase as a function of the implementation of a BID. If this were the case, the adopted model would suggest that adopting a BID has the effect of increasing the incidence of violent crimes. Given that the findings suggest an overall effect of BID on reducing the robbery rate and marginal effects for all violent crimes, we have some confidence in these results.
In addition, if one assumes that BID areas have unique features in terms of the businesses that operate and the communities that encourage their establishment, constructing a group of comparison areas that are matched to the BID areas with respect to certain demographic features of area residents would represent a less conservative test of the effects of BID. We think that the areas that will eventually adopt a BID are the best comparison group for those areas that have already adopted a BID, as there are clearly features of BID areas that are unique in their ability to get a majority of landowners and merchants interested in their adoption. At the same time, our analysis offers no prescription for the various mechanisms by which BID impact robbery rates. BID adopt a variety of tactics, such as mobilising the police, hiring private security officers, street cleaning and environmental redesign to increase a sense of cleanliness and safety of BID areas. Unfortunately, the tactics adopted by each BID area are complex and not easy to approximate in a statistical model.