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Abstract
To take stock of the “neighborhood effects” of drug activity, we combined theoretical insights from the drugs and crime and communities and place literatures in examining the longitudinal relationship between drug activity and crime rates at more spatially and temporally precise levels of granularity, with blocks as the spatial units and months as the temporal units. We found that drug activity on a block one month “pushes” assaultive violence into surrounding blocks the next month. Integrating perspectives form social disorganization theory with Zimring and Hawkins’ (1997) contingency causation theory, we also found that the economic resources and residential stability of the “the larger social environment”—that is, the surrounding quarter-mile egohood area—moderate drug activity’s block-level relationship to crime. These results suggest that drug activity increases assaultive violence and serious acquisitive crime rates on structurally advantaged blocks, producing a significant ecological niche redefinition for such blocks relative to others in Miami-Dade County, Florida.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This research was supported in part by the National Institute of Justice [2012-R2-CX-0010].
Notes
1 We operationally define the theoretical construct the larger social environment as the quarter-mile egohood area encompassing a given block (Hipp & Boessen, 2013). Later on in our manuscript, we theoretically justify examining interaction effects between drug activity and social context across blocks and egohoods.
2 Like Taylor (Citation2015), we use the terms community and neighborhood interchangeably to refer to a geographic area that is larger than a household and smaller than a city. Throughout our manuscript, we constantly use the word “communities” to refer to blocks, which we consider to be “small-scale” communities.
3 Our research setting includes census-designated places (CDPs), the Towns of Cutler Bay and Miami Lakes, and the Village of Palmetto Bay, which are all covered by the Miami-Dade Police Department. We exclude the CDPs of Fisher Island and Homestead Base, given that the former is literally an island and the latter is literally a military base.
4 Among the 11,179 blocks in our research setting, about 45 percent (i.e., 5,014) of these blocks had at least one drug incident during the time period of our study. So, drug activity has some geographic fluidity to it over time.
5 Our inclusion of auto theft as an outcome is consistent with drugs and crime research analyzing the individual-level relationship between drug use and crime (Bennett et al., Citation2008). Auto thefts may indeed be drug-related, for offenders stealing motor vehicles may sell the car parts to “chop shops” for money (Levy, Citation2014).
6 This temporal lag coheres with recently published drugs and crime research, which found that offenders who used drugs one month were more likely to commit drug crime and nondrug crime the next month (Felson & Staff, Citation2017).
7 Our drug arrest data capture overall arrests. Still, communities and place researchers have found the heroin-related mortality rate to be associated with increases in heroin-related drug arrests (Thomas & Dierenfeldt, Citation2018). So, our drug arrest measures may be tapping into the expanding illicit retail market for street heroin, considering the spike in heroin abuse in Miami-Dade County from 2010 to 2014.
8 We use the same procedure for creating spatially lagged versions of the measures derived from the ReferenceUSA business data.
9 We omit percent Latino from our analyses, for it correlates highly negatively with percent black (r = −.82, p < .01) and ethnic heterogeneity (r = −.60, p < .01). Criminologists have noted the importance of controlling for percent black and ethnic heterogeneity in one’s ecological analyses (Pratt & Cullen, Citation2005). Given the high correlations, the effect of percent Latino is effectively captured by these measures in the model.
10 For more information on constructing egohood measures, please refer to the following online resource: http://ilssc.soceco.uci.edu/applications/egohood.
11 We utilize a random-effects, rather than a fixed-effects, estimator, for we are interested in changing monthly crime rates across, rather than within, blocks. Month-to-month fluctuations in crime rates across blocks in our research setting may be due to differences among these blocks in previous levels of drug activity.
12 In ancillary models, using year dummies (with 2014 as the reference category), we tested for the possibility that correlations of time series observed in our analyses were a reflection of common trends. Results were unchanged.
13 In the Figures we present below, we dichotomize our drug activity variable, comparing crime rates across income levels and homeownership rates for blocks with and without drug activity in the previous month.
14 Chronic offenders may indeed be driving the crime rate fluctuations we observed in our analyses. Yet, longitudinal research in the drugs and crime literature suggests that chronic offenders involved in drug markets commit more crime than otherwise given the nature of the illicit drug trade (Menard & Mihalic, Citation2001).
15 To gauge whether drug activity’s spatial effects vary substantially from other incidents, in additional analyses, using calls-for-service (CFS) data, we estimated models with disturbances and vandalism as neighborhood indicators of social and physical disorder, respectively. And, we found variable spatial effects among drug, disturbance, and vandalism incidents. Whereas vandalism incidents exhibited significant “radiation” effects on nonviolent acquisitive crimes (i.e., “pushing” burglaries, larcenies, and motor vehicle thefts into the surrounding area), disturbance incidents posed strong “radiation” effects on the violent crimes of aggravated assault and robbery. Nonetheless, the “radiation” effect of drug activity remained statistically significant in these additional models controlling for physical or social disorder. Disturbances did exert a slightly stronger “radiation” effect on aggravated assault than drug activity (5.0% vs. 2.9% increase in this violent crime type per standard deviation change).
16 Indeed, we found moderate to strong correlations between our drug arrest measures and narcotics-related CFS on a block (r = .44, p < .01) and in the surrounding area (r = .83, p < .01). And, like our drug arrest measures, narcotics-related CFS are positively related to percent black.
Additional information
Funding
Notes on contributors
Christopher Contreras
Christopher Contreras is a PhD student in the Department of Criminology, Law & Society at the University of California, Irvine. His research focuses on neighborhood disorder, drugs and crime, public policy, and the community context of crime. He is a member of the Irvine Laboratory for the Study of Space and Crime (ILSSC).
John R. Hipp
John R. Hipp is a Professor in the Departments of Criminology, Law & Society, Sociology, and Urban Planning & Public Policy at the University of California, Irvine. His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role networks and institutions play in that change. He approaches these questions using quantitative methods as well as social network analysis.