Small Local versus Non-Local: Examining the Relationship between Locally Owned Small Businesses and Spatial Patterns of Crime

Abstract

In the current study, we theorized that businesses in place are subject to two processes: a crime generator effect in which they heighten crime due to increased opportunities and a crime inhibition effect in which certain types of businesses can increase guardianship capability. We explicitly compare the different effects of local vs. non-local and small vs. large businesses on crime in street segments using the data in cities across the Los Angeles metropolitan region by estimating a set of negative binomial regression models for small local, large local, small non-local, and large non-local consumer facing businesses (Retail, Restaurants, Food/Drug Stores, and Services) for violent and property crime. Although we found that most of the business coefficients were positive, local businesses, and particularly small local businesses, have considerably smaller crime-enhancing effects for both violent and property crime.

Keywords:

• Place
• crime
• small local business

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The average length of the street segments in the study area is 476.7 feet with standard deviation of 395.3.

2 Here is the list of 6-digit NAICS codes associated with the business types included in the consumer facing business measure: Retail (448110, 448120, 448130, 448140, 448150, 448190, 448210, 452111, 452112, 452910, 452990, 453310, 453210, 443141, 442110, 442210, 442291, 442299, 444210, 444220, 444130, 444110, 444120, 444190, 453991, 446120, 446199, 453910, 453998, 451211, 451212, 443142, 451140, 451110, 451120, 446130, 453220, 453110, 448310, 448320, 451130); Restaurants (6-digit NAICS code 722511, 722514, 722515, 722513); Food/Drug Stores (445120, 446110, 445110, 311811, 445210, 445220, 445230, 445291, 445292, 445299, 446191); Services (532111, 441310, 441320, 811111, 811112, 811113, 811118, 811121, 811122, 488410, 811191, 811192, 811198, 624410, 447110, 447190, 812320, 812310, 611511, 812111, 812112, 812113, 532220, 532299, 541940, 812191, 812199, 812910, 812990, 541921, 812921, 812922, 561622, 811212)

3 We estimated ancillary models with 40, 30, and 20 employee cutoffs (Table A1). We found that the results are essentially identical compared to those with the 50 employee cutoff (Table 3).

4 In a supplemental analysis, we tested models with a proportional measure of local business as another way to capture the diversity of social activities in place suggested by Jacobs (1961). The results are not substantially different from the models with a measure of small local business heterogeneity (Table A2).

5 The five crime types assessed are Part 1 crimes as defined by the Uniform Crime Reports. These are considered to be serious crimes that have relatively fewer reporting issues (Baumer 2002) compared to other less serious crime incidents such as Part 2 crimes including drug offenses, disorderly conduct, etc. We also analyze these Part 1 crime types because social disorganization and routine activities studies generally focus on these crime types. We excluded homicide because they are too rare on micro places like street segments to show meaningful variation. In ancillary models, we constructed a violent crime measure including homicide and the estimated models showed results not substantially different from those without homicide. We also estimated models with summed counts of violent and property crimes and found essentially identical results.

6 For more information: https://www.census.gov/data/tables/2015/econ/susb/2015-susb-annual.html

7 Negative predicted values in the y-axis occur because we are plotting the logged expected values of the negative binomial distribution with the exposure variable. The predicted values could be expressed with exponentiated values, though the relative ranking of the figures would remain the same.

8 Given the nonlinear relationship of these businesses with violent crime, these values are assessed by comparing the expected log value from Figure 2 when going from 0 to 1 business for each of these business types, and then exponentiating this value to obtain these percentage changes. We perform a similar computation when assessing the change in property crime.

9 In ancillary models, we tested an interaction between concentrated disadvantage in the surrounding area and our business typology variables. There were negative interactions for the local businesses, and positive interactions for the nonlocal businesses, although plotting the effects showed modest relationships.

Additional information

Notes on contributors

Young-An Kim

Young-An Kim is an Assistant Professor in the College of Criminology and Criminal Justice at Florida State University. His research interests focus on various areas such as neighborhoods and crime, criminology of place, immigration and crime, and spatial analysis.

John R. Hipp

John R. Hipp is a Professor in the departments of Criminology, Law and Society, and Sociology, 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.