The spatial distribution of neighborhood safety ties: Consequences for perceived collective efficacy?

ABSTRACT

There is conflicting evidence in the literature regarding the relationship between residents’ social networks and their perceptions of neighborhood collective efficacy. This study proposes addressing this challenge with several theoretically motivated refinements using a large spatially stratified sample of residents in the Western United States. First, we consider various distinct types of social relationships, and find that our novel measure of neighborhood safety ties is much more strongly related to perceived collective efficacy than is a measure of socializing relationships. Second, we explicitly account for the spatial distribution of ties, and find that it is not just local neighborhood ties that increase a sense of cohesion or informal social control, but that more spatially distant ties also matter. Third, we make a distinction between urban and rural areas, finding that in rural areas, social ties from an even broader area are associated with stronger feelings of collective efficacy.

KEYWORDS:

• Neighborhoods
• social networks
• spatial effects
• collective efficacy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Social network scholars define the person reporting on the network as an ego, and the persons they are tied to as alters.

2. As Krackhardt (1992) has noted, the emphasis of most social network research on emically defined relations may obscure the relationships that are most critical for understanding social process; an etic definition of relationships that are theoretically important (even if they do not directly correspond to culturally defined ideal types) is hence a critical step for progress in the field.

3. Note that the emphasis on the role of ethnic heterogeneity and residential instability on tie formation processes is arguably quite similar to work in the social networks literature on homophily and propinquity (Adams et al., 2012; McPherson et al., 2001).

4. We also assessed whether there are differences among rural households by splitting out “pioneers”—those living in very sparse environments. This split was based on those with less than 5,000 persons within 32 kilometers, and those with between 5,000 and 50,000 within 32 kilometers. The results for these two subgroups were essentially identical.

5. Although one can imagine behavioral measures related to collective efficacy, we follow Sampson and colleagues in defining collective efficacy per se to be an inherently perceptual phenomenon.

6. These measures are based on the initial Sampson et al. (1997) study. Nonetheless, we assessed whether the one measure that does not focus on social control of children is different than the others, based on the insight that collective efficacy is task-specific (Wickes et al., 2013). Nonetheless, this measure never had the lowest loading in the confirmatory factor analyses. Furthermore, when we constructed factor scores either including or not including this measure, the correlations were .96 in the urban sample and .97 in the rural sample, indicating that this question does not capture a substantively different task for collective efficacy.

7. We alternatively constructed variables capturing the number of ties more than 50, 100, or 150 kilometers away, and the results were the same as those presented.

8. The education and income measures were based on the 16 categories used by the U.S. Census.

9. The response categories were: (1) never, (2) a few times a year, (3) several times a year, (4) once or twice a month, (5) almost every week, (6) once a week, (7) more than once a week.

10. For variables available in blocks in the Census, it is straightforward to aggregate them to egohoods. For variables only available at larger units of block groups or tracts from the ACS, we first imputed these values to blocks based on the synthetic estimation for ecological inference approach. This strategy combines an imputation model at the larger geographic unit with block level data to impute values from the larger units to the blocks (Boessen & Hipp, 2015). Variables used in the imputation model were: percent owners, racial composition, percent divorced households, percent households with children, percent vacant units, population density, and age structure (percent aged: 0–4, 5–14, 15–19, 20–24, 25–29, 30–44, 45–64, 65 and up, with age 15–19 as the reference category).

11. Although we only focus on social ties outside the household, respondents were also allowed to nominate in-household ties. Interestingly, 23% of neighborhood safety ties are in the same household, highlighting that residents do not only turn to those outside the home for discussing crime problems. And whereas residents will turn to 31% of their kin ties to discuss crime problems, about half of their neighborhood safety ties are kin.

12. This was assessed by estimating multiple group models and constraining the coefficients to be equal and comparing the chi square and BIC values across models.

13. We assessed statistical significance of these differences across urban and rural samples by constraining coefficients equal in the multiple groups models and testing differences.

Additional information

Funding

This research is supported in part by NSF grants [IIS-1526736 and BCS-0827027].

Notes on contributors

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. He approaches these questions using quantitative methods as well as social network analysis. He has published substantive work in such journals as American Sociological Review, Criminology, Social Forces, Social Problems, Mobilization, City & Community, Urban Studies and Journal of Urban Affairs. He has published methodological work in such journals as Sociological Methodology, Psychological Methods, and Structural Equation Modeling.

Adam Boessen

Adam Boessen is an Associate Professor in the department of Criminology and Criminal Justice at the University of Missouri, St. Louis. His primary research interests include neighborhoods and crime, geography and space, and social networks.

Carter T. Butts

Carter T. Butts is a Chancellor’s Professor in the departments of Sociology, Statistics, Computer Science, and Electrical Engineering and Computer Science (EECS) and the Institute for Mathematical Behavioral Sciences at the University of California, Irvine. His research involves the application of mathematical and computational techniques to theoretical and methodological problems within the areas of social network analysis, mathematical sociology, quantitative methodology, and human judgment and decision making. His work has appeared in a range of journals, including Science, Sociological Methodology, the Journal of Mathematical Sociology, Social Networks, and Computational and Mathematical Organization Theory.

Nicholas N. Nagle

Nicholas N. Nagle is a Professor and Head of the department of Geography at the University of Tennessee, Knoxville. His research involves problems of data integration, and small area estimation, and uncertainty in official statistics. He has served various roles with the NASEM Committee on National Statistics to evaluate and advise methodological changes at the US Census Bureau. His work has appeared in Annals of the American Association of Geographers, Journal of the American Planning Association, and Journal of the American Heart Association.

Emily J. Smith

Emily J. Smith is a postdoctoral scholar at the University of North Carolina at Chapel Hill.