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Sense of Community and Informal Social Control Among Lower Income Households: The Role of Homeownership and Collective Efficacy in Reducing Subjective Neighborhood Crime and Disorder

American Journal of Community Psychology

Abstract

We examine the link between homeownership, collective efficacy, and subjective neighborhood crime and disorder. Although prior research suggests that homeownership provides social benefits, the housing downturn and foreclosure crisis, coupled with mounting evidence that people self-select into housing, raise questions about the role of homeownership. We adjust for respondents’ decision to own or rent using a nationwide sample of lower-income households. We account for demographic and neighborhood characteristics as well as ratings of individual efficacy. We present a structural equation model that identifies how sense of community and informal social control jointly contribute to collective efficacy. The latent collective efficacy construct mediates the impact of homeownership on resident’s perceptions of neighborhood disorder. Such perceptions matter because they have been linked to resident’s physical and mental health. Our findings demonstrate that when coupled with sustainable mortgages, homeownership exerts a robust yet indirect effect in reducing subjective neighborhood crime and disorder. Our model also links collective efficacy to neighborhood racial homogeneity, a finding which presents challenges for the study of diversity and community. We discuss sense of community research as well as sustainable mortgages and implications of the foreclosure crisis for the future of homeownership opportunities among lower income households and neighborhoods.

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Notes

  1. Whereas objective disorder can be measured by outsiders, subjective disorder is specific to the perceptions of individual residents. We prefer the terms subjective and objective to perceived and observed because the latter terms erroneously suggest that observed measures are not perceived.

  2. We omit two of the original informal social control items (“if the fire station was threatened with budget cuts” and “if children were skipping school and hanging out on a street corner”) due respectively to potential income disparity effects and conjunctive question wording.

  3. To test this idea, we analyzed two neighborhood level measures affected by the foreclosure crisis: the homeownership rate and residential stability (a standardized measure of occupied housing combined with residents who have lived in same house for 5 years). Two-tailed significance tests indicate that the neighborhood homeownership rate approached but did not reach the 0.05 level for two interactions: homeownership times the homeownership rate on collective efficacy (P < 0.059) and the indirect effect of homeownership times the homeownership rate (via collective efficacy) on subjective crime and disorder (P < 0.076). Interactions with residential stability did not reach or approach statistical significance. These results indicate that we did not empirically identify a mechanism by which the foreclosure crisis itself would differentially affect homeowners’ and renters’ collective efficacy and subjective neighborhood crime and disorder. Thus, we find no evidence that the foreclosure crisis will alter the homeownership effect that we observe.

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Acknowledgments

Funding for this study was provided by the Ford Foundation. Survey data was collected by RTI International. We thank Seb Prohn, Catherine Zimmer, Ling Wang, Sarah Riley, Tianji Cai, Jasperlynn Kao, and other researchers at the UNC Center for Community Capital for consultation and assistance. We also thank anonymous reviewers for helpful comments.

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Correspondence to Mark R. Lindblad.

Appendix

Appendix

Homeownership presents analytic challenges because we cannot randomly assign people to be homeowners or renters, and because people do not randomly become homeowner or renters. People choose to own or rent based on their resources and preferences as well as factors which we do not observe. Without adjustment for these selection effects, bias can result and skew findings.

We draw from Heckman’s (1979) demonstrations that selection effects should be taken into consideration when estimating causal impacts, and that sample selection should be explicitly modeled. In light of Heckman’s insight and those of later researchers, we use a propensity score analysis to adjust for non-random selection into the treatment (homeownership) and control (renters) groups. Propensity score analysis helps address the selection bias that is inherent in observational studies and which is not addressed using traditional regression models which rely on covariate control (Guo and Fraser 2009). Our propensity score analysis helps minimize household and neighborhood differences between renters and homeowners.

Using STATA 10, we perform a one-to-one propensity score match which uses a nearest-neighbor approach with 1/4 standard deviation caliper with replacement. Nearest-neighbor matching relies on an algorithm to identify the renter who is most similar to each homeowner. In order to minimize the possibility that the matching results are sensitive to the matching algorithm, and to avoid extreme matches, homeowners are only matched if there is a renter available within the same caliper. Results yield 375 renters who are matched to 375 homeowners.

Table 4 displays results before and after we match renters to homeowners via propensity score analysis. The column labeled ‘original sample’ shows results of a logistic regression model which uses both household and neighborhood level measures to predict whether a respondent is a homeowner in the original sample (n = 1,902). The column labeled ‘matched sample’ re-runs the same analysis on the matched sample (n = 750). Table 4 shows that following the match, all but 1 of the 36 parameter estimates drops from statistical significance in the prediction of homeownership. Furthermore, a comparison of the odds ratios displayed under each sample reveals that the propensity score match dramatically reduced the magnitude of effects of household and neighborhood characteristics in predicting homeownership. Overall, the results under the ‘matched sample’ column indicate that the selection bias associated with the likelihood of being a homeowner, which is evident in results under the ‘original sample’ column, has been largely negated through the propensity score match.

Table 4 Propensity score analysis predicting homeownership: Pre and post match

Two caveats are noteworthy. First, even after our propensity score match, one variable remains a statistically significant predictor of the decision to own or rent: relative income is positively associated with homeownership. Relative income does not relate conceptually to our outcomes of interest (collective efficacy and crime/disorder), nor does it predict either outcome with statistical significance in the structural equation model. As a further check for potential bias, we ran a separate model (not shown) in which we substitute relative income for homeownership. Findings indicate that even when excluding homeownership, there are no statistically significant effects of relative income on either collective efficacy (estimate = −0.030, standard error = 0.045, P < 0.500) or subjective crime and disorder (estimate = −0.041, standard error = 0.106, P < 0.699). Furthermore, the sign of relative income on collective efficacy is negative, which runs counter the positive effect for relative income on the decision to own a home and the positive effect of homeownership on collective efficacy. Therefore, although we do observe a homeowner/renter difference in relative income, this difference does not substantively alter our evaluation of the impact of homeownership on collective efficacy and subjective neighborhood crime and disorder.

A second caveat relates to our matching procedure. Propensity score analysis matches on observed variables but does not necessarily address unobserved differences between renters and homeowners. Inclusion of a large number of predictors in the propensity score analysis helps reduce such unobserved differences, but the potential remains for unobserved differences to persist even after propensity score matching. In addition, a large number of propensity score methods are available to researchers. Here we used a one-to-one match. An alternate propensity score analysis could produce a different set of matched households.

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Lindblad, M.R., Manturuk, K.R. & Quercia, R.G. Sense of Community and Informal Social Control Among Lower Income Households: The Role of Homeownership and Collective Efficacy in Reducing Subjective Neighborhood Crime and Disorder. Am J Community Psychol 51, 123–139 (2013). https://doi.org/10.1007/s10464-012-9507-9

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