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Research Articles

Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy

Science
15 Aug 1997
Vol 277, Issue 5328
pp. 918-924

Abstract

It is hypothesized that collective efficacy, defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good, is linked to reduced violence. This hypothesis was tested on a 1995 survey of 8782 residents of 343 neighborhoods in Chicago, Illinois. Multilevel analyses showed that a measure of collective efficacy yields a high between-neighborhood reliability and is negatively associated with variations in violence, when individual-level characteristics, measurement error, and prior violence are controlled. Associations of concentrated disadvantage and residential instability with violence are largely mediated by collective efficacy.

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REFERENCES AND NOTES

1
For a recent review of research on violence covering much of the 20th century, including a discussion of the many barriers to direct examination of the mechanisms explaining neighborhood-level variations, see R. J. Sampson and J. Lauritsen, in Understanding and Preventing Violence: Social Influences, vol. 3, A. J. Reiss Jr. and J. Roth, Eds. (National Academy Press, Washington, DC, 1994), pp. 1–114.
2
J. F. Short Jr., Poverty, Ethnicity, and Violent Crime (Westview, Boulder, CO, 1997).
3
For a general assessment of the difficulties facing neighborhood-level research on social outcomes, see
Mayer S. E., Jencks C., Science 243, 1441 (1989).
4
R. Kornhauser, Social Sources of Delinquency (Univ. of Chicago Press, Chicago, IL, 1978); R. J. Bursik Jr., Criminology 26, 519 (1988); D. Elliott et al., J. Res. Crime Delinquency 33, 389 (1996).
5
R. J. Sampson and W. B. Groves, Am. J. Sociol. 94, 774 (1989).
6
M. Janowitz, ibid. 81, 82 (1975).
7
E. Maccoby, J. Johnson, R. Church, J. Social Issues 14, 38 (1958); R. Taylor, S. Gottfredson, S. Brower, J. Res. Crime Delinquency 21, 303 (1983); J. Hacker, K. Ho, C. Ross, Social Problems 21, 328 (1974). A key finding from past research is that many delinquent gangs emerge from unsupervised spontaneous peer groups [F. Thrasher, The Gang: A Study of 1,313 Gangs in Chicago (Univ. of Chicago Press, Chicago, IL, 1963); C. Shaw and H. McKay, Juvenile Delinquency and Urban Areas (Univ. of Chicago Press, Chicago, IL, 1969), pp. 176–185; J. F. Short Jr. and F. Strodtbeck, Group Process and Gang Delinquency (Univ. of Chicago Press, Chicago, IL, 1965)].
8
For example, about half of all homicides occur among nonfamily members with a preexisting relationship: friends, neighbors, casual acquaintances, associates in illegal activities, or members of a rival gang. Illegal markets are especially high-risk settings for robbery, assault, and homicide victimization, whether by an associate or a stranger [A. J. Reiss Jr. and J. Roth, Eds. Understanding and Preventing Violence (National Academy Press, Washington, DC, 1993), pp. 18, 79; A. J. Reiss Jr., in Criminal Careers and “Career Criminals,” A. Blumstein, J. Cohen, J. Roth, C. Visher, Eds. (National Academy Press, Washington, DC, 1986), pp. 121–160].
9
W. Skogan, Disorder and Decline: Crime and the Spiral of Decay in American Neighborhoods (Univ. of California Press, Berkeley, CA, 1990).
10
J. Coleman, Foundations of Social Theory (Harvard Univ. Press, Cambridge, MA, 1990); R. Putnam, Making Democracy Work (Princeton Univ. Press, Princeton, NJ, 1993).
11
A. Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory (Prentice-Hall, Englewood Cliffs, NJ, 1986).
12
See, generally, J. Logan and H. Molotch, Urban Fortunes: The Political Economy of Place (Univ. of California Press, Berkeley, CA, 1987).
13
Kasarda J., Janowitz M., Am. Sociol. Rev. 39, 328 (1974);
Sampson R., ibid. 53, 766 (1988).
14
W. J. Wilson, The Truly Disadvantaged (Univ. of Chicago Press, Chicago, IL, 1987).
15
D. Massey and N. Denton, American Apartheid: Segregation and the Making of the Underclass (Harvard Univ. Press, Cambridge, MA, 1993); D. Massey, Am. J. Sociol. 96, 329 (1990).
16
J. Brooks-Gunn, G. Duncan, P. Kato, N. Sealand, Am. J. Sociol. 99, 353 (1993); F. F. Furstenberg Jr., T. D. Cook, J. Eccles, G. H. Elder, A. Sameroff, Urban Families and Adolescent Success (Univ. of Chicago Press, Chicago, IL, in press), chap. 7. Research has shown a strong link between the concentration of female-headed families and rates of violence [see (1)].
17
D. Williams and C. Collins, Annu. Rev. Sociol.21, 349 (1995).
18
Cluster analyses of census data also helped to guide the construction of internally homogeneous NCs with respect to racial and ethnic mix, SES, housing density, and family organization. Random-effect analyses of variance produced intracluster correlation coefficients to assess the degree to which this goal had been achieved; analyses (37) revealed that the clustering was successful in producing relative homogeneity within NCs.
19
For purposes of selecting a longitudinal cohort sample, SES was defined with the use of a scale from the 1990 census that included NC-level indicators of poverty, public assistance, income, and education (37). Race and ethnicity were also measured with the use of the 1990 census, which defined race in five broad categories: “white,” “black,” “American Indian, Eskimo, or Aleut,” “Asian or Pacific Islander,” and “other.” We use the census labels of white and black to refer to persons of European American and African American background, respectively. We use the term “Latino” to denote anyone of Latin American descent as determined from the separate census category of “Hispanic origin.” “Hispanic” is more properly used to describe persons of Spanish descent (i.e., from Spain), although the terms are commonly used interchangeably.
20
The sampling design of the CS was complex. For purposes of a longitudinal study (37), residents in 80 of the 343 NCs were oversampled. Within these 80 NCs, a simple random sample of census blocks was selected and a systematic random sample of dwelling units within those blocks was selected. Within each dwelling unit, all persons over 18 were listed, and a respondent was sampled at random with the aim of obtaining a sample of 50 households within each NC. In each of the remaining NCs (n = 263), nine census blocks were selected with probability proportional to population size, three dwelling units were selected at random within each block, and an adult respondent was randomly selected from a list of all adults in the dwelling unit. The aim was to obtain a sample of 20 in these 263 NCs. Despite these differences in sampling design, the selected dwelling units constituted a representative and approximately self-weighting sample of dwelling units within every NC (n = 343). ABT Associates (Cambridge, MA) carried out the data collection with the cooperation of research staff at PHDCN, achieving a final response rate of 75%.
21
“Don't know” responses were recoded to the middle category of “neither likely nor unlikely” (informal social control) or “neither agree nor disagree” (social cohesion). Most respondents answered all 10 items included in the combined measure; for those respondents, the scale score was the average of the responses. However, anyone responding to at least one item provided data for the analysis; a person-specific standard error of measurement was calculated on the basis of a simple linear item-response model that took into account the number and difficulty of the items to which each resident responded. The analyses reported here were based on the 7729 cases having sufficient data for all models estimated.
22
Respondents were also asked whether the incident occurred during the 6 months before the interview; about 40% replied affirmatively. Because violence is a rare outcome, we use the total violent victimization measure in the main analysis. However, in additional analyses, we examined a summary of the prevalence of personal and household victimizations (ranging from 0 to four) restricted to this 6-month window. This test yielded results very similar to those based on the binary measure of total violence.
23
The original data measured the address location of all homicide incidents known to the Chicago police (regardless of arrests) during the months of the community survey.
24
The alpha-scoring method was chosen because we are analyzing the universe of NCs in Chicago and are interested in maximizing the reliability of measures [
Kaiser H. F., Caffry J., Psychometrika 30, 1 (1965);
]. We also estimated an oblique factor rotation, allowing the extracted dimensions to covary. A principal components analysis with varimax rotation nonetheless yielded substantively identical results.
25
For a methodological procedure and empirical result that are similar but that used all U.S. cities as units of analysis, see K. Land, P. McCall, L. Cohen, Am. J. Sociol. 95, 922 (1990).
26
S. W. Raudenbush, B. Rowan, S. J. Kang, J. Educ. Stat. 16, 295 (1991).
27
D. V. Lindley and A. F. M. Smith, R. Stat. Soc. J. Ser. B Methodol. 34, 1 (1972).
28
Although the vast majority of respondents answered all items in the collective efficacy scale, the measurement model makes full use of the data provided by those whose responses were incomplete. There is one less indicator, Dpijk, than the number of items to identify the intercept πjk.
29
This degree of intersubjective agreement is similar to that found in a recent national survey of teachers that assessed organizational climate in U.S. high schools [B. Rowan, S. Raudenbush, S. Kang, Am. J. Educ. 99, 238 (1991)].
30
The analysis of collective efficacy and violence as outcomes uses a three-level model in which the level 1 model describes the sources of measurement error for each of these outcomes. The level 2 and level 3 models together describe the joint distribution of the “true scores” within and between neighborhoods. Given the joint distribution of these outcomes, it is then possible to describe the conditional distribution of violence given “true” collective efficacy and all other predictors, thus automatically adjusting for any errors of measurement of collective efficacy. See S. Raudenbush and R. J. Sampson (paper presented at the conference “Alternative Models for Educational Data,” National Institute of Statistical Sciences, Research Triangle Park, NC, 16 October 1996) for the necessary derivations. This work is an extension of that of C. Clogg, E. Petkova, and A. Haritou [Am. J. Sociol. 100, 1261 (1995)] and P. Allison (ibid., p. 1294). Note that census blocks were not included as a “level” in the analysis. Thus, person-level and block-level variance are confounded. However, this confounding has no effect on standard errors reported in this manuscript. If explanatory variables had been measured at the level of the census block, it would have been important to represent blocks as an additional level in the model.
31
The resulting model is a logistic regression model with random effects of neighborhoods. This model was estimated first with penalized quasi-likelihood as described by N. E. Breslow and D. G. Clayton [J. Am. Stat. Assoc. 88, 9 (1993)]. The doubly iterative algorithm used is described by S. W. Raudenbush [“Posterior modal estimation for hierarchical generalized linear models with applications to dichotomous and count data” (Longitudinal and Multilevel Methods Project, Michigan State Univ., East Lansing, MI, 1993)]. Then, using those results to model the marginal covariation of the errors, we estimated a population-average model with robust standard errors [
Zeger S., Liang K., Albert P., Biometrics 44, 1049 (1988);
]. Results were similar. The results based on the population-average model with robust standard errors are reported here.
32
The analysis paralleled that of criminal victimization, except that a Poisson sampling model and logarithmic link were used in this case. Again, the reported results are based on a population-average model with robust standard errors.
33
Although the zero-order correlation of residential stability with homicide was insignificant, the partial coefficient in Table 4 is significantly positive. Recall from Table 3 that stability is positively linked to collective efficacy. But higher stability without the expected greater collective efficacy is not a positive neighborhood quality according to the homicide data. See (14).
34
T. Cook, S. Shagle, S. Degirmencioglu, in Neighborhood Poverty: Context and Consequences for Children, vol. 2, J. Brooks-Gunn, G. Duncan, J. L. Aber, Eds. (Russell Sage Foundation, New York, in press).
35
“Neighborhood services” is a nine-item scale of local activities and programs (for example, the presence of a block group, a tenant association, a crime prevention program, and a family health service) combined with a six-item inventory of services for youth (a neighborhood youth center, recreational programs, after-school programs, mentoring and counseling services, mental health services, and a crisis intervention program). “Friendship and kinship ties” is a scale that measures the number of friends and relatives that respondents report are living in the neighborhood. “Organizational participation” measures actual involvement by residents in (i) local religious organizations; (ii) neighborhood watch programs; (iii) block group, tenant association, or community council; (iv) business or civic groups; (v) ethnic or nationality clubs; and (vi) local political organizations.
36
Similar results were obtained when we controlled for a measure of social interaction (the extent to which neighbors had parties together, watched each other's homes, visited in each others' homes, exchanged favors, and asked advice about personal matters) that was positively associated with collective efficacy. Again the direct effect of collective efficacy remained, suggesting that social interaction, like friendship and kinship ties, is linked to reduced violence through its association with increased levels of collective efficacy.
37
R. J. Sampson, S. W. Raudenbush, F. Earls, data not shown.
38
Major funding for this project came from the John D. and Catherine T. MacArthur Foundation and the National Institute of Justice. We thank L. Eisenberg and anonymous reviewers for helpful comments; S. Buka and A. J. Reiss Jr. for important contributions to the research design; and R. Block, C. Coldren, and J. Morenoff for their assistance in obtaining, cleaning, geo-coding, and aggregating homicide incident data to the NC level. M. Yosef and D. Jeglum-Bartusch assisted in the analysis.

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Science
Volume 277 | Issue 5328
15 August 1997

Submission history

Received: 16 January 1997
Accepted: 20 June 1997
Published in print: 15 August 1997

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Robert J. Sampson
R. J. Sampson is in the Department of Sociology, University of Chicago, Chicago, IL, 60637 and is a Research Fellow of the American Bar Foundation, Chicago, IL 60611, USA. S. W. Raudenbush is at the College of Education, Michigan State University, East Lansing, MI 48824, USA. F. Earls is the Principal Investigator of the Project on Human Development in Chicago Neighborhoods and is at the School of Public Health, Harvard University, Boston, MA 02115, USA.
Stephen W. Raudenbush
R. J. Sampson is in the Department of Sociology, University of Chicago, Chicago, IL, 60637 and is a Research Fellow of the American Bar Foundation, Chicago, IL 60611, USA. S. W. Raudenbush is at the College of Education, Michigan State University, East Lansing, MI 48824, USA. F. Earls is the Principal Investigator of the Project on Human Development in Chicago Neighborhoods and is at the School of Public Health, Harvard University, Boston, MA 02115, USA.
Felton Earls
R. J. Sampson is in the Department of Sociology, University of Chicago, Chicago, IL, 60637 and is a Research Fellow of the American Bar Foundation, Chicago, IL 60611, USA. S. W. Raudenbush is at the College of Education, Michigan State University, East Lansing, MI 48824, USA. F. Earls is the Principal Investigator of the Project on Human Development in Chicago Neighborhoods and is at the School of Public Health, Harvard University, Boston, MA 02115, USA.

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