Neighborhoods and Violent Crime: A Multilevel Study of Collective
Robert J. Sampson,
Stephen W. Raudenbush,
Sampson R, Raudenbush SW, and
Earls F. (1997).
Neighborhoods and violent crime: A multilevel study of collective
efficacy. Science, 277:918-924.
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
residential instability with violence are largely mediated by
R. J. Sampson is in the
Department of Sociology, University
of Chicago, Chicago, IL, 60637 and is a Research Fellow of the
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
Human Development in Chicago Neighborhoods and is at the School of
Public Health, Harvard University, Boston, MA 02115, USA.
For most of this
century, social scientists have observed marked variations in rates of
criminal violence across neighborhoods of U.S. cities.
been associated with the low socioeconomic status (SES) and
instability of neighborhoods. Although the geographical
of violence and its connection with neighborhood
composition are well
established, the question remains: why? What is it, for
the concentration of poverty that accounts for its
rates of violence? What are the social processes that might
mediate this relation (1-3)?
In this article, we report
results from a study designed to address these questions
Our basic premise is that social and organizational characteristics
neighborhoods explain variations in crime rates that are
attributable to the aggregated demographic characteristics of
individuals. We propose that the differential ability of neighborhoods
to realize the common values of residents and maintain effective
controls is a major source of neighborhood variation in violence
Although social control is often a response to
deviant behavior, it should not be equated with formal regulation or
forced conformity by institutions such as the police and courts. Rather,
social control refers generally to the capacity of a group to
regulate its members according to desired principles--to realize collective,
as opposed to forced, goals (6).
goal is the desire of community residents to live in safe
and orderly environments that are free of predatory crime,
especially interpersonal violence.
In contrast to formally or externally induced actions (for example,
police crackdown), we focus on the effectiveness of informal
by which residents themselves achieve public order.
informal social control include the monitoring of
groups among children, a willingness to intervene to
prevent acts such
as truancy and street-corner "hanging" by teenage peer
the confrontation of persons who are exploiting or
Even among adults, violence regularly arises
in public disputes, in the context of illegal markets (for
example, prostitution and drugs), and in the company of
The capacity of residents to control group-level
processes and visible signs of social disorder is thus a
influencing opportunities for interpersonal crime in a
Informal social control also generalizes to broader issues of import
the well-being of neighborhoods. In particular, the differential
ability of communities to extract resources and respond to
public services (such as police patrols, fire stations, garbage
collection, and housing code enforcement) looms large when
the known link between public signs of disorder (such as
housing, burned-out buildings, vandalism, and litter) and
Thus conceived, neighborhoods differentially activate informal
social control. It is for this reason that we see an analogy between
individual efficacy and neighborhood efficacy: both are activated
processes that seek to achieve an intended effect. At the
level, however, the willingness of local residents to
intervene for the
common good depends in large part on conditions of mutual
solidarity among neighbors (10).
Indeed, one is unlikely to
intervene in a neighborhood context in which the rules are
people mistrust or fear one another. It follows that
neighborhoods will prove the most fertile contexts for the
of informal social control. In sum, it is the linkage of
and the willingness to intervene for the common good that
neighborhood context of collective efficacy. Just as
in their capacity for efficacious action, so too do
neighborhoods vary in their capacity to achieve common
goals. And just as individual self-efficacy is situated
rather than global (one has self-efficacy relative to a
particular task or type of task) (11),
in this paper we view neighborhood efficacy as existing
relative to the tasks
of supervising children and maintaining public order. It follows
the collective efficacy of residents is a critical means by
neighborhoods inhibit the occurrence of personal violence,
regard to the demographic composition of the population.
What Influences Collective Efficacy?
As with individual efficacy, collective efficacy does not exist in
a vacuum. It is embedded in structural contexts and a wider political
economy that stratifies places of residence by key social characteristics
Consider the destabilizing potential
of rapid population change on neighborhood social
organization. A high
rate of residential mobility, especially in areas of decreasing population,
fosters institutional disruption and weakened social controls
over collective life. A major reason is that the formation of
social ties takes time. Financial investment also provides homeowners
with a vested interest in supporting the commonweal of
life. We thus hypothesize that residential tenure and
promote collective efforts to maintain social control (13).
Consider next patterns of resource distribution and racial
in the United States. Recent decades have witnessed an
geographical concentration of lower income residents, especially
minority groups and female-headed families. This neighborhood concentration
stems in part from macroeconomic changes related to the
deindustrialization of central cities, along with the
out-migration of middle-class residents (14).
the greater the race and class segregation in a
metropolitan area, the
smaller the number of neighborhoods absorbing economic
shocks and the more severe the resulting concentration of
poverty will be
Economic stratification by race and place thus fuels
the neighborhood concentration of cumulative forms of
intensifying the social isolation of lower income,
single-parent residents from key resources supporting
Perhaps more salient is the influence of racial and economic
exclusion on perceived powerlessness. Social science research has
demonstrated, at the individual level, the direct role of SES
promoting a sense of control, efficacy, and even biological health
An analogous process may work at the community level. The
alienation, exploitation, and dependency wrought by resource
deprivation act as a centrifugal force that stymies collective efficacy.
Even if personal ties are strong in areas of concentrated disadvantage,
they may be weakly tethered to collective actions.
We therefore test the hypothesis that concentrated disadvantage
decreases and residential stability increases collective efficacy.
turn, we assess whether collective efficacy explains the association
neighborhood disadvantage and residential instability with rates
interpersonal violence. It is our hypothesis that collective efficacy
mediates a substantial portion of the effects of neighborhood stratification.
This article examines data from the Project on Human
Development in Chicago Neighborhoods (PHDCN). Applying a spatial
definition of neighborhood--a collection of people and
occupying a subsection of a larger community--we combined
tracts in the city of Chicago to create
343 "neighborhood clusters"
(NCs). The overriding consideration in formation of NCs was
that they should be as ecologically meaningful as possible,
composed of geographically contiguous census tracts, and
internally homogeneous on
key census indicators. We settled on an ecological unit of about
8000 people, which is smaller than the 77 established
community areas in
Chicago (the average size is almost 40,000 people) but large
approximate local neighborhoods. Geographic boundaries (for
railroad tracks, parks, and freeways) and knowledge of
neighborhoods guided this process (18).
The extensive racial, ethnic, and social-class diversity of
Chicago's population was a major criterion in its selection as a
research site. At present, whites, blacks, and Latinos each represent
about a third of the city's population. Table
classifies the 343 NCs according to
race or ethnicity and a trichotomized measure of SES from
the 1990 census (19).
Although there are no low-SES white
neighborhoods and no high-SES Latino neighborhoods, there
neighborhoods in all three cells of SES, and many heterogeneous
neighborhoods vary in SES. Table 1
at once thus confirms the racial and
ethnic segregation and yet rejects the common stereotype
neighborhoods in the United States are homogeneous.
To gain a complete picture of the city's neighborhoods,
residents representing all 343 NCs were interviewed in their
part of the community survey (CS). The CS was designed to
representative sample of households within each NC, with
large enough to create reliable NC measures (20).
Henceforth, we refer to NCs as "neighborhoods," keeping in
that other operational definitions might have been used.
"Informal social control" was represented by a five-item
Likert-type scale. Residents were asked about the likelihood ("Would
you say it is very likely, likely, neither likely nor unlikely,
unlikely, or very unlikely?") that their neighbors could be
counted on to intervene in various ways if (i) children
were skipping school
and hanging out on a street corner, (ii) children were spray-painting
graffiti on a local building, (iii) children were showing
an adult, (iv) a fight broke out in front of their house,
and (v) the
fire station closest to their home was threatened with
"Social cohesion and trust" were also represented by five
conceptually related items. Respondents were asked how
agreed (on a five-point scale) that "people around here are
to help their neighbors," "this is a close-knit
"people in this neighborhood can be trusted," "people in
neighborhood generally don't get along with each other,"
"people in this neighborhood do not share the same values"
last two statements were reverse coded).
Responses to the five-point Likert scales were aggregated to the
neighborhood level as initial measures. Social cohesion and informal
social control were closely associated across neighborhoods (r = 0.80, P < 0.001),
which suggests that the
two measures were tapping aspects of the same latent
we also expected that the willingness and intention to
behalf of the neighborhood would be enhanced under
conditions of mutual trust and cohesion, we combined the
two scales into a summary measure
labeled collective efficacy (21).
The measurement of violence was achieved in three ways. First,
respondents were asked how often each of the following had occurred
the neighborhood during the past 6 months: (i) a fight in
weapon was used, (ii) a violent argument between neighbors, (iii)
gang fight, (iv) a sexual assault or rape, and (v) a robbery or
mugging. The scale construction for perceived neighborhood violence
mirrored that for social control and cohesion. Second, to
personal victimization, each respondent was asked "While you
lived in this neighborhood, has anyone ever used violence, such
as in a
mugging, fight, or sexual assault, against you or any
member of your
household anywhere in your neighborhood?" (22).
tested both survey measures against independently recorded incidents
homicide aggregated to the NC level (23).
Homicide is one of
the most reliably measured crimes by the police and does
not suffer the
reporting limitations associated with other violent crimes,
assault and rape.
Ten variables were constructed from the 1990 decennial census
the population to reflect neighborhood differences in poverty, race
ethnicity, immigration, the labor market, age composition, family
structure, homeownership, and residential stability (see Table
The census was independent of the
PHDCN CS; moreover, the census data were collected
5 years earlier,
which permitted temporal sequencing. To assess whether a
of linear combinations of census characteristics describe
the structure of the 343 Chicago neighborhoods, we
conducted a factor analysis (24).
Table 2. Oblique rotated factor
pattern (Loadings 0.60) in
343 Chicago neighborhoods. (Data are from the 1990 census.)
|Below poverty line
|Less than age 18
house as in 1985
Consistent with theories and research on U.S. cities, the
poverty-related variables given in Table 2
are highly associated and
load on the same factor. With an eigenvalue greater than 5, the
factor is dominated by high loadings (>0.85) for poverty, receipt
public assistance, unemployment, female headed-families, and
density of children, followed by, to a lesser extent, percentage of
black residents. Hence, the predominant interpretation revolves around
concentrated disadvantage--African Americans, children, and
single-parent families are differentially found in neighborhoods with
high concentrations of poverty (25).
To represent this dimension parsimoniously, we calculated a
factor regression score that
weighted each variable by its factor loading.
The second dimension captures areas of the city undergoing
immigration, especially from Mexico. The two variables that define
dimension are the percentage of Latinos (approximately 70% of
in Chicago are of Mexican descent) and the percentage of
persons. Similar to the procedures for concentrated disadvantage,
weighted factor score was created to reflect immigrant concentration.
Because it describes neighborhoods of ethnic and linguistic
heterogeneity, there is reason to believe that immigrant concentration
may impede the capacity of residents to realize common
values and to
achieve informal social controls, which in turn explains an
risk of violence (1-5,
The third factor score is dominated by two variables with high
loadings: the percentage of persons living in the same house
as 5 years
earlier and the percentage of owner-occupied homes. The
of a residential stability factor is consistent with much
The internal consistency of a person measure will depend on
the intercorrelation among items and the number of items in a scale.
The internal consistency of a neighborhood measure will depend
on these factors, but it will hinge more on the degree of
intersubjective agreement among informants in their ratings
neighborhood in which they share membership and on the
sample size of
informants per neighborhood (26).
To study reliability, we
therefore formulated a hierarchical statistical model
variation within persons, person variation within
variation between neighborhoods. Complicating the analysis
problem of missing data: inevitably, some persons will fail
to some questions in an interview. We present our
hierarchical model as
a series of nested models, one for each level in the
Level 1 model. Within each person,
Yijk, the ith response of person
j in neighborhood k, depends on the person's
latent perception of collective efficacy plus error:
Here Dpijk is an indicator
variable taking on a value of unity if response i is to item
p in the 10-item scale intended to measure
efficacy and zero if response i is to some other
p represents the
"difficulty" of item
p, and jk is the
"true score" for person jk and is adjusted for the difficulty
level of the items to which that person responded
The errors of measurement,
eijk, are assumed to be independent and
homoscedastic (that is, to have equal standard deviations).
Level 2 model. Across informants within neighborhoods,
latent true scores vary randomly around the neighborhood mean:
Here k is the
neighborhood mean collective efficacy, and random effects
rjk associated with each person are
independently, normally distributed with variance ,
that is, the "within-neighborhood variance."
Level 3 model. Across neighborhoods, each neighborhood's
mean collective efficacy k varies randomly
about a grand mean:
where is the grand mean collective
uk is a normally distributed random effect
associated with neighborhood k, and
is the between-neighborhood variance.
According to this setup, the object of measurement is
k. The degree of intersubjective agreement
among raters is the intraneighborhood correlation, = /( + ). The reliability of measurement of k
depends primarily on and on the sample size per neighborhood. The
entire three-level model is estimated simultaneously via maximum likelihood
The results showed that 21% of the variation in perceptions of
collective efficacy lies between the 343 neighborhoods (29).
The reliability with which neighborhoods can be distinguished
collective efficacy ranges between 0.80 for neighborhoods with
size of 20 raters to 0.91 for neighborhoods with a sample
size of 50 raters.
Controlling response biases. Suppose, however, that
informant responses to the collective efficacy questions vary
systematically within neighborhoods as a function of
background (such as age, gender, SES, and ethnicity), as
homeownership, marital status, and so on. Then variation
neighborhoods in the composition of the sample of
these lines could masquerade as variation in collective
control for such possible biases, we expanded the level
2 model (Eq. 2)
by incorporating 11 characteristics of respondents as
covariates. Equation 2
where Xqjk is the value
of covariate q associated with respondent j in
neighborhood k and q is the
partial effect of that covariate on the expected response
informant on the collective efficacy items. Thus,
k is now the level of efficacy for
neighborhood k after adjustment for the composition
informant sample with respect to 11 characteristics:
gender (1 = female, 0 = male), marital
status (composed of separate indicators
for married, separated or divorced, and single),
ethnicity and race (composed of indicators for Latinos and
mobility (number of moves in past 5 years), years in
and a composite measure of SES (the first principal
education, income, and occupational prestige).
Association Between Neighborhood Social Composition and Collective
The theory described above led us to expect that
neighborhood concentrated disadvantage (con. dis.) and immigrant
concentration (imm. con.) would be negatively linked to
collective efficacy and residential stability would be
related to collective efficacy, net of the contributions of
the 11 covariates defined in the previous paragraph.
To test this hypothesis,
we expanded the level 3 model (Eq. 3)
where 0 is the model intercept and
1, 2, and 3 are partial
We found some effects of personal background (Table
High SES, homeownership, and age were
associated with elevated levels of collective efficacy,
mobility was negatively associated with collective
ethnicity, and years in neighborhood were not associated
At the neighborhood level, when these personal background effects
were controlled, concentrated disadvantage and immigrant concentration
were significantly negatively associated with collective efficacy,
whereas residential stability was significantly positively associated
with collective efficacy (for metric coefficients and t
ratios, see Table 3).
The standardized regression
coefficients were 0.58 for concentrated disadvantage, 0.13 for
immigrant concentration, and 0.25 for residential
explaining over 70% of the variability across the
Collective Efficacy as a Mediator of Social Composition
Past research has consistently reported links between neighborhood
social composition and crime. We assessed the relation of
composition to neighborhood levels of violence, violent victimization,
and homicide rates, and asked whether collective efficacy
mediated these relations.
Perceived violence. Using a model that paralleled that for
collective efficacy (Eqs. 1,
we found that reports of neighborhood violence depended to
some degree on personal background. Higher levels of
violence were reported by those who were separated or
divorced (as compared with those who were single or married), by
and blacks (as opposed to Latinos), by younger respondents, and
those with longer tenure in their current neighborhood. Gender,
homeownership, mobility, and SES were not significantly associated
responses within neighborhoods. When these personal background
characteristics were controlled, the concentrations of
(t = 13.30) and immigrants (t = 2.44)
were positively associated with the level of violence (see
model 1). The corresponding
standardized regression coefficients are 0.75 and
0.11. Also, as
hypothesized, residential stability was negatively associated
level of violence (t = 6.95), corresponding to a
standardized regression coefficient of 0.28. The model accounted for
70.5% of the variation in violence between neighborhoods.
Table 4. Neighborhood correlates of
violence, violent victimization, and 1995 homicide events.
social composition and collective
Estimates of neighborhood-level coefficients control for gender,
marital status, homeownership, ethnicity, mobility, age, years
neighborhood, and SES of those interviewed. Model 1 accounts for
of the variation between neighborhoods in perceived violence,
model 2 accounts for 77.8% of the
coefficients are adjusted for
the same person-level covariates listed in the first footnote. Model
1 accounts for 12.3% of the variation between
neighborhoods in violent
victimization, whereas model 2 accounts for 44.4%.
1 accounts for 56.1% of the variation between neighborhoods in
homicide rates, whereas model 2 accounts for 61.7% of the
Next, collective efficacy was added as a predictor in the level
model 2). The analysis built in a correction for errors of
measurement in this predictor (30).
We found collective efficacy to be negatively related to
violence (t = 5.95), net of all other effects, and to correspond
standardized coefficient of 0.45. Hence, after social composition was
controlled, collective efficacy was strongly negatively
violence. Moreover, the coefficients for social composition
substantially smaller than they had been without a control
collective efficacy. The coefficient for concentrated
although still statistically significant, was
0.171 (as compared with
0.277). The difference between these coefficients
(0.277 0.171 = 0.106) was
significant (t = 5.30).
Similarly, the coefficients for immigrant concentration and
residential stability were also significantly reduced: The
for immigrant concentration, originally 0.041, was now
difference of 0.023 (t = 2.42); the coefficient
residential stability, which had been 0.102, was now 0.056, a
difference of 0.046 (t = 4.18). The immigrant
concentration coefficient was no longer statistically
zero. As hypothesized, then, collective efficacy appeared
to partially mediate widely cited relations between
neighborhood social composition and violence. The model
accounted for more than 75% of the variation between
neighborhoods in levels of violence.
Violent victimization. Violent victimization was assessed by
a single binary item (Yjk = 1 if
violence in the neighborhood and Yjk = 0
not). The latent outcome was the logarithmic odds of
jk. The structural model for
predicting jk had the same form as before
Social composition, as hypothesized,
predicted criminal victimization, with positive
concentrated disadvantage and immigrant concentration and a
coefficient for residential stability (Table 4,
model 1). The relative
odds of victimization associated with a 2-SD elevation in
were 1.67, 1.33, and 0.750, respectively. These
for background characteristics associated with the risk of
victimization. When added to the model, collective efficacy
negatively associated with victimization (Table 4,
model 2). A 2-SD
elevation in collective efficacy was associated with a
ratio of about 0.70, which indicated a reduction of
30% in the odds of
victimization. Moreover, after collective efficacy was
coefficients associated with concentrated disadvantage and
stability diminished to nonsignificance, and the
immigrant concentration was also reduced.
Homicide. To assess the sensitivity of the findings
when the measure of crime was completely independent of the survey, we
examined 1995 homicide counts (Yk
is the number
of homicides in neighborhood k in 1995). A natural
the expected number of homicides in neighborhood k
E(Yk) = Nkk, where
k is the homicide
rate per 100,000 people in
neighborhood k and Nk is the population
size of neighborhood k as given by the 1990 census (in
hundreds of thousands). Defining k
= log (k), we then formulated
regression model for k of the type in Eq. 5.
This is effectively a Poisson regression model with a
with extra-Poisson variation represented by
Although concentrated disadvantage was strongly positively related
homicide, immigrant concentration was unrelated to homicide, and
residential stability was weakly positively related to homicide (Table
model 1). However, when social composition was controlled, collective
efficacy was negatively related to homicide (Table 4,
2). A 2-SD elevation in collective efficacy was associated with
39.7% reduction in the expected homicide rate. Moreover, when
collective efficacy was controlled, the coefficient for concentrated
disadvantage was substantially diminished, which indicates that
collective efficacy can be viewed as partially mediating the
association between concentrated disadvantage and homicide
Control for prior homicide. Results so far were mainly
cross-sectional, which raised the question of the possible confounding
effect of prior crime. For example, residents in neighborhoods
high levels of violence might be afraid to engage in acts of
We therefore reestimated all models controlling
for prior homicide: the 3-year average homicide rate in
1988, 1989, and
1990. Prior homicide was negatively related (P < 0.01)
collective efficacy in 1995 (r = 0.55) and positively related (P < 0.01)
to all three measures of violence in
1995, including a direct association (t = 5.64)
homicide (Table 5).
However, even after
prior homicide was controlled, the coefficient for
remained statistically significant and substantially
negative in all
Although the results have been consistent, there are still
potential threats to the validity of our analysis. One question pertains
to discriminant validity: how do we know that it is collective efficacy
at work rather than some other correlated social process (34)?
To assess competing and analytically distinct factors suggested
by prior theory (4,
we examined the measure of
collective efficacy alongside three other scales derived from the
the PHDCN: neighborhood services, friendship and kinship ties,
organizational participation (35).
On the basis of the
results in Tables to 5 and also to achieve parsimony, we
constructed a violent crime scale at the neighborhood level
standardized indicators of the three major outcomes:
violence, violent victimization, and homicide rate.
Consistent with expectations, collective efficacy was significantly
(p < 0.01) and positively related to friendship and
kinship ties (r = 0.49), organizational
(r = 0.45), and neighborhood services (r = 0.21).
Nonetheless, when we controlled for these correlated
factors in a multivariate regression, along with prior
concentrated disadvantage, immigrant concentration, and
stability, by far the largest predictor of the violent
crime rate was
collective efficacy (standardized coefficient = 0.53,
t = 8.59). Collective efficacy thus retained
discriminant validity when compared with theoretically
competing social processes. Moreover, these results
dense personal ties, organizations, and local services by
themselves are not sufficient; reductions in violence
appear to be more
directly attributable to informal social control and
A second threat stems from the association of racial composition
concentrated disadvantage as shown in Table 2.
Our interpretation was
that African Americans, largely because of housing
differentially exposed to neighborhood conditions of
Nonetheless, a counterhypothesis is that the
percentage of black residents and not disadvantage accounts
levels of collective efficacy and, consequently, higher
second set of tests therefore replicated the key models
within the 125 NCs where the population was more than
75% black (see the first row of
effectively removing race as a potential confound.
Concentrated poverty and residential stability each had
associations with collective efficacy in these
areas (t = 5.60 and t = 2.50,
respectively). Collective efficacy continued to explain
violence across black NCs, mediating the prior effect of
disadvantage. Even when prior homicide, neighborhood
friendship and kinship ties, and organizational
controlled, the only significant predictor of the violent
in black NCs was collective efficacy (t = 4.80).
These tests suggested that concentrated disadvantage more
than race per
se is the driving structural force at play.
Discussion and Implications
The results imply that collective efficacy is an important
construct that can be measured reliably at the neighborhood level by
means of survey research strategies. In the past, sample surveys have
primarily considered individual-level relations. However, surveys
merge a cluster sample design with questions tapping collective
properties lend themselves to the additional consideration of
Together, three dimensions of neighborhood
disadvantage, immigration concentration, and residential stability--explained
70% of the neighborhood variation in collective efficacy.
Collective efficacy in turn mediated a substantial portion of
the association of residential stability and disadvantage with multiple
measures of violence, which is consistent with a major theme
neighborhood theories of social organization (1-5).
adjustment for measurement error, individual differences in
neighborhood composition, prior violence, and other potentially confounding
social processes, the combined measure of informal social
control and cohesion and trust remained a robust predictor of
rates of violence.
There are, however, several limitations of the present study.
the use of decennial census data and prior crime as lagged
the basic analysis was cross-sectional in design; causal
not proven. Indicators of informal control and social
cohesion were not
observed directly but rather inferred from informant
the scope of the present study, other dimensions of
efficacy (such as political ties) may be important, too.
was limited also to one city and did not go beyond its
boundaries into a wider region.
Finally, the image of local residents working collectively to solve
their own problems is not the whole picture. As shown, what
within neighborhoods is in part shaped by socioeconomic and
factors linked to the wider political economy. In addition
encouraging communities to mobilize against violence through
"self-help" strategies of informal social control, perhaps reinforced
by partnerships with agencies of formal social control (community
policing), strategies to address the social and ecological changes
that beset many inner-city communities need to be considered. Recognizing
that collective efficacy matters does not imply that inequalities
at the neighborhood level can be neglected.
REFERENCES AND NOTES
16 January 1997; accepted 20 June 1997
- 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
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Sources of Delinquency
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K. Ho, C. Ross, Social Problems 21,
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Criminal Careers and "Career Criminals," A. Blumstein,
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Work (Princeton Univ. Press, Princeton, NJ, 1993).
- A. Bandura, Social
Foundations of Thought and Action: A
Social Cognitive Theory (Prentice-Hall, Englewood Cliffs, NJ,
- See, generally,
J. Logan and H. Molotch, Urban Fortunes:
The Political Economy of Place (Univ. of California Press,
Berkeley, CA, 1987).
- See also J. Kasarda
Am. Sociol. Rev.
; R. Sampson,
- W. J. Wilson, The
Truly Disadvantaged (Univ.
of Chicago Press, Chicago, IL, 1987).
- 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,
- 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
- D. Williams and
C. Collins, Annu. Rev. Sociol.
21, 349 (1995).
- 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;
revealed that the clustering was successful
in producing relative homogeneity within NCs.
- For purposes of
selecting a longitudinal cohort sample, SES
was defined with the use of a scale from the 1990 census that
NC-level indicators of poverty, public assistance, income, and
Race and ethnicity were also
measured with the use of the 1990 census, which defined race in
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
- 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
with the aim of obtaining a sample of 50 households within each
each of the remaining NCs (n = 263), nine census
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
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).
Associates (Cambridge, MA) carried out the data collection with the
cooperation of research staff at PHDCN, achieving a final response rate
- "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.
- 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.
- 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.
- 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 [
H. F. Kaiser and
]. 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.
- 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,
W. Raudenbush, B. Rowan, S. J. Kang, J. Educ.
Stat. 16, 295 (1991).
- D. V. Lindley
and A. F. M. Smith, R. Stat. Soc. J. Ser.
B Methodol. 34, 1 (1972).
- 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 jk.
- 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. Kang, Am. J. Educ. 99, 238 (1991)].
- The analysis of
collective efficacy and violence as
outcomes uses a three-level model in which the level 1 model
the sources of measurement error for each of these outcomes. The level
2 and level 3 models together describe the joint distribution
"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
R. J. Sampson (paper presented at the conference "Alternative
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,
and A. Haritou [Am. J. Sociol. 100,
1261 (1995)] and P. Allison (ibid., p. 1294). Note
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.
- 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 [
Results were similar. The results based
on the population-average model with robust standard errors are
- 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.
- Although the
zero-order correlation of residential stability
with homicide was insignificant, the partial coefficient in Table 4
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).
- 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).
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.
- 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.
- R. J. Sampson,
S. W. Raudenbush, F. Earls, data not
- 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
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.