Journal of Criminal Justice 31 (2003) 511 – 521
Strain and violence: Testing a general strain theory model of
community violence$
Barbara D. Warner*, Shannon K. Fowler
Department of Criminal Justice and Police Studies, Eastern Kentucky University, 467 Stratton Building 521 Lancaster Avenue,
Richmond, KY 40475 3102, USA
Abstract
Agnew’s General Strain Theory (GST) has come to be recognized as an increasingly important explanation for
violence at the individual level. Drawing on this individual level theory, Agnew [Journal of Research in Crime and
Delinquency 36 (1999) 123] recently suggested that GST might also be applicable to explaining variations in
community crime rates. This macro level General Strain Theory (MST) has, however, rarely been empirically
examined. This article provides an examination of some of the central ideas in Agnew’s MST using data from
sixty-six neighborhoods in a southern state. The findings presented here suggest that neighborhood disadvantage
and stability significantly affect neighborhood levels of strain. In turn, strain significantly affects levels of
violence. The extent to which the effects of strain on violence are conditioned by levels of informal social control
and social support/capital are also examined in this article. The results are partially supportive of MST.
D 2003 Elsevier Ltd. All rights reserved.
Introduction
In the last two decades there was a significant
renewed interest in explaining variations in crime
rates among neighborhoods. Much of this work was
rooted in contemporary social disorganization theory,
arguing that neighborhood characteristics of disad-
$
This project was supported, in part, by Grant No.
1999-IJ-CX-0052 awarded by the National Institute of
Justice, Office of Justice Programs, U.S. Department of
Justice, and the Eastern Kentucky University’s Department
of Criminal Justice and Police Studies’ Program of
Distinction Research Fellowship. Points of view in this
article are those of the authors and do not necessarily
represent the official position or policies of the U.S.
Department of Justice.
* Corresponding author. Tel.: +1-859-622-1112; fax:
+1-859-622-1549.
E-mail address: [email protected]
(B.D. Warner).
vantage and residential stability decrease informal
social control, thereby allowing crime rates to grow
(Bursik & Grasmick, 1993; Elliot et al., 1996; Sampson & Groves, 1989; Sampson, Raudenbush, & Earls,
1997; Warner & Rountree, 1997). This social control
approach to crime argues that motivations toward
crime do not vary and therefore are not necessary
for understanding variation in crime rates. Only more
recently was there a renewed interest in examining
whether motivation toward criminal offending varied
across neighborhoods. For example, the renewed
interest in cultural deviance theories in the 1990s
led to arguments that community characteristics contributed to the development of values supportive of
violence which in turn affected community rates of
criminal behavior (see for example, Anderson, 1990,
1999). Most recently, there was an attempt to develop
General Strain Theory (GST) into a community
model to explain how variation in levels of neighborhood strain can lead to increased neighborhood
crime rates (Agnew, 1999).
0047-2352/$ – see front matter D 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jcrimjus.2003.08.006
512
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
Agnew (1999) argued that community level crime
rates could best be understood as emanating from
both differences in levels of social control and
motivation for crime, particularly motivation rooted
in strain. Agnew suggested that community characteristics could affect levels of strain by affecting the
likelihood of residents failing to achieve positively
valued goals, losing positive stimuli, and experiencing negative or aversive stimuli. Increased levels of
strain lead to increased rates of negative affect, such
as anger and frustration. Neighborhoods with higher
proportions of strained residents have a higher probability of those residents interacting with each other
and leading to explosive situations. The extent to
which strain leads to crime, however, is argued to be
moderated by several variables, including levels of
social control and social support/social capital within
the community.
Agnew’s (1999) presentation of a macro level
general strain theory (MST) was quite broad and
provided fertile ground for empirical research.1 Few
studies, however, attempted to test MST, and none
examined it in the context of actual neighborhoods.
The purpose of this study was to add to the development of MST by examining the effects of neighborhood characteristics on measures of strain and strain’s
subsequent effects on violence. Further, the study
sought to examine the moderating effects of informal
social control and social support/social capital on the
relationship between strain and violence.
Macro level general strain theory
Drawing on Agnew’s (1992) earlier presentation
of an individual level GST, macro level strain theory
hypothesizes that the aggregate-level of strain within
a neighborhood can have implications for the overall
level of violence within that neighborhood. While
MST focuses on exogenous community level variables similar to other community level models, it
differentiates itself in terms of the intervening processes. MST argues that neighborhood characteristics
such as poverty, inequality, overcrowding, residential
mobility, and high percentages of non-Whites increase the level of neighborhood strain. Following
GST, strain arises from three potential sources. The
first of these is the failure of neighborhood residents
to achieve positively valued goals, including monetary success, status/respect, and ‘‘the desire to be
treated in a just or nondiscriminatory manner’’
(Agnew, 1999, p. 127). Agnew (1999) suggested that
residents of certain types of neighborhoods had a
more difficult time achieving these goals through
legitimate means. For example, residents in disadvantaged neighborhoods have less access to primarysector jobs as well as fewer job contacts and less job
information. Status and respect are also often in
limited supply in distressed neighborhoods. On the
other hand, discrimination, the antithesis of being
treated fairly or justly, may be more likely to occur in
these neighborhoods.
Strain also arises from the loss of positively
valued goals (such as when others take one’s possessions) and the presence of negative or aversive
stimuli (such as personal affronts, provocations, or
harassments). In particular, Agnew (1999) noted that
community characteristics were related to the level of
exposure to aversive stimuli, such as ‘‘signs of
incivility, social cleavages and ‘vicarious strain’’’
(Agnew, 1999, p. 127).
Increased levels of strain within communities
increase the likelihood of residents experiencing
negative emotions, such as frustration and anger.
The increased levels of negative emotion within these
communities then increases the likelihood of persons
within those neighborhoods mistreating or getting
into fights with one another. Hence, increased levels
of strain produce a ‘‘charged’’ environment conducive to crime, particularly violent crime.
While Agnew (1999) viewed strain as an important variable in understanding community level crime
rates, he suggested that community levels of strain
provided an additional rather than an alternative
explanation to community crime rates noting that,
‘‘a full explanation of community differences in
crime rates must draw on a range of theories, including those which examine the ways in which communities motivate as well as control crime’’ (p. 147).
Hence, the effects of strain are additive to the effects
of social control on community level crime rates, and
together these variables should mediate (or explain)
the effects of community structural characteristics on
crime rates.
Agnew (1999) also suggested, however, that the
likelihood of criminal outcomes resulting from strain
is dependent upon, or moderated by, several neighborhood conditions. These moderating conditions
include the level of public knowledge of one’s
personal affairs, the availability of alternative goals
or identities, the presence of subcultures encouraging
external attribution of blame, the number of models
for effective coping available in the community, the
level of social support or social capital, the level of
informal social control of behavior, opportunities for
crime, values conducive to crime and the presence of
criminal groups.
While several examinations of Agnew’s GST at
the individual level suggested support for the strainviolence relationship (Agnew, 1985; Agnew &
White, 1992; Brezina, 1998; Capowich, Mazerolle,
& Piquero, 2001; Mazerolle & Piquero, 1997, 1998;
Mazerolle, Burton, Cullen, Evans, & Payne, 2000;
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
Piquero & Sealock, 2000), there have been few tests
of a macro-level general strain model. Brezina,
Piquero, and Mazerolle (2001) recently examined
MST using school level data from the Youth in
Transition Survey. Their findings showed mixed
support for the model. Specifically, in the aggregate
level model, Brezina et al. found schools with higher
levels of angry students were associated with higher
levels of students who fought or argued with other
students, but higher levels of angry students were not
significantly related to the broader aggressive behavior measure. Further, Brezina et al. did not measure
levels of strain, a central aspect of the theory.
There have not yet been any empirical examinations of MST at the neighborhood level, nor have
there been tests of the conditional effects of strain at
the aggregate level. The current article adds to the
very limited knowledge about the aggregate level
effects of strain on neighborhood crime rates by
examining some of the many hypotheses generated
by MST. Specifically, the following hypotheses suggested by the theory are examined in this article.
Hypothesis 1: Neighborhood characteristics indicative of disadvantage and residential mobility will
increase levels of strain.
Hypothesis 2: Higher levels of strain will increase
neighborhood levels of violence, and will partially
mediate the effects of neighborhood characteristics on
violence.
Hypothesis 3: Strain will add to the prediction of
violence over informal social control models.
Hypothesis 4: The effects of strain on violence will
be moderated by informal social control and social
capital/social ties, with strain being more likely to
lead to violence in neighborhoods with low informal
social control and low social capital.
The study
Sampling
Neighborhoods
The study used census defined block groups as the
unit of analysis. While neighborhoods may be defined
in numerous ways, most recent quantitative studies of
communities and crime relied on geographical units
such as census tracts, electoral wards or nominal
communities (see for example, Bellair, 1997; Sampson
& Groves, 1989; Sampson et al., 1997; Warner &
Pierce, 1993; Warner & Rountree, 1997). Agnew
(1999) noted that MST might best be examined with
data from small, homogeneous areas. Census block
513
groups are relatively small, homogenous areas, but at
the same time, they are large enough to provide some
of the standard census data necessary for this type of
study.
The data for the study were based primarily on
survey responses from residents in sixty-six block
groups in the two largest cities of a southern state.
Each of these cities had a population of over onequarter million (260,512 and 256,231, respectively)
(U.S. Census Bureau, 2000). The survey data were
supplemented with block group level data from the
1990 U.S. Census.2
The survey data used were part of a National
Institute of Justice funded study examining informal
social control in high drug use neighborhoods. Consequently, the sampling plan for the block groups was
developed to assure a sufficient number of high drug
use neighborhoods as well as an adequate distribution
of predominantly White, predominantly minority, and
predominantly racially mixed neighborhoods. To
achieve these goals, census block groups were first
placed into one of three strata: high-drug-use, adjacent
to high-drug-use, and non-adjacent to high-drug-use.
High-drug-use neighborhoods were identified using
data from a previous study that interviewed crack and
injection drug users.3 These high-drug-use block
groups comprised the first strata. Neighborhoods adjacent to these known high-drug-use neighborhoods
were believed to also have the potential for high drug
activity, therefore, all adjacent, non-high-drug-use
block groups were identified and comprised the second
strata. Finally, all remaining census block groups (nonadjacent to high-drug block groups) comprised the
third strata.
Once these three strata were established, census
data for all of the block groups were obtained and
block groups with fewer than one hundred households
were deleted. Block groups within the adjacent to
high-drug-use and non-adjacent to high-drug-use strata were then sub-divided into three further strata predominantly (greater than 67 percent) White, predominantly Black, and predominantly mixed. Approximately one-third of the sampled blocks from the strata
of adjacent and non-adjacent block groups were then
chosen from each of these racial sub-strata to assure an
adequate representation of White, non-White and
racially mixed neighborhoods. All of the block groups
in the high-drug-use neighborhoods were included in
the sample.
Respondents
Once neighborhoods were sampled, the ‘‘street
guide’’ sections of city directories were used to create
sampling frames of all addresses in the neighborhoods. Residences were then sampled using system-
514
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
atic random sampling. Residences with telephone
numbers were interviewed over the phone, while
residences without telephones were interviewed with
face-to-face surveys. Residences found to have nonworking telephone numbers were later entered into the
sampling pool for face-to-face surveys. Approximately 75 percent of the completed surveys were conducted over the telephone and 25 percent were
conducted in person.
Survey data were collected from one person per
household who was eighteen years of age or older and
currently residing at the sampled address. Surveys
lasted approximately twenty minutes and were collected between February and August 2000. Respondents
were paid fifteen dollars for their participation. The
total number of respondents in the study was 2,309
with the average number of respondents per neighborhood being thirty-five. The overall cooperation rate
was 60 percent.
Measures
Exogenous variables
Like other community level theories of crime,
MST identifies neighborhood characteristics associated with high crime rates as low economic status (as
measured by variables such as poverty, education,
welfare, owner occupied housing, etc.), residential
mobility, high percentages of non-Whites and disrupted families, population density, and overcrowding.
Due to the relatively small sample size and the
increased reliability of measures based on more than
one indicator, several census variables were factor
analyzed to capture neighborhood structure. Data for
neighborhood variables were obtained from the 1990
U.S. Census, STF-3A. Variables from the census were
chosen that represented what previous community
level studies identified as disadvantage and residential
stability. These variables included: the percent below
poverty, percent African American, percent with education levels less than a high school degree, percent
female headed households with children under the age
of eighteen, the percent homeowners, and the percent
of residents who lived in the same house five years
earlier (residential stability). These variables were
factor analyzed to determine whether there were one
or more underlying factors that could be used to
describe these variables. The factor analysis (varimax
rotation) produced two factors with eigenvalues greater than one. Together these two factors accounted for
81.15 percent of the variance in these items. Substantively, these factors represented disadvantage and
stability.
The variables that loaded on the disadvantage
factor and their factor loadings were similar to those
variables found in the literature to represent disad-
vantage: the percent below poverty (.82), percent of
female headed households with children (.77), percent African American (.85), and the percent with
less than a high school degree (.79). Both residential
stability (.94) and percent homeowners (.83) loaded
on the stability factor. Regression based factor scores
were computed for each of these factors.
Intervening variables
Community level characteristics have been hypothesized to affect crime rates through both the level
of strain and the level of informal social control.
Agnew (1999) discussed several potential sources of
strain. In terms of failure to achieve positively valued
goals, Agnew included not only economic goals, but
also status/respect and the desire to be treated in a fair
or just manner. He suggested that individuals in
disadvantaged neighborhoods might be more likely
to experience discrimination and negative experiences with the police. Further, Agnew suggested that
disadvantaged communities were likely to provide
the background for the loss of positively valued
stimuli and the presentation of negative or aversive
stimuli. Included here are physical and verbal abuses,
signs of incivilities (such as street harassment), and
social cleavages or ‘‘exploitative and manipulative
relationships.’’ Many of these aversive stimuli are not
only personally experienced, but also experienced
vicariously through witnessing the experience of
other family members and friends.
Three survey items were used to represent these
aspects of strain. Respondents were asked, ‘‘Thinking
back over the last three months, have you or anyone in
your household (1) received verbal threats or insults,
(2) felt cheated by someone, (3) been harassed by the
police.’’ Receiving verbal threats or insults and feeling cheated by someone could be viewed as representing the presence of negative or aversive stimuli. Being
harassed by the police could be viewed as the failure
to achieve positively valued goals, especially the
desire to be treated in a fair or just manner. Respondents answered yes (1) or no (0) to each of these
questions, and the sum of affirmative answers was
calculated for each respondent. These sums were then
averaged within each neighborhood to produce the
average level of strain in the neighborhood. While
these items represented only a limited measure of
strain, the measure of strain did vary substantially
across neighborhoods. The average level of strain
across all the neighborhoods was .33 with a minimum
of .06 and a maximum of .66 (see Table 1 for
descriptive data on all of the variables). Hence, while
strain was not severe in these neighborhoods, there
was significant variation in levels of strain across
neighborhoods. Indeed, an analysis of variance exam-
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
Table 1
Descriptive statistics on sixty-six neighborhoods
Study variables
Mean
St. Dev.
Percent below poverty
Percent African
American
Percent with less than
h.s. education
Percent female-headed
households
Percent homeowners
Percent stable
Community strain
Informal social control
Social support/social
capital
Violence
36.86
48.92
25.26
34.84
Min.
.16
.00
Max.
93.00
100.00
41.11
18.17
4.73
80.00
17.13
17.57
.00
71.67
41.40
48.07
.33
.56
.38
26.78
16.15
.13
.10
.07
.00
11.79
.06
.33
.20
94.78
86.21
.66
.81
.54
7.80
10.28
.20
57.32
ining the extent to which the level of variation was
between neighborhoods rather than within neighborhoods showed significant between neighborhood differences (F = 1.50; df = 2297; p = .007).
While strain is the central variable of concern with
MST, Agnew (1999) suggested that it had both additive and multiplicative effects with measures of informal social control. That is, Agnew argued that for a
full understanding of variations in community crime
rates one must examine both motivations to crime,
such as levels of strain, and informal social control. In
much of the recent community-level social control
models, informal social control was measured in terms
of the likelihood of intervening in inappropriate community behaviors (e.g., Elliot et al., 1996; Sampson et
al., 1997). The items that comprised the measure of
informal social control are shown in Table 2. The
response categories for these items were: ‘‘very likely,’’ ‘‘somewhat likely,’’ ‘‘somewhat unlikely,’’ and
‘‘very unlikely.’’ The percentage of respondents in
each neighborhood stating that neighbors were very
likely to intervene was calculated for each of these six
items, and the percentages were then averaged across
the items to provide a measure of neighborhood
informal social control (a = .87). The average percentage of respondents that stated neighbors were
‘‘very likely’’ to intervene in these neighborhood
behaviors was 56.5 percent, and ranged from a minimum of 33 percent to a maximum of 81 percent.
Moderating variables
MST also suggests that the effects of strain on
criminal offending may be conditioned by a variety of
moderating variables. While Agnew (1999) presented
several potential moderating variables, the current
study focused on the examination of two of those:
informal social control and social support/social cap-
515
ital. Low levels of informal social control are argued
to not only directly increase the level of crime, but
also to increase the probability that strain will lead to
crime. The measure of informal social control was
discussed above.
The level of social support/social capital available
within communities is also argued to moderate the
effects of strain on crime. The level of social support/
capital within communities affects the extent to which
residents are able to successfully cope with strain.
Social support is often thought of in terms of
‘‘networks of interpersonal relationships’’ (Capowich
et al., 2001) and social capital refers to the assets
available to individuals based on their inclusion in
social networks (Bourdieu, 1985; Portes, 1998).
Hence, the measure used here for social support/
social capital was based on responses to items regarding interpersonal relationships among neighbors,
particularly as they related to support gained from
those relationships. Six items regarding social networks among neighbors within neighborhoods were
examined. Respondents were asked how frequently
they engaged in the following behaviors: (1) borrowing or exchanging items with neighbors such as food,
recipes, tools, or other equipment; (2) asking a
neighbor for help, like getting their car started, getting
a ride, or watching their children; (3) having someone
from the neighborhood over to their house or going to
a neighbor’s house for a meal, to play cards, watch
TV, or talk; (4) going out for an evening with
someone from the neighborhood; (5) talking to someone in the neighborhood about personal problems;
and, (6) talking to someone in the neighborhood
about stores and sales, programs for neighborhood
Table 2
Items used to measure informal social control
Items
1. If a fight broke out in front of your house and someone
was being beaten up, how likely is it that someone in your
neighborhood would do something to stop it?
2. If someone was trying to sell drugs to a neighborhood
child in plain sight, how likely is it that someone in your
neighborhood would do something to stop it?
3. If children were spray painting graffiti on a local building,
how likely is it that someone in your neighborhood would
do something to stop it?
4. If someone was breaking into your house in plain sight,
how likely is it that someone in your neighborhood would
do something to stop it?
5. If someone was trying to sell drugs to an adult in plain
sight, how likely is it that someone in your neighborhood
would do something to stop it?
6. If children were showing disrespect to an adult in your
neighborhood, how likely is it that someone else in your
neighborhood would do something to stop it?
516
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
children, church activities, etc. The response categories for these items were: ‘‘about once a day,’’ ‘‘about
once a week,’’ ‘‘about once a month,’’ ‘‘several times
a year,’’ ‘‘about once a year,’’ and ‘‘never.’’ Factor
analysis of these items at the individual level confirmed the unidimensionality of these items and the
reliability analysis demonstrated adequate internal
consistency (a = .82). The percentage of respondents
in each neighborhood answering once a month or
more frequently was calculated for each of these six
items. These percentages were then averaged over the
six items, resulting in the percentage of respondents
in each neighborhood engaged in relatively consistent
social support networks within their neighborhoods.
The average percent of respondents engaged in consistent social support networks was 37.6 percent, with
a range from 20 to 54 percent.
Dependent measure
Agnew’s (1999) discussion of MST focused primarily on aggressive or violent behavior as the most
likely effect of high levels of neighborhood strain.
The measure of violence used here was based on
survey data. In part, this was because official counts
of crime focused predominantly on serious violence
that was reported to the police, and disadvantaged
neighborhoods with high strain might be less likely to
report some crimes to the police (Baumer, 2002).
Further, because self- reports of violence in a general
community survey are unlikely to produce adequate
amounts of reported violence at the block group
level, and victimization from violence is likely to
be a cause rather than a consequence of strain, the
survey measure used relied on respondents as knowledgeable informants about the level of violence in
their neighborhood.
The measure of the neighborhood level of violence
was based on survey questions regarding violent
behavior witnessed or heard about in the neighborhood
in the previous six months. The measure used here was
similar to a measure of neighborhood violence used in
other studies of neighborhood crime (see e.g., Sampson et al., 1997; Sampson, Morenoff, & Earls, 1999).
Respondents were asked about the number of times
they had seen or heard about: (1) a fight in which a
weapon was used; (2) a fight in which no weapon was
used; (3) a sexual assault or rape; (4) a robbery or
mugging; and (5) a spouse or partner being hit,
slapped, punched, or otherwise beaten. Since respondents could be reporting about the same incidents, the
average number of each of these violent behaviors was
calculated for each neighborhood. Each of the averages for the five violent behaviors was then summed to
produce the neighborhood level of violence. The
number of violent crimes witnessed ranged from .2
to 57.32 with an average of 7.8. This variable was
skewed, therefore, its natural logarithm was used in the
analyses.
Results
The analysis began with an examination of the
effects of neighborhood characteristics on strain and
informal social control. As can be seen in Table 3,
disadvantage significantly increases, and neighborhood stability significantly decreases, neighborhood
levels of strain as predicted by MST. In addition as
predicted by contemporary social disorganization
models, disadvantage significantly decreases and stability significantly increases informal social control.
The analysis next turned to an examination of the
effects of strain on violence, controlling for the level
of informal social control, and the extent to which
strain and informal social control mediated the
effects of neighborhood characteristics on violence.
Table 4 presents these results.4 Model 1 (Table 4)
presents the total effects of disadvantage and stability on violence. Consistent with the community and
crime literature, disadvantage had a significant positive effect on levels of violence and stability had a
significant negative effect on violence. Models 2 and
3, respectively, examined the effects of strain and
social control. As can be seen in Model 2 of this
table, strain had a positive significant effect on levels
of violence, and it mediated a small percentage of
the effects of disadvantage and stability on violence
levels. Informal social control also had a significant
(negative) effect on violence levels, and mediated a
more substantial percentage of the effects of disadvantage (25 percent) and stability (41percent) on
violence levels (Model 3, Table 4). When strain
and informal social control were included simultaneously, however, only strain maintained its significant effect on violence. The effect of informal social
control dropped to just below significance level (t =
1.95; p = .06).5
Table 3
Effects of community characteristics on neighborhood strain
and informal social control
Independent
variables
Neighborhood
strain
b (s.e.)
b
Informal social
control
b (s.e.)
Disadvantage .05 (.02) .34 ** .06 (.01)
Stability
.03 (.02) .24 *
.06 (.01)
R2
.18
.61
F
6.67 **
49.32 **
* P < .05.
** P < .01.
b
.54 **
.56 **
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
517
Table 4
OLS regressions of strain and informal social control on violence
Independent
variables
Model 1
Disadvantage
.84
(.09)
.54
(.09)
–
.44 **
–
–
Stability
Strain
Informal
social
control
Drug arrest
rate
R2
F
b (s.e.)
.66
60.28 **
Model 2
b
b (s.e.)
.68 **
–
.74
(.09)
.47
(.09)
2.04
(.70)
–
Model 3
b
b (s.e.)
.61 **
.38 **
.22 **
–
.70
47.75 **
.63
(.11)
.32
(.11)
–
3.71
(1.34)
.69
47.01 **
Model 4
b
b (s.e.)
.51 **
.61
(.11)
.26 ** .33
(.11)
–
1.55
(.73)
.31 ** 2.70
(1.39)
.72
38.37 **
Model 5
b
b (s.e.)
.50 **
.63
(.13)
.27 ** .34
(.12)
.17 *
1.56
(.74)
.22
2.74
(1.41)
.00
(.00)
.72
30.22 **
b
.51 **
.28 **
.17 *
.23
.02
* P < .05.
** P < .01.
Last, because the neighborhoods were sampled to
over-represent neighborhoods with high levels of
drug activity, drug activity was included as a control
variable in the final model (Model 5). While drug
activity at the neighborhood level is notoriously
difficult to measure accurately, recent work by
Warner and Coomer (2003) suggests that drug arrest
data can be used as a relatively valid measure of
drug activity at the neighborhood level. Therefore,
all drug arrests for 1999 were geo-coded and a count
of drug arrests per neighborhood was created. This
count was then divided by the 2000 population
count and multiplied by 1,000 to obtain a drug
arrest rate per 1,000 residents. The average drug
arrest rate across neighborhoods was 28.46. The
drug arrest rate was not found to significantly effect
violence, and none of the other findings changed
substantively.
The analysis next turned to an examination of the
moderating effects of informal social control and
social support. In order to examine moderating
effects, the sample was divided into two groups (high
and low) based on the mean for each of the moderating variables. While this approach had limitations,
the multicollinearity problems that arose in these data
when a (mean centered) multiplicative interaction
term was entered were too severe to allow for
interpretation of the results. Further, this method of
examining moderating effects was used in several
examinations of GST at the individual level (see for
example, Capowich et al., 2001; Hoffman & Miller,
1998; Mazerolle & Piquero, 1997).
Turning first to the moderating effects of informal
social control, the study examined whether the effects
of strain on violence were different in neighborhoods
with low versus high levels of informal social con-
trol. High informal social control neighborhoods
were defined as those having 56.5 percent or more
of the respondents stating neighbors were ‘‘very
likely’’ to intervene and low informal social control
neighborhoods were those having less than 56.5
percent of the respondents stating neighbors were
‘‘very likely’’ to intervene. Findings from these
models appear in Table 5 (Models 1 and 2). The
findings in the models were not consistent with what
MST would predict. MST hypothesized that the
effects of strain would be greatest in neighborhoods
where informal social control was low. The findings
here, however, suggested that strain significantly
increased violence only in neighborhoods with high
levels of informal social control. While the neighborhoods with low informal social control had higher
levels of strain (.39 versus .27) and higher levels of
violent crime (13.13 versus 2.77), the effect of strain
on violence was not significant in these neighborhoods. Since these findings were contradictory to
expectations, they were further examined by testing
for the equality of the strain regression coefficients in
the high and low informal social control models.
Following Paternoster, Brame, Mazerolle, and
Piquero (1998), an unbiased estimate of the standard
deviation of the sampling distribution was used and
the z score for the difference between the two
coefficients was calculated.6 Findings from this analysis showed that the two coefficients were not
significantly different (z = 1.28), suggesting that
the effects of strain on violence were similar across
neighborhood levels of informal social control.
Models 3 and 4 in Table 5 present the results of
the conditional effects of social support/social capital.
High social support was defined as 37.4 percent or
more respondents in each neighborhood engaged in
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B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
Table 5
Moderating effects of informal social control and social support/social capital
Independent
variables
Model 1 High informal
social control
Disadvantage
Stability
Strain
R2
F
Model 2 Low informal
social control
Model 3 High social
support
Model 4 Low social
support
b (s.e.)
b
b (s.e.)
b
b (s.e.)
b
b (s.e.)
b
.78 (.16)
.43 (.14)
2.62 (1.11)
.52
10.74 **
.68 **
.44 **
.30 **
.55 (.13)
.46 (.14)
.78 (.92)
.55
11.56 **
.56 **
.44 **
.11
.71 (.13)
.48 (.12)
.35 (1.08)
.61
16.14 **
.64 **
.48 **
.04
.77 (.13)
.46 (.14)
3.25 (.90)
.75
27.20 **
.60 **
.33 **
.37 **
* P < .05.
** P < .01.
relatively consistent social support networks within
their neighborhoods. As can be seen in Model 3
(Table 5), the effect of strain on violence in high
social support/social capital neighborhoods was nonsignificant, suggesting that neighborhoods with high
social support/social capital were able to control or
diffuse the effects of strain on violence. In contrast,
in low social support/social capital neighborhoods,
strain was found to have a significant positive effect
on violence levels (Model 4, Table 5). A test of
equality between the two coefficients for strain in
these models (as above) confirmed that, indeed, the
coefficients were not equal (z = 2.06), supporting the
finding that social support/social capital did moderate
the effects of strain on violence as predicted by MST.
Finally, because high drug use neighborhoods were
over sampled, the effect of drug activity was also
examined as a moderating variable in the relationship
between strain and violence. For this analysis, the
neighborhoods were divided at the mean of the drug
arrest variable into high and low drug activity neighborhoods and the effect of strain on violence in both
types of neighborhoods was examined. In high drug
activity neighborhoods strain had a significant effect
on violence, while in low drug activity neighborhoods
the effect of strain on violence was only marginally
significant (see Table 6). A test of the difference
between the coefficients for strain in the high and
Table 6
Moderating effects of drug activity
Independent Model 1 High drug
variables
activity
B (s.e.)
b
Model 2 Low drug
activity
b (s.e.)
b
Disadvantage
.61 (.16)
.58 **
.86 (.14)
.70 **
Stability
.15 (.21) .10
.57 (.12) .56 **
Strain
2.54 (1.06)
.36 *
1.70 (.97)
.18
R2
.68
.57
F
13.56 **
17.80 **
* P < .05.
** P < .01.
low drug activity neighborhoods, however, found the
coefficients to not be significantly different (z = .58).
Discussion
While there was a significant amount of research
exploring neighborhood level effects on crime within
the past two decades, most of this research was based
in a social control model, ignoring neighborhood level
differences in motivation toward crime. Recent developments in GST suggested that general strain might
also contribute to neighborhood crime rates. This
study was among the first to examine a MST model
within actual neighborhoods. Further, the study was
unique in that it included measures of both informal
social control and community level strain. In this
study, community levels of strain and informal social
control were found to be affected by community
disadvantage and residential stability rates. In turn,
when examined individually, both strain and informal
social control were found to affect neighborhood
levels of visible violence. When both strain and
informal social control measures were added to the
model simultaneously, however, the effects of informal social control fell to just below the significance
level, while strain maintained its significant effect on
violence. These findings were supportive of MST.
The findings on the variables hypothesized to
condition the relationship between strain and violence, however, were mixed. Strain was found to be
positively associated with violence in neighborhoods
with low levels of social support/social capital and
not in neighborhoods with high levels of social
support. These findings were consistent with MST
and suggested that even in neighborhoods with high
levels of strain, violence was not necessarily a likely
outcome. In neighborhoods that were able to sustain
strong networks of social support/social capital, strain
did not appear to be associated with increased levels
of violence. Further, this was not due to high strain
neighborhoods being unable to sustain social net-
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
works. In fact, the levels of strain, on average, were
found to be higher in high social support neighborhoods than in low social support neighborhoods (.37
versus .29).
Findings from the analysis examining the moderating effects of informal social control, however,
failed to support MST. MST hypothesized that the
effects of strain on crime would be stronger in
communities with low levels of social control, the
findings reported here suggested that, if anything,
strain increased violence only in neighborhoods with
high levels of informal social control. The test of
significance for differences between coefficients,
however, was not significant suggesting that informal
social control might not significantly moderate the
effects of strain on violence. In part, this finding
might be because the sample was simply divided at
the mean to create low and high informal social
control neighborhoods. Hence, many of the neighborhoods in the low social control group (defined as less
than 56 percent say neighbors were very likely to
intervene) may not adequately represent true levels of
low informal social control. Analyses using a more
theoretically derived definition of low informal social
control may provide better results. Unfortunately, the
limited number of neighborhoods in this sample
made it difficult to examine lower levels of informal
social control.
Similarly, while the effect of strain on violence
was found to be significant (p = .03) in high drug
activity neighborhoods, it did not quite reach significance in the low drug neighborhoods (p = .09).
Nonetheless, the test of differences between coefficients was not significant suggesting that the effect of
strain on violence was likely to be the same regardless of the level of drug activity.
While this study was among the first to examine
MST within the contexts of actual neighborhoods and
added to the developing body of literature on MST,
there were several limitations to this study. First, the
method used to examine conditional effects was less
than ideal. Dividing neighborhoods into high and low
categories of the moderating variables was not the
best approach for examining potential interaction
effects. Such an approach severely limited the amount
of variance in the moderating variable, and at the
same time, separated relatively similar neighborhoods
around the mean value into qualitatively different
groups. Introducing multiplicative interaction terms,
however, created severe multicollinearity. A better
examination of the conditional effects of strain may
require a larger sample.
Second, the dependent variable used here was
measured in only one way. It would be preferable
to have two or three different operationalizations of
violent crime, such as respondents’ self-reports of
519
violence or aggressive behavior aggregated to the
neighborhood level. Unfortunately, self reports of
relatively serious violent behavior among the general
population may not be frequent enough at the block
group level to provide reliable measures. This may
necessitate examining neighborhoods defined at a
larger aggregate, such as the census tract.
Third, the response rate for this study, while
adequate, and certainly in line with many other
general community level studies, was not ideal. The
characteristics of the respondents in these neighborhoods were reflective of neighborhood population
characteristics in terms of race and age, but, like other
surveys, they overrepresented females (67 percent of
the sample versus 51 percent of the population) and
homeowners (51percent of the sample versus 38
percent of the population). These differences may
somewhat limit the sample’s representativeness.
Fourth, the measure of strain used in this study
was limited and did not include a measure for the loss
of positively valued stimuli. To the extent that strain
is a homogenous concept, with neighborhoods that
experience high levels of one type of strain being
likely to experience high levels of other types of
strain, the measures used here did not present a
serious limitation. If, however, neighborhoods vary
in terms of the types of strains they face such that one
neighborhood may be high in one type of strain while
another neighborhood is high in another type of
strain, then the findings here become limited in
generalizability. This is a point worthy of further
study. Further studies of MST that include more
varied measures of strain and examine the extent that
different types of strain vary across neighborhoods
should be developed.
Finally, this study did not include a mediating
measure of anger. Like GST, MST suggests that high
levels of strain in a neighborhood are likely to lead to
high levels of anger, which increases the probability
that angry people will interact and violence will
occur. Examination of the intervening role of anger
at the individual level, however, does not generally
support the mediating role of anger. Indeed, several
studies showed that strain continued to produce
significant effects on violent behavior even when
anger was included in the equation (Mazerolle et
al., 2000; Mazerolle & Piquero, 1998; Piquero &
Sealock, 2000). This suggests that the effects of strain
on violence may indeed be direct. Further neighborhood level research on MST, however, should explore
the effects of strain on negative emotions and the
consequential effects of negative emotions on levels
of violence.
MST suggests many interesting hypotheses for
understanding community level crime rates, and
findings from this study provided empirical support
520
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
for some of them. As community level data become
increasingly available, MST should continue to be
used to guide research for a broader understanding of
the dynamics of community violence.
Notes
1. The authors follow previous work in this area and
refer to this theory as MST (see, e.g., Brezina et al., 2001)
even though it was believed that MGST or CGST
(Community General Strain Theory) might be more
appropriate.
2. The 2000 U.S. Census data were not used in this
study as the census data for poverty, a central variable in any
definition of neighborhood disadvantage, were not yet
available. Nonetheless, data on percent African American
and percent renters were obtained from the 2000 census. The
correlations between these variables for 2000 and 1990 were
quite high (.94 for percent African American and .95 for
percent renter), suggesting little change in these neighborhoods over the ten-year period.
3. Respondents to this previous study (Leukefeld et al.,
1999) were asked to identify the street intersection closest to
their home. These intersections were then geo-coded and
neighborhoods with high levels of drug users were identified
as high drug use neighborhoods.
4. All models were examined for outliers and none
were identified.
5. While the drop in significance of informal social
control might be partially due to shared variance with strain,
the correlation between strain and informal social control
was only moderate ( .52). Further, variance inflation
factors (VIF) were well below conventional levels for
concern. The highest VIF was 2.9.
6. Specifically, the formula used was z = (b1 – b2)/
pffiffiffiffiffiffiffiffiffiffi
SEb1 2 + SEb22.
References
Agnew, R. (1985). A revised strain theory of delinquency.
Social Forces, 64, 151 – 167.
Agnew, R. (1992). Foundation for a general strain theory of
crime and delinquency. Criminology, 30, 47 – 87.
Agnew, R. (1999). A general strain theory of community
differences in crime rates. Journal of Research in Crime
and Delinquency, 36, 123 – 155.
Agnew, R., & White, H. R. (1992). An empirical test of
general strain theory. Criminology, 30, 475 – 499.
Anderson, E. (1990). Streetwise: Race, class, and change in
an urban community. Chicago: University of Chicago
Press.
Anderson, E. (1999). Code of the street: Decency, violence,
and the moral life of the inner city. New York: W.W.
Norton & Company.
Baumer, E. P. (2002). Neighborhood disadvantage and police notification by victims of violence. Criminology, 40,
579 – 616.
Bellair, P. E. (1997). Social interaction and community
crime: Examining the importance of neighbor networks.
Criminology, 35, 677 – 703.
Bourdieu, P. (1985). The forms of capital. In: J. G.
Richardson (Ed.), Handbook of theory and research
for the sociology of education (pp. 241 – 258). New
York: Greenwood.
Brezina, T. (1998). Adolescent maltreatment and delinquency: The question of intervening processes. Journal
of Research in Crime and Delinquency, 35, 71 – 99.
Brezina, T., Piquero, A. R., & Mazerolle, P. (2001). Student
anger and aggressive behavior in school: An initial test
of Agnew’s macro-level strain theory. Journal of
Research in Crime and Delinquency, 38, 362 – 386.
Bursik, R. J., Jr., & Grasmick, H. G. (1993). Neighborhoods
and crime: The dimensions of effective community control. New York: Lexington Books.
Capowich, G. E., Mazerolle, P., & Piquero, A. (2001). General strain theory, situational anger, and social networks:
An assessment of conditioning influences. Journal of
Criminal Justice, 29, 445 – 461.
Elliot, D. S., Wilson, W. J., Huizinga, D., Sampson, R. J.,
Elliot, A., & Rankin, B. (1996). The effects of neighborhood disadvantage on adolescent development. Journal
of Research in Crime and Delinquency, 33, 389 – 426.
Hoffman, J. P., & Miller, A. S. (1998). A latent variable
analysis of general strain theory. Journal of Quantitative
Criminology, 14, 83 – 110.
Leukefeld, C. G., Warner, B. D., Schoeneberger, M. L.,
Logan, T. K., Farabee, D., & Cattarello, A. M. (1999).
Final report on prevention education on AIDS in Kentucky. Lexington: Center on Drug and Alcohol Research,
University of Kentucky.
Mazerolle, P., Burton, V. S., Jr., Cullen, F. T., Evans, T. D.,
& Payne, G. L. (2000). Strain, anger, and delinquent
adaptations: Specifying general strain theory. Journal
of Criminal Justice, 28, 89 – 101.
Mazerolle, P., & Piquero, A. (1997). Violent responses to
strain: An examination of conditioning influences.
Violence and Victims, 12, 323 – 343.
Mazerolle, P., & Piquero, A. (1998). Linking exposure to
strain with anger: An investigation of deviant adaptations. Journal of Criminal Justice, 26, 195 – 211.
Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A.
(1998). Using the correct statistical test for the equality
of regression coefficients. Criminology, 36, 859 – 866.
Piquero, N. L., & Sealock, M. D. (2000). Generalizing general strain theory: An examination of an offending population. Justice Quarterly, 17, 449 – 484.
Portes, A. (1998). Social capital: Its origins and applications
in modern sociology. Annual Review of Sociology, 23,
1 – 24.
Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disorganization theory.
American Journal of Sociology, 94, 774 – 802.
Sampson, R. J., Morenoff, J. D., & Earls, F. (1999). Beyond
social capital: Spatial dynamics of collective efficacy for
children. American Sociological Review, 64, 633 – 660.
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997).
Neighborhoods and violent crime: A multilevel study
of collective efficacy. Science, 277, 918 – 924.
B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 511–521
U.S. Census Bureau. (2000). Profiles of general demographic characteristics: 2000. Retrieved from http://
factifinder.census.gov/servlet/BasicFatsServlet.
Warner, B. D., & Coomer, B. W. (2003). Neighborhood drug
arrest rates: Are they a meaningful indicator of drug
activity?: A research note. Journal of Research in Crime
and Delinquency, 40, 123 – 138.
521
Warner, B. D., & Pierce, G. L. (1993). Reexamining social
disorganization theory using calls to the police as a
measure of crime. Criminology, 31, 493 – 517.
Warner, B. D., & Rountree, P. W. (1997). Local social ties in
a community and crime model: Questioning the systemic
nature of informal social control. Social Problems, 44,
520 – 536.