Strain and violence: Testing a general strain theory model of community violence $

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1 Journal of Criminal Justice 31 (2003) 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 , 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 $ This project was supported, in part, by Grant No 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.: ; fax: address: barbara.warner@eku.edu (B.D. Warner). 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 disadvantage 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) /$ see front matter D 2003 Elsevier Ltd. All rights reserved. doi: /j.jcrimjus

2 512 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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;

3 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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 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-

4 514 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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 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 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 disadvantage: 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-

5 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) Table 1 Descriptive statistics on sixty-six neighborhoods Study variables Mean St. Dev. Min. Max. 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 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 capital. 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?

6 516 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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 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 Informal social control b (s.e.) b b (s.e.) b Disadvantage.05 (.02).34 **.06 (.01).54 ** Stability.03 (.02).24 *.06 (.01).56 ** R F 6.67 ** ** * P <.05. ** P <.01.

7 Table 4 OLS regressions of strain and informal social control on violence Independent Model 1 Model 2 Model 3 Model 4 Model 5 variables b (s.e.) b b (s.e.) b b (s.e.) b b (s.e.) b b (s.e.) b Disadvantage ** ** ** ** ** (.09) (.09) (.11) (.11) (.13) Stability ** ** ** ** ** (.09) (.09) (.11) (.11) (.12) Strain 2.04 (.70).22 ** 1.55 (.73).17 * 1.56 (.74).17 * Informal social control Drug arrest rate 3.71 (1.34).31 ** 2.70 (1.39) (1.41).00 (.00) R F ** ** ** ** ** * P <.05. ** P <.01. B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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 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 control. 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

8 518 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) Table 5 Moderating effects of informal social control and social support/social capital Independent variables Model 1 High informal social control 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 Disadvantage.78 (.16).68 **.55 (.13).56 **.71 (.13).64 **.77 (.13).60 ** Stability.43 (.14).44 **.46 (.14).44 **.48 (.12).48 **.46 (.14).33 ** Strain 2.62 (1.11).30 **.78 (.92) (1.08) (.90).37 ** R F ** ** ** ** * 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 variables Model 1 High drug activity Model 2 Low drug activity B (s.e.) b b (s.e.) b Disadvantage.61 (.16).58 **.86 (.14).70 ** Stability.15 (.21) (.12).56 ** Strain 2.54 (1.06).36 * 1.70 (.97).18 R F ** ** * 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-

9 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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 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

10 520 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) 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. pffiffiffiffiffiffiffiffiffiffi 6. Specifically, the formula used was z = (b 1 b 2 )/ SEb SEb 2 2. References Agnew, R. (1985). A revised strain theory of delinquency. Social Forces, 64, Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30, Agnew, R. (1999). 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11 B.D. Warner, S.K. Fowler / Journal of Criminal Justice 31 (2003) U.S. Census Bureau. (2000). Profiles of general demographic characteristics: Retrieved from 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, Warner, B. D., & Pierce, G. L. (1993). Reexamining social disorganization theory using calls to the police as a measure of crime. Criminology, 31, 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,