Explaining crime for a young adult population: An application of general strain theory

Journal of Criminal Justice 33 (2005) 463 – 476

Explaining crime for a young adult population: An application of general strain theory Michael K. Ostrowsky T, Steven F. Messner Department of Sociology, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, United States

Abstract Most research informed by general strain theory (GST) concentrated on the young, particularly adolescents. Using data from the National Youth Survey (NYS) Wave 7, in which respondents were asked about their offending when they were ages twenty to twenty-nine, a model of young adult offending was estimated that incorporated variables reflecting strain, as well as control variables related to differential association and control theories and a lagged measure of offending to account for unmeasured dispositional factors. Results revealed that indicators of strain had significant effects on property and violent offending. In analyses of the role of depression, selected forms of strain were related to depression, and depression affected offending for males but not females. Taken as a whole, these findings demonstrated that GST, which was advanced as a general theory of crime, made an important contribution to the understanding of criminal offending among young adults. D 2005 Elsevier Ltd. All rights reserved.

Introduction Strain theory has a long tradition in the study of crime and deviance. After falling out of favor in the 1970s, this perspective enjoyed a rebirth in recent years, with Agnew’s general strain theory (GST) representing an important development within the tradition and within theoretical criminology. General strain theory enjoyed a fair amount of support since its introduction in 1992 (Agnew, 1992). Yet, the research that was done to date on GST focused primarily on adolescents. Relatively fewer studies examined college-age youth, but many of these had not been based on representative samples, and ages beyond that point were largely neglected. There are several compelling reasons for filling this empirical void. First, although criminal behavior peaks in the teenage years, accumulating evidence indicates that it continues over the life course for some individuals (Gottfredson & Hirschi, 1990; Moffitt, 1993; Sampson &

T Corresponding author. Tel.: +1 518 442 4666. E-mail address: [email protected] (M.K. Ostrowsky). 0047-2352/$ - see front matter D 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jcrimjus.2005.06.004

Laub, 1990; Thornberry, 1987). Explaining the determinants of such offending is clearly an important task for criminological researchers. Second, researchers speculated that the level and nature of strain vary at different ages. On the one hand, the stage of young adulthood typically entails newly emerging demands of a career and family, and thus new opportunities for strain can appear in the form of failing finances and difficulties in fulfilling the roles of husband and wife, father or mother. On the other hand, young adults, in comparison with adolescents, are likely to have greater control over their environment, and they may have developed more effective coping strategies (Agnew, 1997; Hoffmann & Cerbone, 1999). These developments are likely to lessen exposure to strain and lower the likelihood that strain will be manifested in antisocial behavior among young adults. The role of strain as a determinant of offending for such a population is thus unclear on theoretical grounds and is an open empirical question. Finally, GST was originally intended as precisely that: a general theory of crime. The selection of samples of adolescents in initial tests of the theory was based largely on methodological considerations. As Agnew (1992, p. 48)

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explained in an influential statement of the perspective, bThe focus is on adolescents because most currently available data sets capable of testing the theory involve surveys of adolescents.Q The processes described by the theory, however, are presumably general in nature, and thus GST should be applicable to the broad spectrum of age groups. In an attempt to begin to fill this empirical void, this study examined the determinants of offending for a sample of respondents aged twenty to twenty-nine. A model was developed and applied that included measures of strain as traditionally conceptualized, as well as measures associated with the bgeneralQ version of strain theory. The models also contained indicators of some of the more important determinants of offending implied by two other influential criminological perspectives, namely, differential association theory and control theory. To assess the robustness of the results, the models were re-estimated with lagged measures of offending. Lagged offending served as a useful control for dispositional factors formed at early ages for which no explicit measures were available in the data set. Finally, although data limitations precluded a comprehensive analysis of the various forms of bnegative affectQ that allegedly intervene between strain and offending, the role of depression was explored as a possible response to strain that increases the likelihood of criminal involvement among young adults.

Previous research In a widely cited article, Agnew (1992) presented a revised version of strain theory that included, but in meaningful ways went beyond, the original strain model associated with Merton (1938). Agnew suggested that earlier versions of the theory were narrow in their conceptualization of the sources of strain in that they focused exclusively on the failure to achieve positively valued goals. In addition to this source of strain, Agnew contended that strain could also occur when others remove or threaten to remove positively valued stimuli that an individual possesses, and when an individual is confronted with negative or unpleasant circumstances. In his discussion of the removal of positively valued stimuli, Agnew (1992) included such things as: loss of boyfriend/girlfriend, the death or serious illness of friend or parent, moving to a new school district, the divorce/separation of one’s parents, and the presence of a variety of offensive work conditions. In his discussion of the presentation of negative stimuli, Agnew (1992; Agnew & White, 1992) referred to such things as: residence in an unappealing and unsafe neighborhood, child abuse and neglect, negative relationships with adults and peers, criminal victimization, physical punishment, and negative school experiences. According to Agnew’s GST, each of these three types of strain increased the chances that individuals will experience one

or more of a variety of negative emotions. These emotions include disappointment, depression, fear, and anger, with anger being especially consequential for crime and delinquency (Agnew, 1992, p. 59). The experience of negative emotions does not, however, guarantee criminal and delinquent involvement on the part of the strained individual. Like other strain theorists, Agnew recognized that only some individuals respond to strain and the associated negative emotions with crime. For Agnew, whether conforming or deviant behavior occurs depends on coping strategies and the constraints on these strategies. These constraints, which affect the individual’s predisposition to adopt a delinquent response to strain, include self-efficacy, self-esteem, and conventional social support. As noted, to date the majority of the research on GST concentrated on the young, primarily adolescents (Agnew & Brezina, 1997; Agnew, Brezina, Wright, & Cullen, 2002; Agnew & White, 1992; Aseltine, Gore, & Gordon, 2000; Hay, 2003; Hoffmann & Cerbone, 1999; Hoffmann & Su, 1997; Mazerolle, 1998; Mazerolle, Burton, Cullen, Evans, & Payne, 2000; Mazerolle & Maahs, 2000; Paternoster & Mazerolle, 1994; Piquero & Sealock, 2000, 2004). So far GST has enjoyed a fair amount of support in the research that focused on adolescents. Using longitudinal data from the Rutgers Health and Human Development Project (RHHD), in which the respondents were twelve, fifteen, and eighteen years of age, Agnew and White (1992) constructed eight different measures of strain, eight social control/differential association measures, and two measures of deviance (delinquency and drug use). In a cross-sectional regression analysis, they found that five of the strain variables had a significant effect on delinquency or drug use. The results of a subsequent longitudinal analysis were not as supportive of GST. The composite measure of strain was very modestly related to subsequent delinquency, but not to drug use. The longitudinal design, however, was not particularly well suited for the assessment of the effects of strain because of the relatively long time span between waves of the panel. Using data from the first and second waves of the National Youth Survey, in which the eligible youths ranged in age from eleven to seventeen, Paternoster and Mazerolle (1994) replicated Agnew and White’s (1992) original work. They tested the direct effects of general strain on delinquency and examined whether general strain had an indirect effect on delinquency by weakening the social bond and strengthening involvement with delinquent peers. Overall, the findings indicated that general strain had both direct effects on delinquency and indirect effects by weakening the inhibitions of the social bond and increasing one’s involvement with delinquent peers. In a further test with an adolescent sample, Aseltine et al. (2000) used data from a three-wave panel study of high school youths in the Boston metropolitan area to examine three central hypotheses of GST. First, the generality of

M.K. Ostrowsky, S.F. Messner / Journal of Criminal Justice 33 (2005) 463–476

GST was tested using multiple measures of life stresses and relationship difficulties, as well as multiple measures of delinquency (nonviolent, aggressive/violent, and marijuana use). Second, the role of anger and anxiety in mediating the relationship between strain and deviant behavior was examined. Third, the varying impact of strain on deviant behavior depending upon personal and social resources was assessed. Overall, results from this analysis provided limited support for GST. Covariance structure models revealed that anger and hostility in response to negative life events played a causal role in fostering more aggressive types of delinquency, but they were not significantly related to either nonaggressive delinquency or marijuana use. Also, results concerning the conditional effects predicted by GST, in which the impact of strain on delinquency varied by youths’ personal and social resources, were inconsistent. Piquero and Sealock (2000, 2004) focused on a special adolescent population: offenders. In both studies, a small sample of youths who entered the juvenile justice system for various offenses completed self-report surveys. In their original analysis, the authors found mixed support for GST. One measure of negative affect, anger, predicted interpersonal aggression (but not property offending), whereas another measure of negative affect, depression, did not exhibit a significant relationship with either form of offending. Analyses of interactions between coping skills and negative affect were generally inconsistent with GST, although two predicted interactions emerged. Emotional and spiritual coping skills appeared to inhibit the effect of depression on property offending. In addition to the extensive research on the general effects of strain and negative affect on delinquency, a few studies using adolescent samples also considered the possibility of gender differences in the response to strain. Analyses by Hoffmann and Su (1997) and Hoffmann and Cerbone (1999) reported that stressful life events had similar effects on delinquency for female and male adolescents. Other studies indicated that while strain was relevant to both genders, there were some noteworthy differences in the causal processes. Findings reported by Agnew and Brezina (1997) revealed that strain was correlated with both male and female delinquency, but the association was stronger among males. Hay (2003) reported a similar gender differential in his examination of the associations between family strain and delinquency. Evidence also suggested that gender differences might vary depending on the type of offense. Mazerolle (1998) detected no difference between males and females in the effects of GST-related predictors on property offending, but he reported that for violence, exposure to stressful life events and negative relations with adults were particularly criminogenic for males but not females. In their recent research, Piquero and Sealock (2004) provided a test of the gender and GST relationship, again using a small sample of youths who entered the juvenile

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justice system. No differences were found in this crosssectional analysis in the amount of strain across gender. Results also indicated that females reported higher levels of both anger and depression. Strain was positively associated with anger for both sexes, whereas strain was positively associated with depression among males only. Also, males reported higher levels of physical and cognitive coping than did females. The findings revealed that strain was related to anger among both sexes, but strain was not related to depression among females. Lastly, depression was unrelated to property offending (see also Mazerolle & Piquero, 1998) and interpersonal aggression. Empirical tests of GST using older, college-aged youths were relatively scarce, although those that were conducted were reasonably supportive (Broidy, 2001; Capowich, Mazerolle, & Piquero, 2001; Eitle, 2002; Eitle & Turner, 2002, 2003; Mazerolle & Piquero, 1997, 1998). Mazerolle and Piquero (1997, 1998) distributed a questionnaire to university undergraduates to assess respondents’ likelihood that they would engage in deviant behavior. The results of the earlier study were consistent with GST and highlighted the importance of both noxious stimuli and negative emotions in understanding the potential for assaultive behavior. The results of the later study revealed partial support for GST, but only for models predicting intentions to fight. Also, the mediating effects of anger were not observed in models predicting intentions to drive drunk, shoplift, and fight. Research by Capowich et al. (2001), based on a sample of university students, yielded mixed results. Also using the scenario methodology, the findings indicated that situational anger was related to intentions to engage in assault and fighting, but negative emotions were not. The results for shoplifting were reversed: negative emotion affected shoplifting, whereas anger had no effect. An additional study based on college students by Broidy (2001) assessed the relationships among anger and other types of negative affect, legitimate coping, and measures of deviant and criminal behaviors. Using data from a nonrandom, convenience sample of undergraduates, Broidy found that certain types of strain were related with anger and other negative emotions and that strain-induced anger significantly increased the likelihood of deviant outcomes, consistent with GST. Surprisingly, anger was unrelated to legitimate coping, and negative emotions other than anger were positively related to legitimate coping and negatively associated with illegitimate outcomes. Broidy concluded that her analyses boffer some support for general strain theory, but also indicate that the theory does not adequately account for the complexity of the strain/ crime relationshipQ (2001, p. 29). In a series of analyses conducted as part of a South Florida study, Eitle (2002; Eitle & Turner, 2002, 2003) examined the role of stressors on young adult criminal behavior. Using respondents who were between the ages of eighteen and twenty-three, these studies measured stress in a variety of ways that expanded on the range of sources of

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stress previously examined in GST research. With a young adult female sample, Eitle (2002) found that perceived discrimination was an important determinant of both crime and substance use. With a young adult male sample, Eitle and Turner (2003) found that racial differences in criminal behavior were explained by differences in exposure to strain, with African Americans exposed to more stressful events over their lifetimes. In this analysis, stress was measured using thirty-three recent life events, thirty-six potentially chronic stressors including employment stress, relationship stress, child-care stress, residence stress, and the lifetime occurrence of forty-three items that addressed violent and traumatic events. This study also revealed that coping mechanisms did not moderate the stress-crime relationship. Using both young adult males and females, Eitle and Turner (2002) found that exposure to a variety of stressors were all significant predictors of criminal behavior. Even though most of the research on GST to date concentrated on adolescents or college-aged youths, three exceptions deserve mention. In an empirical study of bclassicQ or traditional strain, Agnew, Cullen, Burton, Evans, and Dunaway (1996) explored the determinants and the effects of dissatisfaction with monetary status. Using data from a sample of adults in Cincinnati, results provided support for classic strain theory, in that dissatisfaction had a positive effect on income-generating crime and drug use. Also, as predicted by elaborated classic strain theory, the effect of dissatisfaction or strain on crime was conditioned by criminal beliefs and criminal associates. A second noteworthy study that considered an older population was an unpublished dissertation by Preston (2000). This research tested a model of adult criminality encompassing GST and Hirschi’s theory of the social bond. In this analysis, case subjects were selected from the Survey of Inmates of State Correctional Facilities, 1991, and a control group was selected from the National Household Survey of Drug Abuse, 1993. The results supported Agnew’s contention that crime was one form of coping strategy employed by persons experiencing strain. Finally, recent research by Jang and Johnson (2003) tested hypotheses derived from GST about the relationships among strain, negative emotions, and deviant coping with data from a nationally representative sample of African American adults. OLS regression results generally supported the hypotheses. First, negative emotions had consistently significant effects on deviance, fully mediating the effects of strain on deviant coping. Second, while selfesteem and self-efficacy did not exhibit the expected conditioning effects, religiosity was found to significantly buffer the impact of negative emotions on deviance. Considered together, these three studies suggested that the applicability of GST extended beyond juveniles, but the analyses were limited to selected subpopulations (residents of Cincinnati, case subjects selected from state prisons, or the African American adult population). Furthermore, these analyses were all cross-sectional. As explained below, the

longitudinal nature of the NYS data allowed for the estimation of models with lagged measures of offending, which helped reduce the problem of omitted variable bias. In sum, Agnew’s revised strain theory represents a notable development in criminology. A sizable and growing body of research largely based on adolescent samples offered a fair amount of support for GST. Far less research was done on older populations, and with the few exceptions noted, these studies dealt mainly with college-age populations. While the evidence based on college-aged samples was also suggestive of the utility of GST, the research had been limited in important respects. For example, Mazerolle and Piquero (1997) acknowledged that they were able to examine only one type of violent intention to one specific hypothetical situation. Clearly, other situations might prompt alternative responses. Mazerolle and Piquero (1998) similarly acknowledged a basic limitation of the scenario methodology: intentions to deviate were not necessarily the same as actual deviation (see also Capowich et al., 2001). Also, the generalizability of the research by Broidy (2001) was open to question, given that her data came from a convenience sample. Finally, the samples drawn by Eitle and Turner approximated the ethnic and racial composition of the Dade County school system, such that minorities comprised 75 percent of the sample. Clearly, there are racial and ethnic differences between a nationally representative sample and those drawn by Eitle and Turner in their South Florida studies. The present study attempted to contribute to the accumulating research on GST by assessing the utility of strain measures for understanding criminal offending for a nationally representative sample of young adults that encompassed and extended beyond the ages of traditional college students. Indicators of btraditional strainQ (the failure to achieve positively valued goals) and the additional sources of strain suggested by GST were examined. The data set-the National Youth Survey (NYS)-did not contain useful measures of coping mechanisms or anger, which precluded a thorough investigation of the mechanisms that allegedly intervene between strain and crime.1 Despite these limitations, in addition to assessing the extent to which the bdistalQ causes of crime implied by this theoretical perspective operate for a population of young adults, this study explored the role of one hypothesized mediator-the negative emotion of bdepression.Q The primary hypothesis derived from GST is that measures of the respective types of strain will have significant, positive effects on involvement in property and violent crime, even with variables derived from social control and differential association theories held constant. Another hypothesis is that strain will increase the likelihood that a respondent is depressed, and that depression will have a positive effect on offending. In contrast with much of the research based on samples of college students, this study employed indicators of actual offending behavior rather than intentions. In addition, the robustness of the

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effects of strain variables were assessed by re-estimating models with measures of lagged offending. This was useful because it helped control for dispositional factors that were formed early in life for which explicit measures were unavailable in the data set, such as low self-control (Gottfredson & Hirschi, 1990). Models with lagged offending variables allow for an assessment of the extent to which the experience of strain during young adulthood increases criminal involvement, taking into account earlier levels of offending.

Methods Sample As noted, data for this study came from the National Youth Survey (NYS).2 The NYS was a panel study of delinquency and drug use based on a nationally representative sample of respondents in the U.S. The original sample consisted of 1,725 youths. The bulk of the data for the analyses came from Wave 7 of the survey. For Wave 7, participants were interviewed in early 1987 about events and behavior occurring in 1986, when they were twenty to twenty-nine years of age. This wave of the National Youth Survey thus proved to be particularly useful because the age range was considerably older than most of the samples previously analyzed in GST research. About 52 percent of the sample at Wave 7 was male; about 82 was White; the mean age was approximately twenty-four years old. This study also used data from Wave 6 of the NYS to construct lagged measures of offending. For the Wave 6, respondents were interviewed in 1984 about events and behavior occurring in 1983, when they were seventeen to twentysix years of age. The mean age of the respondents in Wave 6 was approximately twenty-one years old; 53 percent were male, and 78.9 percent were White. Measures Strain and depression Measures of both btraditionalQ strain and the expanded conceptualization of strain associated with GST were included in the analyses. With respect to traditional strain, two components were considered. The first referred to the gap between aspirations and actual outcomes. Respondents were asked about their goals with respect to three dimensions of occupational attainment: being a success at work, getting ahead at work or career, and having a good job or career (e.g., bHow important is the goal of being a success in your work? Q ). Responses were scored on a scale ranging from 1 (not important at all) to 5 (very important). Respondents were also asked a question assessing actual achievements with respect to each of these goals (e.g., bHow are you doing at being a success in your work?Q). Responses here were also scored on a five-point scale

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ranging from 1 (poor) to 5 (good). For each of the three dimensions of work, a goal-discrepancy score was computed by subtracting the outcome score from the aspiration score.3 The three goal-discrepancy scores were transformed into z-scores and then averaged to yield a composite index. A high value on the index indicated that aspirations were being unmet; a low score indicated that outcomes were generally commensurate with aspirations. (Means and standard deviations for all variables are reported in Appendix A.) The second component of traditional strain theory was the perception of blocked opportunities. Respondents were asked whether their chances for job promotion or advancement at work were seriously limited by each of the following factors: interpersonal skills, job skills, level of educational achievement, amount of job experience, sexual discrimination, racial discrimination, and appearance. The responses were coded b0Q for bnoQ and b1Q for byes.Q A composite index of perceptions of blocked opportunities was computed by summing these seven items. These traditional strain measures reflected the failure to achieve positively valued goals. As noted earlier, GST points to two additional important sources of strain: the removal (or threatened removal) of positively valued stimuli and confrontation with negative or unpleasant circumstances. To capture these forms of strain, indicators of victimization strain, neighborhood strain, and life stresses or bhasslesQ4 were selected. Respondents were asked whether or not they had experienced the following types of victimization: been threatened/beaten up by others, had things taken from them, been attacked with a weapon, or had things of theirs damaged. bNoQ responses were coded as b0Q and byesQ responses as b1.Q The measure of victimization strain was the sum of these four items. The neighborhood strain measure was based on respondents’ ratings of the extent to which the following nine conditions constituted a problem in their neighborhoods: high unemployment, racial conflict, vandalism, lawlessness, sexual assaults or rapes, burglaries and thefts, gambling, assaults and muggings, and delinquent gangs. The response categories and codes were: not a problem (1), somewhat of a problem (2), and a big problem (3). The composite measure of neighborhood strain was the sum of these nine items. The final measure of strain-life hassles-was based on responses to four questions. Two questions asked for ratings on a five-point scale of the degree of stress or pressure in relationships with friends or on the job. The other two asked whether the respondent had been fired or laid off in the past year, and whether the respondent had any physical problems that restricted activities (no = 0; yes = 1). An index of life hassles was created by averaging the z-scores for the four items. The measure of depression-a hypothesized mediator of the effects of strain-was a dichotomous indicator of whether

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or not the respondent had two weeks or more during which they felt depressed over the course of the past three years (yes = 1; no = 0). Differential association Differential association theory suggests that an individual becomes delinquent or criminal because he/she has been subjected to more frequent and intense definitions from significant persons in his/her life that favor violating the law rather than obeying it. To measure this construct, an index was computed based on nine items. The items referred to how many of the respondents’ close friends engaged in the following illegal or anti-social behaviors during the past year: destroyed property, used marijuana, stolen something worth less than $5, hit someone, broken into a vehicle, sold hard drugs, stolen something worth more that $50, suggested that the respondent break the law, or used prescription drugs illegally. Responses were coded as follows: none of them = 1; very few of them = 2; some of them = 3; most of them = 4; all of them = 5. The index of differential association was the average score across these nine items. Social control Control theory suggests that involvement in delinquency and crime results when an individual’s bond to conventional society is weakened or destroyed. Measures of five components of control were constructed reflecting religiosity, attachment to friends, conventional beliefs, and familial attachments (being married and having children). The religiosity measure was the average of two five-category items (transformed into z-scores) indicating the respondents’ frequency of attendance at religious services and the importance of religion to him or her. A three-item scale measured conventional social bonding with friends. One item asked respondents to assess their satisfaction with their group of friends on a five-point scale ranging from bvery dissatisfiedQ to bvery satisfied.Q The other two items solicited a rating, also on a five-point scale, of the warmth/affection received from friends and support/encouragement received from friends. An index of attachment to friends was constructed by averaging z-scores for these three friendship items. The bbelief Q component of control was measured by ratings on a four-point scale of the bwrongfulnessQ of the following eight forms of behavior: cheat on income tax, destroy property, use marijuana, steal something worth less that $5, hit someone, break into a vehicle, sell hard drugs, and steal something worth more than $50. The belief index was the average score across these items. Familial control was measured by dummy variables for martial status (not married = 0; married = 1) and having children (no = 0; yes = 1). Demographic control variables Indicators of gender, age, and race were included as controls.5 Age was measured in years. Gender and race were

dummy variables with reference categories of female and non-Black respectively. Dependent variables The analyses differentiated between two types of offending-property and violent-because the causal mechanisms might differ, and in particular, various forms of strain might be more or less relevant to theft or violence. With respect to property offending, respondents were asked about how often in the past year they stole something worth less than $5, stole something from the place they worked, purposely damaged property that did not belong to them, and avoided paying for things such as movies, bus, subway rides, or food. The response categories were coded as 1 = never, 2 = once or twice, 3 = once every two to three months, 4 = once a month, 5 = once every two to three weeks, 6 = once a week, 7 = two to three times a week, 8 = once a day, and 9= two to three times a day. Similar questions were asked about three forms of violent behavior: hit or threatened to hit their supervisor or other employee, hit or threatened to hit anyone else, and attacked someone (the response categories and codes were the same as for property offending). Univariate analyses of the offending items revealed highly skewed distributions. Relatively large numbers reported either no involvement or very infrequent involvement, while small proportions reported heavy involvement. Given the nature of these distributions, offending was operationalized in three ways (the procedures were identical for the lagged measure of offending). First, an offending brateQ was constructed as the sum of the z-scores for the property or violent offending items, respectively. This measure indicated the rate (or frequency) of offending for the one-year period. Each of the four items pertaining to property offending were also converted into dummy variables scored b0Q for no involvement and b1Q for any involvement, and summed over the four items into an aggregate index. An analogous index was computed for violent offending using the three items on violence. In essence, these two indexes measured the bversatilityQ of property and violent offending respectively (i.e., the number of forms of offending engaged in during the one-year period). Finally, a bparticipationQ measure for both property and violent crime was computed. This was a dummy variable score b0Q for no involvement in the respective types of offending, and b1Q for any involvement. The descriptive statistics reported in Appendix A affirmed that offending in the past year was relatively infrequent: slightly over 80 percent of respondents reported no involvement in property offending or in violent offending. By operationalizing offending with these multiple measures, the researchers were able to assess the sensitivity of the results to measurement decisions. Measuring the relevant variables entailed combining information for a large number of items from the question-

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naire. A strict listwise deletion of cases for the independent and control variables resulted in considerable loss of sample size and sacrificed a good deal of information. For example, data were sometimes available for all but one item in a composite measure. The analysis accordingly used mean substitution for missing data on the independent variables, but not for the dependent variables. With such mean substitution, data were available for 1,253 cases in Wave 7 and 1,202 cases in Wave 6 (used for models with the lagged offending measures).

Results The bdistal Q effects of strain The analysis began with an assessment of the effects of the strain measures on property and violent offending, net of controls, for the three ways of operationalizing offending: with respect to the brateQ (i.e., frequency), the bversatility,Q and any bparticipationQ in offending. OLS was used for the offending rate and versatility; logistic regression was used for participation.6 For each operationalization, two models were estimated. The first included strain measures (both btraditionalQ strain measures and the more bgeneralQ strain measures), measures derived from differential association

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and social control theories, and the demographic control variables. The second model added a lagged measure of offending. The results for property offending are reported in Table 1. GST was not supported by the findings for the btraditionalQ strain measure of the aspirations/outcomes discrepancy. All coefficients were trivial in magnitude and below levels of statistical significance. More supportive results for GST were observed for the indicator of blocked opportunities. Although the coefficients for the propertyoffending rate were nonsignificant, those for versatility and participation were significantly positive. Young adults who perceive blocked opportunities were more likely to report that they had engaged in property offending, and they reported involvement in a greater range of property offenses. Turning to the indicators of general strain, the results offered consistent support for GST for victimization strain. The coefficients were significantly positive across the board. The findings for neighborhood strain, in contrast, were mixed. No effects were observed for the property-offending rate nor the versatility measure, while the expected positive effect emerged in the logistic regression equation for participation in property crimes. The measure of life hassles failed to exhibit significant associations with any of the property offending measures.

Table 1 Regression of property offending on strain measures, differential association measures, social control measures, and control variables OLS regression for property offending rate

OLS regression for versatility of property offending

Logistic regression for participation in property offending

(1)

(1)

(1)

(2)

(2)

(2)

Traditional strain Aspirations-outcomes Blocked opportunities

.03 .05

.03 .04

.01 .06T

.01 .06T

.045 .194T

.016 .200T

General strain (GST) Victimization strain Neighborhood strain Life hassles

.08TT .01 .01

.08TT .00 .01

.08TT .02 .02

.06T .02 .00

.295TT .051T .071

.266T .051T .061

.23TT

.12TT

.20TT

.12TT

.749TT

.501T

Differential association Social control Religion Friends Beliefs Married Have children Male Age Race Lagged property crime Adjusted R2 Model chi-square N T P b.05. TT P b.01.

.01 .00 .13TT .00 .07T .05 .05 .06T – .137TT – 1,253

.01 .00 .08TT .01 .06T .02 .02 .04 .40TT .272TT – 1,202

.00 .01 .17TT .01 .09TT .03 .05 .06T – .146TT – 1,253

.00 .01 .12TT .01 .08TT .00 .03 .04 .33TT .236TT – 1,202

.061 .010 1.091TT .057 .746TT .145 .029 .492 – – 162.64TT 1,253

.042 .008 .945TT .041 .750TT .062 .014 .237 1.520TT – 221.46TT 1,202

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The results for the measures derived from other criminological theories were generally in accord with expectations. The indicator of differential association was positively related to property offending. The significant negative effects of the measure of beliefs and the dummy variable for having children were consistent with social control theory, although the measures of religiosity and marriage had no effects. The demographic controls generally were not related to property offending. Finally, as expected, the lagged measure of property offending yielded relatively strong, positive effects in all models. Despite these strong effects of lagged offending, the coefficients for the strain measures were highly similar across Models 1 and 2. Regression results for violent offending are presented in Table 2. Once again, one of the two indicators of traditional strain emerged as a significant predictor of violent offending, but interestingly, the findings were the reverse of those for property offending. The aspirations/outcomes measure was positively associated with all measures of violent crime, whereas the measure of blocked opportunities was nonsignificant. Similar to the results for property crime, victimization strain exhibited significantly positive effects on violent offending. The more extensive the experience of victimization, the greater was the involvement in violent crime. Neighborhood strain once again yielded mixed results. The positive sign of the coefficient was consistent

with GST, but the coefficient reached statistical significance in the models for the versatility and participation measures, but not the violent offending rate. In contrast with the findings for property offending, the life hassles dimension of strain appeared to be highly relevant to criminal violence. The measure of life hassles had significantly positive effects on violent offending in all models. The findings for violent crime supported differential association theory. The differential association measure yielded reasonably strong, positive effects in all models of violent offending. The results for criminal violence, however, offered no support for social control theory as operationalized in the models. The coefficients for the indicators of religiosity, attachment to friends, beliefs, and marriage were nonsignificant. Strangely, the coefficients for having children were significantly positive in the models for versatility and participation, whereas they were significantly negative for property offending. The researchers were unable to formulate any plausible interpretation for these discrepant findings. With respect to demographic variables, males committed violent offenses at a higher rate than did females, and they were more likely to commit any violent offenses and commit more of the violent offenses under investigation. Age and race exhibited nonsignificant associations with violent offending. Once again, the lagged measure of

Table 2 Regression of violent offending on strain measures, differential association measures, social control measures, and control variables OLS regression for violent offending rate

OLS regression for versatility of violent offending

Logistic regression for participation in violent offending

(1)

(1)

(1)

(2)

(2)

(2)

Traditional strain Aspirations-outcomes Blocked opportunities

.10TT .03

.11TT .03

.12TT .03

.12TT .03

.345TT .052

.350TT .104

General strain (GST) Victimization strain Neighborhood strain Life hassles

.22TT .05 .10TT

.20TT .04 .09TT

.24TT .07T .10TT

.20TT .06 .08TT

.721TT .066TT .597TT

.657TT .069TT .508TT

.27TT

.23TT

.24TT

.19TT

1.321TT

1.097TT

.049 .214 .180 .043 .693TT .842TT .051 .085 – – 281.35TT 1,253

.055 .228 .103 .018 .803TT .587TT .015 .086 1.660TT – 333.65TT 1,202

Differential association Social control Religion Friends Beliefs Married Have children Male Age Race Lagged violent crime Adjusted R2 Model chi-square N T P b.05. TT P b.01.

.00 .05 .01 .03 .05 .12TT .03 .01 – .229TT – 1,253

.00 .05 .00 .02 .05 .10TT .00 .01 .14TT .241TT – 1,202

.00 .05 .02 .00 .09TT .13TT .05 .02 – .250TT – 1,253

.00 .05 .01 .01 .11TT .09TT .02 .02 .25TT .296TT – 1,202

M.K. Ostrowsky, S.F. Messner / Journal of Criminal Justice 33 (2005) 463–476

offending had significant positive effects in every equation, but inclusion of this measure did not alter the pattern of results for the strain measures. The role of depression GST explains crime with reference to a causal chain, with strain leading to negative emotions, which in turns promote offending. The only negative emotion that could be examined with the NYS data was depression. To begin to explore the general causal sequence hypothesized by GST with a young adult population, the measure of depression was regressed on the indicators of strain, along with the other variables in the bfullQ models in Tables 1 and 2 (i.e., Model 2). The lagged measure of offending included in the regression for depression refers to the bparticipationQ measure; similar results were obtained for the brateQ or bversatilityQ operationalization of offending. The results in Table 3 revealed that neither of the traditional indicators of strain was associated with depression. In contrast, the coefficients for the three general strain indicators were significantly positive. Consistent with GST, victimization strain, neighborhood strain, and life hassles increased the likelihood that the respondent reported being depressed.

Table 3 Regression of depression on strain measures, differential association measures, social control measures, and control variables Logistic regression coefficient Traditional strain Aspirations-outcomes Blocked opportunities

.126 .021

.109 .016

General strain (GST) Victimization strain Neighborhood strain Life hassles

.236T .053TT .511TT

.219T .053TT .509TT

.867TT

.749TT

.232TT .114 .227 .811TT .099 .725TT .001 .028 .006

.230TT .116 .246 .805TT .098 .784TT .008 .003

Differential association Social control Religion Friends Beliefs Married Have children Male Age Race Lagged property crime (participation) Lagged violent crime (participation) Model chi-square N T P b.05. TT P b.01.

– 149.54TT 1,202

– .519TT 156.32TT 1,202

471

Several additional relationships are significant in Table 3. Respondents who associated with delinquents were significantly more likely to be depressed, while married people and males had a lower probability of depression.7 The positive coefficient for the measure of religiosity probably reflects reverse causation: people who were depressed turned to religion for support. Previous participation in violent crime, but not property crime, increased the likelihood of depression. In Table 4, depression was added to the full models (which included lagged offending measures) predicting the three different operationalizations of offending for property crime and violent crime respectively. The results offered mixed support for GST. Depression was positively associated with violent offending regardless of the specific measure of the dependent variable, consistent with theoretical expectations. Depression, however, was significantly related with only one of the measures of property offending (i.e., brateQ). Furthermore, when the coefficients for the strain measures in Table 4 were compared with the analogous coefficients in the models without depression in Tables 1 and 2, it was clear that the measure of depression failed to mediate much of the effects of the strain measures. Finally, in view of previous research suggesting possible gender differences in causal processes linking strain and crime (Hay, 2003; Mazerolle, 1998; Piquero & Sealock, 2004; Van Gundy, 2002), a product term was computed for gender and depression and introduced into the baseline model predicting the different operationalizations of offending. The coefficients for the product term were significant for five of the six dependent variables (all but bparticipation in violent crimeQ), indicating differential effects of depression for males and females. In Table 5, the unstandardized regression coefficients are reported for the effect of depression on the measures of offending for males and females separately, for the models with statistically significant gender differences. The regressions were based on the fully specified models, but only coefficients for depression are presented to conserve space. The results in Table 5 were quite striking. Depression had a significantly positive effect on both property and violent offending for males, but no effect for females. Evidently, while males were much less likely to respond to strain with depression, as reported in Table 3, depression nevertheless had a greater impact on property and violent offending for males. The pattern of results for depression was inconsistent with Piquero and Sealock’s (2000, 2004) findings. They found strain to be associated with depression among males, but not among females, and depression to be unrelated to property offending and interpersonal aggression among both males and females. These discrepant findings might be attributed to several noteworthy differences in methodology across studies. The present analysis used a measure of depression based on a single dichotomous indicator,

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Table 4 Regression of property and violent offending on strain measures, differential association measures, social control measures, control variables, and depression Property rate Traditional strain Aspirations-outcomes Blocked opportunities General strain (GST) Victimization strain Neighborhood strain Life hassles Differential association Social control Religion Friends Beliefs Married Have children Male Age Race Lagged offending Depressed Adjusted R2 Model chi-square N

Property versatil.

Property particip.

.028 .041

.005 .059T

.010 .202T

.103TT .025

.114TT .027

.335TT .104

.073TT .007 .011

.060T .013 .000

.252T .048 .030

.192TT .039 .084TT

.194TT .055T .073TT

.639TT .064T .458TT

.111TT

.114TT

.453T

.222TT

.174TT

1.019TT

.003 .005 .080TT .004 .060T .025 .021 .038 .402TT .059T .274TT – 1,202

.001 .010 .121TT .011 .082TT .002 .025 .035 .331TT .039 .236TT – 1,202

.007 .044 .001 .034 .051 .111TT .005 .013 .140TT .080TT .246TT

.009 .044 .014 .004 .103TT .103TT .020 .022 .245TT .084TT .302TT – 1,202

.086 .208 .127 .110 .808TT .680TT .017 .120 1.626TT .511TT – 340.71TT 1,202

.054 .015 .960TT .011 .751TT .015 .014 .233 1.528TT .295 – 224.21TT 1,202

Violent rate

– 1,202

Violent versatil.

Violent particip.

Note: OLS regression used for rate and versatility; logistic regression used for participation. T P b.05. TT P b.01.

whereas Piquero and Sealock measured depression with a sixteen-item scale. In addition, they used data obtained from youths who were detained at juvenile detention facilities, whereas this analysis employed data from a nationally representative sample of young adults aged twenty to twenty-nine. Finally, their analysis tested only one measure of strain-experiences of abuse-whereas this study examined

multiple measures of strain: the disjunction aspirations and outcomes, perceptions of blocked nities, victimization strain, neighborhood strain, hassles. Future research is needed to determine these methodological differences can account divergent findings concerning depression.

Table 5 The effects of depression on offending for male and female subsamples

Summary and discussion

Subsample Male

Female

Property offending Rate Versatility Participation

.718TT .130T .736TT

.026 .006 .409

Violent offending Rate Versatility

.655TT .167TT

.162 .058

Note: Unstandardized coefficients are reported. These coefficients are based on the equations with the fully specified models. T P b.05. TT P b.01.

between opportuand life whether for the

The primary objective of this study was to assess the utility of general strain for explaining criminal behavior among young adults. The age range of the respondents was older than that in most of the samples previously analyzed in GST research. Regression models were estimated predicting property and violent offending for this young adult sample with variables representing general strain theory (including indicators of both btraditionalQ and bgeneralQ strain), differential association theory, and control theory, along with demographic controls. Moreover, the robustness of the effects of strain measures was assessed by operationalizing offending in three distinct ways-as a frequency rate, as versatility, and as participation-and by re-estimating the models with measures of lagged offending. Finally, the interrelationships between strain, offending, and one theo-

M.K. Ostrowsky, S.F. Messner / Journal of Criminal Justice 33 (2005) 463–476

retically strategic form of negative affect-depression-were examined. The results were reasonably supportive of GST. All significant coefficients for the strain measures were in the theoretically expected direction, and every measure of strain exhibited significant effects for at least one operationalization of offending. In addition, the effects of strain measures consistently withstood controls not only for variables derived from other criminological theories, but also for prior offending. The pattern of results was indeed quite robust. The coefficients for models with and without lagged offending were in all cases highly similar. The measure of victimization strain yielded the most consistent findings for the strain variables. Young adults who had experienced victimization were more likely to commit both property and violent crimes. Although consistent with the hypothesis from GST, this relationship was open to an alternative interpretation. Previous research suggested that criminal offending was related to a more general bdelinquent life style,Q and empirical analyses indicated that prior offending predicted victimization risks (Lauritsen, Sampson, & Laub, 1991). Nevertheless, the finding that the coefficients for the indicators of victimization strain were relatively stable when measures of lagged offending were included in the models was consistent with the causal sequence implied by GST: the experience of victimization was related to greater offending, controlling for prior level of offending. Most likely, the overall relationship between victimization and offending reflected both strain and routine activities/life styles. Neighborhood strain also appeared to be relevant to both property and violent offending, although statistical significance varied depending on the operationalization of the dependent variable.8 Respondents who reported neighborhood strain were significantly more likely to participate in property and violent offending, and they were also more versatile violent offenders. Life hassles, on the other hand, affected only violent offending, and they did so consistently across operationalizations. The findings for the measures of traditional strain similarly differed depending on the type of offense. The measure of blocked opportunities affected participation in, and the versatility of, property offending but had no effect on violent offending. In contrast, the discrepancy between aspirations and outcomes predicted violent offending (for all three operationalizations) but not property offending. These findings underscored the importance of distinguishing between different kinds of crimes when assessing the effects of strain. More generally, the strain measures tended to have stronger and more consistent effects on violent crime than property crime. These findings were consistent with those of Aseltine et al. (2000). They reported that GST variables played a greater causal role in fostering more aggressive forms of delinquency compared to nonaggressive delinquency. Similarly, Mazerolle and Piquero (1997) found that

473

exposure to GST variables increased intentions to behave violently. They contended that the broad sources of strain might lead to a range of different types of delinquent responses, but that strain was likely to be most directly related to violence given the salience of anger as an intervening mechanism. The results here were consistent with this interpretation. This study was unable to examine directly the mediating effects of anger given the absence of information on this emotion in the data set. The examination of another form of negative affect, depression, however, yielded theoretically interesting results. Consistent with GST, the findings indicated that measures of selected forms of strain (victimization strain, neighborhood strain, and life hassles) were positively associated with depression. Moreover, depression was positively associated with both property and violent offending in five of the six models for the full sample. The overall mediating effects of depression were very small, but this was perhaps not surprising, given that depression represents only one form of negative affect. Subsample analyses, moreover, revealed that the effect of depression on offending was gender-specific. It emerged for males and not females. Thus, while young men were less likely to respond to strain with depression than were young women, this type of emotional response was more likely to instigate offending on the part of men. Further inquiry into gender differences in the mediating role of different forms of negative affect for adult populations is an important task for future research. There were, to be sure, limitations associated with these analyses. The researchers could not claim to have included all potentially relevant variables suggested by other criminological theories. Nevertheless, key variables from differential association and control theory were represented, and the fact that the effects of strain measures persisted when prior offending was controlled lessened concerns about model misspecification. In addition, because of data limitations, the models did not capture the complexity of GST, as they did not include the full range of dimensions of negative affect, and in particular banger,Q or indicators of coping mechanisms. The lack of measures of coping skills was particularly relevant to the interpretation of any gender differences in the processes linking strain and offending. Prior work suggested that males and females were likely to possess different coping resources and coping skills (Broidy & Agnew, 1997), although the nature of any conditioning effect of these traits in the strain/crime relationship remained unclear (see Piquero & Sealock 2004). In any event, a full understanding of gender differences in responses to strain requires the estimation of models with a more comprehensive list of theoretically relevant variables. Lastly, the measure of depression was rather crude-a dichotomous indicator of whether the respondent had two weeks or more during which they felt depressed over the past three years. This indicator did not capture the clinical conceptualization of depression, and it probably reflected

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mild forms of depression (almost one-third of the sample was classified as bdepressedQ). The generalizability of the findings reported about to serious forms of depression must be determined in future research. Despite these limitations, the analyses were able to determine whether the distal determinants of criminal behavior highlighted by GST affected offending for a relatively neglected segment of the population, and whether a measure of common forms of depression was interrelated with strain and offending in theoretically predicted ways for this young adult population. Not only have young adults been rarely investigated through the lens of GST, they are a relatively understudied group more generally. The results of this partial assessment were for the most part consistent with theoretical expectations, suggesting that the claims of GST are not limited to adolescent and traditional college-age populations.

Appendix A (continued) Full sample (N = 1253)

Sample with lagged offending (N = 1202)

Mean SD

Mean SD

Dependent variables Rate of offending Property Violent

.00 .00

2.73 2.19

.00 .00

2.75 2.21

Versatility of offending Property Violent

.25 .26

.66 .58

.25 .26

.65 .58

Participation in offending Property Violent

.16 .20

.37 .40

.16 .19

.37 .40

Notes Acknowledgements An earlier version of this paper was presented at the 73rd Annual Meeting of the Eastern Sociological Society, Philadelphia, PA, February 27–March 2, 2003.

Appendix A. Means and standard deviations for items used in analysis

Traditional strain Aspirations/outcomes Perceptions of blocked opportunities General strain Victimization strain Neighborhood strain Life hassles Differential association Social control Religion Friends Beliefs Married Have children Control variables Gender Age Race Depression

Full sample (N = 1253)

Sample with lagged offending (N = 1202)

Mean SD

Mean SD

.00 .54

.77 .95

.00 .54

.77 .95

.43 11.97 .00

.74 3.48 .54

.43 12.00 .00

.74 3.50 .50

1.35

.43

1.36

.43

.00 .00 3.43 .42 .36

.90 .75 .44 .49 .48

.00 .00 3.43 .42 .36

.90 .75 .45 .49 .48

.53 23.81 .13 .32

.50 1.97 .34 .47

.53 23.80 .13 .33

.50 1.96 .34 .47

1. Other researchers had similarly acknowledged that the National Youth Survey did not contain a rich set of measures of the social psychological factors that allegedly intervene between strain and criminal behavior. See Agnew (2002), Mazerolle (1998), Mazerolle and Maahs (2000), and Paternoster and Mazerolle (1994). 2. Elliott, Delbert. National Youth Survey [United States]: Wave VII, 1987 [Computer file]. ICPSR version. Boulder, CO: Behavioral Research Institute [producer], 1985. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1996. Elliott, Delbert. National Youth Survey [United States]: Wave VI, 1983 [Computer file]. ICPSR version. Boulder, CO: University of Colorado, Behavioral Research Institute [producer], 1992. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1994. 3. In the few instances where outcomes exceeded goals and the discrepancy score was negative, a score of b0Q was assigned. 4. Agnew (2002) reported evidence indicating that both direct experiences of victimization, as well as anticipated and vicarious victimizations, were related to delinquency. See also Agnew (2001) and Eitle and Turner (2002). 5. Also estimated were equations with a measure of respondent’s education. When mean substitution was used for education, the effects of the strain measures were virtually identical to those reported below. There were a few substantively important differences when analyses based on subsamples with non-missing data on education. Specifically, some of the coefficients for traditional strain measures reached statistical significance in the equations for the property offending rate, which offered even greater support for strain theory. The models with education were not reported because extensive missing data on the educational measure resulted in a considerable reduction in sample size. 6. As noted, univariate analyses revealed appreciable skewness in the dependent variables. The error variance was often nonconstant in OLS regressions with skewed dependent variables (Hannon & Knapp, 2003, p. 1428). Following procedures recommended by Fox (1991, pp. 49–50), the researchers plotted studentized residuals from the OLS regressions against fitted values (both in the original metric and smoothed by log transformation). Not surprisingly, the plots suggested heteroske-

M.K. Ostrowsky, S.F. Messner / Journal of Criminal Justice 33 (2005) 463–476 dasticity, especially for offending brates,Q which could impair the efficiency of the estimates. The results of the OLS regressions were nevertheless reported along with those from logistic regressions, given the application of OLS techniques in previous analyses informed by GST. The researchers also assessed multicollinearity by computing variance inflation factors. None of the variance inflation factors exceeded 1.6, indicating that collinearity was not a problem. 7. The researchers also considered the possibility that the effects of strain on depression might differ for males and females. Product terms for gender and each of the strain measures were computed and these product terms were included in the logistic regression models predicting depression. None of the interactions was statistically significant. 8. Consistent with previous research on GST (Agnew et al., 2002; Agnew & White, 1992; Mazerolle, 1998; Mazerolle & Maahs, 2000; Paternoster & Mazerolle, 1994), measures of neighborhood strain were included in this empirical analysis. Admittedly, however, these variables shared conceptual overlap with measures of neighborhood disorganization. For discussions on this matter, see Mazerolle (1998) and Mazerolle and Maahs (2000).

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