Adverse Neighborhood Conditions and Sanction Risk ... - CiteSeerX

J Quant Criminol (2014) 30:505–526 DOI 10.1007/s10940-013-9212-3 ORIGINAL PAPER

Adverse Neighborhood Conditions and Sanction Risk Perceptions: Using SEM to Examine Direct and Indirect Effects Byungbae Kim • Travis C. Pratt • Danielle Wallace

Published online: 1 November 2013 Springer Science+Business Media New York 2013

Abstract

Objectives The present study examines how individuals’ sanction risk perceptions are shaped by neighborhood context.

Methods Using structural equation modeling on data from waves 6 and 7 of the National Youth Survey, we assess the direct and indirect relationships between adverse neighborhood conditions and two dimensions of sanction risk perceptions: the certainty of punishment and perceived shame. In addition, the role of shame as a mediator between neighborhood context and certainty of punishment is also investigated. Results The results indicate that adverse neighborhood conditions indirectly affect both forms of sanction risk perceptions, and additional results show that perceived shame fully mediates the effect of neighborhood conditions on perceptions of the certainty of punishment. Conclusions The perceptual deterrence/rational choice perspective will need to be revised to accommodate more explicitly the role of neighborhood context in shaping sanction risk perceptions. Keywords Sanction risk perceptions Adverse neighborhood conditions Structural equation modeling

B. Kim (&) T. C. Pratt D. Wallace School of Criminology and Criminal Justice, Arizona State University, 411 N. Central Ave., Phoenix, AZ 85004, USA e-mail: [email protected] T. C. Pratt e-mail: [email protected] D. Wallace e-mail: [email protected]

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J Quant Criminol (2014) 30:505–526

Introduction Deterrence theory, which states that individuals restrain themselves from misbehavior out of fear of formal punishment, has a long history in both criminological thought and American criminal justice policy (Gibbs 1975; Pratt 2009). And while the deterrence perspective has adapted over time to accommodate theoretical advancements related to the consideration of the ‘‘non-legal costs’’ of criminal behavior (Braithwaite 1989; Grasmick and Bursik 1990; Tittle 1980) and to the integration of concepts such as self-control, social bonds, and offending experience (Paternoster 1987; Nagin and Paternoster 1993; Stafford and Warr 1993), all of these developments implicitly ignore the broader social-structural conditions that individuals are embedded within (cf. Clarke and Cornish 2001; Pratt and Cullen 2005). Even the most recent twist on the deterrence tradition—the sophisticated Bayesian updating work produced by Anwar and Loughran (2011) and Loughran et al. (2011)—still focuses almost exclusively on how offending decisions are influenced by individual-level characteristics. All of these developments are certainly welcome additions to the deterrence tradition because they have pushed the field forward in several important ways. Nevertheless, the problem, according to recent research (Giordano 2010; Sampson 2012), is that such ‘‘rational’’ decisions cannot be fully understood without an appreciation for the larger structural conditions in which those choices are made. To be sure, in Giordano’s (2010: 32) recent follow-up study of the Children of Highly Delinquent Girls and Boys, she argued from a developmental standpoint that: it is important to underscore that choice making takes place against a backdrop of structural contingencies and constraints. Choices are bounded, that is, never completely divorced from the social systems (macro-level, immediate social networks) within which they unfold. Furthermore, in Sampson’s (2012: 21) recent study of neighborhood dynamics in Chicago, he stated that ‘‘we react to neighborhood difference, and these reactions constitute social mechanisms and practices that in turn shape perceptions… and behaviors’’ (emphasis in the original). Taken together, these perspectives highlight the notion that the ways in which individuals make choices in their lives—including those related to their perceptions of sanction threats and the moral weight they may or may not assign to them—are embedded and situated within their specific structural contexts. The purpose of the present study, therefore, is to examine how neighborhood conditions affect citizens’ perceptions of sanction threats. In particular, we examine the direct and indirect effects of adverse neighborhood conditions on two dimensions of sanction perceptions: certainty of punishment and perceived shame. Both of these factors have been featured prominently in the perceptual deterrence literature (see Pratt et al. 2006), and they tap into both the formal and informal components of sanction threats, the importance of which criminologists have been so careful to highlight repeatedly over the years. To address these questions we use data from waves 6 and 7 of the National Youth Survey (NYS). In doing so, our broader purpose is to determine the degree to which the deterrence perspective should be modified to take into account more explicitly the structural context of the decision to offend.

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Theoretical Framework The Sources of Sanction Risk Perceptions Classical deterrence theory holds that individuals’ perceptions of the certainty, severity, and celerity of punishment determine individual offending behaviors (Beccaria 1764). Thus, most empirical efforts have been devoted to examining the relationship between sanction risk perceptions and subsequent offending behaviors (Pratt et al. 2006). Only recently have deterrence researchers started paying attention to the determinants of sanction risk perceptions, focusing mainly on formal sanction risks (Nagin 1998). This line of research has identified how factors such as impulsivity (Nagin and Pogarsky 2001; Piquero and Pogarsky 2002) and low self-control (Tittle and Botchkovar 2005; Piquero and Tibbets 1996) shape both individuals’ perceptions of formal sanction threats as well as how they respond to such threats. In addition, a variety of situational factors have also turned out to be associated with sanction risk perceptions (Klepper and Nagin 1989; Nagin and Paternoster 1993). Finally, from a series of studies testing Stafford and Warr’s (1993) reformulated model of deterrence, scholars have confirmed that both personal and vicarious experiences of punishment and punishment avoidance are associated with sanction risk perceptions (Paternoster and Piquero 1995; Piquero and Paternoster 1998; Piquero and Pogarsky 2002). And most recently, in a body of studies by Loughran and colleagues, it is being revealed that individuals employ a form of ‘‘Bayesian learning’’ (Anwar and Loughran 2011) in which they will update their estimates of the certainty of punishment over time depending on their offending experiences with either being caught or getting away with it (see Loughran et al. 2011, 2012). At this point, the extant research on the sources of sanction threats indicates that individual characteristics and offense-related experiences are of central interest to perceptual deterrence researchers as sources of sanction risk perceptions. This micro approach is, in and of itself, valid in that a human perception is a cognitive process operating at an individual level. Nevertheless, there are also compelling reasons to believe that the sanction risk perceptions are formulated, shaped, and modified beyond individual level characteristics. To date, researchers have paid relatively little attention to neighborhood context as sources of sanction risk perception—something that we focus explicitly on here. The Direct Effects of Neighborhood Conditions on Sanction Risk Perceptions In general, neighborhoods may affect sanction risk perceptions by influencing: (1) the physical environment of the neighborhood, and (2) the social environment of the neighborhood. Beginning with the physical environment there are currently two theories that would explain the link between neighborhood context and deterrence: broken windows and routine activity theories. Though specifying different causal mechanisms, both theories address how the physical environment of the neighborhood leads individuals to believe that they have a low likelihood of being subjected to sanctions when committing a crime. First, broken windows theory details the relationship between the physical conditions of a neighborhood and the prevalence of crime. This perspective suggests that unattended disorder in a neighborhood signals to residents that ‘‘no one cares’’ about the community. As a result, residents restrict their willingness to engage in all aspects of community life,

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thus lowering levels of informal social control in disorderly neighborhoods (Wilson and Kelling 1982). Crime then begins to flourish as the neighborhood loses its ability to police itself. Nested within this process is a set of assumptions concerning how offenders perceive the nature of formal sanctions within the neighborhood. Disorder, or symbols that local informal social control is weak (Sampson and Raudenbush 1999; Wilson and Kelling 1982), indicates to potential offenders that crime will not be reported or controlled as residents of the neighborhood are no longer willing to take care of their own problems for multiple reasons (e.g., for fear of retaliation or perceptions of the futility of their actions, see Anderson 1999; Pattillo-McCoy 1999). Accordingly, potential offenders think that crime will be easy to get away with (Skogan 1990). While these ideas sound good, the relationship between disorder and crime is highly contested in the criminological literature, with some scholars championing broken windows theory (Kelling and Coles 1996; Skogan 1990), and others finding it lacking (Harcourt 2001; Sampson and Raudenbush 1999). In representing a bit of middle ground, ethnographic work examining how offenders perceive and use disorder to commit crimes demonstrates that drug offenders only partially employ disorder to sort out locations for selling drugs (St. Jean 2007). Taken together, there is conflicting evidence regarding a relationship between neighborhood conditions and how individuals perceive sanction risks, though little research has formally studied the relationship. Routine activity theory also illustrates how the conditions of the physical environment can impact how potential offenders perceive their likelihood of getting caught. Specifically, when a motivated offender, a suitable target, and the lack of capable guardians meet in time and space, the offender’s perceptions of sanction risk drop and crime may occur (Cohen and Felson 1979). Here the neighborhood directly impacts perceptions of sanction risk by influencing the physical distribution of capable guardians. And it is worth noting that over the last four decades, the routine activity perspective has received considerable empirical support (Pratt and Cullen 2005). The social environment of a neighborhood may also have a direct effect on individuals’ perceptions of sanction risks. As but one example, legal cynicism is a cultural orientation within a neighborhood that espouses the idea that those who enforce laws are ‘‘illegitimate, unresponsive, and ill equipped to enforce public safety’’ (Kirk and Papachristos 2011: 1191). Such a belief can impact individuals’ perceptions of sanctions in two ways. First, given the belief that law enforcement agents are unresponsive to neighborhood problems, particularly to crime and violence, offenders may perceive their risk of arrest as low. Second, in neighborhoods where legal cynicism is present, individuals may feel compelled to resort to violence as a means of problem solving since law enforcement is either unable or unwilling to assist (Reisig et al. 2011; Anderson 1999). As legal cynicism emerges, it can be maintained by neighborhood residents through social interaction and reinforcement (Sampson and Bartusch 1998). Adverse neighborhood conditions may also influence residents’ perceived shame or internalization of norms regarding crime and delinquency. Both Shaw and McKay (1948) and Kornhauser (1978), for example, argued that neighborhoods serve not only a supervisory function but also promote collective prohibitions against deviance (Bursik 1988). Specifically, Kornhauser (1978) argued that socially disorganized neighborhoods reduce internal social control among residents, which is analogous to perceived shame. In addition, from his social influence conception of deterrence, Kahan (1997) argued that adverse neighborhood conditions would negatively impact internalized norms of society in general. Furthermore, Bernburg and Thorlindsson (2007), from their study on the effect of community structure on adolescent delinquency, showed that adolescent normlessness mediates

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the effect of community context on adolescent delinquency. Lastly, research by (Elliott et al. 1996) also demonstrated the possibility that neighborhood context affects internalization of norms. One of the key findings of their research was that neighborhood context influenced individual level pro-social competence (in particular, ‘‘commitment to conventionality’’). Overall, these studies point to the possibility that neighborhood context, particularly adverse neighborhood conditions, directly affect both perceived certainty of punishment and perceived shame. Indirect Effects of Neighborhood Conditions on Sanction Risk Perceptions It is possible that neighborhood conditions also exert indirect effects on sanction risk perceptions. A review of extant studies on both perceptual deterrence and neighborhood research suggests there are several possible indirect pathways between neighborhood context and sanction risk perceptions. These include personal offending and arrest experience, peer networks, and parental attitudes. First, traditional deterrence theory states that the more crimes you commit and the less you get caught, the lower your certainty of punishment will be (Gibbs 1975). Neighborhoods certainly contextualize this dynamic regarding offending and prior studies have demonstrated that adolescents living in neighborhoods characterized by high levels of disorganization are more likely to be delinquent (Shaw and McKay 1942; Gorman-Smith 2000). Arrest experience is similar in that the frequency of getting caught or arrested has an inverse relationship with certainty of punishment (Geerken and Gove 1975; Horney and Marshall 1992, cf. Paternoster and Piquero 1995; Pogarsky and Piquero 2003; Piliavin et al. 1986; Bridges and Stone 1986). Thus, neighborhood conditions, like social disorganization and crime rates, are likely to impact individuals’ offending and arrest levels, thereby impacting how they perceive their likelihood of receiving sanctions when offending. Additionally, peer networks, which are also shaped by neighborhood circumstances (Akers 1998; Simons et al. 1996; Haynie et al. 2006), may affect sanction risk perceptions. In particular, Stafford and Warr (1993) argued that individuals form sanction risk perceptions based on vicarious experiences as well as personal experiences. Peers’ punishment and punishment avoidance experiences will therefore impact an individual’s sanction risk perception in the same way that personal experiences do. Studies have consistently shown an inverse relationship between having deviant friends and one’s estimate of the certainty of punishment (Paternoster and Piquero 1995; Pogarsky et al. 2004, 2005). Thus, adverse neighborhood conditions should increase the presence of delinquent peer network, which may, in turn, subsequently lower individuals’ perceptions of sanction risks. Finally, parental attitudes toward crime and delinquency may also mediate link between neighborhood conditions and sanction risk perceptions. There is a well-established relationship between parenting and adolescent developmental outcomes (Gottfredson and Hirschi 1990; Patterson 1982; Sampson and Laub 1993; Turner et al. 2007), and a growing body of research illustrates that parenting is embedded in neighborhood contexts (Simons et al. 2005; Hay et al. 2006; Pratt et al. 2004). Specifically, adverse neighborhood conditions are related to lower levels of parental efficacy (Pratt et al. 2004; Simons et al. 2005). Such neighborhoods may contain less collective parental disapproval of delinquency, not because parents condone delinquency, but rather because the neighborhood setting itself is not conducive to the effective socialization of children (Bursik and Grasmick 1993; see also Sampson and Bartusch 1998). Additionally, there is a large body of

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literature demonstrating the importance of parental reinforcement contingencies (e.g., praise for pro-social behavior and stated disapproval for misbehavior in children) for shaping youths’ attitudes and behaviors (Pratt et al. 2010). Despite this evidence, no research has investigated whether parental disapproval of delinquency influences youths’ sanction risk perceptions. Current Focus Extant research on perceptual deterrence has put more emphasis on individual differences than on the context in which sanction risk perceptions emerge. The current study, therefore, examines the ways in which neighborhood context influences the formation of sanction risk perceptions. In doing so, our efforts will be devoted to not only formal sanction risk such as the certainty of punishment, but also to the informal sanction of perceived shame. This is an important issue since formal and informal sanction processes are intricately related (Zimring and Hawkins 1973; Williams and Hawkins 1986; Piquero and Paternoster 1998; Pratt et al. 2006). Specifically, the current research will (1) examine the direct and indirect effects of adverse neighborhood conditions on both the certainty of punishment and perceived shame, and (2) further investigate whether perceived shame also mediates the effect of adverse neighborhood conditions on the certainty of punishment.1 By answering these two research questions, we will be in a position to evaluate the impact of neighborhood context on sanction risk perceptions in a more holistic way. The specific hypotheses and the hypothesized path diagram (Fig. 1) for the current research are as follows: H1 Adverse neighborhood conditions have a negative direct effect on certainty of punishment and perceived shame. H2 Adverse neighborhood conditions have a negative indirect effect on certainty of punishment and perceived shame. H2-1 Adverse neighborhood conditions have a positive effect on personal offending and personal offending has a negative effect on certainty of punishment/perceived shame. H2-2 Adverse neighborhood conditions have a negative effect on personal arrest and personal arrest has a positive effect on certainty of punishment/perceived shame. H2-3 Adverse neighborhood conditions have a positive effect on deviant peer associations and deviant peer associations have a negative effect on certainty of punishment/perceived shame. H2-4 Adverse neighborhood conditions have a negative effect on parental disapproval of delinquency and parental disapproval of delinquency has a positive effect on certainty of punishment/perceived shame. H3 Adverse neighborhood conditions have a negative indirect effect on the certainty of punishment through the effect of perceived shame.

1

Regarding our latter research question, we are aware that there may be a reciprocal relationship between formal sanction threats and informal sanction risk. Even so, we restrict our focus on the unidirectional pathway from perceived shame to the certainty of punishment since most of the strong arguments involving the relationship between the two dimensions of sanction risks are more focused on the effect of informal sanctions on formal sanctions (Etzioni 1988; Zimring and Hawkins 1973) and not vice versa.

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Fig. 1 The conceptual framework. All the exogenous variables (male, white, age, parent education) are controlled but not shown. Circles represent latent constructs and rectangles represent observed variables

Methods Data This study uses data from the NYS, a longitudinal study of a nationally representative sample containing 1,725 adolescents aged 11–17 in 1977. Respondents were surveyed annually from 1976 to 1980 and then again in 1983 and 1987. This study analyzed data from wave 6 (1983) and wave 7 (1987), which were the only two waves of data that included measures of sanction risk perceptions. Out of the original 1,725 respondents at wave 1 (1977), 1,156 participants are included in the current study from waves 6 and 7. A variety of resources show that both attrition and missing data do not pose any significant impact on study outcomes using the NYS (Elliot et al. 1989). Dependent Variables Two dependent variables are used in this study: certainty of punishment and perceived shame. Certainty of punishment has been chosen as an indicator of formal sanction risk perceptions because, theoretically, a deterrent effect arises only when offenders perceive the possibility of getting caught (Zimring and Hawkins 1973). Second, in terms of empirical evidence, the ‘‘certainty effect’’ has been shown to be the most robust predictor of future offending behaviors among the three components of deterrence theory (Nagin 1998; Pratt et al. 2006). There is relatively less consensus, however, over which factor best

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represents the informal sanction risk perception. Makkai and Braithwaite (1994) classified the informal sanction threats into two broad categories: self-disapproval and disapproval by others. The former includes such concepts as shame (Braithwaite 1989), embarrassment (Grasmick and Bursik 1990), and moral beliefs (Akers et al. 1979), while the latter generally refers to social disapproval by others (Bishop 1984). Following Makkai and Braithwaite’s advice that self-disapproval is a more effective and powerful immediate constraint than disapproval by others, and in conjunction with the argument that shame could be more easily conceptualized as a sanction perception (Grasmick et al. 1993; Paternoster and Simpson 1996), this study uses perceived shame to represent informal sanction threats. Both dependent variables were measured at wave 7 (1987) to circumvent the issue of experiential effects (Paternoster 1987) and to ensure proper temporal ordering between the independent and dependent variables. First, with regard to certainty of punishment, respondents were asked to estimate the probability that they would be arrested if they were to commit the following six crimes: stealing something worth more than $50, stealing something worth less than $5, breaking into a building or a vehicle to steal something, attacking someone, using force to get something from someone, and speeding. The response format ranged from 0 (no chance) to 10 (certain). With regard to perceived shame, the respondents were asked to answer how much guilt, remorse or personal discomfort they would experience if they were to commit the same six crimes. These items were measured by a 5-point Likert scale, which ranged from ‘‘very little (=1)’’ to ‘‘a great deal (=5).’’ Both dependent variables are operationalized as latent variables and the measurement models provide good fits to the data with RMSEA = 0.044, CFI = 0.996, and TLI = 0.990 for certainty of punishment and RMSEA = 0.031, CFI = 1.000, and TLI = 0.999 for perceived shame. Key Independent Variable of Interest Our key independent variable of interest is adverse neighborhood conditions (Pratt et al. 2004). Researchers have used both subjective and objective measurement strategies to describe neighborhood contexts (Taylor 1997; Sampson and Raudenbush 1999; Sampson et al. 2002). The former concerns the nature of perception, emphasizing that in order to be influenced by their neighborhood conditions, respondents must be aware of them (Turner et al. 2007; see also Perkins and Taylor 1996). And in the present context, one of our theoretical expectations is that offenders who recognize the deleterious status of a certain neighborhood would be more likely to perceive that certainty of punishment would be lower in that neighborhood (see Wilson and Kelling 1982). Accordingly, this study uses a subjective measure of adverse neighborhood conditions based on the respondents’ description of the condition of their neighborhoods. Fifteen items were administered at wave 6 to measure adverse neighborhood conditions. Respondents were asked to report whether any of 15 items was ‘‘a big problem’’ (=3), ‘‘a problem’’ (=2), or ‘‘not a problem at all’’ (=1) in their neighborhoods. These items include: unemployment, racial conflict, traffic, vandalism, lawlessness, winos and junkies, prostitution, abandoned houses, gambling, rapes, burglaries, rundown buildings, assaults, organized crime, and delinquent gangs. Adverse neighborhood conditions is also operationalized as a latent variable and the measurement model fits the data well as evidenced by RMSEA = 0.049, CFI = 0.982, and TLI = 0.978. Higher scores on this measure indicate that there are higher levels of perceived problems in the neighborhood.

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We measured neighborhood adversity as a scale rather than as separate variables for two reasons. First, each of the items comprising the scale are strongly correlated with each other, thus if introduced into the model simultaneously, harmful collinearity would pollute the model (Fox 1991). Secondly, in line with a long-standing tradition in community-level research in criminology (Sampson and Raudenbush 1999), we conceptualize neighborhood adversity as a collectivity, where disaggregating neighborhood conditions into a series of constituent components would theoretically—and empirically—miss the larger point: that adverse neighborhood conditions are the result of a confluence of several neighborhood problems. Mediating Variables Personal offending experiences were measured between wave 6 and 7. At wave 7, the respondents were asked to report the frequencies of criminal acts in the preceding year (1986). In addition, at wave 7, respondents were also requested to report the offending behaviors for the years 1984 and 1985. These series of questions allow for the construction of the entire 3-year period of offending behavior index after wave 6. To construct a composite measure, the following offending behaviors were drawn from the 3 year period and recoded into dummy variables:2 car theft, stealing more than $50, breaking into a building, buying stolen goods, attacking someone, gang fights, sexual assault, and using force on someone. These dummy variables were then combined, with approximately 8.9 % of respondents reported having committed at least one of the offenses. Because the distribution of offending was skewed, the variable is logged and ranges from zero to 2.77. Personal arrest experiences were also measured between wave 6 and wave 7. At wave 7, respondents were asked to report the frequency of arrest since January of 1984 (wave 6). The questionnaire was not offense specific, however, so only the single item measuring a general arrest experience was used as the variable of arrest experiences. Approximately 7.7 % of respondents reported having been arrested at least once during this time span and the maximum value was four.3 We decided to treat this as a binary variable, because there were no substantive changes in the results regardless of coding schemes.4 Deviant peers were measured at wave 7. Respondents were asked to give the proportion of their friends who were involved in following six crimes in the preceding year (1986): stealing more than $50, breaking into a building, hitting someone, selling hard drugs, sexual assault, and destroying or damaging properties. The response categories ranged from ‘‘very few of them’’ (=1) to ‘‘all of them’’ (=5). We represent this variable as a latent construct in the model. The measurement model provided a good fit to the data such as RMSEA = 0.034, CFI = 0.997, and TLI = 0.995. Higher scores on this measure indicate that respondents have more deviant friends. Parental disapproval was measured by the respondents’ perceptions of their parents’ attitudes toward crime and delinquency. At wave 7, respondents were asked to rate their 2

The personal offending experience data collected in wave 7 with regard to the preceding year (1986) was measured as categorical variables, but the measures on offending experiences between 1984 and 1985 were measured as binary variables. For these reasons, the personal offending behavior index is constructed using binary indicators.

3

There was one outlying value of 12. This value has been recoded into four since it does not have any meaningful impact on the outcome.

4 Following the lead of Horney and Marshall (1992) and Anwar and Loughran (2011), we also employed an ‘‘arrest to crime ratio’’ variable. However, none of our exogenous variables successfully predicted this new ratio after it was entered in the model. Therefore, we decided to treat the two variables independently.

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parents’ reaction should it become known that they were involved in the following six crimes: stealing less than $5, stealing more than $50, hitting someone, selling hard drugs, deliberately hurting someone, and destroying or damaging properties. The response categories were a five point Likert-scale ranging from ‘‘strongly approve’’ (=1) to ‘‘strongly disapprove’’ (=5). Parental disapproval is also operationalized as a latent variable and the measurement model shows a relatively good fit to the data evidenced by RMSEA = 0.037, CFI = 0.999, and TLI = 0.998. Higher scores on this measure mean that the respondents perceive that their parents’ disapproval of delinquent behavior is high. Control Variables We employed two variables representing the socioeconomic status (SES) of respondents to control for a confounding effect between adverse neighborhood conditions and SES: family income measured at wave 6 (mean = 7,310 dollars) and parents’ education level measured at wave 1 (min = 1, max = 7, mean = 4.00, high school graduate). In addition, major demographic variables such as gender (male = 1), race (white = 1), and age (mean = 20.8) that were measured at wave 6 were also controlled. Analytic Strategy The current study employs structural equation modeling (SEM). The use of SEM has several advantages when compared to other multivariate analysis techniques in the present context, most notably the ability to assess direct and indirect effects. SEM also allows one to correct for measurement errors, which may bias parameter estimates (Bollen 1989). Despite these strengths, SEM is also based on a few basic assumptions that must be satisfied. Among others, multivariate normality is a critical assumption of SEM. Some of our measures violate this assumption since they were measured using categorical scales. In addition, our two mediating variables are treated as observed variables that have skewed distributions. To minimize the impact of violating basic assumptions, M-Plus is used as a main statistical package. M-Plus (V.6.1) allows SEM with a mixture of observed and latent variables and also allows for the estimation of SEM with categorical variables. M-Plus uses the weighted least squares with robust standard errors and mean- and variance-adjusted Chi square (WLSMV)5 as a default estimator when categorical variables are involved (Muthen and Muthen 2010). We follow a two-step process to capture the indirect effects of adverse neighborhood conditions. We start by estimating an SEM model in which our two dependent variables are correlated to determine whether adverse neighborhood conditions differentially influence formal and informal sanction risk perceptions. We then estimate a model where perceived shame is specified as a second-order mediating variable to explore the possibility that neighborhood contexts work through shame, as well as other mediating variables.

5

Maximum likelihood (ML) estimation is most commonly used in SEM. ML estimation assumes that the observed variables follow a multivariate normal distribution. As noted above, however, the categorical indicators of some of our measures in the current study do not allow us to use ML. Instead, oftentimes WLS (Weighted Least Square) has been relied upon as an alternative since it is known to be appropriate when the data do not follow a multivariate normal distribution (Bollen 1989). Nevertheless, Muthe´n et al. (1997) reported that WLS was found to be inferior to WLSMV and therefore WLSMV has been designated as a default estimator in Mplus when categorical endogenous variables or categorical indicators are involved (Muthen and Muthen 2010).

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To that end, the following procedures will be carried out for data analysis. First, measurement models are constructed. Second, following the classic strategy on mediation analysis by Barron and Kenny (Baron and Kenny 1986), a model without mediating variables (a reduced model) is estimated to establish the relationships between the independent and dependent variables. Third, a saturated model is estimated where the mediating variables are included to capture the indirect as well as direct effects of adverse neighborhood conditions. A final (trimmed) model is re-estimated with non-significant paths fixed at zero (Joreskog and Sorobom 1996). We follow the same process for the perceived shame as a mediating model, but only the final trimmed model portion will be presented.

Results Correlation and the Reduced Model Table 1 presents the bivariate correlations for all of the latent variables and endogenous variables.6 There are relatively weak relationships between adverse neighborhood conditions and the certainty of punishment and perceived shame (r = -0.090 for certainty and r = -0.064 for shame). In addition, all of the mediating variables are moderately correlated with the independent variable and the two dependent variables. The only exception is the variable of personal arrest experience. In particular, the association between arrest and certainty of punishment is markedly weak. Following Baron and Kenny (1986), a reduced model was estimated which excluded the proposed mediating variables. The model included only direct effects between adverse neighborhood conditions and the certainty of punishment and perceived shame. This process offers a guideline as to whether further mediation analysis is needed. Table 2 provides the results for the regression models where the two dependent variables were regressed on adverse neighborhood conditions and other control variables. The overall models fit the data well: v2 = 19256.848; d/f = 486, p = 0.000, RMSEA = 0.035, CFI = 0.968 and TLI = 0.964. The results show that adverse neighborhood conditions retain statistically significant effects on the two dependent variables, net of controls. More specifically, a one standard deviation increase in adverse neighborhood conditions decreases the certainty of punishment and perceived shame by 0.098 and 0.073 standard deviations respectively. With regard to the effects of the control variables, gender exerts strong negative effects on both dependent variables and family income also has positive effects on the two outcomes. Race and parental education level are also related to the outcomes. Mediation Analysis: The Saturated Model Given the findings presented so far, the next step is to estimate a full SEM model which includes the four mediating variables. By doing so, the indirect effects of adverse neighborhood conditions on our outcomes can be observed. The overall model fit is reasonable; v2 = 27,954.979; d/f = 1,025, p = 0.000, RMSEA = 0.035, CFI = 0.954 and 6

This correlation matrix could be obtained using the OUTPUT TECH4 option provided by M-plus. This command is useful in that it actually provides the correlation matrix derived from latent constructs rather than observed variables.

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Table 1 Correlation matrix among latent and endogenous variables Variables

1

2

3

4

5

6

7

1. Certainty

1.000

2. Shame

0.508

1.000

3. Neighborhood conditions

-0.090

-0.064

1.000

4. Personal offending (ln)

-0.167

-0.238

0.132

1.000

5. Personal arrest

-0.011

-0.084

0.042

0.041

1.000

6. Deviant peer

-0.219

-0.417

0.235

0.321

0.092

1.000

7. Parental disapproval

0.221

0.563

-0.089

-0.062

-0.055

-0.124

1.000

Mean

5.83

3.89

1.31

0.09

0.07

1.15

4.61

Standard deviation

2.13

0.64

0.34

0.33

0.26

0.33

0.39

Table 2 Unstandardized/standardized coefficients for the reduced model

Variables

Dependent variables Certainty

Shame

Adverse neighborhood conditions

-0.228/-0.098** (0.085)

-0.061/-0.073* (0.031)

Male

-0.595/-0.253*** (0.087)

-0.328/-0.389*** (0.039)

White

0.012/0.004 (0.095)

-0.085/-0.075* (0.037)

Age

0.004/0.006 (0.020)

-0.007/-0.031 (0.007)

Family income

0.013/0.067* (0.006)

0.005/0.078* (0.002)

Standard errors provided in parentheses

Parents education

-0.136/-0.149*** (0.031)

0.009/0.027 (0.011)

* p \ 0.05; ** p \ 0.01; *** p \ 0.001

R-square

0.093

0.153

TLI = 0.949. The data presented in the last two columns of Table 3 represent the direct effects of all the variables on the certainty of punishment and perceived shame. It was hypothesized that adverse neighborhood conditions would have a negative direct effect on the two outcomes. The results show, however, that the significant direct effects of neighborhood conditions on certainty and shame (observed in Table 2) have disappeared. Instead, the findings indicate that the newly added mediating variables become statistically significant. Each of the mediating variables has the expected effects and the magnitudes of these effects are robust: respondents who had more personal offending experiences and more deviant peers were more likely to have a lower certainty of punishment and shame. In addition, as respondents perceived that their parents would not approve of criminal behaviors, the certainty of punishment and perceived shame of crime increased. A comparison of the standardized coefficients among these mediators in Table 3 indicates that the effect of parental disapproval is relatively strong. The only exception to our expectation is the finding that personal arrest experience does not affect either of the outcomes. Considered together, it could be concluded that the effects of adverse neighborhood conditions on certainty of punishment and shame are fully mediated by a combination of personal offending, deviant peer associations, and parental attitudes.

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Mediation Analysis: The Trimmed Model To obtain more accurate estimates of the indirect effects of adverse neighborhood conditions, the saturated model presented in Table 3 was re-estimated using only the statistically significant paths.7 The results of the trimmed model are presented in Fig. 2 and the results of the indirect effects that were calculated from this final model are offered in Table 4. The overall model fit has improved: v2 = 2,080.361; d/f = 904, p = 0.000, RMSEA = 0.034, CFI = 0.959, and TLI = 0.956. In addition, most of the path coefficients slightly improved in the trimmed model. Regarding the indirect effects of adverse neighborhood conditions on the two outcomes, it was hypothesized that adverse neighborhood conditions would influence certainty of punishment and perceived shame through four mediating variables. In line with our hypotheses, Table 4 shows that all the mediating variables account for the effect of adverse neighborhood conditions on certainty and shame except for the personal arrest experience. That is to say, respondents who perceived higher levels of adverse conditions in the neighborhood they resided in (at wave 6), were more likely to commit crimes (during wave 6 and 7), were more likely to have deviant peers (at wave 7), and were more likely to have parents whose disapproval of crime is lower and, as a consequence, have a lower certainty of punishment and a lower level of shame (at wave 7). These findings supported hypothesis H2-1, H2-3, and H2-4. Examining the issue in detail, a comparison of the standardized coefficients for each indirect effect shows that the path involving deviant peer associations turned out to be the most significant contributor to the total indirect effect for both dependent variables. The indirect effects of adverse neighborhood conditions via the deviant peers variable accounted for about 44 % of the total indirect effect for certainty of punishment and 51 % of the total indirect effect for perceived shame. The Final Model: Perceived Shame as a Mediator We estimated our final model in which perceived shame is specified as a second order mediating variable. In this model, shame mediates the effects of both the exogenous variables and other first order mediators on certainty of punishment. Figure 3 shows the results for the final trimmed model. The model fit indicators show that this model fits the data well (v2 = 2,099.931, d/f = 864, p = 0.000, RMSEA = 0.033, CFI = 0.961, and TLI = 0.958). Two things stand out from these results. First, and most importantly, all of the significant effects of the first order mediators on certainty of punishment, which were observed in previous models, totally disappeared, where those effects now work through perceived shame. In other words, the perceived shame variable mediates all the effects of adverse neighborhood conditions, personal offending experiences, deviant peers, and parental approval on the certainty of punishment. Second, the introduction of perceived shame to the model increases the explanatory power of the model substantially (from R2 = 0.154 to R2 = 0.235). Finally, an examination of indirect effects revealed that a one standard deviation increase in adverse neighborhood conditions resulted in a 0.065 standard deviation decrease in certainty of punishment. Therefore, our final hypothesis receives strong support.

7

We also dropped the personal arrest experience variable, since the variable is not related to any of the dependent or the independent variables.

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0.160*** (0.024)

-0.053* (0.023)

-0.047 (0.006)

-0.053 (0.002)

-0.046 (0.008)

–

–

–

–

0.055

Male

White

Age

Family income

Parent education

Personal offending

Personal arrest

Deviant peer

Parental disapproval

R-square

* p \ 0.05; ** p \ 0.01; *** p \ 0.001


0.135*** (0.019)

Personal offending

Endogenous variables

Neighborhood conditions

Variables

Table 3 Standardized coefficients from the saturated model

0.039

–

–

–

–

-0.041 (0.006)

-0.010 (0.001)

-0.030 (0.005)

-0.014 (0.020)

0.185*** (0.022)

0.045 (0.018)

Personal arrest

0.243

–

–

–

–

-0.052 (0.029)

-0.043 (0.006)

-0.166** (0.021)

-0.063 (0.093)

0.382*** (0.073)

0.248*** (0.080)

Deviant peer

0.098

–

–

–

–

0.160*** (0.023)

0.038 (0.005)

-0.041 (0.016)

0.071* (0.078)

-0.224*** (0.057)

-0.096* (0.062)

Parental disapproval

0.153

0.198*** (0.058)

-0.110* (0.069)

0.035 (0.133)

-0.104*** (0.098)

-0.182*** (0.032)

0.040 (0.006)

-0.010 (0.021)

-0.010 (0.092)

-0.165*** (0.095)

-0.027 (0.087)

Certainty

0.503

0.521*** (0.029)

-0.313*** (0.024)

-0.011 (0.036)

-0.110** (0.034)

-0.068 (0.009)

0.037 (0.002)

-0.065 (0.006)

-0.127** (0.031)

-0.142** (0.026)

0.079 (0.032)

Shame

518 J Quant Criminol (2014) 30:505–526


519

Fig. 2 The trimmed model with standardized coefficients. All the exogenous variables (male, white, age, parent education) are controlled but not shown. *p \ 0.05; **p \ 0.01; ***p \ 0.001

Discussion A large body of criminological literature shows that neighborhood conditions are related to crime (Pratt and Cullen 2005). An equally large body of literature shows that individuals’ perceptions of sanction risks are related to their willingness to break the law (Apel and Nagin 2011; Paternoster 1987; Pratt et al. 2006). Nevertheless, neither body of literature seems to care much about the other—a fact that is likely due to variations in scholars’ preferences about what kinds of questions they like to ask which, in turn, dictate the unit of analysis for their research. We hold no allegiance to any particular set of questions or to any particular unit of analysis. Indeed, as Sampson (2012) recently argued, since any unit of analysis is ultimately embedded in a larger one, the ‘‘proper’’ unit is therefore fluid and is always on the lookout for the ones above it, next to it, and below it. Accordingly, the purpose of the present study was to bridge the gap between the macro-level literature on neighborhoods and crime and the micro-level literature on perceptual deterrence. And in doing so, based on the results presented here, two major conclusions are warranted. First, the effect of adverse neighborhood conditions on sanction risk perceptions is indirect only. The notion emerging from broken windows theory that problematic neighborhood conditions send a direct cue to the brains of would-be offenders that their risk of getting caught is low simply does not hold up. This is certainly not the first time that a politically popular idea fails to withstand empirical scrutiny (see, e.g., the discussion by Pratt 2009), nor are we the first to offer evidence that undermines a core assumption of broken windows theory (Gau and Pratt 2008; Harcourt 2001; St. Jean 2007). Our work is, however, an important first step in quantitatively linking neighborhood context with the ways in which people contemplate criminal events. And to that end, our results show that adverse neighborhood conditions are important when it comes to shaping sanction risk

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123 -0.029/-0.013** (0.011) Non-significant -0.071/-0.031* (0.031) -0.064/-0.028** (0.023) -0.164/-0.071*** (0.043)

Adverse neighborhood conditions ? personal offense ? certainty/shame

Adverse neighborhood conditions ? personal arrest ? certainty/shame

Adverse neighborhood conditions ? deviant peer ? certainty/shame

Adverse neighborhood conditions ? parental disapproval ? certainty/shame

Total indirect effects

H2-1

H2-2

H2-3

H2-4

* p \ 0.05; ** p \ 0.01; *** p \ 0.001


Unstandardized/standardized coefficients are presented

Certainty

Hypothesized paths

Hypothesis

Table 4 The indirect effects of adverse neighborhood conditions

-0.089/-0.160*** (0.021)

-0.035/-0.063** (0.012)

-0.046/-0.082*** (0.012)

Non-significant

-0.008/-0.014* (0.003)

Shame

520 J Quant Criminol (2014) 30:505–526


521

Fig. 3 The final trimmed model with standardized coefficients. All the exogenous variables (male, white, age, parent education) are controlled but not shown. *p \ 0.05; **p \ 0.01; ***p \ 0.001

perceptions. Yet to do so, neighborhoods needs to operate through something. We hope that future research within this tradition will continue the practice of measuring directly the social process assumed to be responsible for ‘‘neighborhood effects’’ within the criminological literature. Second, and relatedly, the indirect pathways responsible for the link between adverse neighborhood conditions and sanction risk perceptions are varied and complex. Individuals’ offending experiences, those of their peers, and the level of disapproval they would expect from their parents for their misbehavior all matter when it comes to how they think about the certainty of getting caught and the shame they may experience upon committing crime. In addition, our work clearly shows that informal control (in the form of perceived shame) serves as a necessary precondition for the effectiveness of formal social control (in the form of the certainty of punishment). These results mirror the findings of others, such as Braithwaite’s (1989) work on reintegrative shaming (Hay 2001), Stafford and Warr (1993) reconceptualized theory of deterrence and the importance of vicarious experiences through deviant peers (see also Paternoster and Piquero 1995; Piquero and Pogarsky 2002), and the ‘‘experiential effect’’ noted within the rational choice/perceptual deterrence literature (Paternoster 1987). The problem is that, while these bodies of literature overlap (particularly with respect to the group of scholars most responsible for producing this work) they have matured somewhat independently of each other. Accordingly, our purpose here was to pull them together and to locate these ideas within in a broader structural context. And since no study is without its limitations, there were certain things we simply could not assess here, which represent opportunities for future research. For example, we did not provide offense-specific analyses of sanction risk perceptions. Given that prior research

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suggests that offender decision making processes may differ by crime type (Nagin 1998; Pogarsky et al. 2005), there still remains a possibility that adverse neighborhood conditions would have a differential effect on sanction risk perceptions from one form of offending to another (see Nagin and Pogarsky 2004). In addition, the NYS does not contain a measure of self-control—a factor that has been strongly and consistently linked to a wide range of criminal and deviant behaviors (Pratt and Cullen 2000; Reisig and Pratt 2011; Turanovic and Pratt 2013). This omission is potentially important because research has shown that levels of self-control are partially shaped by neighborhood context (Pratt et al. 2004) and that self-control is linked to offenders’ sanction risk perceptions (Piquero and Tibbets 1996). Future work should therefore include a self-control pathway between adverse neighborhood conditions and sanction risk perceptions to determine whether some of our key conclusions may or may not change as a result of assessing this indirect link. Finally, our measure of adverse neighborhood conditions was somewhat limited. Ideally, we would want to assess alternative indicators of neighborhood conditions (e.g., are the findings robust across objective versus subjective measures of neighborhood conditions?) using a hierarchical sampling strategy that nests individuals within neighborhoods. Measures of concentrated disadvantage or poverty, for example, may provide a more robust analysis, particularly if these measures were included with subjective assessments of neighborhood conditions. Additionally, it is plausible that surrounding neighborhoods play a role in influencing individuals’ risk perceptions above and beyond the neighborhood individuals reside in. Given the sampling design and available measures in the NYS, however, we were simply unable to include more robust measures of neighborhood conditions or spatial dependence—hopefully future studies will undertake that task.8 Our findings are relevant for current criminal justice policies that attempt to change individuals’ perceptions of sanction risks to increase deterrence. A primary assumption over the last few decades is that sanction risk perceptions are easily manipulated (Cullen et al. 2002), yet our study shows that there are complex local and social dynamics behind the formation of sanction risk perceptions as they relate to deterrence. Future policies aimed at increasing deterrence through the manipulation of sanction risks should take into account the neighborhood environment where those perceptions are formed. The problem, however, is that the social conditions within neighborhoods that lower sanction risk perceptions tend to be rather durable and difficult to change (Sampson 2012). Nevertheless, one promising approach would be to target the norms surrounding criminal activity and delinquency in neighborhoods as a means to impact deterrence and to enhance sanction perceptions. One such program, Project Safe Neighborhoods (PSN) in Chicago, aims to increase sanction perceptions and deterrence of gun felons. PSN Chicago is based on the idea that ‘‘changing attitudes toward gun violence requires changing norms and behaviors in the same way other public health efforts have tried to alter behaviors like cigarette smoking, drunk driving, and risky sexual activity’’ (Papachristos 2011: p. 1055). Through offender notification forums (i.e., meetings that the gun offenders need to attend shortly after their release from prison), PSN aims to increase sanction risk perceptions by informing the attendees of their increased odds of prosecution if caught with a gun, while also impacting deterrence by changing the attendee’s legal norms surrounding local law 8

We recognize that, as with any study that uses a non-experimental research design, causality cannot be inferred in the present study. We have, however, established statistical associations with proper temporal ordering along with extensive controls to avoid potential spuriousness. We are therefore confident that the relationships we observed are consistent with the propositions made by the theoretical perspectives we are testing.

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enforcement (Papachristos et al. 2013). Normative theories surrounding deterrence show that believing the law is ‘‘just’’ increases compliance with the law (Papachristos et al. 2007; Tyler 1990). Several studies regarding PSN Chicago show that this programmatic style reduces crime and recidivism (Meares and Kahan 1998; Papachristos et al. 2007, 2013). In the end, it is clear that the rational choice/deterrence perspective needs to do a better job of recognizing the reality that ‘‘context matters.’’ Criminologists have arguably gotten too ‘‘micro’’ in their approach to understanding offender decision making (see, e.g., Sampson 2012). The hyper-focus on individuals is often convenient, particularly when it comes to gathering data with a captive audience of students sitting in college classrooms. But divorcing individuals from their social and structural environments is clearly a mistake when it comes to understanding how they adopt and maintain their sanction risk perceptions. A revision of sorts to the deterrence perspective is therefore warranted—something that has happened often within this theoretical tradition over the years (Becker 1968; Cornish and Clarke 1986; Grasmick and Bursik 1990; Nagin and Pogarsky 2001; Stafford and Warr 1993; Williams and Hawkins 1986). To be sure, Pratt et al. (2006: 386) noted that ‘‘criminologists are not fond of ‘killing’ theories—we are instead more in favor of ‘tweaking’ them in new ways to see if they pan out in their newfangled forms.’’ And so it appears that, once again, the deterrence perspective will need to evolve (or be intelligently designed, if one prefers) to accommodate more explicitly the social context of sanction risk perceptions.

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