The spatial context of the disorder-crime relationship in a study of Reno neighborhoods.

Social Science Research 43 (2014) 168–183

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The spatial context of the disorder–crime relationship in a study of Reno neighborhoods Lyndsay N. Boggess a,⇑, Jon Maskaly b a b

University of South Florida, 4202 E. Fowler Avenue, SOC 107, Tampa, FL 33620, United States East Carolina University, Department of Criminal Justice, MS/505, Greenville, NC 27858, United States

a r t i c l e

i n f o

Article history: Received 2 May 2013 Revised 3 September 2013 Accepted 11 October 2013 Available online 18 October 2013 Keywords: Disorder Violent crime Broken windows Social disorganization Neighborhoods Spatial effects

a b s t r a c t This study extends the current research on the relationship between neighborhood disorder and violent crime rates by incorporating spatial effects and the reciprocal relationship between disorder and violent crime. In particular, we test for both the potential effect of disorder on violence as well as how changes in violent crime rates can impact neighborhood levels of disorder. We control for a variety of factors related to social disorganization theory that can lead to crime and potentially disorder. In order to disentangle these relationships, we use a cross-lagged auto-regressive structural equation model and a unique dataset comprised of calls for police service and reported incidents for 117 neighborhoods in Reno, NV. We find that higher rates of disorder lead to significant, but modest, increases in violent crime, but only aggravated assaults lead to increases in disorder. These effects hold true above and beyond the effect of social disorganization and the influence of spatially proximate neighborhoods. Ó 2013 Elsevier Inc. All rights reserved.

1. Introduction Since Wilson and Kelling’s (1982) breakthrough article on ‘‘broken windows,’’ police departments and policy-makers around the country have based law enforcement strategies on the premise that physical and social disorder are causal antecedents to more serious crime. Wilson and Kelling argued that unchecked signs of minor disorder, such as abandoned buildings, litter, and public drinking signal to criminals that the community does not care and therefore is an ideal place for the commission of more serious offenses. They advocated a policing style that targets disorderly behavior under the premise that increasing arrests for minor offenses would lead to a decrease in more serious crimes (see also Kelling and Sousa, 2001). This community policing strategy, often called ‘‘broken windows,’’ ‘‘zero-tolerance,’’ or ‘‘quality of life’’ policing, has been widely adopted in recent years (Kelling and Coles, 1996; Silverman, 1999); and has been credited for the sharp decline in crime in New York City in the 1990s (Bratton, 1998). Despite popularity among law enforcement, the broken windows thesis is one of the most debated criminological and sociological tenets. Some empirical studies have found evidence of a direct association between increased disorder and higher rates of crime (Perkins et al., 1992; Xu et al., 2005), particular robbery (Skogan, 1990), but other scholars (e.g., Gau and Pratt, 2008; Taylor, 2001) have questioned the causal association of the broken windows premise and the empirical evidence that supports it. Sampson and Raudenbush argue that the theoretical basis of the broken windows premise is flawed since disorder is ‘‘part and parcel of crime itself’’ (1999, p. 608). They theorize that the proposed relationship between disorder and crime is spurious and that both crime and disorder manifest when neighborhoods lack collective efficacy, or the ability of residents to maintain effective community controls. In addition, emerging literature on neighborhood effects indicates that endogeneity often exists between neighborhood characteristics (i.e., Bellair, 2000; Hipp, 2010; Kubrin and Weitzer, 2003). Specifically, structural factors that influence crime, ⇑ Corresponding author. E-mail address: [email protected] (L.N. Boggess). 0049-089X/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ssresearch.2013.10.002

L.N. Boggess, J. Maskaly / Social Science Research 43 (2014) 168–183

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such as residential instability and poverty, can also be influenced by crime. For example, Hipp (2010) found strong evidence that high-crime neighborhoods experience greater residential instability and concentrated disadvantage ten years later. A similar feedback loop can be applied to the disorder model. In his presidential address to the American Society of Criminology, Hunter (1978) was the first to explicitly suggest that crime and disorder operate in a reciprocal relationship and both Wilson and Kelling (1982) and Skogan (1990) discuss that neighborhoods spiral downward due to the feedback effects of disorder and decreased social control (which ultimately leads to an escalation in more serious crime). This suggests that prior research that does not take into account the potential causal association of crime on disorder may be misspecified. Further, studies within a social disorganization framework (one of the few other perspectives linking neighborhood structure to crime) have shown that spatial processes play an important role in the generation of crime. That is, research has shown that neighborhoods influence, and are influenced by, the characteristics of nearby areas (i.e., Cohen and Tita, 1999; Hipp et al., 2009a; Messner et al., 1999; Morenoff and Sampson, 1997). Applying spatial effects associated with social disorganization research to a study of neighborhood disorder is a way to integrate the two perspectives. If, as Sampson and Raudenbush (1999) posit, crime and disorder are at two ends of the same continuum then the same spatial influences that affect crime should also affect disorder. First, given that neighborhood boundaries are artificially defined and that residents living across the street from each other may technically live in different neighborhoods, it is logical to presume that residents incorporate the disorder and social conditions of nearby neighborhoods when assessing fear. Similarly, potential offenders might consider areas larger than the defined neighborhood when deciding where to victimize. Second, Wilson and Kelling (1982) make the claim that disorder is contagious, that is, disorder breeds more disorder; there is no reason to believe that this contagion effect would not cross neighborhood boundaries. Few studies, however, incorporate spatial processes when examining the disorder – crime nexus. In light of these omissions, the true relationship between disorder and crime is not entirely clear. Therefore, we investigate the relationship between disorder and crime using a more fully specified model that not only incorporates the independent impacts of neighborhood factors, but also accounts for the potential reciprocal relationship over time between disorder and crime and the influence of nearby neighborhoods.

2. Theoretical framework 2.1. The broken windows and incivilities theses The broken windows thesis is part of a larger theoretical framework studying the effects of neighborhood incivilities on fear of crime. The incivilities thesis proposes that neighborhood incivilities, generally defined as negative social and physical conditions or behaviors that are seen as nuisances or problematic, lead to increases in fear of crime. There is extensive support for the incivilities thesis from a social-psychological framework; studies consistently find a strong relationship between individual perceptions of incivilities and reported fear of crime (Covington and Taylor, 1991; Perkins and Taylor, 1996) even when controlling for neighborhood level crime rates or social structure. Wilson and Kelling’s (1982) version of the incivilities thesis moved beyond the social-psychological framework to an ecological perspective that incorporates neighborhood levels of crime as an outcome rather than fear of crime. Wilson and Kelling’s (1982) original article, ‘‘Broken Windows,’’ launched a shift in thinking about neighborhoods and crime that has strongly impacted police policy and practices and academic theorizing about the causes of crime. The premise of the article was that levels of minor disorder and subsequent levels of violent crime are inextricably linked and that police can more effectively reduce crime through the control of visible disorder and nuisance behaviors. Disorderly conditions such as literal broken windows, vacant lots, and graffiti, and nuisance behaviors such as loitering, public drunkenness, and panhandling break down existing mechanisms of social control and serve as signals that the neighborhood residents do not care about the community and that the neighborhood is a prime location for would be offenders to target victims with impunity.1 Wilson and Kelling specified that the progression from disorder to more serious crime is a process whereby the accumulation of disorder over time leads to the increase of fearful residents staying indoors, furthering weakening informal controls and making the neighborhood increasingly vulnerable to criminal invasion (see also Kelling and Coles, 1996). The resurgence of interest in the incivilities and broken windows theses after the publication of Wilson and Kelling’s article contributed to Skogan’s book Disorder and Decline (1990). Skogan analyzed residential surveys on neighborhood conditions from 40 neighborhoods in five cities throughout the United States. He was the first scholar to specifically partition disorder into physical (e.g., abandoned buildings, the presence of litter, and disruptive noise) and social disorder (e.g., public drug sales, loitering, and vandalism) in order to more explicitly examine the differential impacts of disorderly people versus disorderly structure. Skogan determined that both perceptions of physical and social disorder are positively and significantly related to rates of criminal victimization.2 1

Given the similarity in the concepts of ‘‘disorder’’ and ‘‘incivilities,’’ we use the terms interchangeably throughout the manuscript. Skogan’s (1990) study has been widely criticized, most notably by Harcourt (1998, 2001) who argues that Skogan’s analysis is methodologically flawed. Specifically, after reanalyzing Skogan’s data Harcourt concludes that (1) Skogan overstates his findings and that when neighborhood poverty, stability, and racial composition are controlled for there is only a significant relationship between disorder and robbery victimization, and (2) that the relationship between disorder and robbery victimization is driven entirely by five neighborhoods in Newark. It is worth noting that Harcourt’s (1998) replication of Skogan’s study has also been critiqued for not having enough statistical power, inappropriately dropping the five neighborhoods from Newark (that Harcourt deemed as too influential), and for mishandling missing data (see Xu et al., 2005 for a more thorough discussion of these critiques). 2

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Since Skogan (1990), other studies have verified a link between disorder and crime or victimization rates (e.g., Sampson and Raudenbush, 1999; Taylor, 1999; Xu et al., 2005; Yang, 2010). Recently Xu et al. (2005) used a survey of residents’ perceptions of neighborhood disorder to test some of the major tenets of the broken windows thesis. They found that disorder exerts a strong direct effect on serious (public assaults, rape, violent attacks on residents, and drive-by shootings) and less serious (burglary, stolen car, and vandalism) crimes even while controlling for social cohesion and community policing efforts. Other studies have relied on observable measures of disorder. Observable or actual disorder is usually measured as an objective assessment and/or count of the presence of dilapidated housing, graffiti, litter, the presence or absence of flower or vegetable gardens, literal broken windows or lights, sidewalks in need of repair, and unkempt lawns; all of which have been found to be associated with higher crime rates (Brown et al., 2004; Perkins et al., 1992; Sampson and Raudenbush, 1999). Overall, studies that rely on residents’ perception of disorder typically show more consistent findings in support of the disorder-crime nexus specified by the broken windows thesis than analyses which use objective measures of observable disorder (Drakulich, 2013; Taylor and Harrell, 1996), though the correlation between perceived and observed disorder is very high (Taylor, 1996; Perkins and Taylor, 1996). Others, however, argue that measures of perceived disorder are biased by the racial composition and economic condition of the neighborhood (Sampson and Raudenbush, 2004; Sampson, 2009) and that perceptions of disorder will be higher in areas where minority and poor residents cluster, regardless of the actual presence of disorder (Sampson, 2009). The majority of empirical studies supporting broken windows have primarily relied on cross-sectional data analysis that cannot capture the process from disorder to crime that happens over time. There is less support for the causal association between disorder and crime when the broken windows thesis is examined over time. First, though many cross-sectional studies support the notion that disorder decreases social control and cohesion (e.g., Kurtz et al., 1998; Liska and Warner, 1991; Perkins and Taylor, 1996; Skogan, 1990) there is only weak support for an association between disorder and subsequent levels of social cohesion (Sampson and Raudenbush, 1999; Taylor, 1996) or informal surveillance by residents (Bellair, 2000). Second, studies that have examined the disorder-crime nexus over time have generally found that disorder has no meaningful impact on crime in the future. For example, Taylor’s (1999) large-scale study of blocks in Baltimore found that economic decline was the primary driving force behind increases in crime. More recently Yang (2010) used trajectory models to study 16 years of disorder and crime in Seattle. She found (1) that neighborhoods without any social disorder are violence free, and (2) locations with high levels of disorder often, though not always, have high levels of crime; the presence of social disorder only leads to high rates of violence 30% of the time (Yang, 2010). In other words, Yang determined that although the location of crime and disorder are frequently the same, having high levels of disorder does not necessarily guarantee high crime. Many studies find the association between disorder and crime is specific to crime-type (e.g., Sampson and Raudenbush, 1999; Skogan, 1990; Taylor, 1996). Higher rates of neighborhood incivilities in Baltimore were associated with increases in homicides, but not robbery, rape, or aggravated assault (Taylor, 1996); and Sampson and Raudenbush (1999) determined that neighborhood level incivilities are predicative of police reports of robbery, but not homicide or burglary. Other studies argue that the link between disorder and crime may be neighborhood specific. Wilson and Kelling (1982) suggested that disorder will be more impactful in neighborhoods that are already deteriorating and on the edge of becoming crime-ridden, but empirical research actually indicates that disorder has a greater effect on crime in neighborhoods that have moderate to higher incomes (Taylor et al., 1985; Taylor and Shumaker, 1990) or more stable residential populations (Taylor et al., 1985). This suggests that the hypothesized link between disorder and crime is weakest in the most deteriorated and disorderly neighborhoods (Gau and Pratt, 2010; Taylor and Shumaker, 1990), although Taub et al. (1984) conclude that incivilities play a larger role in explaining crime in declining neighborhoods just as Wilson and Kelling originally postulated. 2.2. Social disorganization theory Several empirical studies of the broken windows thesis have suggested that neighborhood structural factors lead to both crime and disorder in line with social disorganization theory. Social disorganization theory posits that neighborhoods with high poverty, residential mobility, and a diverse population will have higher crime rates because such communities are unable to realize residents’ common values and maintain effective social control to reduce crime (e.g., Bursik and Grasmick, 1993; Kornhauser, 1978; Shaw and McKay, 1942). The central component of the theory is that certain structural factors negative influence mechanisms of informal social control; neighborhoods that cannot effectively control behavior will have higher crime rates. Indeed, a large body of research has shown that neighborhood conditions such as levels of family disruption, population density, unemployment, the presence of large numbers of young males, and population turnover are significantly associated with neighborhood crime rates (i.e., Bursik and Grasmick, 1993; Sampson and Groves, 1989; Sampson et al., 1997). Sampson and Raudenbush (1999) applied this perspective in their influential article examining collective efficacy on disorder in public spaces. They determined that there is no direct relationship between disorder and crime; rather, both are manifestations of the neighborhood level of collective efficacy (cohesiveness and trust among residents and the willingness to intervene in order to reach collective goals) (Sampson and Raudenbush, 1999; Sampson and Raudenbush, 2004; Sampson et al., 1997; Sampson, 2009); in neighborhoods with low collective efficacy there was both high violence and high disorder. Rather, the level of disorder and crime in a neighborhood varied with neighborhood structural characteristics such as concentrated disadvantage and mixed land use (Sampson and Raudenbush, 1999). Recently, Steenbeek et al.


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(2012) also found that businesses (i.e., supermarkets, schools, fast-food, and bars) as well population density and residential mobility are significantly associated with social disorder, and businesses, density, mobility, and income predict physical disorder. Under a social disorganization framework, disorder has the same structural origins as crime; they are on opposite ends of a continuum of social ills resulting from neighborhood disorganization. In Hunter’s (1978) version of the incivilities thesis he specified that crime rates and incivilities share the same structural origins, such as residential instability, poverty, and a larger minority population. Empirical evidence finds some support of the structural origins of disorder. Skogan’s (1990) analysis in Disorder and Decline found a positive and significant relationship between racial composition and residential turnover and disorder and other studies have shown that urbanization and family disruption are also significantly related to higher rates of disorder (i.e., Markowitz et al., 2001). More recently, neighborhood effects research within a social disorganization framework has begun examining the spatial interdependence of neighborhood characteristics. That is, neighborhoods tend to cluster by characteristics such as crime, economic disadvantage or residential instability. For example, Morenoff et al. (2001) found strong evidence that neighborhoods with high homicide rates were geographically close to other neighborhoods with high homicide rates. Further, these factors can traverse geographic boundaries; it is likely that crime or poverty in one neighborhood may influence crime in adjacent neighborhoods. Cohen and Tita (1999) found a spatial diffusion of youth gang and non-gang homicides across neighborhood boundaries and Hipp et al. (2009a) found that residential change in surrounding neighborhoods influences violence in the focal neighborhood. Given this, if structural factors lead to disorder in the same way they lead to crime it is likely that these factors also cross neighborhood boundaries similarly to influence crime and disorder in the neighborhood of interest. 2.3. The current study Current research examining the disorder-crime nexus is incomplete. There is some research indicating a direct relationship between disorder and crime (Skogan, 1990; Xu et al., 2005) while an additional body of research which indicates the relationship is caused by neighborhood structural factors (Harcourt, 1998; Sampson and Raudenbush, 1999). But many of the existing studies have been limited to narrow cross-sectional datasets (for a notable exception, see Yang (2007)) and have neglected to account for disorder and crime in spatially proximate communities. Therefore, this study moves beyond extant literature in several ways. First, we examine the direct relationship between disorder and crime while accounting for the possible influence that crime can have on future disorder. Second, we investigate whether the disorder–crime relationship is a localized phenomenon as implied in Wilson and Kelling (1982) or whether the characteristics of nearby areas are influential, as specified by social disorganization research. Therefore, we test the potential impact that crime and disorder in neighboring communities may have on future crime and disorder in the neighborhood of interest, referred to as the focal neighborhood. Third, we disaggregate violent crime to separately examine the relationship between disorder and robbery and disorder and aggravated assault. 2.3.1. Reciprocal relationship Research on broken windows and the incivilities thesis has almost exclusively focused on the hypothesized relationship between disorder and crime, though more general studies of neighborhood effects have long recognized the reciprocal relationships among community factors in which crime can lead to the worsening of neighborhood conditions (e.g., Bellair, 2000; Boggess and Hipp, 2010; Morenoff and Sampson, 1997). Methodologically complex analyses of neighborhood effects in general have found a reciprocal relationship between neighborhood conditions and crime. For example, Hipp et al. (2009b) and Boggess and Hipp (2010) determined that violent crime is significantly and positively related to residential instability and Dugan (1999) found that property crime victimization increases the likelihood that a household will move out of a neighborhood. In addition to population migration in general, other research has shown that crime can affect neighborhoods by impacting the racial/ethnic composition. Liska and Bellair (1995) showed that violent crime leads to a decrease in the white population while Morenoff and Sampson (1997) determined that an increase in homicides actually leads to an increase in economically disadvantaged black residents in a neighborhood. The reciprocal nature of disorder and crime was first introduced by Hunter (1978) though little empirical literature has since investigated it. Skogan (1990) recognized the reciprocal relationship between crime and levels of disorder but he did not directly assess it. He conjectured that certain types of crime, including interpersonal violence and crimes committed by strangers in public spaces, would serve to further deteriorate neighborhood conditions over the long run. These crimes in particular would increase residents’ levels of fear causing them to further withdraw from the neighborhood and contributing to elevated levels of disorder and crime caused by the inability of residents to maintain any sort of informal social control. Prior studies have shown that crime has a deleterious impact on the willingness of residents to intervene (Taylor, 2002) and participate in community events (Duncan et al., 2003) and Garcia et al. (2007) have shown that higher crime rates significantly decrease trust among residents. Sampson and Raudenbush (1999) examined the reciprocal relationship of violent crime on collective efficacy and found that both homicide and robbery are negatively related to collective efficacy. Furthermore, they found that the direct effect of disorder on robbery remained while controlling for the reciprocal relationship between robbery and collective efficacy. However, the authors did not test the potential feedback effect of robbery directly on disorder. Markowitz et al. (2001) more

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specifically tested the feedback loop proffered by Hunter (1978) and Skogan (1990) using three waves of the British Crime Survey to study neighborhood cohesion, crime and disorder, and fear over four year lags. They found evidence of a feedback loop in which neighborhood cohesion affects the levels of both crime and disorder through fear. Beyond Markowitz et al. (2001), and in some respects Sampson and Raudenbush (1999), few studies have examined the disorder-crime nexus while accounting for the effect of crime on disorder; though some studies have looked at the reciprocal relationship between fear of crime and homicide (Messner et al., 2004) and total violent crime (Garcia et al., 2007). In part, this is because of data limitations and the predominant use of cross-sectional analysis. One exception is Yang (2007), who performed Granger causality tests on a subsample of Seattle neighborhoods over sixteen years and found no casual association between disorder and future violence. To overcome this limitation we use a unique dataset of crimes reported to the police over a two-year span that allows us to assess the disorder-crime nexus over time while testing for the reciprocal impact of crime on disorder. Though we do not directly account for social cohesion as Markowitz et al. (2001) did, no other study has tested the direct feedback loop between disorder and violent crime while accounting for the impact of nearby neighborhoods.

2.3.2. Spatial effects Studies of disorder in neighborhoods have not yet accounted for the potential influence of nearby neighborhoods, although early proponents of the incivilities thesis (e.g., Hunter, 1978) mentioned that residents likely make inferences about their neighborhood based on residents, events, and public agencies outside of the neighborhood. This suggests that the relationship between disorder and crime is influenced by the conditions of nearby neighborhoods. Furthermore, it has been shown that analyses of spatial data that do not account for the influence of spatial dependence may lead to false indications of significance (Kubrin and Weitzer, 2003; Messner et al., 1999), such that accounting for spatial effects has become a fairly standard practice within most recent neighborhood effects research. Spatial analysis has been used to study neighborhood changes over time (Hipp, 2010), the diffusion or clustering of homicides (Cohen and Tita, 1999; Morenoff et al., 2001), and even fear of crime (Brunton-Smith, 2011; Brunton-Smith and Sturgis, 2011), though most of the spatial effects research falls within a social disorganization framework. Our application of spatial effects to disorder research provides a mechanism to link the two perspectives. But given the lack of prior disorder research to account for geographic space, it is unclear exactly what impact the characteristics of nearby neighborhoods will have on disorder. On one hand, social disorganization research has found that characteristics of nearby neighborhoods influences crime in the focal tract, which suggests that crime, disorder, and social factors in nearby communities should affect crime and disorder in the focal neighborhood. This coincides with Hunter’s (1978) contention that residents make inferences about disorder and crime in other nearby neighborhoods when gauging their own responses to fear. Further, Wilson and Kelling’s (1982) initially postulated that disorder would breed more disorder, implying that disorder is contagious. If so, it is logical to presume that this contagion would cross neighborhood boundaries and that disorder in neighboring areas would impact disorder (and potentially crime) in the neighborhood of interest. On the other hand, Wilson and Kelling (1982) almost exclusively seem to refer to internal actors within the focal neighborhood. It is plausible that disorder has a more localized impact on crime and that disorder in nearby communities does not affect residents of or criminals in the focal neighborhood. We are aware of only two other published studies on disorder and crime that explicitly takes the spatial processes of neighborhood effects into account. Cheong (2012) used a spatial error model to assess the influence of disorder on robbery and burglary in a Midwestern city. Unlike the spatial lag model used in this study which directly tests if the dependent variables are influenced by specific neighboring factors, spatial error models are less specific and account generally for unmeasured factors that are spatially correlated. Cheong (2012) found that accounting for spatial processes matters in the analysis of robbery, but that spatial processes are unrelated to burglary. Similarly, Cerdá et al. (2009) used a spatial error model and

Time-invariant exogenous predictors

Spatially weighted exogenous predictors

Disorder Time 1

Disorder Time 2

Violent crime Time 1


Spatial disorder Time 1

Spatial violent crime Time 1 Fig. 1. Conceptual model predicting disorder and violent crime while accounting for reciprocal and spatial effects.


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local indicators of spatial association (LISA statistics) to demonstrate a clustering of homicides in their study of New York City police precincts, but they did not apply the spatial statistics to disorder. We will test these competing expectations of the influence of spatial processes on disorder and crime by including measures of spatially proximate disorder, violent crime, and social disorganization and other structural factors in our models predicting disorder and violent crime in the focal neighborhood. A model of our conceptual framework incorporating our test for the potential reciprocal relationship between crime and disorder and spatial effects is shown in Fig. 1. 3. Data and methods 3.1. Data The present study investigates the relationship between disorder and crime in 117 block groups – our proxy for neighborhoods – in Reno, NV over a two year period. Block groups have been used in prior studies on neighborhood factors and disorder (e.g., Wallace et al., 2011; Yang, 2010). We divide the data into two one-year periods in order to examine the relationship over time; Time 1 includes data from October 1, 2006 to September 30, 2007 and Time 2 consists of data from October 1, 2007 to September 30, 2008.3 Not only is Reno demographically different from the large industrial urban centers previously used to examine the disorder-crime nexus (e.g., Seattle, Baltimore, Newark, etc.) but incivilities researchers have acknowledged the need to expand the research to smaller cities (Taylor, 2002). The predominant minority group in previously examined cities is Black, but given recent shifts and growth in the U.S. Latino population it is important to examine a city where the largest minority group is of Hispanic or Latino origin. In 2000 the Reno population was 19.2% Latino, 2.6% Black, and 69.2% non-Latino white (U.S. Census Bureau, 2000). Further, Reno is a gambling hub located in the only state with legalized prostitution and could thusly be considered a vice vacation destination. Given this, we uniquely study the disorder-crime nexus in a city that may attract disorder and crime.4 3.1.1. Outcome measures Since we are interested in the reciprocal relationship between crime and disorder over time, we utilize two dependent variables: the neighborhood violent crime rate and the rate of reported disorder. We obtained crime data directly from the Reno Police Department. Our measure of total violent crimes consists of all recorded incidents for aggravated assaults and robberies and we also created independent measures for the aggravated assault and robbery rates per 1000 residents. We exclude murder and rape given the relative infrequency of murder and known reporting issues related to rape/sexual assault. The assault rate excludes domestic violence events. We focus exclusively on violent crime in order to (1) more accurately assess the effect of disorder on the most serious and anticipated outcomes (Kelling and Sousa, 2001) and (2) to reduce any potential overlap in the operational definitions of crime and disorder (a critique that has been levied at prior measures of disorder). We separate aggravated assaults and robberies in order to address the disparate findings for robbery and other violent offenses as well as to examine Sampson and Raudenbush’s (1999) conclusion that robbers differentially respond to visual cues of social and physical disorder unlike murderers, burglars, or thieves. Individual police reports were geocoded to the block group using ARCGIS 10 and standardized for a rate per 1000 residents. On average, each neighborhood experienced 4.80 total violent crimes per 1000 residents, an average of 2.68 aggravated assaults and 2.12 robberies per 1000 persons. Our measure of disorder consists of a subset of calls for police service for incidents related to: intoxicated, unwanted, or undesirable persons, graffiti, abandoned vehicles, litter, illegal dumping, and suspicious persons, vehicles, and circumstances (Katz et al., 2013; Wallace et al., 2011).5 These categories are representative of the most common forms of visible physical and social disorder and are consistent with previous definitions of disorder (Skogan, 1990) and were chosen in conjunction with the communications supervisor of the Reno Police Department (personal communication). This measure of disorder improves upon resident surveys or personal observation since it is independent of memory decay effects and observation bias (Skogan, 2012). Furthermore, calls for police service more accurately record the time and location of the observed disorder than resident surveys and can reduce bias from events that residents report which may have occurred outside the geographically defined neighborhood or specified time period (Skogan, 2012). Since this measure relies on residents calling the police, it indicates that residents are both aware of the disorder in their neighborhood (a factor which may affect their fear and decisions to remain behind closed doors) and are bothered enough by these events to call the police (Kubrin, 2008).6 This measure, however, may underestimate more minor forms of disorder that are less likely to elicit calls to the police. This may particularly be the case in high crime or 3 The data used in this study were collected as part of another research project examining the relationship between casinos and crime during the dates listed. In order to create two equal time periods, the data were divided into years covering the exact same range of dates. Though the dates do not coincide with actual calendar years (January 1 to December 31) our chosen range is essentially equivalent. 4 Prostitution is only legal in licensed and regulated brothels in certain parts of the state; all other forms of prostitution are illegal. All forms of prostitution are illegal in the counties in which Las Vegas and Reno are situated. 5 The suspicious circumstances call type indicates a caller expressing concern over a particular situation although the police dispatcher is unable to specifically identify any criminal incident. 6 Our measure only includes resident calls for service; officer-initiated calls are excluded.

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highly disordered neighborhoods where residents’ tolerance for the presence of disorder may be greater and calls for police service are limited to more serious offenses (Xu et al., 2005). Calls by residents were geocoded by incident address and then aggregated to the block group level using ARCGIS 10.7 In order to account for the variation in the size of the neighborhoods, all disorder measures were standardized to a rate per 1000 residents. The average rate of disorder is 169.07 per 1000 residents, with a range of zero to just over 1481.8 3.1.2. Explanatory and control variables In order to determine if disorder is influenced by the same underlying conditions of social disorganization that generate crime, we control for a general indicator of social disorganization as well as neighborhood demographics. All demographic data was obtained from the 2000 U.S. Census Summary Tape File 3 at the block group level. We created latent indicators of residential instability and disadvantage using confirmatory factor analysis in SAS 9.2. In order to measure residential instability we use the percent of residents who moved into the block group in the past five years (an indicator of new residents) and the percent renters. Higher values indicate greater residential instability. To measure disadvantage we use the percent block group residents receiving public assistance and median household income. Both disadvantage and residential instability are strong, consistent predictors of higher crime rates through their negative impact on social control. Factor loadings indicate these variables accurately represent disadvantage and instability.9 In addition to poverty and social disorganization, prior studies have linked the presence of perceived disorder with minority concentration (i.e., Sampson, 2009; Sampson and Raudenbush, 2004). The presence of graffiti or litter may be more perceptible in minority neighborhoods due to social-psychological cues associating minority context with general disadvantage (Eberhardt et al., 2004), though it is also plausible that calls for service will be limited to more serious offenses due to a greater tolerance for disorder in minority or disadvantaged neighborhoods (Sampson and Raudenbush, 2004; Xu et al., 2005). Therefore, we control for the racial/ethnic composition of the neighborhood by including a measure of the percent nonWhite residents; in Reno this largely consists of a Latino population. In addition, Reno is a particularly interesting city to study disorder because the city has several major casinos that can be additional sources of disorder and crime (Barthe and Stitt, 2007; Barthe and Stitt, 2009). Conventional wisdom argues that casinos may be problematic. First, the alcohol associated with casinos may not only increase calls for service related to intoxicated persons, but the lowered inhibitions can make patrons disruptive, potentially leading to fights and unruly behavior. Indeed, Barthe and Stitt (2009) found that casinos are hotspots for alcohol-related calls for service and disturbances related to arguments or disrupting the peace. Second, casinos attract tourists who may let their guard down while vacationing and become unsuspecting victims. This is exacerbated by the easy availability of cash associated with casinos that tourists may be carrying (Joseph Curran, 1995). On the other hand, the increased presence of security guards and cameras may reduce crime and disorder in and near casinos. In order to control for casinos as potential crime and disorder attractors, we include a dummy variable indicating whether or not the neighborhood has a casino. Lists of unrestricted gaming licenses were obtained from the Nevada Gaming Commission and the addresses were plotted using ARCGIS 10.10 Those block groups containing a casino were given a value of 1 indicating the presence of a casino. Only nine blocks contained a casino and no neighborhood had more than one casino. 3.1.3. Spatial variables In order to account for the effect of disorder, crime, and social characteristics of nearby neighborhoods, we created spatially lagged variables for each variable in the model. Prior research suggests that most offenders do not travel large distances to commit crime (Pyle, 1976) so to assess the impact of nearby neighborhoods, we first created a spatial weights matrix by defining nearby neighborhoods using a distance decay function in which all neighborhoods within two miles of the focal block group are hypothesized to impact the focal block group, but with a distance decay captured as an inverse distance effect such that neighborhoods beyond the two mile radius were estimated to have no effect on the dependent variables in the focal neighborhood. We then multiplied the values of the variables of interest in the nearby tracts by this spatial weights matrix (row standardized) to create spatially lagged variables for disorder at Time 1, violent crime at Time 1, the percent 7 For all geocoding using ArcGIS 10 we were able to successfully geocode over 90% of known addresses. The specific geocoding success rates were 96.38% for violent crime, 95.02% for disorder, and 100% for the casinos. These are very high success rates and will minimize the potential influence of measurement error due to missing addresses (Ratcliffe, 2004). 8 We verified that block groups with zero violent crime and zero disorder are not missing data. These block groups reporting are predominantly wealthy and consist primarily of new construction houses; while these neighborhoods reported no aggravated assaults or robberies, they did have burglaries. Likewise, these neighborhoods had calls for police service for other non-disorder related incidents. 9 The concentrated disadvantage and residential instability measures were created using factor analytic techniques in SAS 9.3. Appropriateness of the model was assessed using the criterion provided by Hu and Bentler (1998). The confirmatory factor analysis suggested acceptable levels of model fit (v2 = 1.53, df = 1, p > .05; SRMSR = .0211; RMSEA = .0674; CFI = .9978). The path coefficients from the latent variable to the indicator variable were all significant and in the expected direction. The latent variables explained between 60% (residential instability) and 85% (median household income) of the variance in the indicator items. 10 Unrestricted gaming licenses are those sought by those locations that designed to primarily serve as a gaming institution; colloquially known as casinos. This excludes smaller venues such as grocery stores, restaurants and bars, or gas stations that offer gambling opportunities (largely slot machines). We limit this study to the large casinos because research suggests that casinos, those places with unrestricted gaming licenses, are disproportionately responsible for elevated levels of disorder and use of police resources (Barthe and Stitt, 2009). Further, including the smaller venues may lead to bias because disorder or crime at those places could be due to the type of establishment (such as liquor store, restaurant, or grocery store) and not due to the presence of gambling.


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Table 1 Summary statistics of neighborhood characteristics in Reno, NV block groups, 2000 (N = 117). Mean

SD

Total population

1750

1001

Racial/ethnic compositions Percent non-Latino White Percent Black Percent Latino

69.08 2.85 16.29

20.19 2.84 14.63

Percent receiving public assistance Residential instability Percent renters Median household income Disorder (per 1000 residents) Violent crime rate (per 1000 residents) Aggravated assault rate (per 1000 residents) Robbery rate (per 1000 residents)

2.77 58.82 45.23 $46,878 169.07 4.80 2.68 2.12

3.95 16.48 31.85 $21,883 216.93 5.42 4.54 3.74

vacant housing units, the percent non-Latino white residents, and our factor variables for concentrated disadvantage and residential instability. Spatially lagged variables were created in Stata 11.0. All explanatory, control, and spatial variables were used to predict both the violent crime and the disorder outcomes. There is no evidence of multicollinearity.11 Average neighborhood characteristics are shown in Table 1 and correlations between the variables in the models are shown in Table 2.12 3.2. Method In order to estimate the dual relationship between violent crime and disorder over time, we use a cross-lagged autoregressive structural equation model. This allows us to measure the effect of disorder on violent crime while accounting for the hypothesized reciprocal effect of violent crime on disorder. At the same time, we test for the effects of time-invariant exogenous variables and spatial effects on both crime and disorder. Similar cross-lagged models have been used to estimate the directionality between crime and residential instability in Columbus, OH (Hipp et al., 2009b), the reciprocal relationship between crime and neighborhood characteristics (Hipp, 2010), and fear of crime and disorder in neighborhoods in the United Kingdom (Brunton-Smith, 2011). We model one-year lags in order to capture the developmental sequence that takes place over time between disorder and violent crime (see Wilson and Kelling, 1982). Though Wilson and Kelling (1982) did not specify an exact time frame for the influence of disorder on crime to emerge, we believe that one-year is the appropriate time frame in order to allow disorder to signal that ‘‘no one cares’’ and estimate future rather than instantaneous effects. We run the model for total violent crime and then separately to test if the relationship is specified by crime type; previous research has continuously found support for the link between disorder and increased robbery rates (Sampson and Raudenbush, 1999; Skogan, 1990), but the findings for aggravated assault are less conclusive. Our conceptual model is shown in Fig. 1. Our final model consists of two equations, one that predicts disorder and one that simultaneously predicts violent crime, as shown in Eqs. (1) and (2):

disorderðtÞ ¼ q1ðtÞ disorderðt1Þ þ b1ðtÞ Wdisorderðt1Þ þ b2ðtÞ crimeðt1Þ þ b3ðtÞ Wcrimeðt1Þ þ C1 X þ C2 WX þ e1ðtÞ

ð1Þ

crimeðtÞ ¼ q2ðtÞ crimeðt1Þ þ b4ðtÞ Wcrimeðt1Þ þ b5ðtÞ disorderðt1Þ þ b6ðtÞ Wdisorderðt1Þ þ C3 X þ C4 WX þ e2ðtÞ

ð2Þ

In Eq. (1) disorder(t) is the rate of disorder during time period t, disorder(t1) represents the rate of disorder in the previous year (t 1) and crime(t1) is the violent crime rate during the previous year. Wdisorder(t1) and Wcrime(t1) represent the spatial lags of disorder and violent crime in the previous year. The variable X represents the control and explanatory variables (social disorganization, vacancy rate, the percent non-Latino white, and casinos) and WX are the spatially weighted control and explanatory variables; 1(t) is the disturbance term. The coefficient q1(t) estimates the effect of disorder in time period t 1 on disorder in time t and b1(t) measures the effect of nearby neighborhoods disorder at t 1 on disorder in the focal neighborhood at time t. The coefficients b2(t) and b3(t) estimate the impact of violent crime at time 11 Variance inflation factors (VIF) estimates of less than 10 are considered to be in the acceptable range (Kennedy, 2008); VIF factors were estimated in a traditional OLS model for both crime and disorder and the values ranged from 1.5 to 8.4; models excluding the spatial variables all had VIF scores below 4. 12 We actually find that the correlation between disorder and neighborhood structural variables is non-significant for the highest disordered neighborhoods (in the top 10% of the distribution) (not shown). In fact, in the most disordered neighborhoods, there is a negative (though non-significant) correlation between the percent non-White and disorder. Thus, although Sampson (2009a, 2009b) argues that the perceptions of disorder will be higher in minority or poor communities, we do not find evidence that our measure of disorder is strongly influenced by the racial/ethnic composition of the neighborhood in our data.

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Table 2 Bivariate correlations between all variables of interest. Value in parentheses depicts the column for that variable.

Violent crime rate time 1 Disorder rate time 1 Aggravated assault rate time 1 Robbery rate time 1 % Non-White % Moved 5 years % Renters Median income (in $ 1000s) % Public assistance Casinos *

Violent crime rate time 1

Disorder rate time 1

Aggravated assault rate time 1

Robbery rate time 1

1.000 .912* .825* .978* .386* .362* .524* .477* .379* .334*

1.000 .838* .894* .310* .336* .466* .464* .338 .369*

1.000 .772* .296* .297* .443* .433* .425* .438*

1.000 .357* .372* .527* .455* .301* .307*

% NonWhite

% Movers

% Renters

Median income

% Public assistance

1.000 .419* .608* .695* .574* .125

1.000 .734* .559* .268* .082

1.000 .794* .475* .155

1.000 .513 .145

1.000 272*

Casinos

1.000

p < .05.

Table 3 Unconditional cross-lagged models examining the relationship between disorder and total violent crime, aggravated assaults, and robbery in Reno, NV neighborhoods (N = 117). Model 1

Violent crime (time 1) Aggravated assault (time 1) Robbery (time 1) Disorder (time 1) R2 v2 (df) p SRMSR RMSEA CFI

Model 2

Disorder (T2)

Violent crime (T2)

0.76 (1.65)

0.63* (0.10)

0.96* (0.03) 0.007* (0.001) 0.97 0.83 9.56(1) p = 0.002 0.0067 0.2716 0.9894

Model 3

Disorder (T2)

Aggravated assault (T2)

2.60* (1.30)

0.51* (0.07)

0.99* (0.03) 0.97 7.81 (1) p = 0.005

0.003* (0.001) 0.81 0.0064 0.2456 0.9905

Disorder (T2)

Robbery (T2)

0.44 (2.26) 0.76* (0.09) 0.99* (0.04) 0.008* (0.002) 0.97 0.81 5.39(1) p = 0.02 0.0050 0.1971 0.9943

Notes: Hu and Bentler (1999) identify the following cutoff values for a good fit in an SEM model: standardized root mean square residual (SRMSR) 6 .08; for the root mean square error of approximation (RMSEA) 6 .06; and for the Bentler comparative fit index (CFI) > .95. There is some disagreement about excessive reliance on fit indices as well as the appropriateness of certain fit indices for longitudinal models or models with few degrees of freedom (see Hayduk et al., 2007; Kenny et al., 2011). p < 0.10. * p < 0.05.

t 1 on disorder in time t for the focal and nearby neighborhoods respectively. C1 and C2 represent vectors of the effects of all coefficients for the focal and nearby neighborhoods on disorder(t). Variable and coefficient interpretations are analogous for Eq. (2) except the model is predicting the violent crime rate crime(t) in time period t. In the aggravated assault and robbery models, the aggravated assault rate and robbery rate at times t and t 1 are substituted for crime(t) and crime(t1). Given that our model specifies that the effects of the endogenous variables occur over one-year lags and not simultaneously, this is a recursive model and does not pose any problems for identification (Bollen, 1989). All models were estimated using SAS 9.2. 4. Results 4.1. Unconditional models One of our primary interests is the hypothesized reciprocal relationship between violent crime and disorder in neighborhoods. Therefore, in order to test just the reciprocal relationship, we first run unconditional models excluding the exogenous variables and the spatially lagged variables for both disorder and violent crime. We run unconditional models first for total violent crime and then separately for aggravated assaults and robbery. Results are shown in Table 3. The results suggest there is a reciprocal relationship between disorder and violent crime, specifically aggravated assault. First, as predicted by Wilson and Kelling (1982), there is a direct relationship between disorder at time 1 and subsequent levels of total violent crime (b = 0.007, p < 0.05), aggravated assaults (b = 0.003, p < 0.05), and robbery (b = 0.008, p < 0.05) but the magnitude of the impact is minimal. This means that an increase of one in the rate of disorder per 1,000 residents will only increase the violent crime rate by 0.007 per 1,000 residents. The average violent crime rate is 4.80 per 1000 residents, so an increase of one in the rate of disorder increases the average crime rate to 4.807. This 0.007 increase in the rate translates into an increase of only 0.012 actual violent crimes based on an average of 1750 people per neighborhood; thus it


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would take an additional 84 instances of disorder to increase the violent crime rate by 1.13 Second, when we look at the reciprocal relationship, the results show that only the rate of aggravated assault in the previous year has a positive and significant impact on the future rate of disorder (b = 2.60, p < 0.05). That is, since the average neighborhood has 1750 residents and an average rate of disorder is 169.07, increasing the disorder rate by 2.60 to 171.67 translates into an increase of 4.55 incidents of disorder for each one unit increase in the aggravated assault rate. The total violent crime and the robbery rates are not significant predictors of disorder.

4.2. Conditional models Our other primary research goal was to test the impact of crime and disorder in spatially proximate neighborhoods on crime and disorder in the focal neighborhood. Therefore, we next looked at conditional models that predict total violent crime and disorder and (1) includes only the spatially lagged versions of the dependent variables, (2) includes the spatially lagged dependent variables and the time-invariant demographic and structural variables, and (3) includes spatially lagged exogenous variables; results are shown in Table 4. The results from the total violent crime model (Model 1, Table 4) show the direct relationship between disorder and crime (b = 0.008; p < 0.05) persists even when controlling for the influence of nearby disorder and violent crime. As in the unconditional model, the magnitude of the impact is fairly small. An increase of one in the rate of disorder results in a rate increase of only 0.008 which is only slightly larger than the unconditional model in Table 3. This translates into an increase of only 0.014 violent crime incidents for the average neighborhood; it would take nearly 72 additional instances of disorder in order to increase the violent crime rate by 1 in a neighborhood of average population size. As in the unconditional models, we find no evidence of a reciprocal relationship: the total violent crime rate at time 1 is not significantly associated with the rate of disorder at time 2. Notably, spatially proximate disorder and violent crime do not significantly impact violent crime or disorder in the focal neighborhood. This is contrary to the extant neighborhood research within a social disorganization framework that has found that characteristics of nearby neighborhoods matter (e.g., Bellair, 2000; Hipp et al., 2009a; Sampson and Raudenbush, 2004). We next analyzed the reciprocal relationship between violent crime and disorder while including explanatory variables associated with social disorganization theory (Model 2, Table 4) and then including spatially lagged versions of these same variables (Model 3, Table 4) in order to isolate the impact of the spatial variables.14 There are several important conclusions from these models. First, when the exogenous variables are included in the models, evidence of a significant reciprocal relationship that was not shown in previous models emerges. That is, increases in the violent crime rate significantly increase future disorder (b = 1.19, p < 0.05) and the impact of disorder continues to be a significant predictor of future violence (b = 0.008, p < 0.05). Second, none of the spatially lagged variables are significant. The results suggest that relationship between disorder and crime is more localized to the conditions in the focal neighborhoods rather than the surrounding neighborhoods. Third, as expected, the results show that some social and structural conditions significantly affect both future increases in the rate of violent crime and disorder (Model 3). We find that neighborhoods with higher levels of disadvantage have significantly greater rates of disorder (b = 2.76; p < 0.05) and future violent crime (b = 0.06; p < 0.05). Casinos are related to higher rates of disorder (b = 322.5; p < 0.05) and the rate of violent crime (b = 6.83; p < 0.05). Residential instability verges on significance, but only for the rate of violent crime (b = 0.05, p < 0.10). Contrary to expectation, the racial composition of the neighborhood is not a significant predictor of violent crime or disorder. Since prior research has suggested that the relationship between disorder and crime may be differential based on the type of crime, we next separated total violent crime into the rates of assaults (Model 1) and robberies (Model 2). We present only the results from the full model including spatial variables. Differences between the types of crime emerge in Tables 5. First, we see that disorder positively and significantly increases both the aggravated assault rate (b = 0.006; p < 0.05) and the robbery rate (b = 0.004; p < 0.05), but that the reciprocal relationship is specific to the rate of aggravated assaults on disorder (b = 2.72; p < 0.05). This translates into an increase of 0.011 aggravated assaults and 0.007 robberies for each additional 1 unit increase in the rate of disorder. On the other hand, an increase of one in the aggravated assault rate is associated with an increase of nearly 4 additional instances of disorder. Second, although none of the spatial variables are significant predictors in the robberies models, the influence of spatial disorder on aggravated assaults approaches significance (b = 0.005, p < 0.10). Thus, these findings show (1) consistent evidence that disorder leads to future violent crimes, though (2) the evidence of a reciprocal relationship found previously is driven by the rate of aggravated assaults on disorder, and (3) that in most cases the characteristics of nearby neighborhoods are not influential on disorder or crime in the focal neighborhood. 13 This is calculated as follows. The average violent crime rate is 4.80 per 1000 residents, which translates into 8.40 crimes for the average neighborhood with a population of 1750. Increasing the violent crime rate to 4.807 translates into 8.412 crimes per neighborhood. The difference between 8.40 and 8.412 represents the average increase in the number of violent crimes for each one unit increase in disorder. To determine the number of crimes needed to increase the rate of disorder by 1, we divided 1 by 0.012. All other translations of rates into actual numbers of events follow this analysis. 14 In ancillary models we included a measure of the percent vacant housing units as an explanatory variable, but since vacancy has also been used as a measure of disorder by other researchers (e.g., Braga and Bond, 2008; Weisburd et al., 2006) we report models without vacancy. In these models, the pattern of results is largely the same with the exception that vacancy suppresses the significant effect of disadvantage. We thank an anonymous reviewer for pointing this out.

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Table 4 Conditional cross-lagged models between disorder and total violent crime with spatial lags and exogenous variables (standard errors in parentheses) (N = 117).

Disorder time 1 Violent crime time 1 W disorder time 1 W violent crime time 1 Disadvantage Residential instability Percent non-White Casinos W disadvantage W instability W percent non-White R2 v2 (df) p SRMSR RMSEA CFI

Violent crime

Disorder

Time 1

Time 1

Time 2 *

0.008 (0.002) 0.63* (0.10) 0.004 (0.006) 0.29 (0.29)

Time 2

Model 2: Including time-invariant exogenous variables Violent crime

Disorder

Time 1

Time 1

*

Time 2 *

0.96 (0.04) 1.21 (1.68) 0.11 (0.11) 2.07 (4.82)

0.68

0.93 9.05 (1) p = 0.003 0.0045 0.2669 0.9924

0.31


0.31 216.25 (18) p < .001 0.0573 0.1176 0.9428

0.96

Disorder Time 2

Time 1

*

0.96 (0.02) 1.19* (0.60) 0.11 (0.11) 2.07 (4.82) 3.83* (1.18) 1.46 (1.24) 0.48 (1.13) 301.1* (76.52)

0.78

Time 1

*

0.008 (0.001) 0.63* (0.04) 0.004 (0.006) 0.29 (0.29) 0.08* (0.03) 0.05 (0.03) 0.02 (0.02) 6.60* (1.94)

Model 3: Time-invariant exogenous variables and spatially lagged exogenous variables

0.008 (0.001) 0.63* (0.04) 0.004 (0.006) 0.29 (0.29) 0.06* (0.03) 0.05 (0.03) 0.01 (0.04) 6.83* (1.95) 0.001 (0.001) 0.005 (0.06) 0.002 (0.06) 0.32

0.78

2.76* (1.39) 1.31 (1.31) 1.27 (1.50) 322.5* (76.09) 0.002 (0.003) 2.20 (2.50) 0.02 (2.60) 0.34 216.04 (24) p < .001 0.0544 0.0804 0.9428

Time 2 0.96* (0.02) 1.19* (0.74) 0.11 (0.11) 2.07 (4.85)

0.97

Notes: Hu and Bentler (1999) identify the following cutoff values for a good fit in an SEM model: standardized root mean square residual (SRMSR) 6 .08; for the root mean square error of approximation (RMSEA) 6 .06; and for the Bentler comparative fit index (CFI) > .95. There is some disagreement about excessive reliance on fit indices as well as the appropriateness of certain fit indices for longitudinal models or models with few degrees of freedom (see Hayduk et al., 2007; Kenny et al., 2011). p < 0.10. * p < 0.05.


Model 1: Spatially lagged dependent variables only

Table 5 Conditional cross-lagged models between disorder and aggravated assaults or robberies with spatial lags and exogenous variables (standard errors in parentheses) (N = 117). Model 2: Robbery rate

Agg. assault Time 1 Disorder time 1 Crime time 1 W disorder time 1 W crime time 1 Disadvantage Residential instability Percent non-White Casinos W disadvantage W instability W percent non-White R2 v2 (df) p SRMSR RMSEA CFI

Disorder Time 2

Time 1

0.006* (0.001) 0.53* (0.04) 0.005 (0.003) 0.02 (0.19) 0.06* (0.03) 0.02 (0.03) 0.01 (0.03) 8.03* (1.57) 0.001 (0.001) 0.03 (0.05) 0.03 (0.03) .34 0.0413 0.1207 0.9808

Robbery Time 2

2.76* (1.39) 1.31 (1.31) 1.27 (1.50) 322.5* (76.09)

.35

Time 1

0.94* (0.02) 2.72* (0.88) 0.08 (0.08) 0.92 (4.20)

0.001 (0.001) 2.20 (2.50) 0.02 (2.60) .97 147.65 (18) p < .001

Disorder Time 2

0.04* (0.02) 0.04* (0.02) 0.01 (0.03) 4.30* (1.34)

.78

Time 1

0.004* (0.001) 0.75* (0.04) 0.004 (0.004) 0.33 (0.27)

0.001 (0.001) 0.01 (0.04) 0.01 (0.05) .34

Time 2 0.98* (0.02) 0.74 (1.09) 0.08 (0.10) 1.18 (6.50)

2.76* (1.40) (1.31) (1.31) 1.27 (1.50) 322.5* (76.01)

.30

0.02 (0.02) 2.20 (2.50) 0.02 (2.60) .97 150.27 (24) p < .001

.80

0.0408 0.8400 0.9546

Notes: Hu and Bentler (1999) identify the following cutoff values for a good fit in an SEM model: standardized root mean square residual (SRMSR) < .08; for the root mean square error of approximation (RMSEA) < .06; and for the Bentler comparative fit index (CFI) > .95. There is some disagreement about excessive reliance on fit indices as well as the appropriateness of certain fit indices for longitudinal models or models with few degrees of freedom (see Hayduk et al., 2007; Kenny et al., 2011). p < 0.10. * p < 0.05.


Model 1: Aggravated assault

179

180


The effects of the social and structural variables largely remain the same as those for total violent crime. One difference is that residential instability significantly influences the robbery rate (b = 0.04, p < 0.05) but not the aggravated assault rate. In both Models, concentrated disadvantage and the presence of casinos are significantly and positively related to rates of disorder, aggravated assault, and robberies. Spatial variables remain non-significant.

5. Discussion and conclusions We set out to more fully model the relationship between disorder and violent crime over time. Specifically, we were interested in estimating both the predicted effect of disorder on future violent crime as well as simultaneously accounting the potential reciprocal impact of violent crime on disorder. We were also interested in examining the neighborhood factors related to crime within a social disorganization theory framework and determine if those factors also predicted disorder (as argued by Sampson and Raudenbush, 1999). In doing so, we moved beyond existing research by incorporating the influence of geographically proximate neighborhoods. In line with Wilson and Kelling (1982), we find consistent evidence that the rate of disorder leads to increases in violent crime in the following year: as untended disorder accumulates, an area becomes more vulnerable to crime. When we control for neighborhood factors such as concentrated disadvantage, residential instability, racial/ethnic composition and casinos, we found that it would take the accumulation of nearly 84 instances of disorder to lead to an increase of one violent crime. The associated increases for aggravated assault and robbery on their own were smaller. This finding holds even while controlling for the potential reciprocal effect of crime on disorder and the exogenous neighborhood factors; it clarifies one-half of the disorder-crime nexus in that we find evidence of Wilson and Kelling’s (1982) contention that disorder is a direct antecedent to more serious crime, though we caution that the effect size is modest and that for robbery it would require 143 additional instances of disorder to increase the robbery rate by one. We also investigated whether crime itself can lead to increased disorder and found that the total violent crime rate leads to future increases in the disorder. Interestingly, this finding was not revealed in the unconditional models but only in the analysis including exogenous variables. This suggests some community social and demographic conditions interplay with the crime rate to affect disorder, likely through an impact on fear of crime (which our analysis could not capture). Ancillary models testing the interaction of the violent crime rate in the prior year with the social variables (not shown), however, found no significant effect on future disorder. Unlike prior studies that tend to find unique results for robbery (i.e., Sampson and Raudenbush, 1999; Skogan, 1990), we found that association between violent crime and disorder is largely driven by the rate of aggravated assaults. This relationship is shown in the unconditional models and continues to be significant even after controlling for neighborhood conditions and spatial effects. This suggests that aggravated assaults – typically considered an expressive crime – differentially impacts neighborhoods than robbery. It is likely that aggravated assaults lead to disorder when residents, fearful of victimization, spend an increasing amount of time inside their home rather than out to monitor behavior on the street. This lack of concern – originally specified by Wilson and Kelling (1982) to lead to more serious crime – also translates into more disorder. Furthermore, increases in violent crime negatively impact neighborliness or trust between residents. It has been previously shown that increases in homicide (Messner et al., 2004) and total violent crime (Garcia et al., 2007) have a detrimental impact on resident levels of trust, even when controlling for the reciprocal influence of trust on crime. This lack of trust could contribute to an increased awareness of disorder, leading residents to be more skeptical and judging of the behaviors of people in the area and call the police more often. This could especially be the case for assaults committed by strangers who do not reside in the neighborhood; in which case, a resident may be more inclined to identify an outsider as a suspicious person and notify police. Relatedly, aggravated assaults could spur residents into becoming more aware and more involved in the social environment (e.g., Taylor, 1996) and consequently more aware of disorder. Prior studies investigating the relationship between disorder and crime at the neighborhood level have ignored the potential impact of space. Given that neighborhood effects research within a social disorganization framework has established the influence of geographically proximate areas on focal neighborhood factors such as racial/ethnic change (Hipp et al., 2009a; Morenoff and Sampson, 1997) and fear of crime (Brunton-Smith, 2011; Brunton-Smith and Sturgis, 2011), we included spatially lagged variables of all predictor and dependent variables in the model. We found little evidence that spatially proximate rates of disorder or spatially proximate rates of crime contribute to those factors within the focal tract. The one exception is the influence of nearby rates of disorder on future aggravated assault rates. The only other study of disorder and crime that included a measure of spatial effects also found limited evidence that space matters: Cheong (2012) found that spatial effects were only present in the robbery models and not burglary. The lack of spatial effects is surprising given extant neighborhood level research, but is in line with Wilson and Kelling’s original development of broken windows that almost exclusively refers to internal actors. It also indicates that the disorder-crime nexus operates at a much more localized scale than other community factors. That is, residents make decisions about crime (and subsequently their fear of crime) and disorder based primarily on their immediate surroundings. This is consistent with recent research on micro-places and crime hot spots (St. Jean, 2007; Weisburd et al., 2012), which emphasizes even smaller units of analysis – blocks – to examine place-based patterns of crime. The lack of significant spatial effects also differentiates the incivilities thesis from social disorganization theory, which has consistently found the neighborhood characteristics of nearby areas


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contribute to the crime rate in the neighborhood of interest. Given the debate between these two perspectives, we find this a particularly important result. Given the localized relationship between disorder and crime, it is surprising that few neighborhood factors significantly impact crime or disorder. We found little evidence that residential turnover or racial/ethnic composition play a role in producing violent crime or disorder, though we did found consistent evidence that the presence of casinos and concentrated disadvantage leads to more disorder and more crimes. This suggests that both crime and disorder have similar structural roots as postulated by Sampson and Raudenbush (1999). Sampson and Raudenbush’s (1999) simultaneous model also failed to find many neighborhood context predictors of the homicide rate or observed disorder once accounting for reciprocal effects. In fact, in their reciprocal model, only collective efficacy and residential stability significantly predict homicide and only disadvantage, immigration, land use, and collective efficacy affect disorder (Sampson and Raudenbush, 1999). Our findings also only partially support Sampson and Raudenbush’s (2004) contention that neighborhood disadvantage and minority concentration influence the perception of disorder and therefore account for high rates of disorder. In line with Sampson and Raudenbush (2004) we find that greater concentrated disadvantage is associated with increased rates of violent crime and disorder. Contrary to their argument, however, we found that although the neighborhood percent of non-White residents is significantly correlated with disorder, we found no evidence that the racial/ethnic composition of the neighborhood leads to more disorder. This could be because the racial composition in Reno is different than the locations of many other social disorganization and disorder studies: the minority population in Reno is primarily Latino and not Black. Given that Latinos are more likely to be integrated with and/or live closer to white residents (see Iceland et al., 2002), it could be that Reno does not have any of the highly disadvantaged and highly minority neighborhoods that exist in Los Angeles, Chicago, and other large cities used in prior studies. We encourage other researchers to continue extending both social disorganization and disorder research into more diverse cities. Although our study addresses two important weaknesses in the current disorder-crime research, the lack of accounting for potential reciprocal effects and ignoring the role of geography, there are some limitations that need to be acknowledged. First, our analysis is unable to account for the social-psychological effects of disorder. Prior work has suggested that the link between disorder and crime is through increased fear (e.g., Brunton-Smith and Sturgis, 2011; Covington and Taylor, 1991; Skogan, 1990; Taylor and Hale, 1986). That is, in highly disordered neighborhoods residents perceive disorder as a proxy for crime and withdraw into their homes, leaving the streets unguarded from predatory crime. Future work should continue to assess the reciprocal relationship between disorder and crime but incorporate measures of fear. Second, we relied on data provided by the police, which can introduce other concerns, most notably the fact that many crimes are never reported to police. In addition, we rely on calls for police service to measure disorder. Calls for service may vary based on the type of neighborhood; in disadvantaged or high crime neighborhoods residents may have a greater tolerance of disorder and limit calls for service for more serious incidents, in which case disorder in disadvantaged or crime-ridden neighborhoods may be underestimated. Likewise, calls for service may be influenced by other factors such as trust in the police, the ability of residents to handle questionable situations on their own (collective efficacy), and the presence of overly cautious individuals who call the police frequently. Third, our analysis is limited to two years of data, which may not be a sufficient time period to fully examine the relationship between disorder and crime. Fourth, by combining our operationalization of disorder to include both physical and social indicators we may be masking differential impacts of social disorder versus physical cues. Prior research (i.e., St. Jean, 2007; Yang, 2010) suggests that social disorder has a stronger association with crime, though the difference may be negligible (Yang, 2007). Our findings suggest that the disorder-crime nexus requires substantially more research. We do, however, find that the influence of spatial effects largely does not matter which further validates the results of existing studies that have not included spatial effects. The most common interpretation of the broken windows thesis focuses primarily on the premise that disorder leads to future increases in more serious crime; an association that our analysis supports. Though a few studies acknowledge that those increases may subsequently lead to more disorder, our results indicate that it may only be necessary to account for the reciprocal effect in the analysis of aggravated assaults, though this too should be interpreted with caution given that our findings contradict the established relationship between robbery and disorder (Sampson and Raudenbush, 1999; Skogan, 1990). We believe that additional data looking at this relationship over a timespan greater than two years will determine whether or not the disorder – crime link needs to be specified in both directions. Likewise, many incivilities researchers have argued that the association between disorder and crime is through fear; subsequent analysis should incorporate levels of fear of crime and changes in fear of crime into a similar model. Acknowledgments The authors wish to acknowledge the gracious assistance of Alyssa W. Chamberlain and the helpful feedback from three anonymous reviewers. 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