The Pennsylvania State University the Graduate School Department of Criminology the IMPACT of SPATIAL SEGREGATION and ISOLATION

The Pennsylvania State University The Graduate School Department of Criminology

THE IMPACT OF SPATIAL SEGREGATION AND ISOLATION ON VIOLENT

VICTIMIZATION

A Thesis in Criminology by Rebecca A. Bucci

Submitted in Fulfillment of the Requirements for the Degree of Master of Arts

December 2017 ii

The thesis of Rebecca A. Bucci was reviewed and approved* by the following:

Eric Baumer Professor of Criminology Head of the Department of Criminology Thesis Adviser

Corina Graif Professor of Criminology

Jeremy Staff Professor of Criminology

*Signatures are on file in the Graduate School

iii

ABSTRACT

Extant research and theory have highlighted the deleterious consequences of segregation and isolation, such as increased poverty rates, joblessness and victimization.

These consequences are often thought to occur in disadvantaged and minority neighborhoods; however, recent research has suggested that segregation may lead to increased crime rates in even the more advantaged communities. While there has been a host of studies that have examined the relationship between segregation and crime, they have had several limitations.

The following study addresses these limitations by using measures of spatial segregation, as opposed to more commonly used a-spatial measures, as well as examining National Crime

Victimization Survey data, which allows for the study of crimes beyond simply homicide, and does not suffer from many of the limitations of police-based crime data. This study also examines many layers of victimization, by first focusing on overall assault victimizations, followed by race-specific victimizations, and lastly, differences in intra and interracial assaults. Results first conclude that the effects of segregation on assault victimization risk in general are non-significant or weakly related, suggesting that the effects of segregation differ by crime type. Additionally, the examination of racially disaggregated assault victimization suggests that isolation has a significant and negative effect on minority assault victimization, a finding contrary to prior segregation and homicide research, as well as theoretical expectations. Lastly, this study finds that spatial segregation increases the likelihood of interracial assaults, however, spatial isolation has the opposite effect on intraracial assaults.

TABLE OF CONTENTS

List of Tables …………………………………………………………………………...... v

List of Figures……………………………………………………………………………....vi

Introduction………………………………………………………………………………….1

Theoretical Background……………………………………………………………………..4

Hypotheses………………………………………………………………………………...... 8

Measuring Racial Segregation……………………………………………………………...10

Prior Research on Segregation and Crime………………………………………………….14

Contributions of the Present Study………………………………………………………....25

Methodology………………………………………………………………………………..26

Data and Sample…………………………………………………………………....26

Measures…………………………………………………………………………....28

Analytic Strategy…………………………………………………………………....33

Results………………………………………………………………………………………36

Further Analyses……………………………………………………………………………55

Discussion…………………………………………………………………………………..56

Conclusion………………………………………………………………………………….61

References…………………………………………………………………………………..64

Appendix A: Correlations…...…………………………………………….………………...69

Appendix B: Metropolitan Statistical Areas……..……………………….…………………70

Appendix C: Spatial Isolation by Metropolitan Statistical Areas……………………………71

LIST OF FIGURES

Figure 1 – Non-Hispanic White to Non-Hispanic Black Spatial Segregation by Metropolitan

Statistical Area…………………………………………………………………………….39

Figure 2 – Non-Hispanic White to Hispanic Spatial Segregation by Metropolitan Statistical

Area……………………………………………………………………………………..…40

LIST OF TABLES

Table 1 – Variables Used in Analysis…………………………………………………....32

Table 2 – Descriptive Statistics…………………………………………………………..37

Table 3 – Hierarchical Logistic Regressions of Segregation on Overall Assault

Victimization……………………………………………………………………...43

Table 4 – Hierarchical Logistic Regressions of Spatial Segregation on Overall Assault

Victimization………………………………………………..…………………….44

Table 5 – Hierarchical Logistic Regressions of Spatial Segregation on Non-Hispanic Black

Assault Victimization………………………………………..……………………49

Table 6 – Hierarchical Logistic Regressions of Spatial Segregation on Hispanic Assault

Victimization………………………………………………..…………………….50

Table 7 – Hierarchical Logistic Regressions of Spatial Segregation on Non-Hispanic White

Assault Victimization……………………………………………………………..52

Table 8 – Hierarchical Multinomial Regressions of Spatial Segregation on Intraracial &

Interracial Assaults ……………………………………………………………….54

Introduction

Over a half century has passed since the Fair Housing Act was enacted, yet many

Americans still live in racially segregated communities. Residential segregation refers to the phenomena when two or more racial or ethnic groups live apart from each other in a given locale (Massey & Denton 1988). For many, residential segregation means social and economic isolation, which often leads to increased poverty and unemployment and decreased access to resources. For many segregated communities, social and economic isolation is coupled with increased crime and victimization risk. However, recent research has concluded that even for those communities that are not socially or economically isolated, segregation still has deleterious effects. Recent research has found that city-level segregation is related to increased crime for all neighborhoods, including both white and nonwhite communities

(Krivo et al. 2009). Unfortunately, these effects are exacerbated and more widespread for minorities; segregation levels have consistently been higher for racial and ethnic minorities

(Krivo et al. 2009), as has violent victimization risk (Peterson & Krivo 1993; Peterson &

Krivo 2005). Despite changes in legislation, residential segregation remains a prominent fixture in many parts of the country. Unfortunately, despite the prevalence of residential segregation, there has been a lack of research on the effect of segregation for black, white and

Hispanic violent victimization, aside from homicide. This study fills this gap by exploring the effect of segregation on non-lethal, racially disaggregated assault victimization.

While prior research has contributed to our understanding of the relationship between segregation and crime, the conclusions that can be drawn remain ambiguous for five key reasons. First, and most importantly, previous studies have uniformly employed “a-spatial” measures of segregation that ignore the spatial distribution of residents within the population 2

(see Reardon & O’Sullivan 2004). These past measures have failed to capture the social environment of each individual and instead have focus solely on the racial composition of an individual’s census tract. Second, much of the extant research has focused on lethal violence, which represents a very small portion of the crime to which Americans are exposed. As homicide accounts for only 1% of violent crime, it is difficult to generalize studies of homicide to overall violent victimization risk. Third, when research has expanded beyond homicide victimization, it has relied primarily on police-based data on arrests made or on crimes reported by citizens. The percentage of violent crimes reported to police, though rising remains at around 50% (Lauritsen & Schaum 2005; Truman & Morgan 2016). As a result, the overreliance on police-based data may yield misleading results. For example, recent research has highlighted the divergence between the number of aggravated assaults in the Uniform

Crime Report, a police-based dataset and the National Crime Victimization Survey (Lauritsen

& Schaum 2005; Lauritsen et al. 2016). In addition, racial segregation may influence both citizen decisions to report crimes to the police (Xie & Lauritsen 2012) and police arrest decisions, creating further ambiguity. Fourth, much of the existing research focuses on aggregate crime rates, without distinguishing by victim race. When research has examined racially disaggregated crime, attention is typically limited to blacks and whites. Hispanics currently make up approximately 17% of the population, (U.S. Census Bureau 2008) while accounting for a similar percentage of violent victimization (Truman & Morgan 2016). In addition to the fact that Hispanics make up a larger percentage of the population than blacks,

Hispanic segregation has been rising in recent years (Logan et al. 2004; Wilkes & Iceland

2004). It is no longer acceptable to disregard such a large percentage of residents and victimizations in this country, especially in studies of segregation’s effects. Finally, the 3 majority of research has not examined differences in inter- and intra-racial crime, despite the theory and research that suggests the impact of segregation differs notably across these two forms of crime (Blau 1977; Sampson 1985; Messner & South 1986; South and Felson 1990).

The majority of research either fails to disaggregate victims or offenders by race entirely, or only examines the race or ethnicity of the victim or offender.

This study will improve upon past segregation research by acknowledging and moving beyond these five limitations. Recent developments in the measurement of spatial segregation will allow for a more nuanced look at the effects of segregation on victimization. Using measures of spatial segregation, as opposed to the misspecified a-spatial measures of the past, will allow for a more conclusive understanding of the effect of segregation on victimization risk. This study will therefore account for the physical distribution of individuals across space

(Reardon & O’Sullivan 2004). By utilizing recent National Crime Victimization Survey data, this study will more accurately capture victimization risk as it relies on victim accounts as opposed to official crime statistics. The use of NCVS data will also allow for examination of nonlethal crimes, instead of homicide. These are two necessary elements of segregation and victimization research that have been ignored in the past. No segregation study has utilized victimization data in this manner, making this study the first to use a nationally representative sample, which includes unreported crimes, to study the effects of segregation on crime. The use of NCVS data will also allow for racially disaggregated victimization, by non-Hispanic black, non-Hispanic white, and Hispanic. This unique application of victimization data will lastly explore intra and interracial crime, which is generally overlooked in segregation research due to data limitations. 4

This study will proceed by first exploring the effect of spatial segregation on overall violent victimization risk across 40 metropolitan statistical areas (MSAs) for the years 2000-

2004. Secondly, this study will examine segregation’s effect on victimization risk by race and ethnicity, as well as examining how this effect may be exacerbated by racial differences in socioeconomic status. Lastly, this study will examine the differential effect of segregation on intra and interracial crime for blacks and whites.

Theoretical Background

There are typically three avenues to explain segregation’s deleterious impact on crime and victimization. The first approach explains segregation’s effect on criminal offending and works to explain overall crime rates. The second approach examines the effect of segregation on offending and victimization risk for different racial and ethnic groups, as well as for those belonging to different socioeconomic classes. The last approach is concerned with how segregation impacts the nature of violence that occurs by examining the differences in intra and interracial crime.

One of the oldest theories typically employed to explain the relationship between segregation and offending is Merton’s social structure and anomie theory. Merton’s theory explains the relationship between segregation and crime rates through inequality (Merton

1938). Those living in segregated communities are often isolated from resources, including jobs, social capital, and adequate housing and schooling. Therefore, inequality results in frustration for those living within segregated communities due to an inability to achieve desired goals as a result of decreased resources. This frustration and alienation is believed to result in increased crime. This theoretical approach is often coupled with General Strain 5

Theory (GST) to further explain why isolation from mainstream society and decreased access to jobs and resources would result in criminal behavior (Agnew 1985). These two theories help to explain why segregation will have negative consequences for community members and why crime rates will be highest within these segregated and isolated communities. These theories are generally used to explain the effects of class segregation on crime, and do not disentangle the relationship between class and race.

In an effort to explain racial segregation explicitly, recent theories have begun to focus on the impact of segregation for minority group members, rather than the population as a whole. This second approach integrates strain theories with insights from urban sociology and emphasizes how segregation may increase exposure to criminal victimization, mainly for minorities (e.g., Wilson 1987; Massey and Denton 1993). Wilson, as well as Massey and

Denton, focus on the lack of access to resources that stem from segregation; however, they are focused on the differences in segregation and inequality by race. In The Truly Disadvantaged,

Wilson (1987) argued that as higher income blacks were able to move into more desirable communities, low-income blacks were left in isolation lacking the resources and institutions that others enjoy. This isolation is also problematic because many isolated individuals lack the ability to interact with more economically and socially advantaged persons living in more

‘stable’ parts of the community (Wilson 1987). When minorities are socially isolated, this often includes decreased job opportunities, decreased levels of social control - both formal

(police protection) and informal (community social control), and weakened family structure and political power. These factors have all been found to have significant negative effects for victimization risk and exposure to crime. While Wilson’s theory focuses on isolation and concentrated disadvantage for blacks, it does not explicitly address the importance of racial 6 segregation. Wilson’s theory instead focuses on segregation as an outcome of concentrated disadvantage instead of a cause.

Alternatively, Massey and Denton (1993) use segregation to explain the relationship between race and concentrated poverty. Referring to segregation as the “missing link” in theories of racial inequality, Massey and Denton define residential segregation as “the institutional apparatus that supports other racially discriminatory processes and binds them together into a coherent and uniquely effective system of racial subordination” (Massey &

Denton 1993, p. 8). This combination of factors is especially detrimental for minority groups

(Massey 1990; Massey & Denton 1993). As a result of institutionalized discrimination, prejudice, and discriminatory public policies that result in segregation, minorities experience increased unemployment, single parent households, lower incomes and even infant mortality

(Massey 2001). Unlike Wilson’s approach, Massey and Denton view segregation as the driving force behind concentrated disadvantage. As opposed to Wilson who views segregation as a result of concentrated poverty, this approach better explains why segregation itself has such negative effects for communities.

The above approaches, while differing on the causal ordering of segregation’s effect, all highlight the role of social and economic isolation. It is believed that when segregation and isolation go hand-in-hand, the consequences are greatest. These theories are useful in explaining why segregation may increase exposure to crime and violence; however, they do little to explain the nature of that violence. For instance, they fail to explain if frustration and isolation lead to out-group or in-group violence. The previous theories are also concerned with the indirect effects of segregation for exposure to violence, (typically mediated by socioeconomic status), but this final approach emphasizes the potential direct effect of racial 7 segregation (Blalock 1967; Blau 1977). By examining the role of community context and highlighting intergroup contact, Blau’s theory focuses on how segregation changes the likelihood of contact between potential offenders and victims, resulting in differences in within race (intraracial) and between race (interracial) crimes. Blau’s (1977) theory of social structure was one of the first theories to explicitly examine how the racial context of the community can play a role in intergroup relations. Despite the benefits of intergroup contact that Blau acknowledged, intergroup contact can have negative consequences as well. Blau proposed that as intergroup contact increases, so would intergroup conflict (see also Nagel

1995). It is believed that segregation will increase contact for same race individuals, while decreasing contact amongst different races (Farley & Frey 1994). Based on this theory, it is anticipated that segregation will decrease interracial crime simply as a function of decreased contact, as well as increasing intraracial crime for the same reason.

Similarly, other theories exist to explain why segregation may have differential effects on intra and interracial crime. While the above theories of anomie and strain, as well as concentrated disadvantage and isolation, are useful to explain why social isolation may increase crime rates, these theories fail to explain why segregation of whites, who are often not socially isolated, may also increase crime. Racial threat theory can be useful to explain why racial segregation may increase offending and victimization for more privileged whites.

As opposed to Blau’s theory of intergroup contact, which states that interracial crime will increase simply do to increased contact, racial threat theory further explains this relationship.

As the minority population increases, whites may become threatened and retaliate (Blalock

1967). Alternatively, the contact hypothesis proposes that increased contact between racial groups leads to decreased crime and conflict (Allport 1979). These theories and prior 8 literature suggest contradictory effect, making it still unclear how segregation effects the likelihood of intra and interracial assault.

The above theories suggest that segregation has a positive relationship with increased crime rates but there are many nuances about the effects of segregation for each racial group.

Furthermore, these effects for each racial group may differ based on the socioeconomic status of that group. This paper will explore the effects of segregation and isolation, as well as socioeconomic status, on overall, racially disaggregated and intra and interracial assault victimization.

Hypotheses

Incorporating theories of isolation and intergroup contact leads to several different hypotheses about the impact of racial segregation on victimization risk. First, structural strain theories suggest that racial segregation in general, and more specifically racial isolation, should have deleterious effects on overall victimization. Because this theory is focused on the importance of social and economic isolation for crime, I hypothesize that segregation and isolation will increase overall victimization risk for residents of segregated MSAs. I also hypothesize that the effects of isolation on assault victimization risk will be larger, given that isolation is more relevant for the economic disadvantage of minority groups.

H1) Racial segregation and racial isolation will increase overall victimization risk, with isolation having a larger effect.

Second, drawing on theories of urban dislocation (Wilson 1987; Massey and Denton

1993), segregation should have differential effects for different racial groups. I hypothesize that segregation and isolation will increase victimization rates for blacks and Hispanics, while offering a protective function for whites.

H2) Racial segregation and isolation will increase non-Hispanic black and Hispanic victimization while decreasing non-Hispanic white victimization risk.

Finally, Blau’s (1977) theory leads to the hypothesis that segregation and isolation will increase intraracial crime while simultaneously decreasing interracial crime for blacks and whites.

H3) Racial segregation and isolation will increase intraracial crime and decrease interracial crime.

Measuring Racial Segregation

Before discussing the innovation of this study and the newly developed measures that will be employed in assessing the impact of segregation on victimization risk, it is important to understand how segregation has been measured and defined in the past. While there are many different dimensions used to measure segregation, these indexes are a-spatial in nature.

This means that past measures have not explicitly accounted for the distribution of individuals across space (Reardon & O’Sullivan 2004). This issues manifests into two common problems, the checkerboard problem and the Modifiable Areal Unit Problem (MAUP). The checkerboard problem implies that the same level of segregation (as measured by commonly used indicators such as the dissimilarity or isolation indices), could either resemble a checkerboard, where different racial groups are scattered across space, or an area where different racial groups are evenly divided into two homogenous groups. The MAUP, on the other hand, arises when adjacent neighborhoods are considered just as different or distant as those on the other side of the city due to the designations of census tracts or blocks. When a tract ends on one side of the street, residents whom we would typically describe as neighbors in everyday language become separated into two distinct census designations. This categorization makes a neighbor who is steps away from another resident equal to a resident on the other side of town. This issue is the most problematic for studies concerned with exposure or isolation, as one is considered to have limited exposure to residents who may in fact be their neighbors. Unfortunately, nearly all past studies of the effect of segregation on crime have relied on a-spatial measures of segregation, making the use of spatial measures a necessary advancement in the literature. 11

There are five aspects of segregation that have been defined in past research: evenness, exposure, concentration, centralization and clustering (Massey & Denton 1988;

Iceland et al. 2002). The unevenness dimension is the most commonly used to study segregation (Massey & Denton 1988). This measure was designed to measure ‘unevenness’ of different groups; for example, a location would be considered even if the racial composition of each neighborhood matched the overall racial composition of the city or state. In other words, if blacks are in the majority at the city level, they would also have to be in the racial majority within each neighborhood. This makes this particular item a relative measure of segregation, as opposed to an absolute (Massey & Denton 1988). This measure does not account for the relative size of the groups within the overall population. Within the evenness measure there are several indexes (Massey & Denton 1988; Wilkes & Iceland 2004), however, the dissimilarity index (D) is the most commonly used measure in studies of segregation and crime. This index ranges from 0 to 1, where 1 means maximum segregation and zero means that no members of a particular racial group would have to change residence in order to achieve the same racial and ethnic demographics in each unit of space (Wilkes &

Iceland 2004). A similar measure of segregation is the information theory index (H) or entropy index. Unlike the index of dissimilarity, the information theory index does rely in part on the relative size of each group in the population (Massey & Denton 1988).

Exposure is another measure that focuses on the likelihood that individuals of different races would physically interact (Massey & Denton 1988; Wilkes & Iceland 2004). This measure, nearly as common as the index of dissimilarity, typically includes elements of interaction or isolation, with the majority of studies typically focused on the latter (Iceland et al. 2002). Unlike measures of unevenness, exposure and isolation measures account for the 12 relative size of each population within the unit of study (Massey & Denton 1988). This is a useful addition, however, it often results in measures of segregation being highly correlated with control variables accounting for percent black or percent foreign-born. Theoretically, the isolation index is valuable as it highlights seclusion from resources and advantaged others that are believed to be detrimental for communities and their residents. This measure has been cited as especially useful because it attempts to capture the feeling of segregation experienced by minority individuals (Massey & Denton 1988).

While these measures appear to capture the effects of segregation, they fail to account for space. Several other dimensions such as concentration, centralization and clustering, have been developed with the intention of measuring the actual spatial distribution of residents, however, even these measures fall short. Concentration examines how much space is actually taken up by each minority group. Similar to the dissimilarity index, this measure captures what percentage of a group would have to change locations in order for the population density to be the same across all spaces within a larger unit (Wilkes & Iceland 2004). Next, centralization measures the distance to the urban center, and is important because racial and ethnic minorities are often relegated to the poorer, older and less desirable urban centers

(Massey & Denton 1988). Lastly, clustering focuses on how proximate minority communities are to one another (Iceland et al. 2002). These measures, while intended to capture the spatial nature of segregation, are often analogous to the measure of unevenness, yet simply measured at a different unit of analysis. For example, Reardon & O’Sullivan (2004, p. 125) explain that when unevenness is measured at the census tract and clustering is measured at the block group, these measures are indistinct. 13

Some scholars have acknowledged that groups may be segregated on more than one of these dimensions, defining this as hypersegregation (Massey and Denton 1989). They have found that blacks are the most segregated across all five of these measures, with the culmination of these different aspects having the highest negative impact. Regardless of this finding, most studies typically focus on only one or two of these dimensions (Wilkes &

Iceland 2004). However, even when all five measures of segregation are included in a study, failure to account for the spatial distribution of individuals has led to incomplete and misleading results.

Recent research has begun to point to the importance of adjacent communities for examining the effects of segregation on crime (Krivo et al. 2015). In the communities and crime literature, it has become clear that, while census tracts are important units of measurement, failing to account for adjacent communities decreases neighborhood effects

(Mears & Bhati 2006). Similarly, using a-spatial measures of segregation fail to account for the “social environment of each individual,” which may have led to inconclusive findings about the effects of segregation (Reardon & O’Sullivan 2004, p. 122-123). Reardon and

O’Sullivan (2004) point out that past measures of segregation have succeeded at capturing how different areas, such as census tracts, differ from one another, but have failed to capture how individuals’ environments differ. This means that past measures of segregation have not acknowledged how census tracts are arranged in space, causing them to examine each census tract as independent, disregarding where tracts are located in space and in relation to other tracts. This problem has been acknowledged for over 30 years; however, it has not been until the past decade that a solution has been offered. Reardon and O’Sullivan (2004) tackled these dilemmas by developing comparable measures of segregation that account for the spatial 14 distribution of residents. Unfortunately these measures have yet to be employed in criminology. This study will be the first to account for spatial segregation in a nationally representative sample of victimization.

Prior Research on Segregation and Crime

The prior literature on segregation is generally split into three categories based on the dependent variable; overall crime, racially disaggregated crime, and finally intra and interracial crime. These categories parallel the three research questions of this paper, which first question the effect of segregation on overall victimization, followed by racially disaggregated victimization, and finally intra and interracial victimization. The literature review will proceed in a similar fashion, first exploring past research that has focused on aggregate levels of crime, secondly looking at differences in crime by race, and finally reviewing the limited research on intra and interracial crime.

Segregation and Overall Crime

Several studies have examined the effects of segregation on overall crime. Typically police-based data, including the Uniform Crime Report (UCR) and the National

Neighborhood Crime Study (NNCS), is used to measure crime, along with the index of dissimilarity (D) to measure segregation. Logan & Messner (1987), for example, examined the role of segregation on violent crime using the UCR and the index of dissimilarity. These scholars found that increased racial segregation positively predicted robbery, aggravated assault and the overall violent crime index in suburban areas across the country. More recent research has pointed to similar conclusions, specifically finding that high levels of citywide segregation lead to increases in crime for all neighborhoods. High racial and/or ethnic 15 segregation has been linked to higher crime rates overall, specifically robbery, burglary, and motor vehicle theft (Hipp 2011). This result holds true when examining neighborhoods by different racial composition. For example, violent crime rates are increased for white, black and Hispanic neighborhoods when they are located within a segregated city (Krivo et al.

2009). In fact, a one standard deviation increase in segregation at the city level increased white neighborhood violence by 20%, black neighborhood violence by 15% and Hispanic neighborhood violence by 18% (Krivo et al. 2009). In general, it has been found that segregation increases overall violent and property crime rates.

More detailed research on the effects of segregation on overall crime for neighborhoods of different racial composition has also lent support for the interaction of racial and class segregation. For example, Krivo and colleagues (2015) examined segregation and crime within over 8000 neighborhoods during 1999-2001, finding that segregation has some positive effects for whites. This study utilized spatially weighted measures of black-white exposure to account for the effects of adjacent communities. This study found that local segregation leads to decreases in both property and violent crime in predominantly white and advantaged areas, making segregation “significantly privileging” for whites (Krivo et al.

2015, p. 314).

In fact, this effect appears to be strong enough to even reduce violence in adjacent communities. Employing spatial-lags to examine the effects of adjacent communities revealed that minority communities bordering white neighborhoods benefit from a buffering effect that reduces violence within those minority neighborhoods (Peterson & Krivo 2009). Violent crimes rates in minority and disadvantaged communities are reduced to similar levels of the white and more advantaged communities that surround them. This may be explained by 16 differences in available resources and income between more advantaged white communities and more disadvantaged minority communities (Alba & Logan 1993; Phillips 2002). This point is further highlighted in studies that have found no effect of racial isolation, but rather have found positive effects of class isolation on crime (Shihadeh 2009). These findings offer further support for the mediating effects of inequality for segregation and crime.

The above research suggests that the presence of racial segregation leads to increases in violent crime rates, including robbery, aggravated assault, rape and the overall violent crime index (Logan & Messner 1987; Krivo et al. 2009). Occasionally research has even found that segregation leads to increases in property crime rates as well (Hipp 2011). The reviewed studies highlight the role of social and economic isolation on crime. These effects have been found to differ by the racial composition and socioeconomic status of each neighborhood (Krivo et al. 2009; Peterson & Krivo 2009; Krivo et al. 2015). While the distinctions between neighborhoods of different racial composition is useful for examining the differential effects of segregation, these results can be misleading. By examining overall victimization rates within these communities, it is unclear how victimizations differ by individual’s race, but rather only allow for conclusions to be drawn based on overall neighborhood composition.

While these studies have been largely informative of the effects of segregation on crime, they encompass many of the limitations that this paper will address. First, studies of this nature typically employ a-spatial measures of segregation. The index of dissimilarity, while widely used, fails to account for the spatial distribution of residents, making conclusions about segregation incomplete and misleading. Apart from Peterson and Krivo

(2009) and Krivo and colleagues (2015), who control for the effects of adjacent communities, 17 these studies have ignored spatial effects entirely (Logan & Messner 1987; Hipp 2011; Krivo et al. 2009; Shihadeh 2009). Employing newly developed spatial measures of segregation will address this concern.

A second and obvious limitation of these studies is their use of official crime data. The

Uniform Crime Report and the National Neighborhood Crime Study allow for generalizable results, as they are national and nationally representative datasets, respectively. However, the use of official crime data alone fails to account for crime unknown to the police. These data may be biased by both differences in policing strategy as well as differences in crime reporting. Past research has found contradictory findings for reporting, showing both increased reporting by minorities (Avakame et al. 1999; Baumer and Lauritsen 2010) as well as decreased reporting (Anderson 1999). By basing our conclusions on police-based data alone, it is unclear if differences in reporting are accounting for some, if not all, of the difference in crime rates by neighborhood. The use of victimization data in the current study will correct this limitation.

Third and finally, these studies have focused on crime rates for the general population, without disaggregating by race. This has lead studies of this nature to make statements about the effects of segregation on “predominantly White and advantaged places” (Krivo et al.

2015), without allowing for victimization risk by race. By failing to disaggregate crimes by the race of the victim, these studies should leave criminologists wondering about the effects of segregation for blacks, whites and Hispanics in general, but also about the victimization risk if one is a racial or ethnic minority in a highly segregated locale. Without examining victimization risk by race, it is unclear if these ‘predominantly white’ areas come with decreased victimization risk for all of their residents, or simply those who are white. 18

Disaggregating the race of the victim, as the current study does, will account for this limitation and address these concerns.

Segregation and Racially Disaggregated Crime

The studies reviewed above, which have failed to disaggregate crime by race of either the victim or offender, cannot capture the effects of segregation for different racial groups. It was not until the early 1990s that the use of race-specific measures of violence in segregation studies became commonplace (Peterson & Krivo 2005). While some research has offered results based on the racial composition of the neighborhood (Peterson & Krivo 2009; Krivo et al. 2015), the use of racially disaggregated crime tells a more complete story. The majority of the early research on race-specific crime has once again focused on the index of dissimilarity

(D) as the primary measure of segregation, however, more recent work has expanded to include the isolation index (P*). The majority of these studies, however, has utilized either police-based data, or has focused on homicide exclusively, to avoid the previously mentioned limitations of official crime data.

One of the first studies to examine the differential effects of segregation by offender race concluded that city-level segregation positively predicted increases in white homicide

(Sampson 1985). Contrary to expectations, however, segregation was found to have no impact on black homicide offending. Homicide victimization, however, follows a different pattern.

Use of the Supplementary Homicide Reports (SHR) has revealed that segregation does in fact lead to an increase in black homicide victimization (Peterson & Krivo 1993). A one standard deviation increase in the index of dissimilarity was related to an increase of over 2 homicides per 100,000 blacks. Similar conclusions were reached when the index of dissimilarity was replaced by the isolation index (Peterson and Krivo 1999). Comparable analysis of the SHR 19 revealed that residential segregation was linked to increased African-American homicide offending (Velez et al. 2003), in contrast to results found two decades earlier (Sampson 1985).

The above research has suggested that the effects of segregation were mediated through either social isolation (Peterson & Krivo 1993) or economic isolation for minority group members (Velez et al. 2003). Related to this point, Lee and Ousey (2007) found that disadvantaged blacks’ exposure to advantaged whites (and not disadvantaged whites or advantaged blacks) had significantly reduced homicide rates for disadvantaged blacks (Lee &

Ousey 2007). These scholars concluded that “socially and spatially isolated” black communities have the highest black homicide rates. This suggests that exposure to whites per se, is not as important as exposure to the resources that are often accompanied with white residential areas. In another study focused exclusively on homicide, the centralization of blacks, another measure of segregation meant to capture the effects of segregation for blacks confined to the inner city, was also positively related to homicide exposure (Shihadeh and

Flynn 1996). This focus on inner city centralization again highlights the importance of disadvantage coupled with segregation.

With this in mind, research began to focus not only on the index of dissimilarity (D), but also on the isolation index (P*). For example, it has been found that black isolation was positively related to homicide victimization for black males, but the negative effects of segregation were largest for blacks in locations that were high on both the isolation index and the dissimilarity index (Collins & Williams 1999).

Similarly, Shihadeh and Flynn (1996) used these two measures to study the deleterious effects of segregation for black homicide, as well as robbery, offending. The isolation index was positively related to both types of black offending. Aside from Shihadeh and Flynn, a 20 recent study on the effects of segregation on nonlethal violence for blacks and whites, is the only study to examine segregation’s effect on racially disaggregated crime other than homicide. This study concluded that segregation serves a protective function for whites, while increasing rates of black victimization (Like 2011).

A handful of studies have examined the effects of segregation on Hispanics, as well as blacks (Feldmeyer 2010; Xie 2010). Black and Hispanic isolation has been found to contribute to both black and Hispanic homicide (Feldmeyer 2010). When super-dimensions of segregation (composite measures of several segregation indices) have been used to predict crime, the results are consistent with the above research. Group separateness (a composite measure of unevenness, isolation and clustering) increased victimization rates by 13% for blacks and 18% for Hispanics (Xie 2010). A one standard deviation increase in centralized concentration (which includes simply concentration and centralization) was related to around a 14% increase in both black and Hispanic homicide victimization as well (Xie 2010). A separate study revealed similar findings for the effects of group separateness and centralization on black homicide (Eitle 2009). These studies once again attribute the positive effects of segregation on crime to decreased resources (Xie 2010), a lack of potential job opportunities (Eitle 2009; Xie 2010) or concentrated disadvantage in general (Feldmeyer

2010).

Although the inclusion of the combined dimensions is meant to better account for the spatial effects of segregation, even these dimensions are actually a-spatial in nature. These dimensions are simply dependent on the unit of analysis used to measure them, meaning if the units of analysis were altered, these measures would be indistinguishable from each other

(Reardon & O’Sullivan 2004). Despite this fact, and the overreliance on the index of 21 dissimilarity (Massey & Denton 1988), many scholars have concluded that the measure of exposure/isolation is best for predicting increased crime rates (Shihadeh & Flynn 1996;

Shihadeh & Maume 1997; Collins & Williams 1999; Lee & Ousey 2007). This is justified as it is believed that the effects of segregation are mediated through contextual factors related to social and economic isolation such as unemployment, poverty, weakened social control, family structure and political empowerment (Peterson & Krivo 1993; Shihadeh & Flynn

1996; Shihadeh & Maume 1997; Peterson & Krivo 1999; Feldmeyer 2010).

Regardless of the measure of segregation, these studies have unanimously concluded that segregation leads to increases in homicide and robbery. However, disaggregated offending or victimization data reveals that segregation has negative consequences for racial and ethnic minorities (Peterson & Krivo 1993; Peterson & Krivo 1999; Velez et al. 2003) and may have no effect on white homicide (Peterson & Krivo 1999; Collins & Williams 1999) or may actually decrease it (Ousey 1999). Unfortunately, there is not much research on segregation’s effect on white crime in relation to other racial or ethnic groups. Despite several studies that have concluded that the effect of segregation is racially invariant for blacks and

Hispanics, these studies failed to examine the differential effects of segregation on racial/ethnic minorities compared to whites (Xie 2010, Feldmeyer 2010). The handful of studies that have examined segregations effect on whites and minorities side by side have either revealed that segregation may only be predictive of homicide for minority groups

(Peterson & Krivo 1999; Collins & Williams 1999) or that segregation is in fact not significant for any groups once controls for socioeconomic disadvantage were included

(Phillips 2002). 22

While the above research has moved beyond the study of aggregate crime and victimization rates, there are still many limitations. Similar to studies of overall violent crime, these racially disaggregated studies still suffer from the use of a-spatial measures of segregation. An additional limitation of this group of studies is their focus on solely lethal crime. The limited research that has explored nonlethal crime (Shihadeh & Flynn 1996) unfortunately utilizes arrest data, which may be biased by differences in both policing practices and/or victim reporting. Apart from Shihadeh and Flynn (1996), all of the reviewed studies have examined either homicide victimization or homicide offending. Homicide is a rare event, accounting for only about 1% of violent crime. Although these studies have advanced our understanding of segregation’s effect on homicide, they still suffer from their inability to generalize beyond one, very rare, type of violent crime. While many scholars use homicide as a proxy for violent crime, some studies have revealed that segregation may predict robbery and assault while not having an effect on homicide (Logan & Messner 1987).

Finally, these studies fail to account for the race of either the victim of homicide (Sampson

1985; Velez et al. 2003) or the race of the offender (Peterson & Krivo 1993). Without a combined analysis, it is unclear how segregation may differentially impact intra and interracial crime. The present study will address these methodological issues, by using spatial measures of segregation, and will address data limitations by utilizing the NCVS, which encompasses non-lethal violent crime, and includes not only victim race, but offender race as well.

Segregation and Intraracial/Interracial Crime

Though there are numerous studies that explore the effects of segregation on violent crime, there are only a handful of studies that have asked how segregation differentially 23 affects intra and interracial crime. The original studies of intra and interracial crime have utilized the National Crime Survey (NCS), as it has been one of the only datasets to include demographic information on both victims and offenders. More recently research has begun to use the National Incident Based Reporting System (NIBRS), which, while similar to the UCR, contains additional offender demographics.

One of the first studies to explore differences in intra and interracial crime examined the effect of racial heterogeneity on rape, robbery, assault and larceny (Sampson 1984).

Racial heterogeneity is measured as 1−∑p2i^2, where p2 equals the percentage of the population that is a specific racial/ethnic group (Sampson 1984, p. 625). Although this measure of heterogeneity is not an explicit measure of segregation, it is similar to the isolation/exposure index, which accounts for the likelihood of contact between individuals of different racial or ethnic groups. Sampson (1984) concluded that intraracial crimes “exceeded chance expectations,” meaning there is a tendency for crimes to be intraracial in nature above and beyond what would be expected simply based on contact alone. Despite this tendency toward intraracial crime, interracial crime was heightened with increased racial heterogeneity

(i.e. decreased isolation/increased exposure). Similar conclusions were found using the index of dissimilarity to explain the relationship between segregation and intra and interracial rape

(South & Felson 1990), as well as the relationship between segregation and intra and interracial robberies (Messner & South 1986). South and Felson (1990) concluded that decreased segregation increased interracial rape, and Messner and South (1986) concluded that increased segregation led to increases in intraracial and decreases in interracial, robbery.

These studies support Blau’s theory of intergroup contact; increased intergroup contact, a result of decreased segregation, increases interracial crime. 24

More recent research has made similar conclusions. In a study of interracial homicide, it was found that residential segregation and an increasing black population negatively predicted black on white homicide (Jacobs & Wood 1999). Similarly, a study of intra and interracial assault and robbery concluded that segregation increased black intraracial assaults but not robbery and that segregation had no effect on either intraracial assault or robbery for whites (Kim 2016). In addition, it has also been found that greater economic competition between blacks and whites increases interracial homicide overall (Jacobs & Wood 1999) as well as solely white on black homicide (D’Alessio et al. 2002). These conclusions support the theory of intergroup contact (Blau 1977) as well as racial threat theory (Blalock 1967).

Although the majority of violent crime is intraracial (Wolfgang 1958; Hagan &

Peterson 1995), contradictory findings about segregations’ positive effect on black homicide victimization (Peterson & Krivo 1993) and non-significant effect on black homicide offending

(Sampson 1985) should raise questions about the effects of segregation on intra and interracial crime. Theory predicts that racial segregation will affect the likelihood of both intra and interracial crime; however, there is little research to explore this phenomenon. Furthermore, recent research has concluded that the likelihood of intraracial crime differs by crime type

(O’Brien 1987). Homicide is believed to be the most intraracial of the violent crimes, therefore only examining the effects of segregation on homicide may yield misleading results.

As others have noted in the past, failing to examine the relationship between segregation and intra and interracial crime may have led to incomplete conclusions about segregation’s effect on victimization (Xie 2010). While the above studies have overcome many of the limitations addressed in previous sections, they unfortunately have still relied on a-spatial measures of segregation, which may be affecting their conclusions. 25

Contributions of the Present Study

Though the reviewed studies have utilized different data sets, measures of segregation and even crime type, the majority of research has concluded that racial segregation increases overall offending and victimization (Logan & Messner 1987; Krivo et al. 2009; Hipp 2011;

Krivo et al. 2015). Racially disaggregated studies have concluded that these effects are stronger for minorities and minority communities (Peterson & Krivo 1993; Collins &

Williams 1999; Shihadeh & Flynn 1996; Shihadeh & Maume 1997; Velez et al. 2003). These studies offer divergent findings for the effects of segregation on whites, however. Some research has concluded that the effects of segregation on crime are negligible for whites

(Peterson & Krivo 1999), while others have found that segregation increases crime for whites and white communities (Sampson 1985; Krivo et al. 2009) and still others have found that segregation decreases crime for whites (Like 2011; Krivo et al 2015).

While it is still unclear if the relationship between segregation or isolation and violence is racially invariant, the general conclusions remain that there is a positive relationship between racial isolation and violent crime rates (Shihadeh & Flynn 1996;

Shihadeh & Maume 1997; Collins & Williams 1999; Lee & Ousey 2007; Feldmeyer 2010).

Despite the trend toward the role of isolation in explaining crime rates, until recently there has been no measure that captures spatial isolation (Reardon & O’Sullivan 2004). The importance of spatial effects for neighborhood studies has recently become clearer, as studies have begun to acknowledge the effect of adjacent communities in predicting violent crime rates for the community of study (Mears & Bhati 2006; Krivo et al. 2015). Furthermore, most of the research on segregation and violent victimization has focused on homicide rates without exploring other types of crime. When this limitation has been overcome by using arrest data 26 to explore crimes beyond homicide, the inability to account for unreported or unsolved crimes raises concern. In addition to these limitations, much of this research has failed to disaggregate crime rates by race of the victim or offender. This has led the majority of segregation studies to draw conclusions about segregation’s impact on violence in ‘majority white’ or ‘majority black’ areas, but has not said much about segregations’ effect for crimes against white or black individuals. Lastly, due to data limitations, studies of segregation have largely ignored the differential effects for intra and interracial crime.

The present study addresses these limitations by first employing a more nuanced measure of segregation that more accurately measures the racial environment and experiences of individuals. Secondly, the use of victimization data allows for a better estimate of crime rates, as well as allowing for the study of non-lethal violent crimes. This data also allows for the study of racially disaggregated victimization rates for blacks, whites and Hispanics, as well as rates of intra and interracial crime for blacks and whites.

Methodology

Data and Sample

The centerpiece for this study is the person-level data from the National Crime

Victimization Survey (NCVS) Metropolitan Statistical Area (MSA) subset file. The NCVS uses a stratified, multistage cluster sample design to obtain interviews from household members age 12 and over about their victimization experiences over the previous six months.

The NCVS is a unique dataset in that it allows for exploration of not only less serious crime than typically found in official crime statistics (UCR), but also accounts for crimes that may have gone unreported by victims, which are not included in police-based crime data. The 27

Census Bureau, in part with the Bureau of Justice Statistics, developed the MSA subset file, which contains victimization data for persons who reside in the core counties of 40 metropolitan statistical areas from which NCVS respondents have been drawn, along with geographic codes that identify these metro areas (Lauritsen & Schaum 2005). These 40 areas are representative of approximately 40% of the US population; including 50% of the black population, 60% of the Hispanic population, as well as 34% of the white population (Xie &

Lauritsen 2012; Xie & McDowall 2014). This dataset has been used in past research to examine metropolitan-area differences in crime reporting (Xie 2014), the effect of racial context on assault reporting (Xie & Lauritsen 2012), and the effect of victimization on resident’s decision to move (Xie & McDowall 2014). Although the data are available from

1979-2004, this analysis focuses on the years 2000-2004. Due to changes in Census Bureau definitions, this NCVS MSA dataset can be paired with Census data only after 1996 (Xie &

Lauritsen 2012). Segregation has also changed considerably over the past three decades

(Logan et al. 2004; Lichter et al. 2015) so this study will focus only on the most recently available years.

The NCVS person-level data set was paired with Census data from the year 2000 to examine the impact of MSA level racial segregation on individual-level victimization risk.

(For similar use of these datasets, see Xie & Lauritsen 2012). While many studies examine the effects of segregation at the city level, recent research has suggested that social processes are not limited to neighborhoods or census tracts but are similar within metropolitan areas

(Sampson & Bean 2006). In fact, geographically distant but socially similar neighborhoods within a metropolitan area have been found to reciprocal relationships when it comes to crime

(Mears & Bhati 2006). Based on these findings, as well as recent conclusions about the effects 28 of macro-level segregation on micro-level (e.g. neighborhood) crime rates (Krivo et al. 2009;

Hipp 2011; Xie & Lauritsen 2012), metropolitan statistical areas are an appropriate unit of analysis.

This study focuses on assault victimizations of non-Hispanic blacks, non-Hispanic whites and Hispanics between 2000-2004. Assault victimizations across these four years will be pooled to account for fluctuations in victimization risk over time (Xie 2010). The final sample includes 376,549 interviews, yielding slightly less than 4000 violent incidents, of which 2,664 are assault victimizations.

Measures

Dependent Variables

This study will proceed by examining three separate research questions, each with a unique dependent variable. The first question examines the relationship between segregation and overall crime rates. For this analysis, the dependent variable will be assault victimizations, measured as a dichotomous variable (1 = “victimization” 0 = “no victimization”). Assault victimizations include attempted and completed aggravated assaults, threatened assaults with a weapon, simple assaults with injury, assault without injury and verbal threats of assault (n=2,664). This variable does not include sexual assaults. Due to the highly skewed nature of victimization, as well as the rarity of multiple violent victimizations within a 6-month period, a dichotomous variable was chosen over a count variable (Lauritsen

2001). The second research question explores the differential effects of segregation on assault for different racial and ethnic groups. This analysis examines three separate dichotomous variables measuring assault victimizations for non-Hispanic blacks, non-Hispanic whites and 29

Hispanics separately. The final research question is concerned with differences in intra and interracial crime. This analysis examines the effect of segregation on intra and interracial crime using one polytomous variable to distinguish intraracial victimization, interracial victimization and no victimization (0,1,2 respectively).1 This final analysis examines intra and interracial crimes for blacks and whites only.2

Independent Variables

The key independent variables include two distinct measures of segregation. These measures will include the spatial information theory index (H) and the spatial exposure/isolation index (P*). The spatial information theory index measures the extent to which an individual’s local environment is less diverse than the total population of the larger area. This measures ranges from zero to one, with one representing maximum segregation

(Reardon & O’Sullivan 2004). The spatial isolation index captures the likelihood of exposure to same race individuals, making it the inverse of spatial exposure, which captures the likelihood of exposure to others of different races. As theory suggests that isolation from mainstream society and advantaged others is a leading factor for the effect of segregation, the isolation index is preferable to the exposure index (Eitle 2009). These measures have been defined as the most comprehensive measures of spatial segregation (Reardon & O’Sullivan

2004), which are complements of the more commonly used a-spatial measures of segregation; the index of dissimilarity (D) and the exposure/isolation index (P*). Similar to the spatial information theory index (H), the index of dissimilarity measure the percentage of individuals who would have to change residence in order for each locale (in this study, block group), to

1 For crimes committed by multiple offenders, when the majority of offenders were specified as black or white these were included to increase sample size (Sampson 1984). 2 While NCVS data has been collecting data on respondent ethnicity for several years, offender data is limited to simply black and white. This analysis therefore includes all blacks and whites, regardless of ethnicity. 30 have the same racial diversity as the overall unit of analysis (in this study, MSA). For comparison, this study will examine differences in the use of these spatial measures compared to a related measure of a-spatial segregation (D) and an analogous a-spatial measure of isolation (P*). Measures of segregation (D & H) are on the same scale and are measured from

0-1. The isolations indices (P* & P*) are also measured from 0-1, with higher scores indicating the probability that a member of that group will interact with members of the same group.

These segregation measures were created using Census data (2000) on race and ethnicity at the block group level for all counties represented in the NCVS data. For the spatial information theory index, measures of non-Hispanic white to non-Hispanic black, non-

Hispanic white to Hispanic, and non-Hispanic black to Hispanic were generated to examine all combinations of intergroup segregation. Measures of spatial isolation include non-Hispanic white, non-Hispanic black, and Hispanic isolation. This yields a total of six spatial segregation measures.

Control Variables

The use of NCVS allows for several control variables typically left out of studies of segregation and crime. The majority of segregation studies do not control for individual level demographics, such as gender, age, marital status or income, which have been found to be highly predictive of victimization risk (Lauritsen 2001). This study controls for demographic characteristics of each respondent, including gender, age, income, education and marital status.3 By including these control variables, this study will examine the effects of segregation net of these individual level characteristics. The analysis also will include other MSA level

3 Income is missing for approximately 30% of the sample. A dichotomous variable for missing income was included in order to preserve a larger sample size. 31 conditions as control variables to account for any possible spurious relationships between segregation and crime, as many of the factors that predict segregation are also predictive of victimization. These factors will be measured at the MSA level using Census data. These variables include measures of socioeconomic disadvantage, comprised of the percent of the population in poverty, the median household income, the percentage of female-headed households with children, the percentage unemployed, the percentage of adults with at least a high school degree, and the percentage of those employed in professional and managerial positions. This study will also control for the region, minority populations, the percentage of males ages 15-29, and population. (For a complete list of control variables, see Table 1.)

Due to the high correlation between poverty, median household income, the percentage of female-headed households, unemployment rate, education level and professional employment, an index of socioeconomic disadvantage that combines these 33 measures was created using principal component factor analysis to reduce multicollinearity

(Land et al. 1990). (For a complete list of correlations for both controls and independent variables, see Appendix A.) Similar indices have been used in previous studies of segregation and crime (Xie 2010). Each of the six control variables loaded on one factor (minimum factor loading is 0.69.) A similar factor of socioeconomic disadvantage was created for each racial or ethnic group for race-specific analyses. Race-specific disadvantage, however, suggested more nuanced correlations between elements of socioeconomic status. While the indicators of socioeconomic disadvantage for non- blacks and whites loaded on a single factor, these measures yielded two distinct dimensions of socioeconomic disadvantage for Hispanics: poverty, median household income, the percentage of female-headed households, and unemployment rate are included in one factor, while education level and professional employment are included in a second factor. Past research has made similar methodological decisions when creating a socioeconomic disadvantage index for Hispanics, as patterns of disadvantage for this group do not appear to follow the typical conventions used for whites and blacks (see Xie 2010).4

Analytic Strategy

The present study examines the impact of racial segregation and isolation on three distinct outcomes: overall assault victimization, race- and ethnic-specific assault victimization, and inter- and intra-racial assault victimization. For the first two outcomes— overall and race-specific assault victimization, the dependent variables are binary measures, coded 1 for respondents who reported being victimized and 0 for those who indicated no

4 Following prior research, the percentage of residents who are foreign born also was considered as a control variable. However, this measure is highly correlated with percent Hispanic (0.736), so only the percentage of Hispanics was included. 34 victimization. These outcomes will be analyzed with a series of models using multilevel

Hierarchical Logistic Linear regression modeling (HLM) with random intercepts. Due to the dichotomous nature of the first dependent variables, this analysis will utilize a Bernoulli model, comparable to a logistic regression. The third and final outcome is a polytomous measure that are assessed with a series of multilevel multinomial logistic models that contrast instances of inter-racial assault victimization and intra-racial assault victimization against cases of no victimization.

Because of the nested design of the data (multiple respondents victimizations within each metropolitan statistical areas), using standard single-level regression procedures to model these outcomes would violate the assumption of nonindependence, resulting in deflated standard errors. Multilevel models yield standard errors that are adjusted for the nesting of respondents within MSAs. Level-1 and level-2 control variables will centered at the grand mean.5 Survey weights will not be applied, as prior research examining individual controls does not include them, as they are somewhat redundant. 6

The first question addressed in this study examines the effect of segregation on overall assault victimization risk. This analysis focuses on assessing the impact of four separate measures of segregation on overall assault victimization. To situate my findings within prior research, I first explore the effects of a-spatial segregation (D) and isolation (P*) on assault victimization. Due to the high positive correlation between segregation and isolation, these measures are typically not included within the same model (Eitle 2009), so this analysis will examine these effects independently. Subsequently, I estimate parallel models that substitute

5 Grand mean centering subtracts the overall mean score of the sample from each individual measure, allowing for comparisons of mean victimization risk across MSAs, holding all controls constant. For example, the mean of spatial segregation is approximately .43 for the entire sample, making the grand mean centered segregation score for Boston equal to 0.016 (0.446-0.43 = 0.016). 6 When weights were applied to account for differences in the population sampled, results remained consistent. 35 spatial measures of segregation (H) and isolation (P*) for the more standard a-spatial indices.

In both sets of analyses, the models examine the effects of non-Hispanic white to non-

Hispanic black segregation (hereinafter white to black segregation) and non-Hispanic white to

Hispanic segregation (hereinafter white to Hispanic segregation) and non-Hispanic black, non-Hispanic white and Hispanic isolation, respectively. The majority of studies of segregation and crime model only white to black segregation and isolation, while failing to account for segregation between whites and Hispanics. Given the substantial growth in the

Hispanic population during the 1980s and 1990s in America, this is a notable limitation and serves as motivation to include a broader approach in the present study. The initial models of overall victimization risk include only MSA measures as controls, while the full models also include individual level controls. Previous research on segregation and crime has been limited to aggregate-level designs that do not permit the inclusion of individual level characteristics.

The capacity to do so with of the NCVS is a strength of this research, as it enables an assessment of the contextual effects of segregation while controlling for compositional differences across communities.

The second question addressed in the study is whether spatial isolation and segregation at the MSA level influence race- and ethnic-specific assault victimization risk.

The analysis of race- and ethnic-specific victimization will parallel the models estimated for overall assault, except that the measures of spatial isolation are in reference to the group under investigation, with non-Hispanic whites as the reference group for segregation. Again these models begin by exploring MSA controls, followed by a full model examining individual and

MSA-level controls together. Individual level and MSA level controls will be race specific within each model, aside from region and population. 36

Finally, this study examines whether segregation differentially affects intra and interracial crime for black and white victims and offenders. As noted, this analysis applies multilevel multinomial logistic regression to predict the effects of segregation on intra-racial victimization (i.e., blacks assaulted by blacks or whites assaulted by whites) and inter-racial victimization (i.e., whites assaulted by blacks or blacks assaulted by whites), compared to non-victimization. While individual level controls would be ideal in this analysis as well, due to the limited number of both forms of victimization, this model will only account for MSA characteristics.

Results

Table 2 provides descriptive statistics for all variables included in the analysis. The first column presents descriptive statistics for the overall sample, while the remaining three columns present racial- and ethnic-specific descriptive statistics. Approximately half of the overall sample is female and married, and the majority of respondents are over age 35. The mean household income for NCVS respondents in the sample was $30,000-49,999, and approximately half the sample has some college education. 37

Race-specific statistics highlight the increased age, income, education, and marriage rate of non-Hispanic whites (hereinafter whites) compared to non-Hispanic blacks (hereinafter blacks) and Hispanics. Notably, blacks and Hispanics have MSA poverty rates over three times the rate of whites. The MSAs in this sample also exhibit higher percentages of unemployment and households headed by females with children, as well as lower rates of professional and managerial employment for minority groups compared to whites.

Victimization risk is similar for all groups (0.01), however, risk varies by MSA, justifying the use of multilevel models. Victimizations range from 13-197 assaults per MSA.

Race specific models reveal more variation in black and Hispanic risk than in white risk, with some MSAs experiencing no assault victimizations for specific racial groups over this four- year period. (See Appendix B for a list of victimizations by MSA.)

For all four independent segregation variables, larger scores are related to higher segregation and isolation within each MSA. Prior research has defined a dissimilarity score below 0.30 as low segregation, and a score over .65 as high segregation (Rugh & Massey

2010). Following this convention, 28 MSAs are categorized as having high white to black segregation, and 34 MSAs experience high white to Hispanic segregation. While these numbers appear large, prior studies of segregation find that these measures usually range from moderate to severe segregation for blacks (Rugh & Massey 2010). Table 2 shows that for this sample, non-Hispanic whites are more segregated from blacks (D=0.65; H=0.43) than from

Hispanics (D=0.49; H=0.25), a finding that is consistent with past research (Logan 2004; Xie

2010; Rugh & Massey 2010). Interestingly, Hispanics are more segregated from blacks than from whites. While these different definitions of segregation capture slightly different elements of segregation for each MSA (i.e. how much less diverse an area is (H) compared to 39 what percentage of residents have to change location to reach full integration (D)), the measures are highly correlated (0.99). (See Appendix A). Notably the spatial information theory index measure is more positively correlated with percent non-Hispanic black as it better accounts for the relative size of each group in the population than the index of dissimilarity. Segregation on average is higher in the North and Midwest (H=0.551) compared to the South and Southwest (H = 0.364). Figures 1 & 2 plot the spatial segregation for white to black segregation and white to Hispanic segregation, respectively.

Figure 1

Figure 2

The same finding holds for isolation of blacks, as blacks are more isolated in the North and Midwest. Conversely, Hispanics are more highly isolated in the South and Southwest compared to the North and Midwest. (For maps of spatial isolation, see Appendix C.)

Isolation measures show that whites have a higher likelihood of interacting with other whites

(P*=0.40) than blacks or Hispanics have of interacting with their own racial/ethnic groups

(P*=0.24 and 0.23, respectively). White isolation is more consistent across MSAs, while there is more variation in black and Hispanic isolation. Isolation measures account for the relative size of each group, therefore it is expected that whites are more likely to interact with other whites, as they are the majority group. The spatial and a-spatial measures of isolation are also highly correlated (0.99) and predict consistent levels of isolation within each MSA, however spatial isolation scores are slightly larger in magnitude than their a-spatial counterparts. These 41 numbers indicate the likelihood that a chosen group member will come into contact with a person of the same race within their neighborhood (i.e. block group).

While different racial constructions of spatial segregation and isolation are not highly correlated with each other, black spatial isolation and white to black segregation are correlated at 0.88. Furthermore, these measures are also both highly correlated with percent non-Hispanic black. Additionally, white spatial isolation is highly negatively correlated with percent Hispanic, which is not surprising given that as the percent Hispanic increases, percent white decreases, causing a decreased likelihood of whites interacting with other whites.

As segregation research is largely concerned with the relationship between segregation and disadvantage, it is interesting to note that socioeconomic disadvantage is most highly correlated with Hispanic isolation. Race specific socioeconomic disadvantage is also surprisingly more correlated with measures of spatial segregation for blacks and Hispanics

(0.64; 0.63) than with spatial isolation (0.58; 0.34) for these same groups. This conclusion is contrary to theoretical expectations of the relationship between isolation and disadvantage, however these results may be a function of the unit of analysis. By capturing Metropolitan

Statistical Areas as opposed to cities, these indices include suburbs and other more advantaged locales, which may be affecting the relationship between segregation and disadvantage.

It is also important to note that in analyses of a-spatial and spatial segregation, controls for percent non-Hispanic black, as well as percent Hispanic are included. While the spatial theory information index takes minority population into account, (contrary to the index of dissimilarity), prior research using this spatial measure of segregation includes the percent of the minority population in the analyses (Hipp 2011). However, prior literature has argued for 42 the exclusion of percent minority alongside isolation indices (Eitle 2009). A-spatial isolation and spatial isolation are correlated with the respective percent of the population by over .80.

These analyses therefore include the minority population alongside the spatial segregation measure as prior research has done, but exclude these controls in models examining racial isolation.

The Impact of Segregation on Overall Assault Victimization Risk

Table 3 examines the effects of a-spatial segregation (D) and isolation (P*), respectively, on overall assault victimizations rates.7 Model 1 parallels prior research by examining the effects of segregation with controls for contextual predictors.8 Model 2 includes individual level controls, as well as MSA level controls. This order of presenting contextual controls alone followed by contextual and compositional controls is followed in the remaining tables. Bivariate analyses (unreported) reveal that a-spatial segregation, measured by the index of dissimilarity (D) at the MSA level, is not a significant predictor of overall assault victimization risk. Full models of segregation yield a similar story: both white to black and white to Hispanic segregation remain statistically non-significant once individual control variables are included. Furthermore, the inclusion of individual level controls explains an additional 37% of the variation in risk by MSA than Model 1.

The effects of a-spatial isolation on overall assault victimization risk are presented in

Models 3 and 4 of Table 3. While past research focuses mainly on the effects of black isolation for crime, this analysis also examines Hispanic isolation on overall crime rates. Both

Hispanic and black isolation are significant predictors of assault victimization risk, however, in the opposite direction than expected. Model 3 reports that both forms of isolation are

7 For all analyses, unit-specific models with robust standard errors are presented. 8 Following typical conventions, only measures of non-Hispanic black isolation are presented, however, the effects of non-Hispanic white as well as Hispanic isolation on overall assault risk are available upon request. 43 related to a 75% reduction in assault victimization risk. Once individual-level controls are added to the models, the effects of both forms of isolation are decreased but remain significant. Again, while this finding is puzzling given prior research, it is interesting to see that racial isolation may play a different role in nonfatal or less serious violent crimes than prior research on homicide would anticipate.

Parallel analyses measuring spatial segregation and isolation are presented in Table 4.

The level of variation in risk that is explained is nearly identical in models of spatial and a- spatial segregation, suggesting these measures are not empirically distinct. It is possible that 44 differences in a-spatial and spatial measures are increased when smaller units are analyzed, while using a large unit of analysis, such as MSA, masks some of these differences. Although spatial segregation is still not significant in these models, it is interesting to note that the effect size of white to black segregation is decreased with spatial segregation, yet increased with white to Hispanic spatial segregation. Spatial isolation is presented in Models 3 and 4. The results are similar to a-spatial models presented above, with black isolation significantly decreasing overall victimization risk. 45

Contextual controls in each model reveal findings consistent with prior research. The percentage of young males 15-29 significantly increases victimization risk and the percentage of Hispanics significantly decreases risk. The relationship between the percentage of

Hispanics and victimization may be explained by recent finding about the effect of immigrant populations on decreasing crime rates (Sampson 2008). Contrary to theoretical predictions, the effect of the percentage of non-Hispanic blacks significantly decreases assault victimization risk, however, this finding is consistent with some prior research on the relationship between segregation and crime (Eitle 2009). Furthermore, socioeconomic disadvantage does not significantly increase victimization risk.

As past research would predict, sex, age, marital status and income are all significant predictors of victimization risk in the expected direction in models of both spatial segregation and isolation; victimization risk is significantly decreased for females, those who are married, those over age 40, and those whose annual household income is over $15,000. It is interesting to note that the significant effects of isolation are decreased once individual controls are included. This finding suggests that prior research may have overestimated the effect of segregation on crime by not accounting for compositional differences in the population.

As these findings are quite unexpected, an additional measure of isolation was also examined. This analysis measures explicitly black and Hispanic isolation – meaning the likelihood of interaction with others of the same racial or ethnic group. In segregation studies that examine two racial groups (e.g. black and white), black isolation is the inverse of black exposure to whites. Because this analysis measures segregation for multiple racial groups, black isolation and black exposure to whites are not perfectly correlated, as they would be in 46 studies of two groups. 9 Prior research that has explored multiple groups often defines black and Hispanic isolation as the lack of exposure to whites (Feldmeyer 2010), as opposed to the exposure of blacks to blacks and Hispanics to Hispanics. In an effort to parallel prior research,

I reexamined previous analyses with a measure more similar to prior conventions. While lack of exposure to whites for both blacks and Hispanics (measured as 1-probability of exposure to whites) is distinct from lack of exposure with any racial/ethnic group other than one’s own

(i.e. isolation in this analysis), the measures are highly correlated with measures of isolation (-

0.84 for blacks and -0.88 for Hispanics). When lack of exposure to whites is used in place of isolation, results are consistently predicting decreases in overall assault risk as black and

Hispanic isolation from whites increases.10 These findings confirm that regardless of the exact measure, (i.e. isolation or isolation from whites), isolation decreases assault victimization risk in this sample. It is noteworthy to add that, despite the high correlation between white to black segregation and black isolation (0.93), segregation appears to have a non-significant effect on overall assault risk while significantly and largely decreasing assault victimization risk.

For both segregation and isolation, it appears that spatial measures predict smaller effects than their a-spatial complements. For example, fully specified models of a-spatial isolation predict an additional 1% decrease in victimization per unit increase in segregation, compared to spatial models. These effects are larger for spatial versus a-spatial segregation, however, this conclusion is hard to draw given the non-significant effects of both forms of segregation. These findings do raise the question of whether a-spatial measures have been overstating the effects of segregation on victimization.

9 For example, in San Antonio and Miami, which have higher than average Hispanic populations, measures of black isolation differ significantly from the lack of black exposure to whites. 10 It is interesting to note that black lack of exposure to whites, (as opposed to black exposure to other blacks), still decreases overall risk, however, the effect is decreased and is no longer significant. Hispanic lack of exposure to whites remains significant, however, and the negative effect on victimization risk is even larger. 47

Despite expectations, both spatial and a-spatial measures suggest that segregation and isolation may have different effects on assault victimization risk than homicide, robbery or aggravated assault. These results may also be a function of data choice, as this analysis captures unreported and less serious crimes, and victimization as opposed to arrest rates.

Divergent findings may also be a result of the level of aggregation, as prior research tends to explore segregation at the city level. Finally, it is possible that these models of overall victimization risk are concealing racial differences in victimization risk. As prior research has concluded, segregation may be detrimental for minorities while having some potential benefits for whites, which may be getting lost in aggregate level research. In order to further explore this finding, racially disaggregated models are presented. While the above findings suggest that there are only minimal empirical differences between measures of spatial and a- spatial segregation and isolation, spatial measures are still conceptually preferred. The following race specific models will present spatial measures only, with differences between spatial and a-spatial measures noted when necessary.

Non-Hispanic Black Assault

Prior research suggests that segregation has significant and positive effects on homicide victimization and offending, as well as robbery offending, for racial and ethnic minorities. Table 5 presents hierarchical logistic regression models of spatial segregation and isolation effects for black victimization risk. Models 2 & 4, which include individual level controls, once again explain more of the variation is victimization risk than Models 1 & 3.

Contrary to models predicting overall risk, the addition of individual controls weakens the effects of segregation and isolation. The contextual and individual level effects remain consistent across models; percent Hispanic significantly reduces black risk of assault, while 48 the percentage of young black males significantly decreases assault risk for blacks. Most importantly, yet contrary to expectations, measures of segregation and isolation both predict decreases in assault victimization risk, however, only spatial isolation is significant. Isolation has a large and negative effect on assault risk; Model 4 demonstrates nearly 100% reduction in the likelihood of victimization. Separate analyses (not shown) were also conducted to explore the effect of the lack of black exposure to whites, as previously mentioned, given that these findings were unexpected, however, the results remain consistent. This racially disaggregated model further suggests that segregation may have a differential effect on assault victimizations compared to homicides and robberies. 49

Hispanic Assault

Table 6 examines the effect of segregation and isolation for Hispanics. Individual characteristics remain significant and in the expected directions, but none of the contextual level controls reach significance. Consistent with past research on immigration and crime, as well as the crime reducing benefits associated with ethnic enclaves, it is not entirely surprising to find negative effects of segregation and isolation for Hispanics, however, neither measure reaches statistical significance. It is unclear why models of Hispanic assault lack significant 50 effects for any MSA independent or control variables, however prior research has concluded that segregation better predicts black victimization than Hispanic victimization (Xie 2010).

Non-Hispanic White Assault

Table 7 presents the effects of racial segregation and isolation on white victimization.

The majority of segregation and crime studies do not focus explicitly on the effects of segregation for whites, making this exercise somewhat exploratory in nature. Bivariate 51 models (unreported) reveal results similar to past research; segregation significantly decreases victimization risk for whites. However, once MSA controls are added, the effects of segregation are no longer significant.11

Models 3 and 4 of Table 7 present the findings for white spatial isolation. While past research generally examines the effect of black isolation on crime, white isolation has been found to decrease both violent and property crime for white areas (Krivo et al. 2015). The fully specified model shows that white spatial isolation is a positive predictor of white assault victimization. A one-unit increase in white spatial isolation is related to a 6% increase in white assault victimization risk. The relationship between white spatial segregation and assault risk is significant at p<0.001, however, the standard errors are very large which suggests caution when interpreting these effects. In all models, the percentage of young white males increases risk, while the percentage of non-Hispanic blacks decreases risk, consistent with prior research.

11 Non-Hispanic white to black segregation reaches marginal significance (p<.10) in Model 2. 52

Intraracial & Interracial Assault

The final analysis explores the differential effects of segregation on intra and interracial assault for blacks and whites. Table 8 presents the results of a multinomial logistic regression. Due to the small number of intra and interracial assaults, it was not possible to include individual level controls in this analysis.12 Models 1 & 2 examine the likelihood of no

12 I attempted to include victim race to examine differences in black intra racial assaults compared to white intraracial assaults, but due to the small amount of assaults, models would not converge. 53 victimization compared to intraracial assault. As expected from previous models, white to black segregation decreases the odds of no victimization risk while black isolation increases the odds of no victimization, relative to intraracial assault. Models 3 & 4 present similar findings for the likelihood of no victimization compared to interracial assault. Models 5 & 6 are more straightforward, as they compare intraracial to interracial assault. As contact theory would predict, segregation increases the likelihood of intraracial assaults compared to interracial assaults. Most notably, black isolation is related to a decreased likelihood of intraracial assaults compared to interracial assaults. While this relationship is not significant, this finding helps to further explain the unexpected findings of the previous models. The negative effects of black spatial isolation on overall assaults, as well as black assaults, are driven by large decreases in intraracial assault risk. Although this model cannot explicitly disentangle white and black intraracial crimes due to the limited number of crimes, this finding suggests that black isolation leads to a decrease in black on black assaults.

54 (0.069) (0.861) (0.011) (0.065) (0.126) (0.168) (0.186) *** 0.000 0.008 48.524* Model 6 Model 1.461 0.005 0.007 0.009 -0.960 -0.066 -0.270 (0.068) (0.738) (0.011) (0.011) (0.067) (0.090) (0.162) (0.204) *** * 0.049 0.222 43.379 Model 5 Model Intraracial Assault:Interracial Assault Assault:Interracial Intraracial 1.448 1.078 0.000 0.020 0.205 -0.027 -0.008 -0.305 (0.072) (0.943) (0.009) (0.086) (0.120) (0.131) (0.215) * 0.083 0.287 Model 4 Model 60.492** 0.134 0.058 0.082 -6.821 -0.614 -0.018 -0.153 (0.070) (0.814) (0.015) (0.009) (0.089) (0.120) (0.135) (0.206) Model 3 Model *** * 0.101 0.317 59.506** Interracial Assault:No Assault Assault:No Interracial 0.071 0.153 0.085 0.064 -6.812 -0.010 -0.020 -0.072 (0.052) (0.645) (0.007) (0.064) (0.083) (0.113) (0.148) *** * 0.098 0.312 Model 2 Model 153.644*** 0.068 0.064 -5.365 -1.592 -0.013 -0.186 -0.153 (0.046) (0.542) (0.009) (0.008) (0.055) (0.076) (0.106) (0.162) *** * *** * * * 0.067 0.259 Intraracial Assault:No Assault Assault:No Intraracial Model 1 Model 109.934*** 1.149 0.145 0.106 -5.364 -0.037 -0.020 -0.241 -0.133 ) H

Segregation ( Segregation Non-Hispanic White to Non- to White Non-Hispanic Isolation Black Non-Hispanic Black Non-Hispanic Percent Hispanic Percent Males Young Disadvantage Socioeconomic Population Region Hispanic Black Spatial Spatial Black Hispanic Table 8 - Hierarchical Multinomial Regressions of Spatial Segregation on Intraracial & Interracial Assaults & Interracial on Intraracial of Spatial Segregation Regressions Multinomial 8 - Hierarchical Table Intercept Variables Independent Variables Control Context Metropolitan Component Variance Standard Deviation Intercept Chi-Squared parentheses in given errors are Standard N= 348,554 * P<.05, ** P<.01, ***P<.001 55

Further Analyses

Studies of victimization are forced to choose between measuring the prevalence of crime (i.e. the number of victims over the total population) compared to the incidence of crime (i.e. the number of crimes over the total population). This analysis chose the former, which does not account for repeat victimizations. It is possible that while these results suggest that isolation decreases the risk of assault victimization, isolation may have different effects when repeat victimizations are included. For example, high levels of isolation may experience higher percentages of repeat victimizations than single victimizations, which may partly explain the negative relationship found between isolation and assault risk. In order to explore this relationship, I performed a multilevel over-dispersed Poisson regression (equivalent to a negative binomial regression), to examine the relationship between segregation and victimization counts.13 These results (unreported) conclude findings similar to those presented in this study; isolation still decreases the likelihood of assault when accounting for repeat victimizations; offering further support for the findings that isolation is in fact related to decreased assault risk.

In an effort to further explore the relationship between segregation and assault, I estimated similar models with Uniform Crime Report data from the year 2000. Examination of the log of aggravated assaults rates reveals conclusions similar to past research; OLS regression reveals that measures of black and Hispanic isolation increase overall aggravated assault rates (contrary to the findings presented in this study) within the core counties of the

40 MSAs.14 This analysis further suggests that segregation differentially impacts more serious crime, such as aggravated assaults, robbery and homicide, compared to less serious crime (i.e.

13 These findings are not presented but are available upon request. 14 These findings are not presented but are available upon request. 56 simple assaults.) However, it is still possible that this finding is simply an artifact of the differences in victimization data compared to police-based data.15

A further test of the NCVS, however, lends some support for the hypothesis that isolation and segregation behave differently for aggravated assault than simple assault. A reexamination of the effect of segregation on assault victimization in the NCVS, disaggregated by simple and aggravated assault, further supports this hypothesis. Overall simple assault, as well as racially disaggregated simple assault, shows that the effects of black and Hispanic isolation remain negative and are increased compared to an analysis of both simple and aggravated assaults combined. While these effects are not significant, they do suggest that future analyses should examine these forms of assault separately. The relationship between black isolation and black simple assault, is however significant. Black isolation has a large and significant negative effect on black simple assault risk. In fact, the effect of black spatial isolation on black simple assault is larger and more significant than this relationship between spatial isolation and combined simple and aggravated assaults.

Discussion

Prior literature has led to several hypotheses about the effects of segregation on crime.

While previous studies have explained variation in homicide offending and victimization, as well as robbery and occasionally aggravated assault offending, it is clear that segregation has a different effect on assault victimization. Contrary to hypothesis 1, as well as expectations of prior literature and theory, spatial segregation (as well as a-spatial segregation) does not appear to significantly increase overall assault risk. Most interestingly, spatial isolation significantly reduces overall assault victimization risk. While somewhat surprising, these

15 NCVS aggravated assault risk is correlated with UCR aggravated assault risk -0.23. 57 finding would occur if segregation and isolation differentially impacted whites and minorities.

For example, an analysis of aggregate victimization risk may be capturing increased risk for some groups while decreased risk for others.

Race-specific analyses (Tables 5-7) reveal, however, that segregation does not significantly predict assault risk for blacks, whites or Hispanics. Furthermore, race specific analyses of isolation’s effect reveal interesting findings. Spatial isolation significantly reduces assault victimization risk for blacks in this sample. Additionally, white spatial isolation is related to an increase in white assault victimization. This finding is also somewhat contradictory to prior research, which has found that white isolation decreases white robbery and homicide (Krivo et al. 2015). Together these findings contradict theories of segregation and concentrated disadvantage, which state that black isolation leads to a geographic concentration of negative factors such as low employment, high disorder, a high percentage of female-headed households and poverty, which in turn, leads to higher rates of criminal victimization.

It is possible that these findings differ from expectations of concentrated disadvantage’s effect due to the large level of aggregation. Typically concentrated disadvantage is believed to be a neighborhood or community phenomena. While segregation measures are created at the block group level, an appropriate proxy for neighborhood, they are averaged across all neighborhoods within an MSA.16 This may affect the results in two ways.

First, if isolation is quite large in several locations this may drive the isolation index to increase, despite the otherwise racially integrated areas of the MSA. Secondly, as victimization data is aggregated to the MSA and not on a smaller scale, this study cannot draw

16 Segregation research has found that macro level segregation has been on the decline in recent years while micro segregation has been increasing (Lichter et al. 2015). Conclusions drawn from MSA level segregation may be understating the effects of more micro level segregation. 58 conclusions about the effects of victimization for isolated individuals within isolated MSAs.

Past research has begun to explore this relationship by examining the effects of city level segregation on individual neighborhoods within the city (Peterson & Krivo 2009), but future research may wish to explore the possible additive effects of isolated communities within particularly segregated places. Moreover, it is possible that segregation only has deleterious effects for individual minorities living either within isolated communities or in adjacent communities. Data on victimization risk by MSA will inherently be capturing both segregated/isolated and non-segregated/isolated individuals. Victimization data has been useful to remove many of the biases of police-based crime data, however, lacking the exact location of individual victimizations is somewhat problematic. As previously stated, without this information it is impossible to make conclusions about segregation’s effect on isolated individuals. Future research can build upon this study by utilizing the restricted NCVS data, which contains more specific information on incident location.

In addition, future research may benefit from a larger dataset with more variation in segregation levels. The relationship between black isolation and crime appears to be nonlinear; locations with low levels of isolation experience increasing assault victimization rates, while areas with isolation scores between 0.2 and 0.4 appear to experience the majority of decreases in risk, and finally MSAs with black spatial isolation over 0.4 appear to also experience increases in assault risk. The current dataset has a lack of highly isolated MSAs, and includes several MSAs with a low percentage of Hispanics and blacks.17 It is possible that for highly isolated areas, assault victimization risk is increased, but this study does not capture many of these locations. Future research that includes more variation in segregation and

17 All MSAs included have at least 20,000 non-Hispanic blacks and 12,500 Hispanics. While the threshold of segregation studies tends to be at least 5000 minority group members, it is unclear what the cut off point for minority population should be when examining MSAs. 59 isolation may also benefit from studying neighborhoods based on high, low and medium isolation levels, which will account for the nonlinear effect of segregation and isolation.

Aside from the limitations of this study, there are several strengths of this analysis which may help to explain why these results differ from expectations of both theory and prior literature. First, unlike most prior research, the data used in this study is unbiased by differences in policing and arrest decisions, as well as differences in victim reporting. Past research utilizing police-based data may have an inflated number of assaults in highly segregated and isolated communities due to differences in police presence and policing strategies. Isolated communities, (both all white as well as all black), may simply have increased police presence which leads to a higher likelihood of arrest compared to less isolated locations. A contradictory explanation, however, may be that victimization data is also biased as assaults may be so prevalent and commonplace that residents do not consider verbal assaults a serious enough matter to report to interviewers. Furthermore, residents may fear increased police presence if true levels of crime are reported, even to NCVS interviewers.

Both issues should be explored further as they are of great importance to criminologists who often hold victimization surveys as the holy grail of crime data.

Secondly, differences in reporting rates across segregated and isolated communities may inflate official police data on aggravated assault rates within some isolated communities while decreasing them in more homogenous locations. Recent research supports this possibility; Xie and Lauritsen (2012) have found that in MSAs with higher than average segregation, both black and white victims are more likely to report black offenders than white offenders. As a result, reporting rates in isolated communities may over inflate police-based crime rates, causing segregation’s effect on crime to appear larger than in isolated 60 communities and smaller in integrated locations, compared to the reality of crime’s occurrence.

In addition to overcoming several of the limitations of police-based data, this analysis has examined non-lethal violence, which may account for some of the differences in results compared to prior studies of segregation and homicide. For one, assaults are simply less serious than homicide, while also far more prevalent. It is entirely possible that segregation plays a role in lethal crime while not having a significant or positive effect on less serious violence. Additionally, further analyses of simple and aggravated assaults suggest that isolation may increase aggravated assault risk (as extant literature would expect), while largely decreasing simple assault risk. Quantitative research offers some support for this finding, as blacks may in fact be less likely to experience assault compared to whites (Felson

2010). Qualitative research also suggests that simple assaults may be less likely in disadvantaged or minority communities, as the ‘code of the streets’ or presentation of self may protect against these less violent crimes, despite potentially increasing more serious crime (Anderson 1999). A reexamination of Table 5 will reveal that the percentage of young black males 15-29 significantly decreases the risk of black assault victimization in general, as well as the likelihood of black simple assault (unreported), further supporting Anderson’s theoretical expectations. Table 8, which explores intra and interracial assault, also offers some explanation as to what crimes are actually decreased in areas with high spatial isolation.

Increases in black spatial isolation are related to decreases in intraracial assault, contrary to theories of intergroup contact, but in further support of ‘code of the streets’ (Anderson 1999).

Finally, the use of victimization data highlights the potential importance of controlling for individual or compositional effects when studying segregation and isolation. Decreased 61 effects of isolation in models that control for individual level effects suggest that the relationship between isolation and victimization is mediated by compositional effects, such as population age structure, education level or income. Prior research that does not control for these effects may be overstating the impact of isolation, while simultaneously missing more nuanced explanations for why isolation is so detrimental for many communities.

Several explanations based on choice of data and crime type have been presented to explain why black spatial isolation appears to decrease assault victimization rates. However, there are other factors that may account for a decrease in assault risk in highly isolated communities. Highly isolated communities may be more likely to experience a high level of incarceration, thus decreasing the number of potential male offenders and male victims (Clear

2008). Inclusion of a measure of incarceration or the ratio of male to female residents may be necessary in further research to help explain the mechanism behind the negative effect of black isolation on assault. A decrease in potential offenders, coupled with gender norms about violence toward women, may help to explain the negative effects of black isolation on assault risk.

Conclusion

Criminological research is largely concerned with what leads to differences in victimization risk across place, with segregation often cited as an important factor in explaining this variation. In general, this study suggests that segregation may not be as integral to assault victimization risk as previously expected, in fact, locations with high levels of black spatial isolation may actually experience decreased black assault victimization, especially black simple assault victimization. 62

These findings suggest that segregation may have not only differential effects on victimization by racial and/or ethnic group, but that segregation and isolation’s effect may also be crime specific. Though this research has advanced beyond the use of strictly homicide data and police based data more broadly, as well as the use of a-spatial measures of segregation, future research should continue to explore the more nuanced relationship between segregation and different crime types. It is unclear if the mechanism explaining this relationship is due to a decrease in potential offenders, differences in victim reporting or policing strategies, or a ‘code of the street’ mentality, where simple assaults are less likely due to fear of retaliation.

Although the findings of black spatial isolation are quite strong, caution should be used when generalizing these results beyond the effects of isolation on simple assault or assault in general. While segregation may decrease less serious crimes, or change the quality of violence that is occurring, there are still noted strong positive effects of segregation on fatal crime, as well as many deleterious effects of segregation beyond crime and victimization. The fact that even the most disadvantaged white communities experience higher unemployment and access to resources than the average black community should continue to raise concern over the impacts of segregation (Peterson & Krivo 2005). Furthermore, ethnographic research has found that even middle class segregated black communities are more likely to be surrounded by high crime areas than a similar white community (Patillo 1998). Recent research about the spillover effects of adjacent communities suggests that these communities will still experience higher crime rates than similar status white communities, which are afforded more distance from high crime areas.

Future research should begin to explore beyond the role of simply racial segregation 63 and isolation to include measures of race and class interaction, including social ties and access to resources that span racial and class boundaries. Research has found that even when integration exists, social life tends to still be segregated (Molotch 1972), therefore, it may be important to move beyond these measures of solely racial segregation. In reality, measures of segregation are often believed to be important because they are proxies for more difficult to measure items, such as social capital, social networks and access to resources and opportunity.

Efforts to increase integration and decrease segregation should also acknowledge the potential difficulties and negative consequences. Simply bringing jobs to isolated communities, while if successful could ease the effects of racial segregation, can fail as residents lack the social capital to be aware of these positions and employers often hire outside of the neighborhood due to stigma and fear of community residents (Kasinitz &

Rosenberg 1996). In order to be successful, racial integration must take place within social networks as well as in residential neighborhoods. Advances in technology and methodology should make studying these factors more accessible in years to come, but criminologists must continue to explore unique datasets and different dependent variables in order to fully understand the deleterious consequences of segregation.

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Appendix

Appendix C1

Appendix C2

Appendix C3