Black Out-group Marriages and Hate Crime Rates: A Cross-sectional Analysis of U.S. Metropolitan Areas

Abstract

Objectives:

This study introduces an indicator of racial out-group marriage to the research on hate crime. Drawing upon a variant of group threat theory, we hypothesize that Black out-group marriage with Whites will be positively related to anti-Black hate crime rates insofar as such marriages are perceived as transgressions of cultural boundaries. Informed by Allport’s contact theory, we hypothesize that Black out-group marriage with Whites will be negatively related to anti-Black hate crime rates insofar as such marriages indicate intercultural accommodation.

Methods:

Using data for a sample of U.S. Metropolitan Statistical Areas circa 2010, we assess our hypotheses with two operationalizations of levels of hate crime—incidence rates and victimization rates.

Results:

Our results reveal that levels of Black out-group marriages with Whites are positively related to the Black hate crime victimization rate but not related to the incidence rate.

Conclusions:

Our analyses suggest that any salutary effect of intercultural accommodation associated with interracial marriage is overwhelmed by the influence of the perceived cultural threat and intensification of animus for the “at-risk” population for perpetrating anti-Black hate crimes.

Keywords

hate crime metropolitan statistical areas Black out-group marriages

Over the course of the past few decades, research on the correlates of levels of crime motivated by bias has flourished. This literature has been guided primarily by theories of intergroup crime and bigoted violence (Blalock 1967; Blumer 1958; Disha, Cavendish, and King 2011). These theories assume that hate crimes arise out of the perceived threat posed by the subordinate group to the privileged position of the dominant group. The most commonly studied structural conditions under investigation have been those that can be theorized to constitute a “realistic” threat to group dominance. These structural conditions include the relative size of the minority population, economic competition, and differential political power (Disha et al. 2011; Green, Strolovitch, and Wong 1998; Lyons 2007, 2008). Another body of research depicts hate crimes against out-group members as a function of perceived threat to the cultural integrity of the in-group more so than as a reaction to the direct competition between the groups over scarce resources (Glaser et al. 2002; Green, Abelson, and Garnett 1999; Huddy and Sears 1995; Lyons 2007; Suttles 1972). Applying terms of the social psychological integrated threat theory (Stephan and Stephan 2000), this line of research focuses on “symbolic threat” as opposed to realistic threat in predicting crime motivated by bias.¹

In the present study, we extend prior work by introducing interracial marriage into the hate crime literature. Prior research on extremist populations suggests that such marriages might constitute a potentially potent symbolic threat (Glaser et al. 2002; Green et al. 1999). We focus specifically on the frequency with which members of the potentially threatening minority group (Blacks) marry with members of the dominant group (Whites) in the United States. Such Black out-group marriage is a particularly strategic dimension of social structure to introduce into the hate crime literature because there is a long-standing perspective on intergroup relations, the “contact hypothesis” proposed by Allport (1954), that directs attention to alternative, more cordial processes than those implied by group threat theory. These two perspectives provide a rationale for formulating opposing hypotheses about the association between the levels of Black out-group marriages with Whites and rates of anti-Black hate crimes. More precisely, these hypotheses target distinct but potentially intertwined processes that apply to differentiated segments of the population. We assess these hypotheses with a unique data set that links aggregated counts of racially motivated hate crimes against Blacks from the Federal Bureau of Investigation (FBI)’s Uniform Crime Reports (UCRs) with data on interracial marriage from the American Community Survey circa 2010.

Theoretical Background and Prior Research on Hate Crime

As noted above, much of the research on crime motivated by bias has focused on structural conditions that members of a dominant group might view as a threat to their privileged standing in society (Disha et al. 2011; Green, Strolovitch, et al. 1998; Lyons 2007, 2008; Piatkowska, Messner, and Yang 2018a, 2018b). The logic has been that any gains on the part of the subordinate group are likely to be perceived as having the potential to undermine the privileged position of the dominant group. Researchers have devoted particular attention to the relative size of minority groups as a threatening structural condition. A number of studies showed that the relative size of minority population is positively related to violence against minorities (Beck and Tolnay 1990; Disha et al. 2011; Krueger and Pischke 1997). Other research, however, revealed that the relative size of minority population is negatively related to hate crime victimization rates (Disha et al. 2011; Krueger and Pischke 1997; Piatkowska et al. 2018b; Stacey, Carbone-Lopez, and Rosenfeld 2011). These latter findings have been interpreted with reference to the power-differential hypothesis. According to this view, an increase in minority group size yields power, which reduces the likelihood that hate crimes will be perpetrated against members of these groups as the dominant group is more fearful about acting on their bias, given the minority group’s enhanced power (Levine and Campbell 1972).

The findings with respect to economic conditions are also inconsistent (Green, Glaser, and Rich 1998; Lyons 2007). Historical analyses of anti-Black violence revealed that lynching often resulted from adverse macroeconomic conditions (Hovland and Sears 1940). Building on the frustration–aggression hypothesis (Dollard et al. 1939), several subsequent studies (e.g., Olzak 1990; Pinderhughes 1993; Tolnay and Beck 1995) likewise focused on the relationship between economic threat and intergroup violence, with the theory of realistic group conflict (Bobo 1988) as possibly the most prominent one.

In contrast with the work that has focused mainly on structural conditions that are readily theorized as realistic threats to the position of the dominant group, analyses of historical data link hate crime against African Americans with perceived threat to the integrity, separateness, and hegemony of the in-group—symbolic threats—more so than with economic concerns or material competition (Green, Glaser, et al. 1998, 1999). These studies underscore the importance of the territorial incursion of Blacks trespassing into predominantly White communities (Green, Strolovitch, et al. 1998; Levine and Campbell 1972; Lyons 2007, 2008). This is consistent with Ezekiel’s (1995) theory of the racist mind, which argues that White racists often fear for their own survival, and their belonging to dominant groups gives them comfort and reassurance. This body of work also aligns with the aforementioned integrated threat theory offered by social psychology (Stephan and Stephan 2000), which distinguishes symbolic threats (concerning group differences in morals, which threaten the identity and the way of life of the in-group) from realistic threats (pertaining to threats to the welfare of the in-group including its political and economic power). As described in social identity theory (Tajfel 1982), out-groups, particularly those perceived as culturally distinct, indicate change and intermixing in society, which can be perceived as a threat to the in-group. Moreover, cultural threats proved to be perceived as more enduring (Huddy and Sears 1995) than economic threats, which are assumed to depend more strongly on short-term economic fluctuations (Malhotra, Margalit, and Mo 2013).

Prior research thus suggests that symbolic threats to the cultural integrity of a dominant group might play a particularly important role in stimulating crimes motivated bias. Moreover, an empirical examination of the possible association between interracial marriage as a symbolic, cultural threat and crimes motivated by bias is of particular interest, given ambivalence in the broader general public about interracial marriage—particularly between Blacks and Whites. Public opinion data indicate that while the majority of the U.S. public is not generally opposed to intimate interracial marriage, nontrivial portions feel otherwise. In a 2009 Pew Foundation survey, 83 percent agreed that it was acceptable for Whites and Blacks to date each other (Taylor et al. 2010), but only 66 percent of non-Blacks in the Pew survey indicated that they would “be fine” with having a member of their family marry a Black person. The Black exceptionalism becomes visible in actual marrying behavior, as the share of Blacks who outmarry Whites is substantially lower than the share of other ethno-racial minorities who outmarry to Whites (Lichter and Qian 2004).

Previous Research on Interracial Marriage

Although indicators of interracial marriage have yet to be incorporated in the hate crime literature, there is a research tradition that relates such marriage to crimes other than hate crime. These studies evolved with reference to Blau’s macrostructural theory of intergroup relations (1977), which is concerned with the structural determinants of social associations in general. The theory explicates how structural arrangements constrain or facilitate opportunities for social contacts, which are necessary for any social association including interracial marriages. The underlying assumption is that structural constraints affect marriage notwithstanding cultural values that might influence in-group or out-group marriage.

The macrostructural theory implies a set of hypotheses pertaining to four structural conditions relevant for the occurrence of intergroup social associations. One, the rate of out-group associations is higher when the relative size of the out-group is smaller because members of large groups can more easily interact primarily among themselves, while members of small groups are more constrained to look outside their group for partners in social relations. Two, a higher degree of heterogeneity is related to more intergroup relations (and higher interracial marriage) because the greater the heterogeneity, the greater are the chances that any fortuitous encounter involves persons of different groups. Three, the degree of inequality between groups is negatively related to the rate of intergroup and out-group associations because inequality along status dimensions inhibits contacts between members of the unequal groups. Four, when the degree of residential segregation among groups is lower, intergroup and out-group associations are more prevalent because opportunities for fortuitous contacts vary along with the location of persons in physical space. Consistent with this theory, Blau and colleagues (1982) showed that the smaller the group’s relative size, the higher is its outmarriage rate, that is, the proportion of that group whose spouse is not a member of the same group. A few years later, South and Messner (1986) tested Blau’s (1977) theory of social structure in their analyses focused on interracial marriages and interracial violent crime. They detected moderate positive correlations between interracial marriage and interracial crime—two very different forms of social association (South and Messner 1986:1420).

More recently, after decades of extraordinary influx of new immigrants, research has reported that even as the overall number of interracial marriages in the United States increased, the outmarriage rates among some rapidly growing immigrant groups declined (Qian and Lichter 2007). Generally, the percentage of racially endogamous marriages still proved to be inversely related to group size (Qian and Lichter 2011). In short, the literature on racial intermarriage lends credibility to the general hypothesis that this type of social relationships is likely to be related to other forms of intergroup associations, including criminal victimizations, while drawing attention to selected features of social structure that might be determinants of both. These findings imply that such features of social structure need to be taken into account when attempting to assess any causal connections between indicators of racial intermarriage and other forms of interracial associations.

The Current Study

We examine the extent to which hate crimes are influenced by racial out-group marriage across metropolitan statistical areas (MSAs), drawing upon two theoretical perspectives that direct attention to different processes among different segments of the population that might generate opposing relationships between levels of racial out-group marriage and anti-Black hate crime rates. The first one is the group threat perspective, with a particular focus on cultural, symbolic threat for those members of the dominant White population who harbor racial animus. Marriage is a socially supported union that serves as the basis of and foundation for family and offspring. From the perspective of White racists, Black out-group marriages with White persons involved might therefore be associated as a very direct threat to the White race. Additionally and more generally, Black out-group marriages with Whites pose a threat to the privileged position of the dominant Whites in the contemporary United States. For those with racial animus, Black out-group marriages with Whites are perceived as the willingness of Blacks to transgress cultural boundaries and dilute these boundaries. This should tend to promote anti-Black hate crimes by triggering these acts among this segment of the White population. Hate crimes against Blacks can then serve the purpose of sending a message of retaliation and intimidation to protect Whites from further racial trespassing.

The potential importance of a symbolic, cultural threat is suggested by research on extremist populations. For example, Glaser, Dixit, and Green (2002) examined the endorsement of interracial violence among 38 White racists in Internet chat rooms. Their results revealed that the respondents felt most threatened and most supportive for the use of violence by genetic threat (interracial marriages) and to a lesser extent by economic (job competition) or even by territorial threat (minority in-migration). Green and colleagues (1999), meanwhile, reported that White supremacists were neither more economically frustrated nor pessimistic about future finances, but they were considerably more opposed to interracial marriage.

This research on resistance to interracial marriage, along with the general group threat perspective, would suggest a positive relationship between Black–White marriages and anti-Black hate crimes insofar as Black–White marriages represent the undesired trespassing of highly salient cultural boundaries and a threat to the privileged position of Whites in the United States. Black–White interracial marriage essentially serves as a trigger for potential hate crime among the segment of the dominant White population harboring racial animus.

By contrast, Allport’s (1954) contact hypothesis calls attention to different processes that are likely to be associated with interracial marriage. The contact hypothesis is based on the notion that negative intergroup relations arise out of ignorance that derive from limited contact with out-group members. The lack of knowledge about members of different groups encourages negative, stereotypical views, whereas frequent intergroup contact promotes more positive attitudes, as contact allows in-group members to gain information, make positive experiences, and build friendships with members of other groups. Moreover, contact reduces intergroup fears, strengthens empathy (Pettigrew and Tropp 2008), and it “deprovincializes” (Pettigrew 1998), that is, contact facilitates the insight that one’s own cultural standards and habits are not the only thinkable and possible ones. The more and the better the nature of the contact between groups, the lesser the prejudice and the better the intergroup relations. Allport further suggested four conditions for successful intergroup contact: status equality, cooperative events, common goals, and support by authority figures. A meta-analysis on studies on the contact hypothesis by Pettigrew and Tropp (2006) showed a medium overall correlation (r = −.21) between intergroup contact and reduced prejudice and confirmed the relevance of the contact conditions, as the effects were amplified when the conditions were met.

More recently, further studies showed that not only direct but also indirect contact is able to enhance intergroup relations. Different forms such as “imagined contact” (Turner, Crisp, and Lambert 2007), “virtual contact” with the help of social media (Lemmer and Wagner 2015), and “extended contact” (Wright et al. 1997) in the form of knowledge about positive contacts between an in-group member and an out-group member have been associated with reduced prejudice.

The contact hypothesis not only proved to be worthwhile for reducing attitudes such as prejudice but also for enhancing intergroup relations in terms of behavior and behavioral intentions. Several studies showed how contact also reduces discriminatory intentions (e.g., Wagner, Christ, and Pettigrew 2008), as well as avoidance tendencies and readiness for aggressiveness (Asbrock, Wagner, and Christ 2006). Emerson, Kimbro, and Yancey (2002) showed the effect of prior experiences of interracial contact in schools and neighborhoods on the likelihood of adults having more racially diverse general social groups, friendship circles. Moreover, they showed that respondents with prior experiences of interracial contact tend to intermarry more later on.

Moreover, a widespread environmental “hostile climate” (Piatkowska and Hövermann 2019) where many persons share prejudices and discriminate against Blacks and potentially legitimize and turn a blind eye to—if not commit—hate crimes becomes less likely in such regions, when many persons decide to marry a person of another race. Accordingly, insofar as interracial marriage is understood as a marker for intercultural accommodation, we would expect less visible and defining cultural and racial boundaries among the population at large and lower rates of anti-Black hate crimes.

The processes of symbolic, cultural threat and intercultural accommodation enumerated above are not necessarily mutually exclusive. Higher levels of Black out-group marriage with Whites might signify and facilitate more cordial racial relations in the population at large, while at the same time intensifying the animus of those with racist leanings. There are thus plausible theoretical grounds for anticipating either a positive or a negative relationship between levels of Black out-group marriage with Whites and rates of anti-Black hate crime, reflecting the relative strength of the potentially countervailing processes of symbolic, cultural threat and intercultural accommodation associated with racial intermarriages.

In the present study, we examine empirically the association between the levels of Black out-group marriage with Whites and anti-Black hate crimes. We operationalize rates of these offenses in two ways—as incidence rates and as victimization rates experienced by Blacks—given the findings in past research that the structural correlates of hate crimes differ across these two operationalizations (Disha et al. 2011; Piatkowska et al. 2018a). By introducing Black out-group marriage rates, we thus extend prior research that has largely focused on the effects of the relative group size and economic conditions in predicting crime motivated by bias. Our analyses with data for MSAs also allow for an assessment of the stability of structural correlates of hate crime rates across units of analysis, given that their effects have been mainly explored at the community and county levels (e.g., Disha et al. 2011; Lyons 2007, 2008).

Data and Methods

To test the hypotheses outlined above, we assembled data from several sources: the FBI’s UCRs (U.S. Department of Justice 2009, 2010a, 2010b, 2010c, and 2011), the U.S. Census (U.S. Census Bureau 2010, 2017), the American Community Survey (ACS; U.S. Census Bureau, American Community Survey 2010), Diversity and Disparities Project (Logan and Stults 2011), and the Anti-Defamation League (ADL 2010). Our measure of Black out-group marriage with Whites was constructed using data from the ACS, 2010 (1 percent sample), provided by the Integrated Public Use Microdata Series (Ruggles et al. 2017). The ACS is collected on an annual basis from a national random sample of the population. Approximately 3 million households are selected in an attempt to create a nationally representative population sample (U.S. Census Bureau 2014, 2015). The ACS is suitable for this study because it offers a wide array of information regarding a respondent’s characteristics including race and marital status. Moreover, the ACS provides information on the characteristics of other persons living in the household, including the spouse’s race and marital status. This enables us to identify interracial marriages between Black and White individuals within the sample. The ACS is also well suited because it identifies the MSA within which the respondent resides. This allows us to combine data on interracial marriage from the ACS, 2010, with data on anti-Black hate crimes provided by the UCRs as well as with data on other measures included in the regression models, which yielded a maximum sample of 250 MSAs for the analysis.² MSA accordingly constitutes our unit of analysis.

MSAs have proven to be a suitable unit of analysis to study the structural determinants of interracial marriage and their effects on interracial crime (Blau et al. 1982; South and Messner 1986). Blau et al. (1982:48) used American Standard Metropolitan Statistical Areas (SMSAs) to assess their macrostructural theory of intergroup relations, suggesting that SMSAs are apt units for such a test because “they are large collectivities where group size is likely to exert less constraint than in small communities.” A similar logic can be ascribed to Black–White out-group marriage and its association with anti-Black hate crime rates. Large units such as MSAs are likely to offer a larger marriage market that is not constrained by the relative minority group sizes. Accordingly, members of minority groups in MSAs may be less inhibited looking outside their group for partners in social relations, as could be the case in small communities. To that end, any potential relationships between Black–White out-group marriage and anti-Black hate crime rates across MSAs are likely to reflect the perceptions and reactions to the symbolic, cultural threat posed by the minority members or, by way of contrast, constitute a reflection of a cultural accommodation rather than serve as a function of the relative minority group size in a given MSA.³

Dependent Variables

Our dependent variables are anti-Black hate crime rates. Data on these measures were taken from the FBI’s UCRs as provided by the Interuniversity Consortium for Political and Social Research (U.S. Department of Justice, 2009, 2010b, 2011). According to the UCR, 6,628 incidents were reported in 2010: anti-Black incidents constituted 33.21 percent and anti-White 8.68 percent (U.S. Department of Justice 2010b).

Prominent issues related to hate crime reporting and recording warrant attention. In 1990, the U.S. Congress passed the Hate Crimes Statistics Act of 1990 mandating that law enforcement agencies submit annual reports of hate crimes to the UCR (McDevitt et al. 2003; Perry 2010). Nonetheless, many agencies failed to comply with this requirement. Given that many law enforcement agencies report zero incidents, it remains uncertain whether the total count of hate crimes reported reflects the actual hate crimes occurring within the jurisdiction. The reported zero may reflect either the absence of hate crimes or simply a failure to comply with federal requirements. We also note that undercounting may result from inadequate police training or failure to report hate crime due to the lack of trust in the criminal justice system. Furthermore, factors such as social movement outcomes, population composition, and political incentives have been found to influence hate crime reporting (King 2007; McVeigh, Welch, and Bjarnason 2003). With these limitations in mind, we proceed given that data on hate crime as provided by the UCR continue to be “our best source of national hate crime data” (McDevitt et al. 2003:78). Moreover, these data are useful for our study because they offer information on the geographic location of hate crimes within the United States. This allows us to aggregate county-level data to the corresponding MSA and subsequently to link these outcomes with data on interracial marriage.

Nevertheless, in an attempt to address hate crime nonreporting, we adopt an approach utilized by previous research (Piatkowska et al. 2018a). In their work, Piatkowska et al. addressed ambiguities surrounding reporting in the UCR data on hate crime by analyzing two samples: one based on all counties that treat no-reports as “true zeroes” and the second based on counties that report at least one hate crime of any type during the time period under investigation, but not necessarily a racial hate crime. Thus, in the second sample, the county received a missing value for racial hate crime if no hate crime of any type was recorded but received a zero for racial hate crime if any type of hate crime, not necessarily racial hate crime, was recorded. The authors’ reasoned that “the absence of a record of a racial hate crime can more plausibly be interpreted as a ‘true zero’ if the law enforcement agency has complied with reporting requirements as reflected in some kind of reported hate crime” (Piatkowska et al. 2018a:2). In the present study, we also tested the robustness of our results with a second sample—a sample based on counties that report at least one hate crime of any type during the time period under investigation, but not necessarily a racial hate crime. This “Definite Recorders Sample (DRS)” consists of 236 MSAs as compared with 250 MSAs in the full sample and serves as sensitivity analyses. The results differ not substantively between the two samples, as can be seen in the results table in the Appendix.

As noted above, our dependent variable of hate crime incidents is operationalized in two different ways: an incidence and a victimization rate. First, we computed a hate crime incidence rate with the total population used as the offset. This measure indicates the occurrence of hate crimes relative to the total population. It is a measure that focuses on the location of the incidents and therefore indicates where the hate crimes occurred. It has also been appropriately labeled as the “spaces of hate” in prior research (Disha et al. 2011). We additionally computed the hate crime victimization rate with the total Black population serving as the offset. The hate crime victimization rate indicates where on average the experience of being victimized by a hate crime is high in a particular group. This number captures the risk of being subject to hate crime among members of a group, as the size of the population at risk serves as the denominator.⁴

Following previous work (Piatkowska et al. 2018a), anti-Black hate crime rates were based on three-year averages (2009 to 2011).

Independent Variable

Our focal independent variable is the number of Black out-group marriages with Whites relative to the number of married Blacks. To calculate the Black out-group marriage rate, we created dummy variables indicating Black–White marriages and any type of Black marriage within the ACS data set. These dummies have been subsequently multiplied by personal weights as provided by the ACS to correct for the sampling (Ruggles et al. 2017). The results have been next aggravated to the corresponding MSA units. The Black–White out-group marriage rate has been accordingly constructed by dividing the total number of weighted Black–White marriages by the total number of weighted Blacks married.

Control Variables

We also include a number of additional variables found to be related to crimes motivated by bias (Disha et al. 2011; Green, Strolovitch et al. 1998; Lyons 2007, 2008; McVeigh et al. 2003; Piatkowska et al. 2018a): the relative size of the Black population (percentage of Black population), percent change in the Black population from 2000 to 2010, and the young population (percentage of population between 15 and 24 years). These variables are available to download from the Census Bureau (U.S. Census Bureau 2010, 2017). Our models also include the percent divorced (percent of the population divorced aged 15 years and older) and income per capita in the past 12 months gleaned from the 2010 ACS one-year estimates (U.S. Census Bureau, ACS 2010). Following previous work (Blau 1977; Blau et al. 1982; Blau, Beeker, and Fitzpatrick 1984; South and Messner 1986), we also incorporated indicators of racial inequality and segregation. Racial inequality has been measured by the ratio of White mean income per capita to Black mean income per capita, data for which were available to download from the 2010 ACS one-year estimates (U.S. Census Bureau, ACS 2010). The level of segregation in a given MSA was assessed with an index of dissimilarity between Whites and Blacks as reported by the Diversity and Disparities Project based on the information from the Census Bureau (Logan and Stults 2011). The dissimilarity index, which ranges from 0 to 100, measures the residential distribution of one group across census tracts in the metropolitan area relative to another group. Scores of 60 and above are considered as very high, indicating that for an equal distribution to take place between two groups in a given MSA, about 60 percent (or more) of one group would need to change their residence by moving to a different track (Logan and Stults 2011). Previous research revealed that region constitutes an important factor in predicting compliance with hate crime reporting (King 2007; see also King, Messner, and Baller 2009). Following this line of research, we capture the regional impact by including a dummy variable for South (Non-South serving as the reference category).

Studies on hate crimes also found that county rates for serious criminal offenses are related to crimes motivated by bias (Disha et al. 2011; McVeigh et al. 2003). We accordingly incorporate serious criminal offences from the UCR (U.S. Department of Justice 2010a). Following previous work (Disha et al. 2011; McVeigh et al. 2003), serious criminal offences constitute the sum of reports for murder, forcible rape, robbery, aggravated assault, burglary, larceny, and motor vehicle theft. This measure has been converted into a rate (per 100,000 population). We also incorporate a dummy variable capturing the presence of a state data collection statute in line with previous research (Disha et al. 2011). This variable was available to download from the ADL (2010). Finally, we include police force size (measured as the number of officers per 1,000 population) as control variable (U.S. Department of Justice 2010c). Although the findings are somewhat inconsistent, previous studies have indicated that police force size can serve as a factor that contributes to lower levels of crime (Carriaga and Worrall 2015; Greenberg, Kessler, and Loftin 1983). Increased police size can reduce crime by increasing arrests rates but also through a deterrence mechanism. Such an argument can be applied to crime motivated by bias. Thus, it is plausible that MSAs with larger police forces will have lower levels of hate crime due to higher numbers of personnel to control, report, and deter crime motivated by bias.⁵

The independent and control variables diverged considerably in terms of their measurement units. Accordingly, we converted the original values of all continuous independent variables into standardized scores. These scores were then used in the regression analysis. This approach facilitates the interpretations of the estimates in the regression models and allows us to compare the coefficients more easily.

Analytic Strategy

As noted, our dependent variables are anti-Black hate crime rates. Given that these measures are positively skewed with zero counts, we applied a negative binomial regression model, which offers unbiased estimates for highly skewed event counts and has been used in research on crime motivated by bias (e.g., Disha et al. 2011; Lyons 2007, 2008). Unlike Poisson models, negative binomial regression models allow for overdispersion such as that found in this study (see Table 1).⁶ Our regression models incorporate the dependent variable of anti-Black hate crime as the count measure. We converted this measure into rates using the offset function to adjust for the differences in the “population at risk” across units. In the anti-Black victimization hate crime models, the offsets are the log of the Black population, whereas in the anti-Black hate crime incidence models, the offset constitutes the log of the total population. With the regression coefficient fixed at 1, the “offset” allows for hate crime counts to be interpreted as rates. Our initial analysis revealed that few MSAs recorded a very small number of married Blacks, which resulted in out-group marriage rates of questionable reliability given that they are based on only very few cases. Accordingly, we examine only MSAs with at least 10 or more married Blacks for the anti-Black hate crime models.⁷

Table 1.

Descriptive Statistics for Variables Used in the Analysis.

Variables	Mean	Standard Deviation
Anti-Black hate crime
Anti-Black hate crime (count)	23.69	53.61
Anti-Black hate crime incidence rate (per 100,000)	0.74	0.76
Anti-Black victimization rate (per 100,000)	13.43	32.75
Independent variable
Black out-group marriage relative to the Black married	0.12	0.11
Control variables
Percent Black	13.77	10.38
Percent Black change	20.16	20.96
Index of dissimilarity	50.02	11.47
White/Black income	1.75	.33
Divorce rate	11.44	1.78
Percent young	14.97	3.11
Income per capita	24,524.23	4,374.68
Total crime rate (per 100,000)	3,621.97	898.07
Mandate for data collection	0.62	0.49
Police size (per 1,000)	2.45	1.60
South	0.47	0.50
Total population	3,231,252	6,807,236
Black population	488,949	1,362,301
N	206	206

Results

We begin by displaying the spatial patterning of hate crime rates across MSAs. The quintile maps for anti-Black hate crime incidence and victimization rates are displayed in Figures 1 and 2. These figures reveal a considerable amount of overlap. Consistent with previous research (Piatkowska et al. 2018a), high anti-Black hate crime incidence rates, as well as high anti-Black victimization rates, can be found across MSAs in the Northeast and Midwest and across MSAs located in the Southern and Western parts of the West. In the Southeast, both anti-Black hate crime incidence rates and anti-Black victimization rates are considerably lower. However, similar to previous research, there are some differences in spatial patterning of anti-Black hate crime incidence rates and anti-Black hate crime victimization rates. That is, in the Southeastern parts of the East, the anti-Black hate crime victimization rates appear to be even lower than the anti-Black hate crime incidence rates.

Figure 1.

Anti-Black hate crime incidence rate, colors based on quintiles.

Figure 2.

Anti-Black hate crime victimization rate, colors based on quintiles.

Table 1 displays descriptive statistics for variables used in this analysis. Recall that in the regression analyses, we control for either the total population or the number of the Black population by the use of an offset. For the sake of readability of descriptive statistics, the anti-Black hate crime count has been converted into rates (per 100,000 population) in Table 1. These results reveal that, on average, an MSA experienced 13.43 anti-Black victimization hate crimes (per 100,000 population) with a standard deviation of 32.75 and 0.74 anti-Black hate crime incidence rate (per 100,000 population) with a standard deviation of 0.76. The hate crime victimization rates are considerably higher than the incidence of hate crime rates, which reflects different populations at risk utilized. These findings are in line with previous research, which indicates that Blacks are by far the most frequently victimized group according to federal statistics (U.S. Department of Justice 2010b, 2017).

Table 1 also reveals that the mean of the Black out-group marriage rate relative to the number of married Blacks is equal to 0.12 with a standard deviation of 0.11. Income per capita was found to be positively skewed. Accordingly, we applied a natural logarithm to reduce this skewness. With a natural logarithm, the mean for income per capita was equal to 10.09 with a standard deviation of 0.17. The results from the bivariate analyses and analyses of the variance inflation factors (VIFs) revealed that multicollinearity is not a problem in the current analyses, indicating the highest VIF of 2.38 for percent Black in Table 2.

Table 2.

Anti-Black Hate Crime Rates Regressed on Black Out-group Marriage and Control Variables.

Independent Variables	Anti-Black Hate Crime Incidence Rate	Anti-Black Victimization Rate
	Model 1	Model 2
Black out-group marriage relative to the number of married Blacks	0.053 (.152)	0.444** (.154)
Percent Black	−0.090 (.098)	−0.715*** (.098)
Percent Black change	−0.234 (.142)	−0.165 (.148)
index of dissimilarity	−0.084 (.095)	−0.239* (.097)
White/Black income	0.011 (.091)	0.051 (.094)
Divorce rate	0.195* (.086)	0.155 (.090)
Percent Young	0.272** (.084)	0.257** (.086)
Income per capita (ln)	0.364*** (.081)	0.361*** (.084)
Total crime rate	0.104 (.077)	0.162* (.077)
Mandate for data collection	−0.118 (.127)	−0.158 (.133)
Police size	0.019 (.062)	−0.038 (.065)
South	−0.687*** (.166)	−0.758*** (.175)
Constant	−11.58*** (.126)	−8.95*** (.131)

Note: Full sample, N = 206. Standard errors are in parentheses. Coefficient estimates were based on standardized scores.

*p < .05. **p < .01. ***p < .001 (two-tailed test).

Table 2 displays the results of regressions of hate crime rates on Black out-group marriage with Whites relative to the total number of Blacks married, net of the control variables. Model 1 reports the results of anti-Black hate crime rates with the total population used as the offset, while model 2 reports the results of anti-Black hate crime victimization rates with the Black population as the offset. The estimates reveal that Black out-group marriage with Whites relative to the total number of Black marriages yields a significant and positive effect on anti-Black hate crime victimization rates (model 2: b = .444, p < .01). Net of other predictors, a one unit increase in the standardized Black–White out-group marriage rate (e.g., one standard deviation, or 0.11 in Table 1) leads to an increase of approximately 55.89 percent ([(exp(.444)) − 1] × 100) in the anti-Black hate crime victimization rate. In other words, high levels of Black out-group marriage with Whites relative to the total number of Black married are associated with high anti-Black hate crime victimization rates. The results also reveal that Black out-group marriages with Whites exhibit no effect on the incidence of anti-Black hate crimes, that is, the “spaces of hate.”

Turning to the control variables, the results in Table 2 show several noteworthy findings across both models. In line with previous literature (Disha et al. 2011; McVeigh et al. 2003; Piatkowska et al. 2018b), we find significant positive effects for the percentage of young population and income per capita across two models. A relatively large proportion of young population and higher income per capita are associated with high levels of anti-Black hate crime victimization rates and anti-Black hate crime incidence rates. Another interesting finding is the effect of the regional variable South. The regression coefficients for this measure are significant and negative in both models, suggesting that hate crimes are less likely to be recorded in the South versus non-South regions. Net of other predictors included in this study, the likelihood of the incidence of anti-Black hate crime in the South decreases by approximately 49.69 percent ([1 − (exp(−.687))] × 100), whereas the risk of victimization decreases by approximately 53.14 percent ([1 − (exp(−.758))] × 100) relative to the other U.S. regions. This is consistent with previous work on crime motivated by bias (King 2007; see King et al. 2009). Some striking differences across both models also emerge, however. In contrast to the findings for anti-Black hate crime incidence rates, percent Black (model 2: b = −.715, p < .001) and the index of dissimilarity (model 2: b = −.239, p < .05) are negatively associated with anti-Black victimization rates. The results also show that the regression coefficient for total crime rates is positive and significant (model 2: b = .162, p < .05) in model 2 but falls below conventional levels of statistical significance for anti-Black hate crime incidence rates. Finally, the divorce rate yields significantly positive effect on anti-Black hate crime incidence rates (model 1: b = .195, p < .05) but only reaches a level of significance at 0.10 level with two-tailed test (b = .155, p = .084) for anti-Black hate crime victimization rates.

We next considered the extent to which the impact of Black out-group marriage relative to the total number of Blacks married is conditioned by the regional variable South. As shown in Table S.3 and Table S.4 in the Online Supporting Information, none of the interaction terms between Black out-group marriage with Whites relative to the total number of Blacks married and the regional variable South were significant.

Summary and Conclusion

The overarching objective of the present study has been to examine the association between the Black out-group marriage and rates of the incidence of anti-Black hate crimes relative to the total population and anti-Black hate crime victimization rates across U.S. MSAs circa 2010. Drawing upon the variant of group threat theories that highlight the salience of symbolic, cultural threats and the well-known contact theory (Allport 1954), we explicated different processes that serve as the basis for two alternative hypotheses. On the one hand, we hypothesized that levels of marriages of Blacks with Whites will be positively related to anti-Black hate crime rates to the extent that such marriages are perceived as symbolic, cultural threats by the segment of the population harboring racial animus. On the other hand, we suggested the levels of marriages of Blacks with Whites will be negatively related to anti-Black hate crime rates insofar as levels of Black out-group marriage reflect and facilitate intercultural accommodation. Our analyses incorporated a well-established set of control variables, thereby allowing for an assessment of the consistency of the relationships between the structural correlates and hate crime rates across the units of analysis (MSAs in comparison with previously examined neighborhoods and counties).

We began by investigating the spatial distribution of the incidence of anti-Black hate crime rates and anti-Black hate crime victimization rates across MSAs. The figures reveal that the spatial patterns for anti-Black hate crimes across MSAs generally correspond to those reported at the county level in previous research (Piatkowska et al. 2018a). Specifically, we found both the anti-Black hate crime incidence rates and anti-Black victimization rates tend to be high across MSAs in the Northeast and Midwest, and in the Southern and Western parts of the West, but to be substantially lower in the Southeast. The comparatively low rate in the Southeast is especially prominent for anti-Black hate crime victimization rates, which is also consistent with previous research (Piatkowska et al. 2018a).

The empirical findings from the regression models are consistent with the processes derived from the cultural, symbolic variant of group threat theory. Specifically, we find that when the share of Black respondents married to White spouses (relative to the total married Blacks) increases, the victimization risk for anti-Black hate crimes in a given MSA also increases. These findings are consistent with the view that among the segment of the population that harbors racial animus, interracial marriage represents the trespassing across especially salient cultural boundaries, which can trigger violence against the threatening group to preserve the dominant position of the privileged group. The present study thus accords with speculations in previous research that interracial marriages can be strongly perceived as a symbolic threat to the in-group’s identity and can lead to vengeance in terms of hate crimes (i.e., Glaser et al. 2002). More generally, this work aligns with a body of work indicating that marriages and sexuality between Whites and Blacks has been sanctioned historically (Kennedy 2003; Moran 2001; Yancey 2003).

Our finding of the positive association between the Black out-group marriage rate with Whites and anti-Black hate crimes does not necessarily discredit the more general contact hypothesis. It is conceivable and even likely that interracial marriage is indeed both a reflection of and a facilitator of a general climate of intercultural accommodation. Moreover, such a climate might under certain conditions contribute to lower levels of hate crime (Piatkowska and Hövermann 2019), when community condemnation inhibits residents from turning a blind eye to hate incidents and from perpetrators having the belief that they are acting in the name of the people. At the same time, intercultural accommodation among the population at large might itself intensify the racial animus of the segment of the population at risk of committing crimes of racial bias. These persons might perceive the more widespread intercultural accommodation as an additional threat, which could serve to trigger acts of retaliation. It seems reasonable to expect that this segment of those with extreme racial bias in the population is largely isolated and marginalized, having limited direct or indirect contact with Blacks, which might counteract their prejudices. Our results are thus consistent with the speculative interpretation that any salutary impact of an intercultural accommodation among the population at large accompanying high levels of racial intermarriage are overwhelmed by the influence of the perceived cultural threat and intensification of animus for the population “at risk” of perpetrating anti-Black hate crimes. To the extent that there is merit in this interpretation, it underscores the importance of considering distinct but potentially intertwined processes that apply to differentiated segments of dominant groups.

While the results reveal a positive association of levels of Black out-group marriage with Whites and anti-Black hate crime victimization rates, we find no significant effect of this predictor on anti-Black hate crime incidence rates. The observation of different associations for varying operationalizations of hate crime rates is in line with previous research that compared different hate crime rate conceptualizations (Disha et al. 2011; Piatkowska et al. 2018a). Recall that incidence rates—indicators of the location of incidents or the “spaces of hate”—are calculated relative to the population at large. These rates thus appear to be impacted by features of the general rather than the race-specific population, as reflected in our findings of significant impacts of income levels, age structure, and family structure.

Turning to the control variables, the empirical findings with respect to the effect of racial composition on anti-Black hate crime victimization rates are compatible with the power-differential hypothesis, given the observed negative regression coefficient. These results are in accord with those based on U.S. counties (Piatkowska et al. 2018a). Recall that the power-differential hypothesis predicts that hate crime against Blacks is lower in places with a higher proportion of the Black population because the perpetrators are more likely to fear reprisal (Levine and Campbell 1972). Our findings with respect to the effect of the regional variable are also in accord with previous research, which has revealed that region constitutes an important factor in predicting compliance with hate crime reporting (King 2007; see King et al. 2009).

Additional findings reveal that both incidence rates of anti-Black hate crimes and anti-Black hate crime victimization rates are influenced by age structure and by our measure of average economic well-being—income per capita, showing consistency across the unit of analysis (Disha et al. 2011; McVeigh 2003; Piatkowska et al. 2018a). The authors of previous work interpreted the findings with respect to income as consistent with the defended neighborhood hypothesis (Lyons 2007, 2008; Suttles 1972) and with the argument that more affluent places have stronger informal control and more economic resources, and as such their reaction to the perceived threat is more intense. Our study also indicates that total crime rates are related to hate crimes, which is in accord with prior research utilizing different unit of analysis (Disha et al. 2011; Piatkowska et al. 2018a), and that divorce rates are positively associated with the rates of incidence for Blacks, while reaching α of 0.10 with a two-tailed test for anti-Black victimization rates.

We acknowledge limitations of the present study. Despite our efforts to address the issue of hate crime nonreporting by replicating the analyses with a filtered DRS sample, ambiguities about the interpretation of nonreports of racially biased crimes in the UCRs remain. Another important limitation pertains to the lack of information on the race of the offender in the hate crime files as recorded in the UCRs (U.S. Department of Justice 2010b). Accordingly, we were unable to differentiate between hate crimes committed by the presumably largest group of perpetrators of anti-Black hate crimes (Whites) and other race-specific populations such as Asians or Hispanics. This could very well have implications for our understanding of the relationship between racial out-group marriage and crime motivated by bias. An important task for future research is thus to incorporate information on race of the offender in predicting hate crime rates and to create the appropriate race-specific out-group marriage rates. With these measures, the potential identity threat (and therefore hate crime legitimization) for the presumably largest group of perpetrators of anti-Black hate crimes—Whites—would be considered explicitly.

Another important caveat of the current research pertains to the sample constraints. As discussed in note 2, the sample of MSAs utilized in the present study is not comprised of all the counties that reported anti-Black hate crimes in the UCR files. Moreover, about 43 percent of the U.S. counties (with 135 anti-Black hate crimes) did not belong to either Micropolitan Statistical Areas or MSAs. Micropolitan Statistical Areas (with 210 anti-Black hate crimes) were also not included in the analysis. As the data continue to improve, the generalizability of our findings will be improved by including data that cover a larger share of territories if not the entire United States in future research.

The current research has the potential to stimulate additional future studies as well that focus on hate crimes against other ethnic, cultural, or religious groups. In light of the current political environment and galvanizing events, such as the Pittsburgh synagogue shooting in October 2018, much insight could be gained from studies that examine the relationship between interreligious marriages and crime motivated by religious affiliation. Such an investigation is beyond the scope of the present study; however, it is possible that the theoretical arguments outlined above could apply to anti-Muslim and anti-Jewish hate crimes, as well. These relationships could diverge before and after triggering events and/or be amplified by the political environment.

As the data continue to improve, future research may also seek to disaggregate the dependent variable by the type of offense to better understand the relationship between racial out-group marriages and crime motivated by bias. We interpret the positive association between Black out-group marriages with Whites with reference to the transgression of cultural boundaries that trigger crimes motivated by bias among populations with racial animus. This relationship may be particularly strong for serious violent crimes rather than for less serious crimes like vandalism or larceny, which may often be committed by youths for the sake of thrills only. Indeed, thrill seekers refrain from using guns as opposed to, for instance, mission offenders (McDevitt, Levin, and Bennett 2002; see also Walters 2011). Unfortunately, the requisite data on hate crime at the county level to conduct these analyses are not available. The UCR record type files utilized in this study offer information on the location of the offense (i.e., residence/home, highway/road/alley) and the motivation for the offense (i.e., anti-Black, anti-Asian) only. Consequently, we are unable to distinguish violent hate crime from nonviolent in this study.

Another plausible avenue for future research is to examine the effects of sex-specific indicators of interracial marriage. For example, statistics reveal that among newlywed Blacks in 2013, men (25 percent) were twice as likely as women (12 percent) to marry someone of a different race. This pattern is quite the opposite for Asians, with 37 percent of Asian women and 16 percent of Asian men marrying someone of a different race (Wang 2015). Accordingly, it would be instructive to see how these patterns are reflected in crime motivated by bias, especially when data on the latter improve and provide information on hate crime incidents by gender.

In conclusion, our analyses extend the body of literature on bias-related crimes by directing attention to interracial marriage rates as an important yet largely neglected structural condition. Our analyses reveal a previously undetected but important risk factor for hate crime victimization: When Blacks tend to choose Whites as marriage partners, they increase the likelihood that the Black population in their area will experience crimes of hate. Understanding the racialized processes that link intermarriage with hate crimes constitutes a topic of considerable academic and public policy concern.

Supplemental Material

Supplementary_Materials - Black Out-group Marriages and Hate Crime Rates: A Cross-sectional Analysis of U.S. Metropolitan Areas

Supplementary_Materials for Black Out-group Marriages and Hate Crime Rates: A Cross-sectional Analysis of U.S. Metropolitan Areas by Sylwia J. Piatkowska, Steven F. Messner and Andreas Hövermann in Journal of Research in Crime and Delinquency

Footnotes

Appendix

Table A1.

Anti-Black Hate Crime Rates Regressed on Black Out-group Marriage and Control Variables—“Definite Recorders Sample.”

Independent Variables	Anti-Black Hate Crime Incidence Rate	Anti-Black Victimization Rate
	Model 1	Model 2
Black out-group marriage relative to the number of married Blacks	−0.020 (.138)	0.373* (.149)
Percent Black	0.020 (.093)	−.555*** (.098)
Percent Black change	−0.042 (.126)	0.026 (.136)
Index of dissimilarity	−0.071 (.087)	−0.259** (.093)
White/Black income	0.018 (.084)	0.047 (.090)
Divorce rate	0.184* (.081)	0.122 (.087)
Percent young	0.295*** (.078)	0.255** (.084)
Income per capita (ln)	0.224** (.075)	0.237** (.083)
Total crime rate	0.006 (.071)	0.041 (.075)
Mandate for data collection	−0.140 (.117)	−0.099 (.129)
Police size	0.041 (.056)	−0.013 (.063)
South	−0.561*** (.155)	−0.677*** (.170)
Constant	−11.31*** (.116)	−8.75*** (.127)

Note: N = 198. Standard errors are in parentheses. Coefficient estimates were based on standardized scores.

*p < .05. **p < .01. ***p < .001 (two-tailed test).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The preparation of the manuscript was supported and funded by a Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) research fellowship to Andreas Hövermann [HO 5858/1-1].

ORCID iD

Sylwia J. Piatkowska

Supplemental Material

Supplemental material for this article is available online.

Notes

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