“Seeing” Hate Crime in the Community: Do Resident Perceptions of Hate Crime Align With Self-Reported Victimization?

Abstract

Hate crimes undermine tolerance and social inclusion by conveying an “outsider” status of the victim and other group members to the broader community. Yet, limited research considers whether non-victims recognize hate crime incidents when they occur. Using census and survey data for 4,000 residents living in 145 communities, we ask whether local residents “see” hate crime when it happens in their neighborhood and whether the neighborhood context influences the association between residents’ perceptions of hate crime and self-reported hate victimization. We find that residents’ perceptions are positively related to victim self-reports; however, this relationship weakens in ethnically diverse and disadvantaged areas. This suggests that residents’ perceptions of hate crime may be more dependent upon the community context than non-hate crimes.

Keywords

hate crime ethnic diversity victimization disadvantage

Introduction

Criminal victimization is considered a hate crime when it is motivated by an offender’s hostility or prejudice toward a victim on the basis of their race, religion, sexual orientation, disability, or gender identity (Harlow, 2005; Home Office, 2013). Crimes motivated by hate are widely believed to cause greater harm than equivalent crimes without bias intent (Iganski, 2001; McDevitt, Balboni, Garcia, & Gu, 2001). Compared with non-hate victims, hate crime victims more commonly report feeling unsafe, fear future victimization, and face greater difficulties overcoming the incident (Home Office, 2013; McDevitt et al., 2001). Hate crimes are also unique in their ability to send a message to minority group members that they are neither safe nor welcome in the community (Iganski, 2001). Perry (2014) argues that the impact of hate crime extends even further, affecting “all members of the civic culture in question” (p. 47). Hate crimes are therefore considered more damaging to the social fabric of society than parallel crimes as they attack collective values, disrupt social harmony, and fuel intolerance for diversity (Iganski, 2001; McDevitt et al., 2001).

While there has been significant scholarly attention on the short- and long-term effects of hate crime victimization for hate crime victims (Ehrlich, Larcom, & Purvis, 1994; Gerstenfeld, 2013; Herek, Gillis, & Cogan, 1999; Iganski & Lagou, 2014; Lawrence, 1999; Leets, 2002; Levin, 1999; Meyer, 2010) and fellow minority group members (McDevitt et al., 2001; Perry, 2014; Perry & Alvi, 2012), there is limited research that considers the impact of hate crime on the wider community. For instance, we do not know whether non-hate crime victims are aware that hate crime has occurred in their local area, and scant evidence suggests that the general public views hate crime as more harmful to the community than non-hate crime. This is largely due to the lack of available data for a large-scale analysis of hate crime. Although hate crime victimization studies exist (Chakraborti, Garland, & Hardy, 2014; Home Office, 2013; Williams & Tregidga, 2013; Wilson, 2014), they do not capture community-level variation in hate crime nor can they assess whether residents of these communities reliably detect hate crime victimization.

In this article, we examine whether or not community residents “see” hate crime incidents. Specifically, we assess whether residents’ perceptions of hate crime align with self-reported hate crime incidents that have taken place within the community. We employ unique data from more than 4,000 residents, including an ethnic booster sample of residents from Indian, Vietnamese, and Arabic speaking backgrounds, living in 145 communities across Brisbane, Australia. In our analyses, we focus on the association between perceptions of hate crime motivated by the skin color, race, religion, or ethnicity—one of the most frequently reported categories of hate crime (Home Office, 2013; M. Wilson, 2014)—and self-reported hate crime victimization. Drawing on a long history of disorder research (Kanan & Pruitt, 2002; LaGrange, Ferraro, & Supancic, 1992; Mellgren, Pauwels, & Levander, 2010; Skogan, 1990, 2008; J. Wilson & Kelling, 1982), we consider the utility of using residents as neighborhood informants on hate crime and explore the community-level structural characteristics that make hate crime more or less visible.

Literature Review

Hate crime scholarship has advanced significantly in the last 20 years. We now have a solid understanding of the motivations associated with hate crime perpetration (Byers, Crider, & Biggers, 1999; Craig, 2002; Dunbar, Quinones, & Crevecoeur, 2005; McDevitt, Levin, & Bennett, 2002) and the effects of hate crime on victims and fellow minority group members (Herek et al., 1999; Home Office, 2013; Iganski, 2001; Iganski & Lagou, 2014; Lawrence, 1999; McDevitt et al., 2001). Despite important advances in hate crime scholarship, we know little about the broader community context and its influence on hate crime and perceptions of hate crime. Where scholars have considered the ecological context of hate crime, they have relied predominantly on official data (Brimicombe, Ralphs, Sampson, & Tsui, 2001; Grattet, 2009; Green, Strolovitch, & Wong, 1998; Lyons, 2007, 2008). Yet, police records only capture the “tip of the iceberg” (Levin, Rabrenovic, Ferraro, Doran, & Methe, 2007, p. 247), significantly underrepresenting actual levels of hate crime across communities (Chakraborti et al., 2014; Levin, 1999; Perry, 2001). However, what we know from studies using official hate crime data is that hate crime is more likely to occur in communities where there is increasing competition for scarce economic and political resources (Lyons, 2007; Stephan & Stephan, 2000) or where minorities are perceived as a cultural threat (Grattet, 2009; Green et al., 1998). Under these conditions, hate crimes serve as a mechanism of informal social control by sending a message to minority group members that they are not welcome in the community (King, Messner, & Baller, 2009; Perry, 2001).

Missing from this literature is a focus on the extent to which hate crime sends a message to the wider community. We simply do not know whether residents “see” hate crime when it occurs. Unlike other types of crimes, understanding and recognizing an offender’s motivation is central to distinguishing a hate crime from a non-hate motivated offense (Cronin, McDevitt, Farrell, & Nolan, 2007). From the broader literature, we know that this is not always a simple task. For police, the ambiguity surrounding legal definitions of hate crime, its relatively infrequent occurrence, and the need to investigate motivation regardless of the seriousness of the offense has proven challenging (Cronin et al., 2007). Even victims are often unaware that a particular incident meets an official definition of hate crime (Chakraborti et al., 2014). If police officers and victims have difficulties in recognizing hate crime, community residents might also struggle to identify hate crime incidents.

The broader criminological literature indicates that residents are relatively reliable informants of crime problems in their local community as they are “observers of the local scene” (Skogan, 2012, pp. 176-177). Thus, they have local knowledge of community life which can provide considerable insight into the more significant problems occurring in their area. Residents who live in the same community generally offer fairly consistent responses to the problems occurring in their neighborhood (Sampson & Raudenbush, 2004; Skogan, 2012). Studies testing the reliability of community surveys against other types of measurements including observations and official data reveal a moderate to high level of agreement between the data sources (Hipp, 2007; Perkins & Taylor, 1996; Sampson & Raudenbush, 1999). Yet, research also reveals that perceptions of crime are highly subjective, often driven by the racial/ethnic composition of a particular neighborhood and levels of disadvantage (Sampson & Raudenbush, 2004; Wickes, Hipp, Zahnow, & Mazerolle, 2013).

There is little evidence to suggest that residents recognize hate crime when it happens in their community. Unlike non-hate crimes, residents’ ability to correctly identify the existence of racially motivated attacks in their neighborhood goes beyond just witnessing or knowing about violent crime. It requires residents to first be aware of hate crime as a distinct crime category and second to recognize an offender’s hate motivation by picking up on additional cues like the use of racial slurs or racist graffiti. Whether members of the broader community accurately perceive such incidents as hate crime and the degree to which the socio-structural context influences these perceptions is uncertain.

To date, only two studies shed light on this relationship. The first is Perry’s (2010) study of hate crime perceptions among college students. Of the 807 students surveyed, many respondents reported that they had either witnessed or heard about hate crime incidents ranging from offensive comments and verbal harassment to property damage and sexual assault. However, students did not attribute hate crime incidents to the structural or cultural context of the university. Few students from the majority group believed that the university community was particularly harmful to university students. This led Perry (2010) to conclude that students may “see” hate crime but they may not associate hate crime with the context in which they occur, thus allowing “campus bigotry and its related forms of violence” to remain unacknowledged and unaddressed (p. 274).

The second study examined the extent to which local residents recognize and report hate crime as a significant problem in their residential community. From a survey of 4,000 residents living across 148 communities in Australia, Sydes, Wickes, and Higginson (2014) find that people did report hate crime as a problem and that these perceptions were significantly influenced by the community context. In line with ecological studies of hate crime, their study revealed that residents living in communities with higher levels of ethnic diversity and disadvantage were more likely to perceive hate crime to be a problem. Interestingly, minority and majority residents did not differ in their perceptions of hate crime. Thus, there was no discrepancy between the racial/ethnic background of the participant and the likelihood that they viewed hate crime as a problem in their community.

Together, this research provides some evidence that non-victims do recognize hate crime incidents as attacks that target an individual because of their association with a particular minority group. Considering the serious limitations of official data on hate crime incidents, these studies suggest that residents or bystanders could be an important source of information on the level of hate crime occurring in the community. Indeed this is the central thesis underpinning the recent growth in third party hate crime reporting (Her Majesty’s Government, 2012; Victorian Equal Opportunity and Human Rights Commission [VEOHRC], 2011). Yet, what we do not know is whether people who witness such incidents can reliably distinguish them from non-bias crimes. Our study therefore advances the hate crime scholarship in two important ways: by examining whether shared perceptions of hate crime incidents align with self-reported hate crime incidents; and by identifying the community contexts that might make hate crime more or less visible to community residents.

Method

This article draws on unique survey data from the Australian Community Capacity Study (ACCS), census data from the Australian Bureau of Statistics (ABS), and crime data from the Queensland Police Service (QPS). The ACCS is a study of urban communities in Australia that is supported by Australian Research Council funding (Mazerolle et al., 2012; Mazerolle et al., 2007; Wickes, Homel, McBroom, Sargeant, & Zahnow, 2011). The overarching goal of the ACCS is to understand and analyze the key social processes associated with the spatial variation of crime across urban communities over time.

The Research Site

Brisbane statistical division

Located in Australia, Brisbane is the capital city of the state of Queensland with a population of 2.1 million people (ABS, 2013). In 2011, 29.7% of the Brisbane population were born overseas, with the leading countries of birth including New Zealand, England, India, China, and South Africa (ABS, 2013). In addition, 17.9% spoke a language other than English (LOTE), most commonly, Mandarin, Vietnamese, Cantonese, Samoan, and Spanish (ABS, 2013). Furthermore, Brisbane had the highest indigenous population of the major capital cities¹ at 2.02% (ABS, 2012).

The ACCS survey participants

The 2010 ACCS survey included 4,396 participants, randomly sampled from 148 randomly sampled communities. The ACCS survey was conducted from August to December 2011 by the Institute for Social Science Research at the University of Queensland. Trained interviewers used computer-assisted telephone interviewing to administer the survey, which lasted approximately 25 min. The in-scope survey population comprised all people aged 18 years or above who were usually resident in private dwellings with landline telephones in the selected communities. The response and cooperation rates for this survey were 41.81% and 59.67%, respectively² (for further information, see Mazerolle et al., 2012). The 2010 ACCS survey also included a booster sample of 237 participants from Vietnamese, Indian, and Arabic speaking backgrounds. Face-to-face interviews were conducted with these residents in the respondents’ native language. Participants were sampled using common surnames, which involved generating lists of the most common surnames in the three ethnic groups of interest. These lists were used along with the Electronic White Pages telephone directory to randomly select and contact participants. Of the 237 respondents in the booster sample, 89 were Arabic speaking, 67 were Indian speaking, and 73 were Vietnamese speaking (Murphy, Cherney, Wickes, Mazerolle, & Sargeant, 2012).³

Census Data

In our analyses, we include a number of community-level socio-structural variables from the ABS census data and the QPS. These variables are empirically driven and are associated with officially recorded hate crime incidents (Grattet, 2009; Green et al., 1998; Lyons, 2007, 2008) and residents’ perceptions of crime (Franzini, Caughy, Nettles, & O’Campo, 2008; Hipp, 2010; Quillian & Pager, 2001; Sampson & Raudenbush, 1999, 2004; Wickes et al., 2013). Our variables are described in further detail below. Descriptive statistics on the variables in our analyses are presented in Table 1. The correlations between our variables can be found in the appendix.

Table 1.

Descriptive Statistics.

Variable	M	SD	Minimum	Maximum
Hate crime victimization	0.554	1.096	0	6
Hate crime as a problem (%)	13.906	13.623	0	55.882
Median income	1,227.426	332.140	613	2,323
Different address 5 years ago (%)	42.319	8.971	23.455	79.319
LOTE (%)	14.561	9.759	1.939	54.545
Disadvantage	0.000	1.00	−1.492	3.489

Note. LOTE = language other than English.

Dependent variable: Hate crime victimization

In the ACCS, victimization was measured by the following item: “While you have lived in this community, has anyone ever used violence such as in a mugging, fight or sexual assault against you or any member of your household anywhere in the community?” If the participant responded with yes to this question, they were then asked whether they felt the incident occurred because of the victim’s skin color, ethnicity, race, or religion. These questions were repeated for residential property damage as well as break and enter.⁴ Participants who reported household victimization, and believed it to be due to certain racial/ethnic characteristics, were classified as being hate crime victims. Eighty-two households reported 96 hate crime incidents.⁵ We observed that the number of households who reported hate crime victimization ranged from 0 to 6 within each community. The number of hate victimizations per community is displayed in Table 2.

Table 2.

Hate Crime Victimization Counts Across ACCS Communities.

Number of hate incidents	0	1	2	3	4	5	6
Number of communities	101	31	6	6	1	1	2

Note. ACCS = Australian Community Capacity Study.

We acknowledge in the case of general crimes, violence, property damage, and break and enter are committed for very different purposes (Andresen & Linning, 2012). However, we argue that in the case of hate crime, these offenses share a similar motivation—that is, to send a message of hate and instill fear (Boyd, Berk, & Hamner, 1996). Thus, due to the relatively rare occurrence of hate crime and the shared underlying motivation of various types of hate crime victimization, our dependent variable represents household victimization of any kind that is perceived to be racially or ethnically motivated.

Independent variable: Perceived hate crime in the community

As our primary independent variable, we include a community-level measure of perceived hate crime. In the ACCS, respondents were asked to what extent attacks or harassment of residents in their community based on their skin color, race, ethnic origin, or religion was a problem. Response categories included “no problem” (coded as 0), and “some problem” and “big problem” (both coded as 1). From this, we obtained a total number of respondents who perceived incidents motivated by prejudice to be a problem for each community. Next, the perceptions of respondents who reported hate victimization were removed to avoid their perceptions skewing the results. We then converted this figure to a percentage of respondents who perceived hate crime to be an issue in each community. This figure ranged from 0% in some communities to 54.54% in others with a mean of 13.49% (SD = 13.14%). Figure 1 shows the spatial distribution of residents’ perceptions of hate crime and counts of self-reported hate crime incidents across ACCS suburbs.

Figure 1.

Hate crime victimization and perceptions of hate crime as a problem across ACCS suburbs.

Control variables

Extensive scholarship suggests that residents’ perceptions of crime and disorder are influenced by the community’s socio-demographic composition. Therefore, our control variables are derived from both the ecological studies of hate crime (see Grattet, 2009; Green et al., 1998; Lyons, 2007, 2008) and the disorder literature more broadly (Franzini et al., 2008; Quillian & Pager, 2001; Sampson & Raudenbush, 1999, 2004; Taylor, 2001; Wickes et al., 2013).

We created a factor representing the level of disadvantage in the community that included the proportion of unemployed residents, the proportion of low-income households, the proportion of single-parent households, the proportion of people in social housing, and the proportion of indigenous residents. These variables loaded strongly on one factor (Eigenvalue = 3.78), and more than 75% of the variation in this factor was explained by these variables. All variables had loadings of 0.83 and higher.

We captured the racial/ethnic composition of the community using the proportion of residents speaking a LOTE at home. Research from the United States and elsewhere suggests that the racial/ethnic composition of the community may play an important role in shaping crime perceptions (Quillian & Pager, 2001). We further argue that the racial/ethnic composition of suburbs may be particularly pertinent for hate crime perceptions as it may provide a cue to residents on the number of potential victims in their community (Sydes et al., 2014).

In line with previous ecological hate crime work (Grattet, 2009; Lyons, 2007, 2008), we included a measure of residential stability—the proportion of people living at a different address 5 years ago. Finally, we included a measure of the average violent crime rate per 100,000 people in each suburb between 2007 and 2009 drawn from QPS crime data. This was done to ensure that perceptions of hate crime were not just based on living in a high crime area.

Analytic Strategy

Considering that many communities did not have a self-reported hate crime incident (N = 101), we employed a negative binomial regression model to examine the relationship between residents’ reports of hate crime and self-reported hate crime victimization. A test of equidispersion identified that there was overdispersion in our data, necessitating a negative binomial model (overdispersion = 1.955). A Vuong test rejected the need to use a zero-inflated model (z = .22, ns). In all negative binomial models, we controlled for the number of respondents in the community as the exposure variable. Three suburbs were missing violent crime data and were thus dropped from the analyses. Two of these suburbs reported zero counts of hate victimization and one reported one count of hate victimization. We therefore proceeded to our analyses with a total sample of 145 communities.

We conducted our analyses in a stepwise fashion. Model 1 examined whether residents’ perceptions of hate crime in their community predicted hate victimization. As residents’ perceptions of disorder are shaped by the community context (Franzini et al., 2008; Sampson & Raudenbush, 2004; Wickes et al., 2013), Model 2 included the structural characteristics of the community likely to influence perceptions. We then tested two interaction terms. Model 3 evaluated the predictive strength of perceptions of hate crime and concentration of residents speaking a language other than English on hate crime victimization. Model 4 considered the interaction between disadvantage and perceptions of hate crime.

Results

To answer our first research question, we first examine the relationship between perceptions and victimization without accounting for any other factors in Model 1 (see Table 3):

Research Question 1: Do local residents “see” hate crime?

Table 3.

Model Results.

	Model 1		Model 2		Model 3		Model 4
	Coefficient (SE)	IRR	Coefficient (SE)	IRR	Coefficient (SE)	IRR	Coefficient (SE)	IRR
Perceptions	0.050*** (0.001)	1.051	0.018 (0.013)	1.018	0.046* (0.018)	1.047	0.029* (0.013)	1.029
Disadvantage			0.311* (0.147)	1.365	0.348* (0.140)	1.416	0.812** (0.262)	2.253
Mobility			−2.617 (1.771)	0.072	−3.052 (1.774)	0.047	−3.645* (1.854)	0.026
Violent crime			0.001 (0.001)	1.000	0.001 (0.001)	1.000	0.001 (0.001)	1.000
LOTE			2.654* (1.349)	1.026	0.073** (0.025)	1.076	0.024 (0.013)	1.024
LOTE × Perceptions					−0.002* (0.001)	0.997
Disadvantage × LOTE							−0.018 (0.008)	0.982
Pseudo-R²	.088		.133		.148		.1514
	χ²(1) = 25.27***		χ²(5) = 37.65***		χ²(6) = 41.60***		χ²(6) = 42.32***

Note. LOTE = language other than English.

p < .05 (two-tail test), ** p < .01(two-tail test), *** p < 0.01(two-tail test).

Here, we find a link between the communities where hate crime is perceived to be a problem and self-report victimization. Specifically, a 1% increase in residents who perceive hate crime to be a problem is associated with an increase in hate crime victimization by a factor of 1.05 (Incident rate ratio (IRR) = 1.051, p < .001). The model statistics demonstrate that perceptions of hate crime are significantly associated with the likelihood of hate crime victimization, χ²(1) = 25.27, p < .001.

We then addressed our second research question:

Research Question 2: Under what conditions do residents’ perceptions of hate crime align with victims’ self-reported hate crime?

Acknowledging that the bivariate relationship found above may be due to other signals of community disorder (Sampson & Raudenbush, 2004; Wickes et al., 2013), we included several socio-structural variables, including the disadvantage factor, violent crime, mobility, and minority concentration to control for this effect. We found that once we included these variables in the model, residents’ perceptions of hate crime were no longer associated with hate crime counts in the community. However, the percentage of residents speaking a language other than English in the community did predict self-report hate crime counts (IRR = 1.027, p = .049). Disadvantaged communities were also more likely to have a higher count of hate crimes (IRR = 1.364, p = .035). Interestingly, we find that the average violent crime rate and residential mobility were non-significant in this model. Together, the variables included in Model 2 were significant predictors of hate crime in the community, χ²(5) = 37.29, p < .001. Furthermore, the addition of these socio-structural control variables increased the pseudo-R² from .08 in Model 1 to .13 in Model 2.

We then assessed whether there was a moderating effect between perceptions of hate crime incidents, LOTE, disadvantage, and self-reported hate crime incidents. Here, we were interested in whether the strength of relationship between perceptions of hate crime and hate crime victimization was a function of particular community characteristics that might make hate crime incidents more or less visible. We constructed an interaction term in Model 3, to examine the combined effects of LOTE and perceptions of hate crime on hate crime counts (pseudo-R² = .15). This interaction term was significant (IRR = 0.997, p = .033). We found that in low LOTE communities, residents’ perceptions were more likely to align with self-reported victimization incidents. Yet, in high LOTE areas, the relationship between seeing hate crime and actual hate crime victimization was much weaker (see Figure 2).

Figure 2.

Interaction between perceptions of hate crime and LOTE.

To further examine this interaction, we split the LOTE variable into those above and below the mean of 10% of the population speaking LOTE in the community. Next, we ran these variables in separate models. In the model with the low LOTE communities, perceptions of hate crime significantly predicted hate crime counts. However, in high LOTE communities, this relationship was no longer significant. We altered the cut point at low and high LOTE communities to determine whether there was a point at which LOTE began to break down the relationship between perceptions and victimization. We identified this to be 17.1% LOTE or higher, which is equivalent to around 1 standard deviation above the mean LOTE. Of the 145 suburbs, 120 were below this cut point.

In Model 4, we included an interaction term of perceptions of hate crime and our factor of community disadvantage. This interaction term was also significant (IRR = 0.982, p = .025), and the pseudo-R² was similar to Model 3 (R² = .15). This suggests that in disadvantaged communities, residents’ perceptions of hate crime are more likely to align with self-reported hate crime incidents. The relationship between seeing hate crime and actual hate crime counts was weaker in more advantaged areas (see Figure 3).

Figure 3.

Interaction between perceptions of hate crime and disadvantage.

Discussion

In contrast to a growing scholarship on the experiences and consequences of hate crime victimization, there is surprisingly little research that considers the wider impact of hate crime. In this article, we examined whether non-victims perceive hate crime as a significant problem in their community and the extent to which these perceptions align with self-report hate crime victimization experiences. Two questions guided our research. First, we asked, “Do local residents see hate crime?” We found that they do. Yet, the bivariate relationship between perceptions of hate crime problems and actual victimization reports was not as strong as the relationship between perceptions of non-bias crimes and actual crime as reported in the literature (Hipp, 2007; Perkins & Taylor, 1996; Sampson & Raudenbush, 1999).

As we were interested in whether particular community-level socio-structural characteristics made hate crimes more or less visible to community residents. Our second research question asked under what conditions residents’ perceptions of hate crime align with victims’ self reported hate crime. Specifically we wanted to assess if the strength of this relationship varied across different contexts. Here, we found that community disadvantage and the concentration of non-English language speaking residents were directly associated with self-reported victimization counts. In fact, when these variables were entered into the model, residents’ perceptions of hate crime became non-significant. This is likely because the relationship between perceptions of hate crime and self-report hate crime incidents is conditioned by the community context. Thus, we created interactions to assess these moderating effects. We found in neighborhoods with higher concentrations of LOTE residents, the relationship between perceptions of hate crime and self-reported hate crime incidents weakened. This suggests that hate crime may be less visible in neighborhoods where there are multiple minority groups residing. Here, residents may mistake acts motivated by hate or violence for intergroup violence. Alternatively, residents in high LOTE areas may have developed a heightened level of tolerance for racism and hate crime due to its relative regularity, and therefore do not perceive it as a particular problem. Additional research is needed to better understand how residents perceive and make sense of hate crime incidents occurring in their neighborhood.

We also found that the relationship between perceptions of hate crime and self-reported hate crime incidents was stronger in disadvantaged neighborhoods. Interestingly, previous research indicates that working-class individuals involve the police in neighborhood issues and complain to elected officials about community problems (Baumgartner, 1988). In contrast, residents living in more affluent areas tend to view problems like hate crime as anomalies and not reflective of the structural characteristics of their community. Furthermore, these residents may be less willing to acknowledge hate crime as a problem in their area due to the stigma attached to racism. Indeed, studies have found that some individuals are not only likely to defend themselves from allegations of racism and prejudice but will defend others of those allegations (Condor et al., 2006). This defensiveness may translate to neighborhoods. Residents living in wealthier neighborhoods may be more likely to reject and defend the notion that hate crime is a problem in their neighborhood. This is an area for further research.

Our results have implications for research and policy. In the literature, three measures are used to evaluate the level of crime in a particular area (official statistics, victimization reports, and, to a lesser extent, perceptions of crime problems). Yet in the case of hate crime, using official statistics (when recorded) to determine trends and identify problem neighborhoods is extremely difficult as high levels of underreporting and errors in police recording render these data largely incomplete (Balboni & McDevitt, 2001). Furthermore, large-scale victimization studies of hate crime across communities are both rare and expensive. Thus, residents’ perceptions of hate crime as a problem can offer some potentially useful and much-needed insight into determining areas where hate is an issue. Indeed after evaluating the alignment of residents’ perceptions of violent crime and official violent crime rates, Hipp (2007) argues that “dismissing such reports by residents as simply being perceptions of crime as opposed to ‘real’ levels of crime seems misguided as residents seem to do a reasonable job of assessing the crime that exists in a neighborhood” (p. 25).

Contrary to the evidenced ability of residents to accurately report levels of non-hate crime in their community (Hipp, 2007), our results suggest that perceptions of hate crime may be highly subjective and largely influenced by the socio-structural context of the neighborhood. Thus, while individuals may be very good at identifying what Sampson (2009, p. 7) calls the “tangible manifestations of disorder” (e.g., buildings, graffiti, or disorderly public behavior) as well as other forms of offending like violent crime, “seeing” hate crime is decidedly more complex and in some contexts more difficult to recognize . These findings reaffirm the argument that is commonly put forward in the hate crime literature—that hate crime is a unique form of offending, and thus theories developed to understand and explain general crime may not necessarily apply to hate crime.

This finding has potential consequences for policies concerned with the development of third-party hate crime reporting systems. In the United Kingdom, Scotland, and Australia, third-party reporting is considered as a way to advance the targeting of hate crime incidents without relying solely on victim reports (Her Majesty’s Government, 2012; Police Scotland, 2015; VEOHRC, 2011). Third-party reporting effectively removes the police (and at times the victim) from the data collection process, overcoming some of the underreporting issues (VEOHRC, 2011). Proponents claim that this reporting system will allow key agencies to respond more effectively to hate crime by filling some of the reporting gaps and identifying high-risk hate crime areas (VEOHRC, 2011). Yet, our findings suggest that under particular conditions, witnesses may find it difficult to distinguish a hate crime from a non-hate motivated incident. We argue that a central feature of any third-party reporting strategy must include an awareness campaign that highlights what constitutes hate crime and provide tools to assist witnesses of hate crime to differentiate harassment or more serious acts driven by hate and/or prejudice from non-hate acts. This would assist in better identifying places where hate crime is occurring and would be useful to police who at times find it difficult to collect sufficient evidence from witnesses to determine hate crime motivation.

Although our research advances hate crime scholarship, it is not without limitations. First, our measure of hate crime victimization is broad and only captures household victimization of ethnically or racially motivated hate crime. Thus, we cannot identify the victim nor can we assume that our findings will generalize to hate crime incidents for other minority groups (e.g., those targeted due to their sexual orientation). We are also unable to assess whether hate crimes against some ethnic groups are more visible than others. Another limitation of our research is the small victim sample. As we are relying on self-reports from a cross-sectional survey, we only have a small number of households reporting hate crime victimization. While our analyses suggest that we have significant predictive power, it is possible that with a higher base rate of victims, we would see stronger relationships between our key variables of interest.

Despite these limitations, our results strongly support a greater focus on the broader impact of hate crime beyond the victim and the victims’ minority group members. Furthermore, we suggest that more research is needed to better understand what guides perceptions of hate crime. While there is some evidence that observers can successfully classify hate crimes when evidence of hate motivation is clear such as the use of racial slurs (Levin et al., 2007), victims and observers do interpret hate motivation differently (Nielsen, 2002). Understanding the cues that non-victims use to “see” hate crime would no doubt be useful in raising awareness that hate crime is a significant social problem that affects not only the victim but the wider community as well.

Footnotes

Appendix

Correlations.

	Hate crime	Perceptions	Disadvantage	Mobility	Violent crime	LOTE
Hate crime	1.00
Perceptions	.546	1.00
Disadvantage	.501	.671	1.00
Mobility	−.109	.098	.007	1.00
Violent crime	.136	.135	.151	.278	1.00
LOTE	.343	.460	.281	.076	.284	1.00

Note. LOTE = language other than English.

Acknowledgements

The authors would like to thank the Queensland Police Service (QPS) and the Australian Research Council Centre of Excellence in Policing and Security (CEPS) for their support in the collection of these data.

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: This work was supported by the Australian Research Council (RO700002; DP1093960, and DP1094589).

Notes

Author Biographies

Rebecca Wickes is a senior lecturer in criminology in the School of Social Science at the University of Queensland (UQ) in Brisbane, Australia. She is the lead investigator of the Australian Community Capacity Study, and her academic interests include the ecology of crime and hate crime, race and ethnicity, community engagement, and social exclusion.

Michelle Sydes is a PhD student at the School of Social Science at UQ in Brisbane, Australia. Her academic interests center around urban criminology with particular focus on the relationship between immigration and crime and the neighborhood contexts of hate crime.

Kathryn Benier is a PhD candidate and research assistant in the School of Social Science at UQ. Her research interest includes hate crime in the neighborhood context. She has also researched terrorism–hate crime connections and the effects of hate motivated victimization.

Angela Higginson is a lecturer in criminology in the School of Social Science at UQ. Her research evaluates policing and community crime control interventions. Recent work includes systematic reviews of the predictors and prevention of youth gangs in low- and middle-income countries.

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