Hate Crime in the Wake of Terror Attacks

Abstract

This article asks what happened to racially motivated hate crimes in the wake of the 7/7 terror attack that hit London in July 2005 and the 9/11 terror attack that hit the United States in September 2001. There is anecdotal and descriptive evidence of an increase in bias-motivated crimes since the 9/11 terrorist attacks in the United States, but little quantitative research on the issue. This study offers empirical evidence on the effects of 7/7 and 9/11 on hate crime using rich data from four police force areas in England with sizable Asian/Arab populations. We find significant increases in hate crimes against Asians and Arabs that occurred almost immediately in the wake of both terror attacks, which subsequently decayed, but remained at higher than pre-attack levels a year later. We argue that this demonstrates a significant link between terror attacks and subsequent increases in hate crime and hypothesize that attitudinal changes resulting from media framing and coverage may act as a conduit linking terror attacks and hate crimes.

Keywords

hate crimes terror attacks

Introduction

A growing literature has studied empirical issues surrounding the economic and social effects of terrorism. Attempts have been made to quantify the effects of terrorism on a number of outcomes, including gross domestic product (GDP; Abadie & Gardeazabal, 2003; Bloom, 2009), financial markets (Chen & Siems, 2004), social attitudes or well-being (Bozzoli & Mueller, 2009; Frey, Luechinger, & Stutzer, 2004), birth weight (Eskenazi, Marks, Catalano, Bruckner, & Toniolo, 2007; Lauderdale, 2006; Smits, Krabbendam, de Bie, Essed, & van Os, 2006), and mental health (Metcalfe, Powdthavee, & Dolan, 2011). Perhaps surprisingly, the evidence seems to suggest that the total effect on GDP and financial markets of a single terrorist incident is relatively short-lived, while the effects on well-being and health outcomes are large and can be persistent.

In this article, we explore a different question, asking what happened to racially motivated hate crimes in the wake of the 7/7 terror attack that hit London in July 2005 and the 9/11 terror attack that hit the United States in September 2001. This is a relevant outcome to study if, for whatever reason, terror attacks alter individuals’ perceptions of other groups in society. The article empirically models the impact of terror attacks on hate crimes, in a setting with a credible research design where focus is placed on the possible impact on a particular sub-group of society. To do so, we investigate what happened to hate crime against Asians and Arabs in four regions of England after the 9/11 attack in the United States in 2001 and the 7/7 attacks that hit London in 2005.

While there is anecdotal and descriptive evidence of an increase in hate crimes against Muslims since the 9/11 terrorist attacks (which we review below), we are not aware of much quantitative research that tries to accurately pin down the impact of terror attacks on the incidence of hate crimes. This is what we offer in this article, where we analyze detailed monthly administrative data before and after the terror attacks in four English police force areas (PFAs) with a significantly sized Asian/Arab (predominantly Muslim) population.

We quantify the increased number of hate crimes against U.K. Muslims that occurred as a result of both the 9/11 attacks and the 7/7 bombings, using data that sub-divide hate crimes by victim ethnicity. Thus, we can study hate crimes against Asians and Arabs before and after the attacks, and generate credible estimates by using hate crimes against Blacks and Whites as a control group.

One clear advantage of studying hate crimes as recorded by the English police is that they are explicitly defined and quantifiable. Standardized information is collected by police forces with clear definitions on victims of hate crimes contained in the National Crime Recording Standard (NCRS), which was instituted in April 2002. These well-defined victim data therefore facilitate accurate study of time trends in a way that is not possible with the kind of opinion survey attitudinal, self-reported well-being, or newspaper coverage data that have been more commonly studied in the terrorism literature. Moreover, actual hate crimes have greater implications—there is a direct cost to the victim, which may not be the case with attitudinal changes.

To preview our main findings, we report sizable increases in hate crimes against Asians and Arabs—of the order of 25% to 30%—that occurred almost immediately in the wake of the two terror attacks. Moreover, while subsequently the increase did not stay as high as the initial impact, in both cases, it persisted and was still significantly higher sometime after the terrorist events occurred. In the case of the 7/7 attacks in Britain, where we have better data to estimate duration effects, cumulative increases remained significantly higher a year after the attacks.

The structure of the rest of the article is as follows. In the section “Theoretical Background and Existing Evidence,” we consider some theoretical background motivations of our questions of interest and discuss relevant existing evidence. In the section “Data and Descriptive Analysis,” we describe the data we use and offer some initial descriptive analyses. The section “Modeling Approach and Statistical Results” explains the modeling approach and presents statistical estimates of the impact of the 9/11 and 7/7 terror attacks on hate crime. The final section is the “Conclusion.”

Theoretical Background and Existing Evidence

Hate Crimes in the Economics of Crime

Becker’s (1968) seminal article was the first to consider crime in the economic framework of rational behavior. As with other economic activities, in this approach, crime results from a simple cost–benefit choice under uncertainty, and the Becker model generates clear empirical predictions about incentive and deterrence effects on crime. It is not so clear how useful this approach is for studying hate crime as, in the original Becker model, harm or loss to the individual is considered an externality, essentially an unintentional side effect of the offender’s actions. In the case of a hate crime, however, it has been suggested that loss to the victim is the intention of the crime (Craig, 2002; Gale, Heath, & Ressler, 2002). As well as causing harm to the victim, a hate crime is often intended to convey a message to the wider group to which the victim belongs (or is perceived to belong).

Gale et al. (2002) and Medoff (1999) have extended the individual economics of the crime model to consider hate crime as a consumption good that generates utility, but at the same time incurs some kind of cost.¹ In this utility setting, hate crimes can be thought of as being driven by factors that alter preferences and they arise, for example, if the propensity to commit hate crimes is affected by some kind of shock. One can ask what kinds of shocks may occur that could make an individual choose to dislike a hated group more or less at different times. At the micro level, this may be about personal experiences, education, culture, and environmental changes. At the macro level, however, we might expect the biggest driver to be current affairs. So in the specific context of the hate crimes we study, namely, those targeting Muslims, the media framing and coverage of news events that some individuals may interpret as showing Muslims in an unfavorable light could be expected to increase the incidence of hate crime. We could plausibly consider the 9/11 and 7/7 terrorist attacks we study as featuring an extreme form of this media exposure.

Hate Crimes in a Behavioral Approach

In the economist’s rational decision-making framework, an individual decides to commit a hate crime because the expected utility from the action is positive. An alternative view, which is maybe more applicable to the context we study, is offered by contributions from behavioral economics. Particularly relevant are those areas that try to understand why agents make seemingly irrational decisions, even once factors such as limited information and limited decision-making time have been taken into account.

It seems reasonable to think of hate crimes in a behavioral context in that, while the prospective gains from acquisitive crime are self-evident, the potential “gains” from committing a pure act of violence against others are less clear (unless people have a taste for discrimination of this sort, though ultimately this is a theoretical proposition that is hard to test in practice). An alternative perspective might consider a hate crime to be an action of passion or emotion—where feelings of anger and rage dominate the individual’s rational decision-making process. This is the assertion of Gordon and Arian (2001) who claim that “when one feels very threatened, the decision-making process is dominated by emotion rather than logic or rational considerations” (Gordon & Arion, 2001, p. 197).²

Hate crimes also tend to be committed by groups of people rather than individuals (see Craig, 2002) and this suggests that there may be some element of group interaction, such as peer pressure or removal of social barriers, which can cause individuals to commit hate crimes when in groups. The concept of “herding” is well known to economists, in particular in relation to financial markets. For example, economists explain the formation of stock market bubbles as being caused by investors valuing assets according to how they believe others to value assets rather than based on private valuations. This kind of group behavior can lead to seemingly irrational choices and can cause instability in financial markets (Baddeley, 2010). In the context of hate crime, we can imagine that group mentality has the power to overcome social taboos or persuade individuals to commit acts they would not otherwise have considered to impress the group. Escalation may occur when group members second guess the value that other members place on committing hate crimes.

How do these notions connect to terror attacks? It is evident that a terrorist attack can trigger sharp changes in behavior, which may not be rational responses (see Viscusi & Zeckhauser, 2003, or Sunstein, 2003). However, the supposedly irrational “certainty premia” phenomenon is accounted for in a rational framework developed by Becker and Rubinstein (2011). They argue that, when considering shock mass-fear type events, the standard state-dependent utility model is not sufficient. In fact the model they develop assumes that a negative utility shock occurs only in a “bad” state (like when the terrorist attack occurs), and not in good states.

Thus, there are both rational and behavioral arguments that have been proposed to explain why hate crimes occur.³ In terms of empirical analysis, testing the distinction between the rational and behavioral arguments is outside the scope of this study (and it is indeed difficult to even begin thinking how this might be done). Instead, the focus in what follows will be on empirically pinning down the magnitudes and durations of the effect of the 9/11 and 7/7 attacks on subsequent patterns of hate crime incidence.

Existing Evidence Linking Hate Crimes and Terrorist Attacks

Quite a lot of descriptive evidence exists on whether terror attacks induce increases in hate crime. In the United States, it seems that the 9/11 terrorist attacks were followed by an increase in the number of hate crimes against Muslims, Arabs, and those perceived to be Middle Eastern.⁴ FBI annual statistics on hate crimes show that, prior to 2001, incidents of anti-Islamic crime were in the magnitude of 20 to 30 incidents per annum.⁵ This number jumps from 28 incidents in 2000 to 481 in 2001, and then remains steady in the 100 to 150 range per annum thereafter. These FBI numbers convey the magnitude of the backlash against Muslims, in particular with the 3 months after the attacks appearing to be a time of intense anti-Muslim violence.

Other sources confirm this impression. First, a report by the American-Arab Anti-Discrimination Committee (ADC) counts 700 violent attacks on U.S. Muslims in the 9 weeks following 9/11; they report that “the intensity of the backlash, especially in terms of hate crimes and discrimination, was at its peak in the first six months following the attacks, and particularly during the first nine weeks” (Ibish & Stewart, 2003, p. 15). Second, Swahn, Mahendra, and Paulozzi (2003) conducted a survey of newspaper reports during the period from September 1, 2001, to October 11, 2001. They found evidence of 100 incidents of hate crimes against Middle Easterners in the United States, of which just one occurred in the 10 days between the 1st and 11th of September (the “baseline”). Of the remaining 99, 77 occurred in the period 10 days after 9/11. Incident types ranged from assault and intimidation to murder and attempted murder. Although this is not a rigorous scientific study (in part as the baseline period is so short, and may be subject to seasonal variation), it does support the hypothesis of a relatively short and intense “shock period.” What is more, it gives direct evidence that the perpetrators of these hate crimes were motivated by the terrorist event: “the perpetrators in at least 30 of the incidents specifically mentioned the September 11 terrorist attacks, or accused the victims of being terrorists” (Swahn et al., 2003, p. 188).

Furthermore, there is some evidence to suggest that the effects of the 9/11 terrorist attacks were not limited to the United States. Surveys of Muslims in both the United Kingdom and Australia find a significant increase in experiences of hate crime post 9/11. In the United Kingdom, Sheridan and Gillett (2005) surveyed 398 respondents from various religious groups in Leicester and Stoke-on-Trent during the period from October to December 2001. They estimate regressions to predict an aggregated “change score” (showing change in experience of hate crimes since 9/11) and find that both Muslims and Hindus report increases post 9/11 (with a much larger effect for Muslims), whereas the other religious groups report small decreases. A similar, but smaller scale, study conducted in 2003 surveyed 186 Australian Muslims and Christians (Poynting & Noble, 2004) and found similar results, with Muslims being far more likely to report an increase in experiences of racism since September 11 than Christians.

Adopting a more rigorous quantitative analysis, Disha, Cavendish, and King (2011) study hate crime offending against Arabs and Muslims using FBI data on hate crime in the United States before and after 9/11. They report the number of anti-Arab/Muslim hate crimes rising sharply after 9/11, in contrast to the majority of racially or ethnically motivated hate crimes that declined post 9/11. Deloughery, King, and Asal (2012) focus on the direction of causation between hate crime offenses and terrorism. Their evidence shows that that hate crimes occur in response to terror attacks, but that there is no evidence of causation working in the opposite direction where hate crimes would act as a precursor to terrorist activity.

Thus, there is evidence that the 9/11 terrorist attacks were immediately followed by a dramatic rise in the incidence of hate crimes against American Muslims, with a peak lasting for around 2 to 3 months, and with the effects persisting for perhaps years afterward. Other than the survey evidence already discussed, there exists little evidence of the experiences of British Muslims following 9/11. Even scarcer is evidence on the effect of 7/7, which we would presume likely to have caused similar effects to 9/11. Our empirical work will study the impact of both attacks.

Data and Descriptive Analysis

Data

Data requirements to study the impact of terror attacks on hate crimes are stringent. Adequate, reliable data to study the subject are hard to come by. This is for a number of reasons. First of all, we need data on hate crimes measured in a consistent and accurate manner. Second, we also need information on the ethnicity or religiosity of hate crime victims. Third, hate crime data at a high frequency (at least monthly) are required to carry out our empirical analysis of what happens to hate crimes before and after the 9/11 and 7/7 terror attacks.

Fortunately, for our purposes, data collected on hate crimes and on the victims of hate crimes by police forces in England is of good quality owing to clear definitional guidelines that police forces need to follow in their crime recording practices. The NCRS was introduced in April 2002, with the explicit purpose of standardizing crime recording practices across police forces to allow between-force comparisons and to generate a better estimate of the national crime level. The purpose of the NCRS was also to move toward victim-focused crime recording, with “victimless crimes” not being recorded under the new guidelines. The way in which racially motivated crime is identified is via offenses identified with a “racist flag” by offense category and victim ethnicity. The term racist flag signifies that when entering incident details into the police database, the responsible officer considers the incident to have been racially motivated.⁶

Because data on monthly numbers of hate crimes broken down by ethnicity of victim are not publicly available, we therefore obtained such data by direct application to police forces through a freedom of information (FOI) request.⁷ We submitted FOI requests to four police force areas in England—the Metropolitan Police Services (MPS) in London, the West Midlands, Leicestershire, and West Yorkshire. These were chosen for two main reasons. First, because all four have a sizable Muslim population, and thus hate crimes against Muslims may occur relatively frequently, and second, because the 7/7 attacks occurred in London, and so a comparison of London versus non-London areas was sought (the MPS covers all of central London, with the exception of City of London); the other three areas are independent of London (although it should be noted that the 7/7 bombers were from West Yorkshire).

We obtained monthly data from all four police forces, with information being supplied to us on the major offense category and ethnicity for both victims and offenders of all crimes listed as racially motivated (as defined above).⁸ For Leicestershire, London, and the West Midlands, we have data before and after both 9/11 and 7/7, and for West Yorkshire only for before and after 7/7.

Because of the significant NCRS crime recording changes that occurred in April 2002, this constrains us in our ability to look at before/after changes in hate crimes associated with the two terror attacks. In fact, it means that the feasible time series we can study differ around the window of the two attacks. We can do a much better job on having consistent data before and after 7/7 and so our main focus is placed on studying what happened to hate crimes in response to this terror attack. We thus study the 7/7 attacks first and then look at 9/11 effects using a shorter time series that stops when the recording changes occurred in April 2002. The actual periods we use in our analysis are as follows: 7/7 attack, January 2003 to July 2006 (1 year after the attack); 9/11 attack, February 2000 to March 2002.⁹

Hate Crimes by Victim Ethnicity

There is a distinction between racial and religious discrimination, although often the two co-occur. While it is clear that the 9/11 and 7/7 terrorist attacks triggered animosity toward Western Muslims, research from the United States (discussed above) has found that it is not just Muslims who were targeted—hate crimes were also carried out on Middle Easterners and Arabs who were not practicing Muslims, and Sikhs, who may have been mistaken for Muslims.

The vast majority of Britain’s Muslim population are South Asian, most of whom originate from Pakistan, Bangladesh, and India. The Pakistani and Bangladeshi populations are almost entirely Muslim, whereas the Indian population sub-divides into Hindus, Muslims, and Sikhs. Thus the U.K. Muslim population is almost entirely contained within the ethnic category “South Asian.” People in this category may be the victim of either racial or religious discrimination. In some cases, religious discrimination may be misplaced—individuals may be discriminated against because they are mistaken for being Muslims, or because of some kind of statistical profiling (i.e., discriminators target South Asians because they are the ethnic group most likely to contain Muslims).

Since religious data were unavailable, we use ethnicity to define our main groups of interest. Ethnicity categories used in crime statistics differ from one police force to the next, and so some aggregation was required to standardize the numbers from the different sources. The following six broad categories were created: Asian/Arab, White, Black, Oriental, Unknown, and Other. The latter three contain very small numbers and so are dismissed from the analysis. We thus consider the impact of the terror attacks on Asian/Arab hate crimes and use hate crimes against Whites and Blacks as a control group in a difference-in-difference (D-i-D) setting when we formulate our statistical models.

Descriptive Analysis

Figure 1 plots the monthly time series of hate crimes by ethnic group and police force area for the time window for which we study the 7/7 attacks (January 2003 to July 2006).¹⁰ Hate crimes where the victim was Asian or Arab are shown by the dark solid line, and hate crimes where the victim was White or Black are the two dashed lighter lines. There are several interesting features of the overall patterns. First, while the monthly time series do jump around to an extent, all four graphs show a discernible spike up in the Asian/Arab victim hate crime series in the month of July 2005, suggesting an immediate impact. Second, eyeballing the graphs is suggestive of the notion that the time series patterns of hate crimes before the 7/7 bombings for all three ethnic groups look similar (this is considered formally in more detail below).¹¹

Figure 1.

Trends in hate crimes by ethnicity of victim, four police force areas, January 2003 to July 2006.

There are also two police force area specific observations that are relevant:

In the West Midlands, there is a large spike caused by the Birmingham race riots that occurred in October 2005. The riots were sparked by the alleged rape of a Black girl by a group of South Asian men. This event seems to have been completely unrelated to the terrorist attacks that occurred 3 months previously.

The pre-recording change data for West Yorkshire were not good enough to study the 9/11 attacks for this police force area. Also, a third-party recording scheme ("True Vision") was launched in June 2005, just 1 month before 7/7.

We deal with these two data issues in our empirical models below by including specific variables to control for any data jumps unrelated to our interest that result from these.

An analogous set of charts for a shorter time window around the 9/11 attacks (February 2000 to March 2002) is given in Figure 2. The chart this time covers only three police force areas excluding West Yorkshire. While the length of the post-attack time period is constrained by the recording changes of April 2002, the figure does seem to show a blip up in hate crimes against Asians/Arab in the month of 9/11 and then higher relative levels afterwards (despite subsequent falls) compared with the White and Black hate crimes. We scrutinize these patterns in more detail by means of the statistical models described in the next section of the article.

Figure 2.

Trends in hate crimes by ethnicity of victim, three police force areas, February 2000 to March 2002.

Modeling Approach and Statistical Results

Basic Approach

We begin the statistical analysis by developing an empirical model that permits us to study the question of how the 7/7 and 9/11 terror attacks affected hate crime. We ask what happened to hate crime against Asians and Arabs before and after the terror attacks relative to hate crime against two other ethnic groups (Blacks and Whites).

Because crime is seasonally highly persistent,¹² and our time units cover monthly data across years, we express our model in 12-month differences (thereby differencing out area and month fixed effects from a level model). We operationalize our estimator in terms of the following D-i-D equation (with Δ₁₂ being a 12-month differencing operator) determining 12-month changes in hate crimes for ethnic group e in area j in time period t:

Δ_{12} H_{e j t} = α + β T_{t} + θ (A A_{e} \times T_{t}) + γ A A_{e} + λ X_{j t} + τ_{t} + Δ_{12} ε_{e j t},

(1)

where H denotes hate crimes, AA is a dummy variable indicating the Asian/Arab ethnic group (relative to Whites and Blacks), T is a dummy variable equal to 1 in months where the terror attack occurred (or for a window comprising several post-attack months, see below), X is the control variables for the data issues specific to particular police forces discussed above, τ is a time variable (see below), and ϵ an error term.

This equation enables us to ascertain the impact of terror attacks on hate crimes against the Asian and Arab group relative to the White and Black groups via the D-i-D estimate of θ. When T_t is set to 1 in the month of the terror attack, the estimate of θ reveals whether Asian/Arab hate crimes differentially increased in the month of the terror attack. When the timing of attack indicator T_t is set equal to 1 to cover a longer post-attack duration, the estimate of θ will show that Asian/Arab hate crimes evolved subsequently in the wake of terror attacks.

Basic D-i-Ds

Table 1 reports descriptive statistics showing the basic differences-in-differences for the 7/7 and 9/11 terror attacks. They compare what happened to hate crimes against Asians and Arabs relative to hate crimes against Whites and Blacks in the month of the terror attack as compared with before. Panel A of Table 1 shows a pre-attack time period of all months between January 2003 and June 2005 for 7/7 and between February 2000 and August 2001 for 9/11. Panel B of Table 1 uses a pre-attack period of the same month in the previous years, thus corresponding more closely to the seasonal difference approach we use to control for unobservables in our econometric analysis that follows.

Table 1.

Numbers of Hate Crimes in Pre-Attack and Attack Months.

	7/7			9/11
A. All pre-attack months	January 2003 to June 2005	July 2005	Gap and D-i-D	February 2000 to August 2001	September 2001	Gap and D-i-D
Asian and Arab hate crimes	271	367	96 (17)	435	532	97 (22)
White and Black hate crimes	428	482	53 (5)	709	635	−74 (50)
			Levels D-i-D: 43 (13)			Levels D-i-D: 171 (71)
			Logs D-i-D: 0.15 (0.04)			Logs D-i-D: 0.28 (0.04)
B. Same pre-attack months	July 2003 and July 2004	July 2005	Gap and D-i-D	September 2000	September 2001	Gap and D-i-D
Asian and Arab hate crimes	311	367	56 (10)	420	532	112 (34)
White and Black hate crimes	512	482	−30 (43)	699	635	−64 (38)
			Levels D-i-D: 87 (39)			Levels D-i-D: 176 (72)
			Logs D-i-D: 0.20 (0.08)			Logs D-i-D: 0.37 (0.05)

Note. All the specifications are population weighted; standard errors are clustered by area (with small cross-section sample adjustment from Cameron, Gelbach, & Miller, 2011) and given in parentheses. D-i-D = difference-in-difference.

Considering first the 7/7 attacks, it is clear from the table that the number of hate crimes against Asians and Arabs rose faster in July 2005 as compared with the control group of Whites and Blacks. In Panel A of Table 1, they rose by 96 crimes, going up to 367 from an average of 271 per month in the 2.5 years before. Hate crimes against Whites and Blacks also went up, but not by anywhere near as much and so the D-i-D of 43 hate crimes, or 0.15 log points, shown in the table is strongly significant in statistical terms.

That there is a seasonal, monthly, aspect to this is revealed in Panel B of Table 1. In the same-month comparison presented there, hate crimes against Asians and Arabs rise from 311 to 367, whereas those against Whites and Blacks fall a little, resulting in a D-i-D estimate of 56 hate crimes, corresponding to a 0.20 log point change.

For the 9/11 analysis, a strong and significant D-i-D estimate also emerges. The estimates shown in the last column of the table show a sharp increase in hate crimes against Asians and Arabs as compared with Whites and Blacks of 171 (when compared with all pre-attack months) or 176 (when compared with the previous September), or 0.28 to 0.37 log points.

Pre-Attack Trends

Although the results of Table 1 show there to be a significant increase in hate crimes against Asians and Arabs relative to the control group immediately after the terror attacks, it remains the case that a prerequisite for our research approach to yield unbiased estimates is that pre-attack trends of hate crimes against the treatment group (Asians/Arabs) are no different to trends in hate crimes against the comparison groups (Whites and Blacks). A glance back to Figures 1 and 2 makes it graphically clear how this operates in practice, as the Asian/Arab, White and Black hate crime trends do seem to show similar temporal evolution in the pre-attack periods.

This is tested more formally in statistical terms for the 7/7 attacks in Table 2. The results in the table show estimated coefficients for pre-attack trends in 12-month differenced hate crimes against Asians/Arabs in Specification 1, for Whites and Blacks in Specification 2, and for the gap between the two in Specification 3. Panel A shows results for all four areas pooled together, and Panels B and C separately for London and the other three police force areas.

Table 2.

Pre-7/7 Trends in Hate Crimes Against Asians and Arabs and Whites and Blacks (Four Police Force Areas, January 2003 to June 2005).

	Δ₁₂Log (Asian and Arab hate crimes)	Δ₁₂Log (White and Black hate crimes)	Δ₁₂Log (Asian and Arab hate crimes) − Δ₁₂Log (White and Black hate crimes)
	(1)	(2)	(3)
A. All areas (N = 72)
Trend	−0.001 (0.002)	−0.003 (0.007)	0.002 (0.007)
B. London (N = 18)
Trend	−0.001 (0.005)	−0.011 (0.007)	0.009 (0.006)
C. Leicestershire, West Midlands, and West Yorkshire (N = 54)
Trend	−0.000 (0.005)	0.008 (0.005)	−0.008 (0.010)

Note. All models are estimated on monthly data across four police force areas from January 2003 to June 2005. All the specifications are population weighted, seasonally differenced across the same months in adjacent years. Standard errors clustered by area (with small cross-section sample adjustment from Cameron, Gelbach, & Miller, 2011) are given in parentheses.

In all cases, the estimated coefficients on the trend variables show there to be no differential pre-attack trends between Asian/Arab hate crimes and those against Whites and Blacks. Thus, the common trend assumption required for our estimator to be valid appears to be upheld by the data.

Statistical Estimates of the 7/7 Impact

Table 3 shows D-i-D estimates for the case of the 7/7 attacks. There are four panels in the table, where each gives a 7/7 impact over different durations. Panel A of Table 3 shows the immediate impact via a dummy variable defined for the 7/7 month only. Panels B to D of Table 3 further refine the dummy variable definition to cover a wider post-attack window (Panels B, C, and D, respectively, refine the dummy variables to cover 3, 6, and 12 months post attack).

Table 3.

Hate Crimes and the 7/7 Terror Attacks.

	D-i-D: Δ₁₂Log (Asian and Arab hate crimes) − Δ₁₂Log (White and Black hate crimes)
Treatment period	(1)	(2)
A. Month of the terror attack (N = 76)
7/7	0.267 (0.095)	0.256 (0.039)
B. 3 Months from the terror attack (N = 84)
7/7	0.226 (0.064)	0.212 (0.043)
C. 6 Months from the terror attack (N = 96)
7/7	0.170 (0.048)	0.170 (0.048)
D. 12 Months from the terror attack (N = 120)
7/7	0.100 (0.033)	0.154 (0.072)

Note. All models are estimated on monthly data across four police force areas from January 2003 to June 2006. Estimates are population weighted from regressions that are estimated on seasonally differenced models across the same months in adjacent years. In these differenced models, the pre-attack period covers January 2004 to June 2005 (sample size, N, is 72) and the post-attack period is defined sequentially in moving from Panels A to D. Standard errors clustered by area (with small cross-section sample adjustment from Cameron, Gelbach, & Miller, 2011) are given in parentheses. A dummy for the Birmingham race riot in October 2005 and for the introduction of the True Vision recording scheme in West Yorkshire from June 2005 onward is included where relevant, and the specifications in column 2 include a monthly time trend. D-i-D = difference-in-difference.

Results from two specifications are included in each panel. The first includes only the 7/7 dummy in the 12-month differenced model. The second models common aggregate effects through the inclusion of a monthly time trend.¹³ All the specifications are population weighted and report standard errors clustered by police force area (adopting the small cross-section sample adjustment from Cameron, Gelbach, & Miller, 2011).

Consider first the immediate impact results in Panel A of Table 3. Specification 1 produces a 0.27 coefficient on the 7/7 dummy, showing a 27% significant spike up in hate crimes against Asians/Arabs in the attack month. Specification 2 shows a very similar estimated coefficient of 0.26 that remains strongly significant. This analysis based on the seasonally differenced data very much confirms the earlier, more descriptive analysis.¹⁴

Panel B of Table 3 considers impact in the 3 months following the terror attacks. The estimated impact comes down, but remains strong and significant at 0.21 to 0.23, depending on specification. The window is further widened in Panels C and D of Table 3 where the effects again fall but remain strongly significant. Six months on from the 7/7 attacks, the magnitude of the hate crime increase is around 17% and still around 10% to 15% after a year.

The results of Table 3 show a strong impact of 7/7 on hate crimes against Asians and Arabs. The immediate impact is the largest, followed by subsequent decay, but the cumulative effect persists even 12 months after the attack occurred. Four specific estimates were chosen to be reported. We can, however, estimate an impact for every month sequentially to study the duration of impact in more detail. Estimated coefficients from carrying out this sequential modeling exercise reproduced the large immediate impact at 27% in July 2005, which falls to 20% if the window is defined as 4 months after the attack. After that, it stabilizes in a range that stays over 10% higher. All of the individual estimates were significantly different from zero (for a 5% significance level), thus showing a significantly higher level of hate crimes against Asians and Arabs in all 12 months after the 7/7 attack.¹⁵

The results in Table 3 are very supportive of the idea that 7/7 caused a strong immediate increase in hate crimes against Asians and Arabs, and that while the scale of the increase tempered off in the following months, it remained around 10% higher than the pre-attack levels. We are reluctant to extend the window much beyond a year, as other factors that could affect the relative hate crimes variable are likely to come into play and so confound the picture, but it does seem that the increase in hate crimes against Asians and Arabs that occurred in the wake of the 7/7 bombings persisted for some time.

Separate Estimates by Police Force Area

In Table 4, we report separate estimates of the 7/7 impact, at the different chosen post-attack durations, by police force area. More specifically we consider London alone and the other three areas together. There are at least two reasons for doing this. First, as highlighted above, there are certain PFA-specific data issues of relevance. Second, we wish to explore possible heterogeneities in the magnitude and duration of impact across areas.

Table 4.

Separate 7/7 Impact Estimates by Police Force Area.

	D-i-D: Δ₁₂Log (Asian and Arab hate crimes) − Δ₁₂Log (White and Black hate crimes)
Treatment period	London	Leicestershire, West Midlands, and West Yorkshire
A. Month of the terror attack (N = 76)
7/7	0.322 (0.049)	0.181 (0.075)
N	19	57
B. 3 Months from the terror attack (N = 84)
7/7	0.244 (0.049)	0.193 (0.059)
N	21	63
C. 6 Months from the terror attack (N = 96)
7/7	0.162 (0.051)	0.186 (0.082)
N	24	72
D. 12 Months from the terror attack (N = 120)
7/7	0.073 (0.050)	0.156 (0.055)
N	30	90

Note. As for the specifications in column 1 of Table 5. D-i-D = difference-in-difference.

The table confirms there to be some variation. In terms of immediate impact, it is higher at 0.32 in London, as compared with 0.18 in Leicestershire, the West Midlands, and West Yorkshire. The rate of decay of the effects, however, is seen to differ by area with, interestingly, there being no impact remaining in London 12 months after the terror attacks, but the effects still persisting strongly in the other three police force areas. One possible interpretation of the more heightened persistence outside of London is the presence of historically more entrenched race issues that have engendered deeper seated issues of anger and resentment in communities in the other areas.¹⁶ The capital city has also been characterized by much more rapid population movements through migration over this time period as well, suggesting a more dynamic environment where perhaps faster adjustment can take place.¹⁷

Statistical Estimates of the 9/11 Impact

We have also estimated variants of Equation 1 for the impact of 9/11. However, we should say that this analysis is more limited than for the study of the 7/7 impact. There are several dimensions to this. First, as noted above, we only have usable data for three police force areas. Second, we are not able to define a symmetric time series window around the attack as we did with the 7/7 analysis. This is because we have to stop due to the recording practice change that occurred in April 2002. One consequence of this is we can only look as far as 6 months following the terror attack. Third, because we only have 2 years’ data, our ability to difference across months in the years is more limited.

The results are reported in Table 5. The table is structured in a comparable way with the 7/7 results, though we can only look at shorter duration effects. Column 1 results show a strong immediate impact effect from 9/11. Hate crimes against Asians and Arabs rose by 28% in September 2001. This effect dampens down by 3 months after the attacks to 22%, and falls further to 11% after 6 months, but remaining statistically different from zero.

Table 5.

Hate Crimes Against Asians/Arabs and the 9/11 Terror Attacks.

	D-i-D: Δ₁₂Log (Asian and Arab hate crimes) − Δ₁₂Log (White and Black hate crimes)
	(1)	(2)	(3)
	Month of the terror attack(N = 24)	3 Months from the terror attack (N = 30)	6 Months from the terror attack(N = 39)
9/11	0.280 (0.082)	0.223 (0.066)	0.109 (0.070)

Note. Estimates are from monthly data across three police force areas from February 2000 to March 2002. Estimates are population weighted from regressions that are estimated on seasonally differenced models across the same months in adjacent years. In these differenced models, the pre-attack period covers February 2001 to August 2001 (sample size, N, is 21) and the post-attack period is defined sequentially in moving across the specifications in columns 1 through 3 in the table. Standard errors clustered by area (with small cross-section sample adjustment from Cameron, Gelbach, & Miller, 2011) are given in parentheses. D-i-D = difference-in-difference.

Conclusion

Despite the importance of the subject, credible statistical evidence on the impact of terror attacks on hate crime is sparse. In this article, we look at the impact of the 7/7 and 9/11 terrorist attacks on hate crimes against Asians and Arabs in four police force areas of the United Kingdom. We estimate a strong immediate impact on Asian/Arab hate crimes from both terror attacks, and find that while the effects do reduce through time, they remain significantly higher than post-attack levels at least 6 months (in the case of 9/11) or a year later (in the case of 7/7). The highly similar pattern of results from the separate study of the respective impacts of 7/7 and 9/11 on hate crime in four areas with sizable Asian/Arab populations is highly suggestive that we can attribute a causal interpretation of the impact of terror attacks on hate crime from the empirical approach implemented in the article.¹⁸

The findings add to the literature on the economic and social effects of terror attacks. They show that, in line with some of the theoretical discussion in the early part of the article, for individuals the cost of terror attacks is not just limited to the victims of the attacks. That hate crimes perpetrated against Asians and Arabs significantly rose in the wake of 9/11 and 7/7 points to an additional social cost of terrorist activity.

Moreover, if attitudes toward groups like British Muslims are altered by attacks and by media coverage of attacks, then these findings fit with the proposition of “attitudinal shocks,” where a driver of hate crimes is the level of hatred or bigotry for a particular group in society, which may be influenced by media framing and coverage of attacks. In this setting, such shifts in underlying bigotry from attitudinal change following events like terrorist attacks seem to be potentially important determinants of hate crime incidence.

Thus, the determinants of hate crimes may be different from, or certainly more complex than, the kind of incentive or deterrence effects that emerge as crime determinants in the standard economics of the crime model. Of course, to more firmly establish whether this is the case, continued work on the causes of hate crime and on the behavioral motives that individuals have to engage in crime against different ethnic or religious groups forms an important future research agenda.

Footnotes

Acknowledgements

We acknowledge helpful comments and suggestions from the Editors, two referees, Mirko Draca, Edwin Leuven, Kalle Moene, Steve Pischke, and other participants in seminars at the London School of Economics and Oslo, the 2012 National Bureau of Economic Research Summer Workshop Economics of Crime session, the European Association of Labour Economists’ annual conference in Bonn, and at the 4th Annual Institute for the Study of Labor (IZA) Meeting on the Economics of Risky Behaviors in Istanbul.

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) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Emma Hanes works as an economic advisor to the UK Government. She has worked in fields including crime, security and transport economics. She studied for an MSc in Economics at University College London.

Stephen Machin is Professor of Economics at University College London and Research Director of the Centre for Economic Performance at the London School of Economics. He is a Fellow of the British Academy, has been President of the European Association of Labour Economists, is a Fellow of the Society of Labor Economists and a member of the UK Low Pay Commission. His current research interests include inequality, education and crime, and the interactions between them.

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