Did Brexit increase hate crimes? Probably, yes.*

By Dan Devine. Dan is a PhD student in Politics at Southampton. He specialises in comparative politics, political attitudes and quantitative methods (@DanJDevine, personal websiteAcademia.edu).


Tl;DR: Brexit probably caused an increase in hate crimes. I provide descriptive and statistical (linear regression and regression discontinuity) evidence for this claim, but the claim that there was a rise in reporting rather than hate crimes per is also plausible. It’s also positive to see that this is not a lasting effect (at least in the data), although there is still an upward trend in hate crime since 2013.


In the wake of Brexit – when the UK voted to leave the European Union – there was a flurry of activity in newspapers and across the internet reporting a rise in racial tensions and hate crimes. In the following weeks and months, this was widely reported in the Guardian (a lot), the BBCHuman Rights WatchSky NewsThe Telegraph, and I’m sure some others that I’ve missed. Nevertheless, some individuals and outlets (such as Spiked and ConservativeHome) remain extremely sceptical of the claim that the vote to leave the EU was behind a rise in hate crime – and indeed, call into question the validity of the numbers at all. 

As many outlets have picked up in the last week, the government have recently released the full figures of hate crime that cover the referendum and post-referendum months and days. This allows us a much closer look at what exactly was going on around that time (and gives me a chance to try out some new ideas at visualising data). Here, I take a look at these numbers, put them through some rough-and-ready statistical tests, and look at some other explanations of the findings. In general, though, the evidence is overwhelming that Brexit did cause a rise in hate crime. Nevertheless, it is encouraging that (at least according to the data) this does not seem to be a ‘lasting effect’, as The Independent reports.

What is hate crime?

Many of the biggest critiques of the data concern what is meant by ‘hate crime’. Hate crimes in general are defined as ‘any criminal offence which is perceived, by the victim or any other person, to be motivated by hostility or prejudice towards someone based on a personal characteristic’. However, the data I use here is focused specifically on racially or religiously aggravated offences (from now, I will just call these hate crimes). This includes crimes such as: assault with or without injury, criminal damage, public fear or distress, and harassment. This also includes graffiti of a racist nature (presumably under the latter two categories), and attacks on asylum seekers or refugees (regardless of their race). 

This does mean that essentially, anyone can report something as a hate crime if they perceive it as such. In addition, it’s true that a majority of these cases go unsolved – about a quarter of offences are taken to court. I don’t want to get into the territory of disagreeing with the very definition of hate crimes (or how they’re reported) – but it’s worth being open about what is behind the analysis.

An increase in hate crime is descriptively clear

At first glance, it is clear that there was a rise in hate crime surrounding the Brexit referendum. The first graph below shows hate crimes by month since 2013. Although there is always a seasonal effect – hate crimes increase over summer – the sharp rise in June and July 2016 is startling, and the drop off in August is not particularly drastic (or at least as drastic as we would hope). From this longer-term perspective, the summer months of 2016 are outliers in the recent history of hate crime. It should be noted, however, that there is a clear upward trend in hate crime since 2013; the low point of 2016 is around the same as the high point of 2013. This upward trend should send a warning to those interested in social cohesion.

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It’s also possible, with the Home Office data, to have a more fine-grained analysis. The graph below presents daily data for the months of May, June, July and August. Once again, the dashed horizontal line indicates when the referendum took place. The interesting part of this is the sudden increase the day after the referendum, which persists for several days, peaking approximately a week after (more on this later).  There is, as in the monthly data, a slow decline to pre-referendum levels.

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From both of these graphs, it is clear that there was a peak in hate crime surrounding the referendum. But there is also a lot of variability, and some claim that this is not necessarily down to the referendum. In lieu of suitable data to test the competing claims, I wanted to look at this statistically as best I could.

And the differences are statistically significant

To do this, I took two approaches. I make no claim that these are conclusive. They are relatively back-of-the-envelope tests, but I think they are nonetheless strong evidence for the impact of Brexit on hate crimes (for those interested, details are at the end). The first tests how much variation can be explained by the referendum, and how many hate crimes can be attributed to Brexit. What the results indicate is that Brexit increased hate crimes by about 31 a day (if we use the daily data), or by 1600 a month (if we use the monthly data). Due to the few months following the referendum, I would say the daily data is more accurate. Importantly, the results indicate that Brexit explains about 35% of the crimes in the days following Brexit – which is a statistically and substantively significant amount.

But regressions are flawed for a range of reasons, especially when done like this. As is clear from above, hate crimes slowly decrease after the peak. In other words, June and July are huge outliers. So, as another check I carried out a regression discontinuity test (again, details at the end). This narrows the focus to the days surrounding the referendum, and essentially treats the referendum as an experiment: the day of the referendum and afterwards are those ‘treated’ with the experiment, whilst those before are the control group. In other words, there should be no real difference between June 21st and June 24th other than the referendum.

The results are the same. It is statistically significant. Moreover, in the ‘RD Plot’ at the end of the post, you can see how this relationship changes dramatically. Put another way: the days either side of the referendum are fundamentally different, and the only plausible explanation is the referendum. Indeed, this is what the regression discontinuity provides extremely strong evidence for.

But was there really an increase in hate crime?

The evidence in the data is extremely strong. However, there can be a few objections which are more theoretical. The first, and most important, is that the difference might be due to an increased awareness and therefore increased reporting (this is what the police claimed at the time). In other words, hate crimes did not increase, but the reporting of them did. This is certainly plausible.

In the days following the referendum, I find this hard to believe. Why would people be more likely to report hate crimes following a referendum? This did not happen after the Charlie Hebdo attacks, or Paris attacks, or other elections, or the start of the Palestine-Israel conflict – events which are more closely tied to the potential for hate crime. It only increased slightly even after the murder of Lee Rigby. The reverse is much more plausible: that hate crimes (remember, this includes damage to property and graffiti) ensued after the referendum. However, the peak of hate crimes occurred a week after the referendum. This is surely likely to be influenced by media coverage of the previous rises. Again, I think it is likely that there was indeed increased reporting of hate crimes, in response to national media coverage and the existence of more hate crime in general. In other words, I think it was a bit of both, with more hate crimes leading to coverage and more reporting (we must also remember that hate crimes are still hugely under-reported). 

Other claims I find less appealing. One might just say it is a coincidence. The statistical weight of evidence is, for me, far too strong for that. It is far less than a 1% chance that this was just a random increase which happened to occur at the exact time of the referendum. Other claims might argue about the definition of hate crime, how they are accounted for, and how few are brought to court. These are not the focus of this post – not because they’re not important, but because they can’t be drawn from the evidence here.

Brexit, hate crime and the future

A lot of coverage has argued that the atmosphere in the UK is increasingly toxic and intolerant. The data released only extends a few months after the referendum, so we cannot be sure of what’s actually happening. But from the existing data, I would conclude that the actual impact of Brexit on hate crimes was a short-lived one, and that the effect will decrease over time.

However, I would also suggest, on a more negative note, that all Brexit did was mobilise latent attitudes into behaviour. In other words, I do not think it changed attitudes that much, but acted as a catalyst to change those attitudes into actual actions – and hate crimes. This is in part evidenced by the general upward trend in hate crimes since 2013. For what it’s worth, going forward, the media and politicians need to be extremely careful not to stoke the flames of these attitudes. The referendum has shown that it does not necessarily take much to spark an increase in hate crimes. Other catalysts are possible. And it’s important that, when the next one comes, it is much harder to translate these beliefs into actions. 

*Probably = almost certainly

 


Statistical/methodological notes:

Summary

Graphics and tests were produced in the software package R, using data from the Home Office. The background design for the graphs was taken from code by Max Woolf

Summary statistics for the two data sets used (monthly and daily data):

Statistical Tests

Firstly, I ran a basic regression on both the daily and monthly data. This uses the referendum to ‘predict’ the variation in hate crimes after the referendum. The regressions were run using the variable ‘brexit’ as a binary predictor for the dependent variable ‘hate.crime’. Clearly for the monthly data, this is hugely unbalanced, so should be treated with a bucket load of caution. The daily data is more stable.

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The regression discontinuity used the day after the referendum as the cut off. This is because the referendum really would not have had an effect until the result. Nevertheless, it is centred around 0, the day of the referendum. It is statistically significant as well. Additional analysis by Professor Will Jennings, using a time series intervention model, confirmed the findings here. The debate about whether to use a time series or discontinuity approach continues…

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The R code used is as follows. You will need the theme function downloadable from here. If you’d like the data, please contact me (D.J.Devine@Soton.ac.uk)

setwd(“”) # set your working directory

hate <- read.csv(“day.hate.csv”) # read in the data
hate2 <- read.csv(“month.crimes.total.csv”)

install.packages(“rdrobust”) #install packages
library(“rdrobust”)
install.packages(“ggplot2”, dependencies = TRUE)
library(“ggplot2”)
library(“stargazer”)
library(“lubridate”)
library(“tseries”)
library(“scales”)
library(“grid”)
library(“RColorBrewer”)
install.packages(“extrafont”)
library(“extrafont”)
loadfonts() # note, loading the fonts package will take considerable time depending on the machine
pdf(“plot_garamond.pdf”, family=”Garamond”, width=4, height=4.5)

rdrobust(hate$hate.crime, hate$since.ref, c = 1 ) # regression discontinuity
rdd_est <- rdrobust(hate$hate.crime, hate$since.ref, c = 1 )
rdplot(hate$hate.crime, hate$since.ref, c = 1)

stargazer(hate, type=”html”,
title = “Summary Statistics for Daily Data”)
stargazer(hate2, type=”html”,
title = “Summary Statistics for Monthly Data”) # summary statistics, output in HTML

linear.day <- lm(hate$hate.crime ~ hate$brexit) # regular regression on day
linear.month <- lm(hate2$hate.crime ~ hate2$brexit) #… and months
stargazer(linear.day, linear.month, type=”html”,
title = “The Effect of Brexit on Hate Crimes”,
column.labels = c(“Daily Crime”, “Monthly Crime”),
coviariate.labels=”Brexit”) # table of the regression

month.crime.plot <- ggplot(data=hate2, aes(x=id, y=hate.crime)) +
fte_theme() +
geom_line(color=”#c0392b”, size=1.45, alpha=0.75) +
geom_vline(xintercept=42, linetype = “longdash”, color = “gray47″, alpha = 0.7) +
geom_text(aes(x=42, label=”Referendum”, y=2300), colour=”gray36″, size=8, family=”Garamond”)+
ggtitle(“Hate Crimes in England and Wales, 2013-2016”) +
scale_x_continuous(breaks=c(6,12,18,24,30,36,42),
labels=c(“June 2013”, “Dec 2013”, “June 2014”, “Dec 2014”, “June 2015”, “Dec 2015”, “June 2016”)) +
labs(y= “# Hate Crimes”, x=”Date”) +
theme(plot.title = element_text(family=”Garamond”, face=”bold”, hjust=0, size = 25, margin=margin(0,0,20,0))) +
theme(axis.title.x = element_text(family=”Garamond”, face=”bold”, size = 20, margin=margin(20,0,0,0))) +
theme(axis.title.y = element_text(family=”Garamond”, face=”bold”, size = 20, margin=margin(0,20,0,0))) +
geom_hline(yintercept=2000, size=0.4, color=”black”) # monthly graph

day.crime.plot <- ggplot(data=hate, aes(x=id, y=hate.crime)) +
fte_theme() +
geom_line(color=”#c0392b”, size=1.45, alpha=0.75) +
geom_vline(xintercept=54, linetype = “longdash”, color = “gray47″, alpha = 0.7) +
geom_text(aes(x=54, label=”Referendum”, y=85), colour=”gray36″, size=8, family=”Garamond”) +
ggtitle(“Hate Crimes in England and Wales, May-August 2016”) +
scale_y_continuous(limits=c(75,220)) +
scale_x_continuous(breaks=seq(14,123, by=14),
labels=c(“14 May”, “28 May”, “11 June”, “25 June”, “9 July”, “23 July”, “6 August”, “20 August”)) +
labs(y= “# Hate Crimes”, x=”Date”) +
theme(plot.title = element_text(family=”Garamond”, face=”bold”, hjust=0, size = 25, margin=margin(0,0,20,0))) +
theme(axis.title.x = element_text(family=”Garamond”, face=”bold”, size = 20, margin=margin(20,0,0,0))) +
theme(axis.title.y = element_text(family=”Garamond”, face=”bold”, size = 20, margin=margin(0,20,0,0))) +
geom_hline(yintercept=75, size=0.4, color=”black”) # daily graph


 

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