Tsukuba Economics Working Papers
No. 2018-003

Did the Presence of Immigrants Affect the Vote Outcome in the Brexit Referendum?
byMizuho Asai
andHisahiro Naito
May 2018

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANDid the Presence of Immigrants Affect the Vote
Outcome in the Brexit Referendum?
Mizuho Asai∗
College of International Studies
University of Tsukuba

Hisahiro Naito †Program of Economic and Public Policy
Graduate School of Humanities and Social Sciences
University of Tsukuba

May 2018∗†

Address: Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573; e-mail: s201410344@u.tsukuba.ac.jp
Address: Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573; e-mail: naito@dpipe.tsukba.ac.jp

Abstract
This study examines the effect of the presence of immigrants on voters’ behaviour,
using a data set of the UK referendum on the exit from the European Union andsurvey data on individuals’ attitudes towards accepting immigrants. We apply the
instrumental variable estimation to control for the bias due to the endogeneity of theimmigrant ratio and omitted variables. To construct the instrumental variable, we use
information on past industrial composition, including current industry composition asa control variable. We also instrument the current claimant rate. Contrary to popular
media coverage, we find that the effect of the presence of immigrants on the voteoutcome is small and not statistically significant. We also show that the survey resulton attitudes towards accepting immigrants is consistent with the estimation results
based on the vote outcome.
JEL Classification: C25
Keywords: Voting behaviour, immigration, Brexit

1

Introduction

The presence of immigrants and influx of refugees have had a major effect on domestic
politics in Europe and the United States. One point of contention for the Britishreferendum on Brexit in June 2016 concerned stopping the influx of immigrants fromother EU countries by withdrawing from the Union. In the 2016 US presidential
election, then candidate Donald Trump argued for a tough stance on illegal immigrants,
with building a wall on the border with Mexico one of his campaign promises. He
also suggested that he wanted to restrict visa issuance for legal immigrants and guest
workers, including a stop on green cards. Despite being distinctly against immigration,
he was nonetheless elected as the 45th President of the United States. In the firstround of the French presidential election on 23 April 2017, anti-immigration and antiEU advocate Marine Le Pen came second, thus advancing to the final round. Similar
developments have also unfolded in other European countries.1
A natural question here is whether voters’ support for anti-immigration ideas stems
from the presence of immigrants whose languages and customs are different from theirown, the influence of the media or politicians, or the economic difficulties occurringaround the same time. If the presence of heterogeneous immigrants itself is causing theanti-immigration behaviour of fellow citizens, then progressive economic globalization
should intensify domestic anti-immigration trends. Yet, if anti-immigration voting
behaviour stems from the influence of the media and politicians or concurrent economic
difficulties, then an alleviation of those difficulties or spread of correct information will
reduce anti-immigration sentiment.
This study examines whether an increase in the proportion of immigrants affected
Brexit voting behaviour, using both vote outcome data on the referendum and British
survey data on people’s feelings towards immigrants.
For any empirical study that examines the causal effect of the presence of immigrants on voting behaviour, two issues need careful treatment from the point of view
1

In Hungary, Prime Minister Orbàn, re-elected in 2010, is using the refugee issue to provoke antiEU feelings, such as becoming more authoritarian, restricting the right to free speech, and limiting
the power of the EU. In the 2015 Polish parliamentary election, the anti-immigrant conservative party
Law and Justice (PiS) won an absolute majority. PiS gained control over the Polish Constitutional
Court and is turning more authoritarian.

1

of econometrics. The first issue is omitted variable bias. When a multivariate analysis
is applied, the inclusion of a limited set of control variables may be insufficient. In
such a case, the estimated coefficient includes not only the effect of an increase in theimmigrant ratio but also the effect of omitted variables such as local characteristics.
The second issue is the bias due to the endogeneity of the immigrant ratio. If there isan area where local residents are more tolerant of immigrants, immigrants may inflow
to such an area. In this case, if we run an ordinary least squares (OLS) regression
and regress the number of approval votes to remain in the EU, which implies not
opposing the influx of immigrants, on the immigrant ratio, the estimated coefficient of
the immigrant ratio can be positive. However, such a relationship does not necessarilymean that an exogenous increase in the immigrant ratio would increase the approval
vote to remain in the EU. An estimated positive coefficient simply reflects the sortingof immigrants through their endogenous location choice.
To address omitted variable bias and endogeneity bias, we employ an instrumental variable (IV) estimation method, using as the IV the predicted immigrant ratio in
2001, which was predicted by using information on past (2001) industry composition,
while controlling for current (2011) industry composition. Once we control for current
industry composition and past and present demographic characteristics, industry composition in 2001 is unlikely to affect attitudes towards immigrants. On the contrary,
past industry composition affects the past immigrant ratio, which then influences thecurrent immigrant ratio because of the slow mobility of immigrants across electoral areas. This finding implies that the predicted past immigrant ratio, which is a function of
past industry composition, satisfies the condition of a valid IV. To check the robustness,
we conduct various regression estimations by using different sets of control variables
and regressing individual attitudes towards accepting immigrants on the immigrant
ratio. Our results are found to be stable after these various robustness checks.
In the literature, there is increasing interest in the effect of immigrants on voters’
behaviour. Regarding Brexit, Becker et al. (2017) categorize various potential variables
that could have affected the vote outcome into four groups. Then, within each group,
they regress the vote outcome on the variables in each group by using OLS. Amongthe variables related to immigration and trade, they show that the immigrant ratio in
2001 is negatively related to the number of votes approving exit from the EU and that2the growth in immigrants from accession countries is negatively related to the vote toapprove exit from the EU. Goodwin and Heath (2016) conduct a multivariate analysis
and examine factors that might affect the vote count with a limited set of explanatory
variables. Since both studies use a simple OLS with limited variables, they are subject
to omitted variable bias and endogeneity bias. Curtice (2017) examines how the various
perceptions of voters affect the outcome, using a single explanatory model.
For cases other than Brexit, Becker and Fetzer (2016) analyse how the inflow ofimmigrants to each electoral area affects the vote share of UKIP, Mayda (2006) analyses those against accepting immigration, and Scheve and Slaughter (2001) analyse
how labour market competition with immigrants affects attitudes towards immigrants.
Facchini et al. (2013) analyse the effect of the skill of natives on attitudes towards immigrants, using data on South Africa. Mendez and Cutillas (2014) analyse the causal
relationship between a voter’s behaviour and the immigrant ratio in Spain, finding thatthe inflow of immigrants from Latin America has increased the support for left-wing
political parties. Otto and Steinhardt (2014) analyse the effect of the immigrant ratioon voter’s behaviour in Germany and Facchini and Mayda (2009) analyse whether thewelfare state affects attitudes towards immigrants, using cross-country data.
We contribute to previous studies in three ways. First, we analyse the causal effectof the share of immigrants on voting behaviour. Although several studies analyse
the cause of the Brexit referendum, all of them use simple OLS with a limited set of
explanatory variables. Thus, they are subject to omitted variable bias and endogeneitybias. By contrast, this study uses the IV estimation method.
Second, we analyse both voting data and survey data. Our analysis based on voteoutcome data and individual survey data show the robustness of our estimation results.
In our IV estimation (two-stage least squares, 2SLS), we find that, contrary to thefinding by Becker et al. (2017), the immigrant ratio does not affect the vote outcome.
The effect is not only not statistically significant, the estimated magnitude is small.
We conduct several robustness checks such as changing the dependent variable andinvestigating individual survey results, and confirm that our estimation result is robust
to these robustness checks.
The organization of this paper is as follows. In section 2, we explain how theimmigration issue was addressed during the Brexit negotiation process and how it
3became an important issue during the referendum. Section 3 discusses the estimation
model and identification strategies. Section 4 explains the data sets. Section 5 presents
the estimation results and section 6 discusses the implications of the estimation results
and concludes.
2

Background of the Brexit Referendum and Immigration Issues

In the Brexit referendum, immigration restrictions from other EU countries were oneof the key issues. This issue had existed from the beginning when the negotiations
with the EU were discussed in UK politics.
On 22 January 2013, Prime Minister David Cameron announced that if the Conservative party won the next election, it would renegotiate the United Kingdom’s relationship with the EU and give the British people the simple choice between staying
in the EU under newly negotiated terms or leaving.
In May 2015, the Conservative party unexpectedly won the general election. Soon
afterwards, the European Union Referendum Act 2015 was introduced into Parliament
to enable the referendum. Among the topics that Prime Minister Cameron sought torenegotiate with other member countries of the EU was restricting EU immigration.
In June 2015, during the EU summit, Cameron started to negotiate the United
Kingdom’s position in the EU. By February 2016, the outcome of the renegotiations
was announced and some limits to in-work benefits for new EU immigrants were agreed.
However, before they could be applied, the United Kingdom had to receive permission
from the European Commission and European Council. On 20 February 2016, Cameron
announced that the United Kingdom’s in/out referendum would be held on 23 June
2016.
During the referendum campaign, one of the key issues was restricting immigrants
from EU member countries to the United Kingdom. According to a study of the media
during the referendum, the economy was the most covered campaign issue, with 7,028
articles compared with 4,383 about immigration; health was the third most reported
issue with 1,638 articles (Moore and Ramsay, 2017).
To understand how people voted in the referendum, one post-election survey foundthat the immigration issue was the second most important. This finding suggests

4that it is reasonable to examine how the presence of immigrants in each electoral area
affected the vote outcome.
3

Estimation Model and Identification Strategy

WE first estimate the following equation:
Remainj = β0 + β1 ImmigrantRatioj + β2 Xj + uj

(1)

where j is an index of electoral area. Remainj is the share of the vote to remain in the
EU. ImmigrantRatioj is the ratio of immigrants in total residents in area j. Xj is avector of the control variables and β2 is a vector of the coefficient of Xj . The coefficientof interest is β1 , which measures how an exogenous increase in the current immigrant
ratio affects the share of votes to remain in the EU. The error term is an unobserved
factor that affects the share of the vote to remain such as the preferences of voters.
It is difficult to estimate β1 consistently by applying OLS to equation (1) since
ImmigrantRatioj is not randomly assigned and, as a result, it could be correlated withthe error term uj . For example, immigrants may flow into areas where natives arefriendly towards them. In such a case, running OLS on equation (1) would generate apositive coefficient of β1 even when an exogenous increase in immigrants generates anegative effect on voting to remain in the EU.
To discuss how to solve the endogeneity of RatioImmigrantj , let Pkj be the numberof residents (including both natives and immigrants) working in industry k in area j.
When k=0, Pkj indicates the number of residents who are not working. Let Mkj be thenumber of immigrants working in industry k in area j. The immigrant ratio in area j
can be written as follows:P
Mkj
ImmigrantRatioj = Pk
k Pkj
X
=
ρkj zjk(2)kwhere zkj is the current composition of industry k in area j defined as zkj = Pkj /ρkj is immigrants’ relative share of industry k in area j defined as ρkj =5

Pk Pkj .
Mkj
. zj
Pkj

is a vector composed of zjk . Equation (2) suggests that we can predict the current
immigrant ratio in area j by using information on zjk assuming that ρkj is different fordifferent k. The idea that we can use zjk to predict the immigrant ratio comes fromthe observation that immigrants and natives have different skills and preferences forjobs. Hence, local industry composition, zk , can be a good predictor of the immigrant
ratio.
Our key assumption is that natives and immigrants have different skills and preferences for jobs. Table 1 shows the share of individuals working in several industries whoreport that their ethnicity is related to the United Kingdom and that their ethnicity is
not UK-related. For example, among those who report that their ethnicity is related toTable 1. Share of Employment in Different Industries for Different Ethnicities
Industries
AccommoPublicAgriculture,
Transport
Self‐
dation and
Manufactu Constructi
administra Education
andreported energy and
food
ring
on
tion
water
storageethnicities
serviceUK related

2.59%

9.37%

8.30%

4.74%

4.73%

6.42%

10.21%

Others

1.25%

7.06%

5.04%

5.88%

9.37%

4.09%

8.54%

Notes: UK‐related ethnicities are individuals who reported that their ethnicity is English, Welsh, Scottish,
Northern Irish, or British. Others are all other individuals who reported other ethnicities.

the United Kingdom (those who report that their ethnicity is English, Welsh, Scottish,
Northern Irish, or British), the percentage of individuals who work in the agriculture,energy, and water industry is 2.59 percent, while among other ethnicities, it is 1.59
percent. Among UK-related ethnicities, the percentage of those working in the accommodation and food service industry is 4.73 percent, while among other ethnicities, it
is 9.37 percent. Thus, industry composition is likely to be different for different ethnicities. This implies that local industry composition can be a good predictor of theimmigrant ratio.
On the contrary, using information on current industry composition to construct
the instrument for the current immigrant ratio is problematic since current industry
composition can be correlated with current natives’ preferences for immigrants. For
example, when immigrants inflow to a certain area because of natives’ generosity to im6

migrants, the proportion of a certain industry for which immigrants have more specifichuman capital will rise. In such a case, information on current industry composition
is correlated with natives’ preferences for immigrants.
To avoid a correlation between the IV and natives’ preferences for immigrants, we
use past (2001) industry composition to construct the IV, while including current industry composition as a control variable. More specifically, we divide the cross-sectionalvariation in past industry composition into two parts: one that can be correlated withcurrent industry composition and one that cannot be explained by current industry
composition. Then, it is reasonable to assume that once we control for current industry composition, the variation in past industry composition that cannot be explainedby current industry composition is not correlated with current natives’ preferences for
immigrants. Since this component is not correlated with current industry composition
by definition, it is unlikely that this component is correlated with natives’ preferences
for immigrants.
o
Let zjk
be the past (2001) industry composition of industry k in area j and let zjo beoa vector composed of zjk. As we defined earlier, let zj be a vector of current industry

composition. Our assumptions on zj and zjo can be restated asE[uj |zjo , zj ] = E[uj |zj ](3)

By using the standard argument of conditional independence, it is straightforward toshow that the 2SLS estimator of β1 using zjo while controlling for zj is consistent.2
On the contrary, directly using zjo is not an efficient way in which to use information
on past industry composition since there are many industries. To use the informationof zjo effectively, we construct the IV as follows. Let the immigrant ratio in 2001 be
Immigrant Ratiooj . We estimate the immigrant ratio in 2001 as a function of zjo by
assuming the following relationship:
ImmigrantRatiooj = F (zjo , γ) + j(4)where γ is a vector of the parameters to be estimated. For a functional form F, we
2

For example, see the discussion of Stock and Watson (2011) Chapter 12 Appendix 12.6.

7

find that a logistic function fits very well for predicting the immigrant ratio in 2001
as a function of industry composition in 2001.3 Let γ̂ be an estimated parameter of γby the nonlinear regression. Then, we use F (zjo , γ̂), the predicted immigrant ratio in
2001, as the IV for the current immigrant ratio.
When we use the predicted immigrant ratio in 2001 above, F (zjo , γ̂), as the IV
for the current immigrant ratio, we need to include current industry composition asa control variable because without it, zjo is correlated with current industry composition and current industry composition is correlated with current natives’ preferences
for immigrants. For the same reason, we include the current and past demographic
characteristics of each electoral constituency. We also control for the current claimantrate, using the past claimant rate as the IVs. In other words, we compare similarelectoral areas whose past and present demographic characteristics, current industrial
compositions, and past claimant rates are similar, whereas past industry composition,
which is not corrected with current industry composition, is different. By comparing
the vote outcomes in those areas, we estimate the effect of the immigrant ratio on thevote outcome.
There is one concern about using the predicted past immigrant ratio, which ispredicted by past industry composition, as the IV for the current immigrant ratio.
The first concern is that once we control for current industry composition, there islittle variation in past industry composition. However, this concern is not valid in thedata. Table 2 shows industry composition in 2001 and current industry composition.
This table shows that industry composition changed at the macro level during these
10 years. For example, in 2001, the share of residents working in the manufacturingsector was 15 percent; however, in 2011 this had decreased to 9 percent. Similarly, theshare of residents working in the healthcare industry was 10.8 percent in 2001 but 12.5
percent in 2011. The first-stage results of our 2SLS show that the predicted immigrant
ratio, which is constructed by past industry composition, has strong explanatory powerfor predicting the current immigrant ratio—even if we control for current industry
composition.
3

Since the dependent variable takes a value from 0 to 1, it is natural to use a logistic function.
When we apply a linear function to F, the R-squared is 0.7. On the contrary, when we use the logistic
function, the R-squared becomes 0.94.

8

Table 2: Percentage of Top Five industries in 2001 and 2011
2001

2011

Wholesale and retail trade

16.8%

Wholesale and retail trade

16.0%

Manufacturing

15.0%

Healthcare

12.5%

Real estate

13.0%

Education

9.9%

Human health

10.8%

Manufacturing

8.9%

Education

7.8%

Construction

7.7%

Notes: The data source is the 2001 and 2011 censuses. The number shows the percentage of residents
of England and Wales aged 16 to 74 in employment by industry. Industry category is based on thecensus. Wholesale and retail trade include the repair of motor vehicles. Real estate includes renting
business activities. Healthcare includes social work activities.

As a robustness check, we also use individual survey data on attitudes towards
accepting more immigrants. By using the index of a respondent’s opinion as the dependent variable, we run the same regression as in equation (1) and examine how theimmigrant ratio affects natives’ opinions about accepting immigrants.
4

Data Set

For the referendum vote counts in each local authority area (district), we obtain information from the UK Electoral Commission (The UK Electoral Commission, 2017).
We use the code of the local authority before April 2015 to match the vote data andother economic and demographic data. We select electoral areas in Wales and Englandand exclude Scotland and Northern Ireland because of the availability of demographic
and other economic data. In total, we have 333 electoral areas in our data set.4 For
the current ratio of non-UK-born individuals, we use the annual population survey
in 2015. Although previous studies use census data in 2011 to calculate the ratio of
non-UK-born individuals, it seems better to use the ratio of non-UK-born individuals
just before the referendum since the ratio kept increasing until the referendum. Owing
to the sampling design of the annual population survey, there may be a measurement
error in the ratio of non-UK-born individuals. The use of IV is likely to solve the problem of bias due to any measurement errors in the ratio of non-UK-born individuals.
4

In addition, the share of the vote to remain in Scotland is substantially higher than that in
England. This fact suggests that the coefficient of immigrants could be different for Scotland and forEngland and Wales and that the equation should be estimated separately.

9

We use the claimant rate in 2015 since the claimant rates in 2011 and 2015 differ inmany areas. For the age and educational composition of UK-born individuals, we need
to use the census data in 2011. For information on past non-UK-born individuals’
age and educational composition, we use the census data in 2001. For current andpast industry composition, we use the census data in 2011 and 2001, respectively. For
the claimant count in 2015 and 2001, we use the database of the Office for National
Statistics.5
Regarding individual attitudes towards accepting immigrants, we use YouGov’s
Election Study data set.6 YouGov, a UK-based company that focuses on consumer
surveys, conducts various types of Internet-based surveys almost every day. One such
survey is the British Election Study. In the YouGov system, the company’s software
continuously looks for respondents. When a panel, a group of consumers that represent
the nation, is formed, none of the panel knows what kinds of questions will be asked.
YouGov asks a variety of questions including on politics, international issues, health,life, and the environment. The YouGov software tries to form a panel that represents
the nation’s opinion. If people drop out of the panel, the software tries to find a person
who has similar demographic characteristics.
In the YouGov data, we use wave 10 of the British Election Study conducted between 24 November 2016 and 12 December 2106. Among wave ten respondents, we
exclude those who live in the Isle of Man and Isle of Sicily since there are no corresponding census data with information on demographic characteristics. We also drop
individuals aged less than 18 and individuals who stated that their nationality is not
British. As a result, we have 10,365 individuals in this YouGov data set.
5

Results

5.1

Results based on the Vote Outcome

Figure 1 shows the ratio of non-UK-born individuals over UK-born individuals in thepast 15 years. It also shows the ratio of EU-born residents (except residents bornin the United Kingdom) over UK-born residents and the ratio of residents born in
5
6

See https://www.nomisweb.co.uk/.
See https://yougov.co.uk/news/categories/politics/.

10accession countries over UK-born residents. Figure 1 shows that the ratio of non-UKborn individuals to UK-born residents has increased consistently over the past 15 years.
It also shows that the increase in non-UK-born individuals is parallel to the increase
in residents born in accession countries.

0

.05

.1

.15

Figure 1: Immigrant ratio

2000

2005

2010

2015

year
Ratio of Non−UK Born
Ratio of Acession Countris

Ratio of EU Born (except UK)

Notes: The source is the National Population Survey from the Office for National Statistics. The
ratio of non-UK-born individuals is the number of non-UK-born residents divided by the number ofUK-born residents. The other ratios are calculated in a similar way.

Table 3 shows the summary statistics of the main variables used in this study. In
our data set, the average share of the vote to remain in the EU is 46 percent and theaverage ratio of non-UK-born individuals is 12 percent in 2011 and 7.8 percent in xxxx.
Table 4 shows the estimated coefficients of our OLS estimation where the dependent
variable is the share of the vote to remain in the EU and the main explanatory variable
is the ratio of non-UK-born individuals in each electoral area. Table 4 shows that ahigher ratio of non-UK-born individuals leads to a lower share of the vote to remain in
the EU when we control for the claimant rate, demographic characteristics in 2011, and11Table 3. Summary Statistics of the Main Variables
Variables
Ratio of Vote for Remaining in EU
Ratio of Non‐UK‐born individuals in 2015
Ratio of Non‐UK born‐individuals in 2001
Predicted Ratio of Non‐UK born in 2001
Claimant Rate in 2015
Claimant Rate in 2001
Ratio of aged ≥ 65 in 2011

(1)
mean
0.457
0.119
0.07750.07580.0181
0.0217
0.175

(2)
sd
0.09700.105
0.07900.07630.01040.01220.0400

(3)
min
0.244
0.008130.01130.004350.004250.00400
0.0613

(4)
max
0.786
0.541
0.466
0.440
0.05500.06000.297

Ratio of aged ≥ 65 in 2001

0.165

0.03210.08950.296

Ratio of Education level 1 and 2 in 2011
Ratio of Education level 3 in 2011
Ratio of Education level 4 or more in 2011

0.292
0.122
0.271

0.03840.01650.07550.136
0.0916
0.142

0.361
0.192
0.536

Notes: N=333. The unit of the sample is the local authority (district) before April 2015 in Englandand Wales. The sources of the data are the national censuses of 2001 and 2011, Annual Population
Survey 2015, and Office for National Statistics database on claimant count.

Table 4: OLS Estimation Results
The Effect of the Immigrant Ratio on the Vote to Remain in the EU
Ratio of Vote for Remaining in EU
Dependent Variable
(1)
(2)
(3)
(5)
(4)
Ratio of Non‐UK born in 2015
‐0.00344 ‐0.0226 ‐0.00105 ‐0.0136 ‐0.0898**(0.0416) (0.0421) (0.0409) (0.0415) (0.0432)
Claimant rate in 2015
1.161*** 1.884*** 2.008*** 0.928**(0.394) (0.421) (0.439) (0.448)
Ratio of Education Level 4
1.209*** 1.069*** 0.992***
or More in 2011(0.225) (0.231) (0.218)
Ratio of No Qualificaation in 2011‐0.3050.194(0.231) (0.244)
Ratio of Aged 65 or More in 2011
‐0.565***(0.103)2011 industry compostion
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
2001 educaiton & age compositionR‐squared
0.877
0.882
0.894
0.894
0.905
Observations
333
333
333
333
333
Notes: Heteroscedasticity‐robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

industry composition in 2011. Column (1) shows that a one percentage point increasein the ratio of non-UK-born individuals decreases the vote to remain in the EU by 0.003
percentage points when no control variable is included in the regression equation; this12

is not statistically significant and small. Columns (2)–(4) add control variables suchas the claimant rate and demographic characteristics. Column (4) shows that a one
Table 5: First‐Stage Results of the 2SLS Estimation
Panel A.
Dependent Variable
Predicted Ratio of
non‐UK born in 2001
Claimant Rate in 2001
in 2001
F‐statistics
R‐squared
Panel B.
Dependent Variable
Claimant Ratein 2001
Predicted Ratio of
non‐UK born in 2001
F‐statistics
R‐squared
Control Variables
2011 industry compositin
2001 education & age composition
Ratio of Level 4 or More in 2011
Ratio of No‐qualification in 2011
Ratio of aged 65 or more in 2011
observations

Ratio of Non‐UK born in 2015
(1)
(2)
(3)
(4)
(5)
0.951*** 0.900*** 0.894*** 0.868*** 0.805***(0.107) (0.107) (0.113) (0.120) (0.116)
0.839
0.816
0.927
0.304
(0.583) (0.586) (0.570) (0.552)
79.12
41.12
35.09
29.91
25.21
0.843
0.844
0.844
0.846
0.865

Claimant Rate in 2015
(1)
(2)
(3)
(4)
(5)
0.650*** 0.689*** 0.650*** 0.615*** 0.583***(0.0627) (0.0634) (0.0611) (0.0610) (0.0627)‐0.0205**‐0.0309***‐0.0228***‐0.0265***(0.0103) (0.00957)(0.00880)(0.00916)
149.1
81.51
82.89
96.95
41.88
0.840
0.843
0.864
0.878
0.884

yes
yes
no
no
no
333

yes
yes
no
no
no
333

yes
yes
yes
no
no
333

yes
yes
yes
yes
no
333

yes
yes
yes
yes
no
333

Notes: Heteroscedasticity‐robust standard error in parentheses. F‐statistics show theheteroscedasticity‐robust F‐value of the null hypothesis that the coefficients of the
excluded instruments are equal to zero.

percentage point increase in the ratio of non-UK-born individuals decreases the shareof the vote to remain in the EU by 0.09 percentage points. Although the coefficient isstatistically significant at the 10 percent level, the magnitude is small. For example,
a 5 percentage point increase in the ratio of non-UK-born individuals decreases theshare of the vote to remain in the EU by only 0.45 percentage points. Given that the
average share of the vote to remain in the EU is 46 percent, this effect is small.
In Table 4, we do not control for the endogeneity of the ratio of non-UK-born
13individuals and the claimant rate and do not control for omitted variable bias. In
Tables 5–7, we conduct a 2SLS regression to handle those issues where the dependent
variable is the share of the vote to remain in the EU and the endogenous explanatory
variables are the ratio of non-UK-born individuals in 2015 and the claimant rate in2015. The IVs are the predicted ratio of non-UK-born individuals in 2001, which waspredicted by using information on industry composition in 2001 and the claimant rate
in 2001.
Table 5 shows the estimated coefficients of the first-stage 2SLS regression. The ratio
of non-UK-born individuals and the claimant rate are denoted in decimal numbers tosimplify the comparison. For the regression, we include industry composition in 2011
and the educational and age composition in 2001 as control variables in all cases,
since industry composition in 2001 can affect the vote outcome without those control
variables. Panel A shows the first-stage result when the dependent variable is theratio of non-UK-born individuals. The estimated coefficients of the predicted ratio
of non-UK-born individuals are around 0.8–0.9 and these are stable under different
specifications. The heteroscedasticity-robust F-statistics show that the IVs are strong.
In Panel B, the dependent variable is the claimant rate. For comparison purposes, theclaimant rate is measured as a decimal number. It shows that the estimated coefficientsof the claimant rate in 2001 are stable under various specifications, around 0.58–0.05.
The heteroscedasticity-robust F-statistics show that the IV is strong.
Figure 2 presents the relationship between our IV and the ratio of non-UK-born
individuals in 2015 after controlling for the effect of industry composition in 2011. It
shows that our first-stage result is not driven by outliers and that there is a strong
positive relationship between our IV and the ratio of non-UK-born individuals in 2015.
Table 6 shows the second-stage estimation result of our 2SLS estimation. All the
specifications in Table 6 control for the past and current industry composition and theage composition. The Kleibergen-Paap Rank Wald F-statistics show that our IVs are
not weak. Column (1) shows that a one percentage point increase in the immigrant
ratio decreases the share of the vote to remain in the EU by 0.016 percentage points.
The effect is not statistically significant and the magnitude is small. Column (2)additionally controls for the claimant rate in 2015. Since the claimant rate can beendogenous, it is instrumented by the claimant rate in 2001. In columns (3)–(5), we
14

Non−UK born Ratio in 2015 (adjusted)
−.1
0
.1
.2

.3

Figure 2: Relationship between Non-UK-born individuals in 2015 and our IV afterControlling for Current Industry Composition

−.1

0
.1
Predicted Non−UK born Ratio in 2001 (adjusted)
Non−UK born Ratio in 2015 (adjusted).2

Fitted values

Notes: The vertical axis is the residual after regressing the ratio of non-UK-born individuals in 2015
on the control variables (current industry composition and educational composition in 2001). The
horizontal axis is the residual after regressing the IV, the predicted ratio of non-UK-born individuals
in 2001, on the same set of control variables. The predicted ratio of non-UK-born individuals in 2001
is constructed by using information on industry composition in 2001.

additionally control for the educational and age composition in 2011. Column (5) showsthat a one percentage point increase in the ratio of non-UK-born individuals decreases
the share of the vote to remain in the EU by 0.006 percentage points. The effect is notstatistically significant and the size of the magnitude is small. On the contrary, Table
6 shows that the effect of the claimant rate in each electoral area is strong. Column(5) shows that a one percentage point increase in the claimant rate in 2015 increases
the share of the vote to remain in the EU by 3.6 percentage points.
It is known that the standard error of 2SLS is larger than the standard error of OLS.
One might think that the nonsignificant results of Table 6 might come from the natureof 2SLS. Table 7 shows the reduced-form regression where the dependent variable isthe vote outcome and the explanatory variable is the IV, the predicted ratio of nonUK-born individuals in 2001. To focus on the coefficient of the effect of the ratio of15

Table 6: Second‐Stage Results of the 2SLS Estimation
The Effect of the Immigrant Ratio on the Vote to Remain in the EU
Ratio of Vote for Remaining in EU
Dependent Variable
(1)
(2)
(3)
(5)
(4)
Ratio of Non‐UK born in 2015
‐0.0160 ‐0.0818 0.0322 0.0138 ‐0.00581(0.0739) (0.0767) (0.0739) (0.0739) (0.0820)
Claimant Rate in 2015
2.924*** 3.567*** 3.773*** 3.598***(0.809) (0.875) (0.977) (1.023)
Ratio of Education Level 41.610*** 1.442*** 1.408***
or More in 2011(0.263) (0.243) (0.248)‐0.380‐0.289Ratio of No Qualificaation in 2011(0.297) (0.313)‐0.356
Ratio of Aged 65 or More in 2011(0.335)2011 industry composition
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
2001 education & age composition
Kleibergen‐Paap Rank Wald
79.12
40.31
33.12
28.85
24.95
R‐squared
0.889
0.875
0.887
0.886
0.889
Observations
333
333
333
333
333
Notes: Heteroscedasticity‐robust standard errors in parentheses. The endogenous
explanatory variables are the ratio of non‐UK‐born individuals in 2015 and the claimant rate
in 2015. The ratio of non‐UK‐born individuals in 2015 is instrumented by the predicted ratio
of non‐UK‐born individuals in 2001. The claimant rate in 2015 is instrumented by theclaimant rate in 2001. The claimant rate is denoted as a decimal number. *** p<0.01, **p<0.05, * p<0.1

non-UK-born individuals in 2001, we make the other control variables the same as in
Table 6 and instrument the claimant rate in 2015 by using the claimant rate in 2001.
Thus, the estimated coefficient of the reduced form of the predicted ratio of non-UKborn individuals in 2001 divided by the estimated coefficient of the same variable in thefirst-stage regression becomes the estimated coefficient of the second-stage equation of
the ratio of non-UK-born individuals in 2015. Table 7 shows that even in the reduced
form, the effect of the instrument, the predicted ratio of non-UK-born individuals in
2001, is not statistically significant.
One natural question from Table 6 is whether natives respond negatively not tothe level of immigrants, but to a change in the level of immigrants. In other words, inan electoral area where the rate of increase in immigrants is high, do natives vote toleave the EU? To check such a hypothesis, we calculate the rate of the inflow of non16

Table 7: Reduced‐Form 2SLS Regression
The Effect of the Predicted Ratio of Non‐UK‐Born Individuals in 2001 on the Vote
Ratio of Vote for Remaining in EU
Dependent Variable
(1)
(2)
(3)
(4)
(5)
Predicted ratio of non‐UK‐born‐0.0152 ‐0.0672 0.0339 0.0231 ‐0.000481
individuals in 2001
(0.0729) (0.0720) (0.0681) (0.0670) (0.0666)
2011 industry compositin
yes
yes
yes
yes
yes
2001 education & age composition
yes
yes
yes
yes
yes
claimant rate in 2015
no
yes
yes
yes
yes
ratio of Level 4 or More in 2011
no
no
yes
yes
yes
ratio of No‐qualification in 2011
no
no
no
yes
yes
ratio of aged 65 or more in 2011
no
no
no
no
no
R‐squared
0.878
0.868
0.886
0.888
0.896
observations
333
333
333
333
333
Notes: Heteroscedasticity‐robust standard errors in parentheses. The table shows theestimated coefficient of the predicted ratio of non‐UK‐born individuals in 2001 and theclaimant rate in 2015. The claimant rate in 2015 is instrumented by the claimant rate in 2001.
To see the pure effect of the predicted ratio of non‐UK‐born individuals in 2001 on theoutcome variable, for the other control variables, we use the same set of variables as in
Table 6. *** p<0.01, ** p<0.05, * p<0.1

UK-born individuals as follows and use this variable as an endogenous explanatory
variable, applying 2SLS:
Rate of inflow of non-uk born =non-UK born residents2015 − non-UK born residents2001
UK-born residents2001(5)

where Non-UK born residentst and UK born residentst are the number of non-UK-born
residents and UK-born residents in year t.
We use the same IV, which works well as indicated by the Kleibergen-Papp Rank
Wald Statistics in Table 8.7 This table shows the results of our 2SLS regression.
Table 8 shows that the estimated coefficients of the effect of the rate of the inflow of
non-UK-born individuals on the vote outcome are similar to those in Table 7. Column(5) of Table 8 shows that a one percentage point increase in the rate of the inflow
of non-UK-born individuals decreases the share of the vote to remain in the EU by
0.0061 percentage points and that the effect is not statistically significant. Thus, bymeasuring the presence of immigrants in terms of the ratio or rate of inflow, the effect7

To save space, the first-stage results are available from the authors upon request.

17

Table 8: The Effect of the Rate of the Inflow of Non‐UK‐born Individuals
on the Vote to Remain in the EU (2SLS)
Ratio of Vote for Remaining in EU
Dependent Variable
(1)
(2)
(3)
(4)
(5)
0.103 ‐0.0412 0.0813 0.0495 ‐0.000631
Rate of the inflow of(0.0868) (0.0952) (0.0823) (0.0823) (0.0873)
non‐UK‐born individuals
3.232*** 3.502*** 3.524*** 2.733***
Claimant Rate in 2015
(0.809) (0.795) (0.797)(0.929)1.524*** 1.328*** 1.316***
Ratio of Education Level 4(0.264) (0.248)(0.237)or More in 2011‐0.419*‐0.105
Ratio of No Qualificaation in 2011
(0.234)(0.287)‐0.475
Ratio of Aged 65 or More in 2011(0.316)
yes
yes
yes
yes
yes
2011 industry composition
yes
yes
yes
yes
yes
2001 education & age composition
Kleibergen‐Paap Rank Wald
48.66
21.11
29.13
23.85
19.19
R‐squared
0.875
0.867
0.883
0.886
0.897
Observations
333
333
333
333
333
Notes: Heteroscedasticity‐robust standard errors in parentheses. The endogenous explanatory
variables are the rate of the inflow of non‐UK‐born individuals in 2015 and the claimant rate in2015. The rate of the inflow of non‐UK‐born individuals in 2015 is instrumented by thepredicted ratio of non‐UK‐born individuals in 2001. The claimant rate in 2015 is instrumented
by the claimant rate in 2001. The claimant rate is denoted as a decimal number. *** p<0.01, **p<0.05, * p<0.1

of immigrants on the vote outcome is virtually zero.
5.2

Results using Survey Data on Attitudes Towards Immigrants

Tables 6–8 show that contrary to the general perception, the presence of immigrants
did not affect the vote outcome once we control for the endogeneity of the immigrant
ratio.
To check the robustness of our results, we further examine whether attitudes towards accepting immigrants during the period of the Brexit referendum show a similar
pattern. For the regression equation, we use attitudes towards accepting immigrants as
the dependent variable. This number takes from 1 to 10 and a higher number implies
that the respondent answered more favourably towards accepting immigrants. To compare this result with those in the previous tables, we standardize the answer use thatas the dependent variable. The endogenous explanatory variables are the immigrant18

Table 9: Second‐Stage 2SLS Results (Individual Sample)
The Effect of the Ratio of Non‐UK‐born Individuals on Attitudes towards Accepting Immigrants
Dependent Variable
Ratio of Non‐UK bornindividuals in 2015 ×100
Claimant Rate in 2015 ×100
individual characteristics
2011 industry compositin
2001 education & age composition
Claimant rate in 2015
Ratio of Level 4 or More in 2011
Ratio of No‐qualification in 2011
Ratio of aged 65 or more in 2011
R‐squared
Kleinberg‐Papp‐Stat
Observations

How much immigrant should be accepted
(1)
(2)
(3)
(4)
(5)
‐0.00296‐0.00467‐0.00271‐0.00305‐0.00506(0.00420) (0.00450) (0.00450) (0.00480) (0.00540)
0.084**
0.088**
0.088**
0.062**(0.0370)(0.0380)(0.0380)(0.0410)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
no
yes
yes
yes
yes
no
no
yes
yes
yes
no
no
no
yes
yes
no
no
no
no
yes
0.152
0.152
0.152
0.152
0.153
105.5
32.92
35.55
34.39
29.06
9,789
9,789
9,789
9,789
9,789

Notes: Clustered‐robust standard errors in parentheses. Individual attitude towards accepting immigrants
is standardized. A higher number implies that more immigrants should be accepted. To simplify thecomparison with the previous table, we display the effect of non‐UK‐born individuals and theunemployment rate when those variables are multiplied by 100. Thus, the number measures the effect of
one percentage point increase of the corresponding variable on the standardized value of the attitude. Table
A1 and Table A2 show the original results.

ratio in 2015 and the claimant rate in 2015. We use the same IVs as in the previous regression (see Tables 5–8) and the F-statistics and KleibergenPaap Rank Waldstatistics show that the IVs are strong. Table A1 presents more detailed results of our
2SLS regression. Table 9 reports the estimated coefficient of the ratio of non-UK-born
individuals in 2015, which was instrumented by the predicted ratio of non-UK-born
individuals in 2001. The estimated coefficients are all nonsignificant and small. Since
the ratio of non-UK-born individuals in 2015 is measured as a decimal number, column(5) implies that a one percentage point increase in non-UK-born individuals decreases
the standardized opinion by 0.005, which is virtually equal to zero. This result is
consistent with our 2SLS results based on the vote outcome reported in Tables 6–8.

19

6Discussion and Conclusion

In this study, we examine the causal relationship between the ratio of non-UK-born
residents and the vote to remain in the EU. After controlling for endogeneity, we findthat the effect of an increase in the ratio of non-UK-born residents is not only notstatistically significant, but also small. For example, the estimated coefficient implies
that even a 10 percentage point increase in non-UK-born residents decreases the vote
to remain in the EU by only 0.05 percentage points, which is small given that the shareof the vote to remain in the EU is 47 percent. On the contrary, we find that the effectof an increase in the claimant rate for the vote to remain in the EU is statisticallyand economically significant. We also confirm that the effect of the rate of increasein non-UK-born individuals displays a similar pattern and does not affect the voteoutcome. In addition, we check whether the presence of immigrants affects attitudes
towards accepting them. Our analysis shows that the presence of immigrants does notaffect individual opinion in this way, which is consistent with our results based on thedata on the vote outcome.
One natural question is why the poll after the referendum shows that immigration
was an important issue even though the presence of immigrants in each electoral area
did not affect the vote outcome and nor did attitudes towards accepting immigrants.
One possible hypothesis is that attitudes towards accepting immigrants are determinedby predetermined demographic characteristics such as age and education level. Indeed,
the more detailed regression results on individual attitudes towards accepting immigrants in Table A1 show that once we control for many individual characteristics, economic factors do not affect such attitudes; on the contrary, demographic characteristics
do matter. Table A1 shows that younger and more educated people have a positive
attitude towards accepting immigrants, whereas older and less educated individuals
have a negative attitude.
Although we do not go further to examine why this is so, it is an important implication if this hypothesis is true. As globalization proceeds, the immigrant ratio mightrise in many parts of the world. However, opinions regarding immigrants will be determined more or less by demographic factors, and among demographic characteristics,
only education can be influenced by policy. This implies that the role of education will20

have an additional role in society, namely understanding globalization and influencing
attitudes towards globalization.
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district-level analysis. Economic Policy, 32(92):601–650.
Curtice, J. (2017). Why leave won the UK’s EU referendum. JCMS: Journal of
Common Market Studies, 55(S1):19–37.
Facchini, G. and Mayda, A. M. (2009). Does the welfare state affect individual attitudes toward immigrants? Evidence across countries. The Review of Economics andStatistics, 91(2):295–314.
Facchini, G., Mayda, A. M., and Mendola, M. (2013). What drives individual attitudes towards immigration in South Africa? Review of International Economics,
21(2):326–341.
Goodwin, M. J. and Heath, O. (2016). The 2016 referendum, Brexit and the left behind:An aggregate-level analysis of the result. The Political Quarterly, 87(3):323–332.
Mayda, A. M. (2006). Who is against immigration? A cross-country investigation ofindividual attitudes toward immigrants. The Review of Economics and Statistics,
88(3):510–530.
Mendez, I. and Cutillas, I. M. (2014). Has immigration affected Spanish presidential
elections results? Journal of Population Economics, 27(1):135–171.
Moore, M. and Ramsay, G. (2017). UK media coverage of the 2016 EU referendum
campaign. Centre for the Study of Media, Communication and Power, King’s College
London. Available at:https://www.kcl.ac.uk/sspp/policy-institute/CMCP/UK-

media-coverage-of-the-2016-EU-Referendum-campaign.pdf [Accessed on 10 January2018].
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Otto, A. H. and Steinhardt, M. F. (2014). Immigration and election outcomes: Evidence from city districts in Hamburg. Regional Science and Urban Economics,
45:67–79.
Scheve, K. F. and Slaughter, M. J. (2001). Labor market competition and individual preferences over immigration policy. The Review of Economics and Statistics,
83(1):133–145.
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Avail-

able at: https://www.electoralcommission.org.uk/our-work/our-research/electoraldata/electoral-data-ne [Accessed on 10 January 2018].

22

Appendices

Table A1 (1): The Detailed Results of Table 9 (Individual Sample 2SLS)The Effect of the Ratio of Non‐UK‐born Individuals on Attitudes towards Accepting Immigrants
How much immigrant should be accepted

VARIABLES
Ratio of Non‐UK bornindividuals in 2015
Claimant Rate in 2015

(1)
‐0.296(0.421)(2)‐0.467(0.455)8.405**(3.732)(3)‐0.271(0.447)8.844**(3.783)3.060**(1.355)(4)‐0.305(0.477)8.817**(3.782)2.702*(1.464)‐0.636(1.402)(5)‐0.506(0.538)
6.198(4.163)2.320(1.530)0.270(1.447)‐1.136(0.862)0.0281(0.0210)0.367***(0.0515)0.130***(0.0478)‐0.0664*(0.0342)‐0.142***(0.0350)‐0.158***(0.0353)

0.0276(0.0210)0.368***(0.0515)0.132***(0.0479)‐0.0664*(0.0342)‐0.142***(0.0351)‐0.157***(0.0354)0.0278(0.0211)0.368***(0.0516)0.132***(0.0480)‐0.0665*(0.0342)‐0.142***(0.0351)‐0.157***(0.0353)

0.0276(0.0210)0.369***(0.0516)0.133***(0.0480)‐0.0661*(0.0341)‐0.140***(0.0350)‐0.155***(0.0351)

Ratio of Education level 4 ore morein 2011
Ratio of Noqualification in 2011
The ratio of aged 65 ore more in 201
Indivdaul Characteristics
GenderAge group = 2, 18‐25
Age group = 3, 26‐35
Age group = 5, 46‐55
Age group = 6, 56‐65
Age group = 7, 66+
Continued on the next page23

Table A1 (2): The Detailed Results of Table 9 (Individual Sample 2SLS)The Effect of the Ratio of Non‐UK‐born Individuals on Attitudes towards Accepting Immigrants(continued from the previous page)
Highest Qualification = 2,
Youth training certificate/skillseekers
Highest Qualification = 3,
Recognised trade apprenticeship completed
Highest Qualification = 4,
Clerical and commercial
Highest Qualification = 5,
City and Guild certificate
Highest Qualification = 6,
City and Guild certificate ‐ advanced
Highest Qualification = 7,
onc
Highest Qualification = 8,
CSE grades 2‐5Highest Qualification = 9, CSE grade 1,
GCE O level, GCSE, School Certificate
Highest Qualification = 10,
Scottish Ordinary/ Lower Certificate
Highest Qualification = 11,
GCE A level or Higher Certificate
Highest Qualification = 12,
Scottish Higher Certificate
Highest Qualification = 13,
Nursing qualification (eg SEN, SRN, SCM, RGN
Highest Qualification = 14,
Teaching qualification (not degree)
Highest Qualification = 15,
University diploma
Highest Qualification = 16,
University or CNAA first degree (eg BA, B.Sc)
Highest Qualification = 17,
University or CNAA higher degree (eg M.Sc, Ph.D
Highest Qualification = 18,
Other technical, professional orindividual characteristics
yes
2011 industry compositin
yes
yes
2001 education & age composition
Unemployment rate in 2015noRatio of Level 4 or More in 2011noRatio of No‐qualification in 2011noRatio of aged 65 or more in 2011noR‐squared
0.152
Kleinberg‐Papp‐Stat
105.5
Observations
9,789

0.488*(0.267)0.207**(0.104)0.142*(0.0855)
0.0694(0.0617)0.214***(0.0768)
0.238**(0.102)‐0.0619(0.0729)
0.0925**(0.0431)‐0.00577(0.177)0.327***(0.0472)
0.695***(0.222)0.408***(0.0995)
0.521***(0.0725)
0.492***(0.0664)0.717***(0.0443)0.865***(0.0526)0.401***(0.0449)
yes
yes
yes
yes
no
no
no
0.152
32.92
9,789

0.491*(0.267)0.208**(0.104)0.141*(0.0854)0.0684(0.0616)0.217***(0.0763)
0.242**(0.102)‐0.0615(0.0726)
0.0907**(0.0431)‐0.0108(0.175)0.326***(0.0471)0.689***(0.223)0.405***(0.0995)
0.523***(0.0726)
0.491***(0.0663)
0.715***(0.0443)0.865***(0.0523)
0.399***(0.0449)
yes
yes
yes
yes
yes
no
no
0.152
35.55
9,789

0.491*(0.267)0.208**(0.104)0.142*(0.0857)
0.0685(0.0616)0.217***(0.0762)
0.242**(0.102)‐0.0605(0.0724)0.0908**(0.0432)‐0.00793(0.174)0.327***(0.0472)
0.690***(0.223)0.406***(0.0995)
0.523***(0.0726)
0.492***(0.0663)
0.715***(0.0443)0.865***(0.0523)
0.399***(0.0449)
yes
yes
yes
yes
yes
yes
no
0.152
34.39
9,789

0.486*(0.268)0.210**(0.104)0.143*(0.0858)
0.0713(0.0615)0.220***(0.0764)0.243**(0.102)‐0.0573(0.0723)
0.0938**(0.0431)‐0.0109(0.174)0.329***(0.0471)
0.692***(0.223)0.407***(0.0998)
0.528***(0.0721)0.494***(0.0662)0.716***(0.0442)0.864***(0.0523)
0.402***(0.0450)
yes
yes
yes
yes
yes
yes
yes
0.153
29.06
9,789

Notes: Clustered‐robust standard error in parentheses. Individual attitude towards accepting
immigrants is standardized. A higher number implies that more immigrants should be accepted. The
ratio of non‐UK‐born individuals and unemployment rate are in decimal numbers. Thus, the estimatedcoefficients measure a 100% increase in the ratio of non‐UK‐born individuals and the claimant rate.