1066 and All That: Some Deep Determinants of Voting

Shares in the 2016 Referendum on EU Membership

David Fielding, University of Otago

Abstract

Recent evidence from UK opinion surveys suggests that inhabitants of areas where there is a

high density of universities have opinions that are significantly more liberal with regard to

immigration and minority rights; this effect is robust to controlling for the individual’s own

age and education level, suggesting that university towns have a distinctive culture.

Moreover, variation in the density of universities is explained partly by variation in the

density of earlier educational institutions, and the variation in the density of these earlier

institutions is associated with medieval exposure to religious and ethnic diversity. Support for

EU membership is known to be correlated with liberalism, and in this paper I show that

patterns of voting in the 2016 referendum are also associated with the density of universities

and earlier educational institutions, and with medieval exposure to religious and ethnic

diversity.

JEL Classification: D72, F52, N33, Z13

Keywords: Voting; EU membership; Medieval Jewish communities; Crusades

1. Introduction

The Department of Economics at the University of Nottingham has established a reputation

as a world-leading centre for scholarship in the economics of international trade, in large part

through the leadership of Chris Milner and David Greenaway over the last 20-30 years. In

addition to the large volume of high-impact research, Nottingham has educated generations

of undergraduates in trade theory and its application, so that the average Nottingham

economics graduate is well able to understand the complex issues relating to international

trade policy and UK membership of the European Union. However, these issues appear not to

have been to the fore during the 2016 referendum on EU membership: the Remain campaign

did not try very hard to educate people about comparative advantage, the Leave campaign

spent little time discussing the distributional implications of the Stolper-Samuelson Theorem,

and econometric estimates of trade creation and trade diversion effects have not featured

prominently in recent parliamentary debates about membership of the EU customs union. At

the end of this paper, I will discuss some of the implications of this disconnect for economics

departments such as Nottingham’s, but this discussion will be informed by an analysis of

some of the factors that do explain voting in the referendum.1 For reasons explained below,

this analysis will focus on England rather than the Celtic nations, and on the “deep

determinants” of voter choice embedded in English social and economic history.

2. EU Membership, Liberal Values and English History

Fielding (2017a) presents an analysis of some of the long-run historical factors that explain

regional variation in the attitudes expressed in surveys such as the British Election Study

(BES). It appears that in the 21st century, inhabitants of locations showing evidence of

exposure to medieval ethnic and religious diversity are significantly more likely to express

1 The analysis in this paper is informed by Fielding (2017b), which includes a brief discussion about

attitudes towards EU membership but no discussion or analysis of voting patterns in the 2016 referendum.

1

positive views about immigration and equal rights for minority groups.2 One possible

explanation is that the initial exposure weakened prejudice towards other groups and that this

new cultural norm was transmitted to subsequent generations. The exposure effect would be

consistent with findings in social psychology which suggest that under certain conditions –

including equal social status and an absence of direct competition – personal contact with

members of another group can have a positive effect on attitudes towards them.3 Moreover,

the formal theoretical model of Cavalli-Sforza and Feldman (1973) implies that inter-

generational transmission through socialization of the young could lead to persistent regional

variation if the socialization takes the form of “many-to-one” interactions – i.e. the whole

village raising the child – see Cavalli-Sforza (1981) for more detail. Evidence presented in

Fielding (2017b) suggests that at least part of the inter-generational transmission has been

through an educational channel: ceteris paribus, locations with more exposure to medieval

diversity had a significantly greater density of educational institutions during the

Enlightenment, suggesting that diversity was associated with a taste for new ideas. The

regional variation in the density of these early institutions is strongly correlated with variation

in the density of modern universities, and inhabitants of modern university towns are

significantly more likely to express positive views about immigration and equal rights than

are inhabitants of other towns. This is partly because inhabitants of university towns have

markedly different personal characteristics (for example, they are younger, on average, and

have better educational qualifications), but a significant effect remains even when we control

for these characteristics. In other words, we might expect the average taxi driver in a

university town to have views on immigration and minority rights that are significantly more

positive than those of taxi drivers elsewhere.2 Other papers which explore the historical origins of regional variation in modern attitudes include Jha

(2013), Voigtländer and Voth (2013), and Alesina et al. (2013).3 The intergroup contact literature began with Allport (1954); see Dovidio et al. (2005) and Pettigrew and

Tropp (2012) for an overview of this literature.

2

There is evidence that opinions about the UK’s membership of the EU are also

strongly correlated with an individual’s education level (e.g. Bruter, 2005: appendix 3) and

one explanation for this similarity is that feelings about EU membership, immigration and

minority rights all reflect one’s position on a socially liberal / socially conservative spectrum,

this position depending partly on one’s level of education. In this case, it is possible that

voting patterns in the 2016 referendum reflect the same deep historical determinants that have

been shown to explain some of the variation in attitudes towards immigration and minority

rights. Before discussing the data and evidence relating to voting patterns, we provide some

context by briefly reviewing the correlates of exposure to medieval diversity and early

educational institutions introduced in previous papers.

2.1. Medieval diversity #1: Jewish settlement

The one large immigrant community in early medieval England were the Jews. The first

Jewish immigrants arrived from France at the end of the eleventh century, and tax records

indicate that by the end of the twelfth century, Jewish communities had been established in

about 20 English towns (Hillaby, 2003). Jewish settlement appears to have been encouraged

by Norman and Angevin kings, and the Jews fulfilled two main economic functions (Mundill,

2010): the provision of financial services at a time when Christians were forbidden to lend

money to each other at interest, and the creation of a tax base. Unlike any other commoners,

the Jews were vassals of the king, so they were the only group on which he could impose

direct taxes: the existence of the Jewish community gave the king more financial autonomy

and enhanced his bargaining power with the barons. Movement of Jews in the twelfth century

seems to have been largely unregulated, but the thirteenth century saw more extensive

regulation: each Jewish family was obliged to live in one of about 30 designated towns

containing an archa (or chest) where all contracts between Jews and Christians had to be

deposited (Brand, 2003; Brown and McCartney, 2005), the inspection of the archae forming

3

the basis for all major tax assessments. Growing anti-Semitism finally forced the king to

agree to expel all Jews from England in 1290; this edict was not officially revoked until the

seventeenth century, and there were no other large immigrant groups until modern times.4

It is likely that anti-Semitism was endemic in early medieval England, and attacks on

individual Jews appear to have been frequent. However, genocidal attacks on entire

communities seem to have occurred during only two periods. The first period was 1189-90, in

the aftermath of fighting between Jews and Christians at the coronation of Richard I, and the

second was 1263-65, during the civil war between the king and the barons (Mundill, 2010).

The twelfth-century attacks seem to have been perpetrated mainly by protagonists from

outside the local area: for example, foreign merchants instigated the attack at Lynn. During

the civil war, genocidal attacks on Jewish communities were instigated by baronial forces as

a way of terrorizing groups who supported the king (Hillaby, 2003; Stacey, 2003). Compared

with the attacks in Germany at the time of the Black Death (Voigtländer and Voth, 2013),

there is relatively little evidence that genocidal attacks on English Jewish communities were

instigated by their close neighbours.5

Fielding (2017a) includes a discussion of whether direct contact with a medieval

Jewish community would be likely to improve or worsen someone’s attitudes towards the

Jews. In this context, it is relevant to note that most ordinary people would meet Jews on the

basis of roughly equal social standing, since the Jews were vassals of the king and

commoners were vassals of the local lord. The most common place to meet would have been

the market, where Jews would buy goods from local merchants: Jews lent money only to

wealthy nobles and religious groups with collateral; they were prohibited from activities that

would put them in direct competition with Christians. The evidence from social psychology 4 For reasons discussed in Fielding (2017a), the Huguenot immigration in the seventeenth century is

unlikely to have had any lasting impact on attitudes.5 The only genocidal event in which locals were directly implicated was at York: here the attack appears to

have been orchestrated by minor nobles with large debts.

4

suggests that such contact would result in more positive attitudes towards Jews. Langmuir

(1963), reviewing documentary evidence on this point, observes that ‘the majority of the little

evidence that there is suggests that it was primarily those who lived in close contact with

Jews who were friendly with them.’ In this case, we might expect those towns that were

home to a Jewish community in the twelfth and thirteenth centuries to have had residents who

were less anti-Semitic than average. If this difference in social norms has been transmitted

across the generations, and if the norm is broad enough in scope to encompass attitudes

towards a variety of out-groups, then English towns that once contained an archa may still

exhibit attitudes towards outsiders that are more positive than average. Amongst other things,

these towns may exhibit less Euroscepticism. Note that the absence of detailed records

relating to Jewish settlement in Wales and Scotland is the main reason for excluding these

nations from our later analysis.

2.2. Medieval diversity #2: The crusades

Although the Jews were the only large ethnic minority in medieval England, there was one

other way in which Englishmen came into contact with other religious and ethnic groups: the

crusades. When considering the effect of the crusades, it is important to note that most of a

crusading army was made up of servants, foot soldiers, and knights of lower rank: it is likely

that most of these men had little choice about their participation, except perhaps in the case of

the First Crusade. In theory, feudal obligations did not include military service outside

England, but in practice, most vassals were financially dependent on their lord, so if he chose

to go on crusade they had little choice but to follow (Benjamin, 2015). In other words,

crusading represented a treatment effect. Once the crusaders were in the Holy Land, the

fighting itself might not have encouraged positive attitudes towards Arabs and Muslims, but

this fighting made up a small proportion of their total time, especially after the establishment

of the Crusader Kingdoms. Europeans who settled in the Crusader Kingdoms were eventually

5

engaged in a range of activities, and documentary evidence suggests that these activities led

to the acquisition of a reasonable understanding of local religions, languages and cultures, an

understanding that stood in stark contrast to the ignorance prevalent across Western Europe at

this time (Hamilton, 1997; Attiya, 1999). The division of labour in a Crusader Kingdom

between the Europeans (employed mainly in administration and services) and the Arabs

(employed mainly in agriculture) is likely to have mitigated any day-to-day competition

between them, making it more likely for inter-group contact to promote positive attitudes

towards the out-group. Contemporary documents written by crusaders include sympathetic

depictions of individual Arabs (Rouleau, 2005; Khanmohamadi, 2010), and this eventually

influenced popular Western European literature, which included Arab heroes as well as Arab

villains (Hamilton, 1997; Calkin, 2012).

This influence was partly a consequence of the fall of the Crusader Kingdoms in the

late twelfth and thirteenth centuries, which led to the resettlement of their first-, second- and

third-generation European inhabitants back in Western Europe. Where exactly did the

migrants settle? One indicator of crusader influence, or at least of the salience of the crusades

to a local community, is an inn named the Saracen’s (i.e. Arab’s) Head. We must be careful

when interpreting the Saracen’s head design: in a small number of cases, this heraldic device

does appear to have shown a decapitated head, implying a particularly high level of

xenophobia on the part of the noble family who bore it on their arms. However, these cases,

in which the blazon describes a head that is erased, distilling blood, or on the point of a

pheon, are much less frequent than that of a head that is couped, i.e. depicted in the same way

as the Queen’s head on a coin. For example, only 10% of the Saracen’s heads on the English

coats of arms listed by Burke (1884) are of the violent type. It is therefore likely that the vast

majority of Saracen’s Head inn signs were originally modelled on heraldic devices which

reflected respectful (or at least neutral) interest in a foreign culture.

6

Unfortunately, there is no documentary source that lists medieval inn names in

individual towns. However, Fielding (2017b) includes a list of 88 towns with Saracen’s Head

inns that appear in the census of 1851.6 If a Saracen’s Head inn reflects the salience of the

crusades to the town, if returning crusaders were more open to foreign cultures than other

medieval Englishmen, and if there is some inter-generational persistence in this social norm,

then, ceteris paribus, there should be less Euroscepticism in the 88 Saracen’s Head towns

then elsewhere.

2.3. Inter-generational transmission of social norms: The education channel

One possible mechanism for the inter-generational persistence of regional variation in social

norms is through variation in the size of local educational institutions. Medieval exposure to

diversity could be associated with a taste for new ideas, encouraging the early adoption of

educational innovations such as the printed book. This could facilitate the development of a

local educational infrastructure and eventually, in the 21st century, to a greater density of

tertiary education institutions. If university staff and students tend to promote liberal values in

their local community then we will observe a correlation between 21st-century social norms

and medieval exposure to diversity.

One key educational institution during the Enlightenment was the private subscription

library, public libraries coming into existence only after the Public Libraries Act of 1850

(Raven, 2006). Given the unusually small number of universities in England – until the mid-

nineteenth century there were only two, in Oxford and Cambridge – libraries performed a key

role in the transmission of ideas. Using historical library location data compiled from Alston

(2011), Fielding (2017b) shows that up to 1850, the number of libraries and bookstores in a

6 In most cases it is impossible to date the foundation of these inns precisely. However, the excavation of

some of the Saracen’s Head inn sites has produced evidence for a date contemporary with or not much

later than the crusades, and in a small number of cases, there is some documentary evidence for a medieval

foundation date. See Fielding (2017b) for more details.

7

town was significantly higher if the town once contained an archa or if it had a Saracen’s

Head inn. These effects are robust to controlling for a range of town-specific characteristics,

including whether the town had a medieval school or library, its population level, its

occupational composition, and the total number of inns there. Moreover, using data from the

2011 census, it can be shown that there is a strong correlation between the proportion of the

modern population in a parliamentary constituency who are university students and the

number of libraries in the constituency’s largest town prior to 1850. This association is robust

to controlling for a range of constituency-specific characteristics, including socio-

demographic factors and overall population density. Interpreting student numbers as a

measure of the importance of the tertiary education sector in a constituency, this suggests

significant inter-temporal persistence in the relative importance of education to local

communities. Finally, white respondents in the 2015 BES are significantly more likely to

have positive views about immigration and equal rights for ethnic minorities, gays / lesbians

and women if they live in a constituency with high student numbers. This association is

robust to controlling for a range of individual characteristics (e.g. the individual’s own age

and education level) and other constituency characteristics (e.g. total population density and

ethnic minority population density).

These results suggest the following hypotheses:

(i) The share of the Remain vote in the 2016 referendum was higher in locations with

medieval exposure to diversity, as reflected in the presence of an archa or Saracen’s Head

inn.

(ii) With education as at least one channel for this effect, the share of the Remain vote in the

2016 referendum was higher in locations with more libraries prior to 1850, and the

association with medieval characteristics is smaller once we control for library numbers.

(iii) The association between the number of libraries and the Remain vote is smaller once we

8

control for modern student numbers.

In the next section, we discuss the model and data that we use to test these hypotheses.

3. Modelling Voting in the 2016 Referendum

The UK Electoral Commission has published the 2016 referendum results at the local

authority district level.7 This level of aggregation is not ideal for a statistical analysis, because

there is so much variation in the relative sizes of districts: compare the Scilly Isles (an

electorate of 1,799) with Birmingham (an electorate of 707,293). Fortunately, Hanretty

(2017) has published a set of referendum results interpolated to the parliamentary

constituency level, and parliamentary constituencies are designed to have roughly equal

population sizes. Our main results will be based on constituency-level referendum data.

Suppose that the probability that an individual voter i in constituency j will vote

Remain can be expressed in terms of a Logit function:8

(1)

Here, is a binary variable equal to one if the voter chooses Remain and equal to

zero if she chooses Leave; stands for the pth observable characteristic of constituency j

and is a coefficient to be estimated; is a random variable reflecting unobserved

heterogeneity at the constituency level. Rearranging equation (1) gives us the following

equation:

7 See https://www.electoralcommission.org.uk/find-information-by-subject/elections-and-referendums/past

-elections-and-referendums/eu-referendum/electorate-and-count-information?

8 Alternatively, we might specify a Probit function , in which case

the dependent variable in equation (3) should be . Using this alternative dependent variable produces results very similar to those shown in Table 2 below.

9

(2)

By the Law of Large Numbers, the share of the Remain vote in total votes in constituency j,

denoted , will be determined by the following function:

(3)

Here, is the share of votes for Leave. This is the regression equation that we will fit to

the data.

The results presented below include coefficient estimates for a number of alternative

sets of constituency-level explanatory variables .9 The first set of variables is comprised

only of characteristics which relate to the medieval history of towns in the constituency; the

model using this set of variables relates to hypothesis (i) above. The characteristics include

the presence of an archa or a Saracen’s Head inn; they also include correlates of medieval

population size and infrastructure that could have influenced the socio-economic

development of a region (and hence modern opinions), but are also correlated with the

location of archae and Saracen’s Heads.

• if-archaj equals one if there was an archa in the constituency, and zero otherwise;10

data on the location of archae come from Hillaby and Hillaby (2013).

• if-saracenj equals one if there was a Saracen’s Head inn in the constituency in the

9 In addition to the variables listed here, each set of coefficient estimates also includes fixed effects for the

different administrative regions of England: the North East, the North West, Yorkshire-Humberside, the

East Midlands, the West Midlands, the East of England, the South East and the South West. Estimates of

these fixed effects are not reported in the tables but are available on request.10 When a single town is divided into different constituencies, all constituencies in the town take the same

value of if-archa; the same is true of if-saracen.

10

1851 census, and zero otherwise.11

• if-cathedralj equals one if there was a cathedral in the constituency before 1400, and

zero otherwise.

• if-schoolj equals one if there was a school in the constituency before 1400, and zero

otherwise; data on the location of schools come from Orme (2006).

• if-coastalj equals one if the constituency contains a port or coastal settlement, and

zero otherwise.

• log(pop-med-townj) is the logarithm of the adult male population of the largest town

in the constituency, as recorded in the 1377 Poll Tax records and listed by Dyer

(2000). If there is no listed town in the constituency then log(pop-med-townj) equals

zero.

• if-med-townj equals one if log(pop-med-townj) > 0, and zero otherwise.

The second set of variables combines the medieval characteristics with data on the number of

libraries prior to 1850 and on mid-nineteenth-century population levels;12 the model using

this set of variables relates to hypothesis (ii) above.

• log(1+librariesj) is the logarithm of one plus the number of libraries in the largest

town in the constituency prior to 1850; data on the location of libraries come from

Alston (2011).13

11 This parameterization of the archa and Saracen’s Head effects assumes additive separablility. Results

from models with non-linear parameterizations, for example models incorporating the variables if-archa ×

if-saracen (‘both’) and (1 – if-archa) × (1 – if-saracen) (‘neither’), are available on request. As indicated in

Table 1, if-archa = 1 in only 8% of the sample, so the non-linear results may be quite fragile.12 Population size is correlated with the number of libraries, and population estimates are included in case

nineteenth-century population levels influenced the socio-economic development of a region (and hence

modern opinions). However, the inclusion of population size makes no substantial difference to any of the

other coefficient estimates.13 The number of libraries has a distribution that is approximately Poisson, with some zeroes; the

distribution of the logarithmic transformation is slightly left-skewed but approximately Normal. Replacing

11

• log(pop-1841-townj) is the logarithm of the population of the largest town in the

constituency in the 1841 census, as listed in Bennett (2011: appendix 3). If there is no

listed town in the constituency then log(pop-1841-townj) equals zero.

• if-1841-townj equals one if log(pop-1841-townj) > 0, and zero otherwise.

The third set of variables also includes an estimate of modern student numbers in each

constituency; the model using this set of variables relates to hypothesis (iii) above.

• studentsj is the proportion of the adult population in the constituency who are in full-

time tertiary education, as recorded in the 2011 census.

Care must be taken in interpreting the estimated coefficient on studentsj: student numbers are

correlated with a range of other constituency characteristics that may be associated with

voting in the referendum. These include the average education level of residents in the

constituency, its unemployment rate, its age and wealth profiles, its overall population

density and its immigrant population density. For this reason, there is a final set of variables

which also includes the following characteristics.

• no-qualificationsj is the proportion of adults in the constituency with no formal

education qualifications, as recorded in the 2011 census.

• graduatesj is the proportion of adults in the constituency with a university degree, as

recorded in the 2011 census.

• unemploymentj is the constituency unemployment rate, as recorded in the 2011

census.

• pop-densityj is the constituency population density in thousands of people per

hectare, as recorded in the 2011 census.

• minoritiesj is the proportion of individuals in the constituency who identify with a

log(1+libraries) with libraries makes little difference to the results.

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religion other than Christianity, as recorded in the 2011 census.14

• pensionersj is the proportion of the constituency population aged over 64 years, as

recorded in the 2011 census.

• acornNj is the proportion of households in the constituency belonging to ACORN

socio-economic group N, where N {1,2,3,4}. The omitted category is group 5; see

CACI (2014).15

Table 1 includes descriptive statistics for all of these variables in a sample comprised of the

460 English constituencies outside London. We exclude London from our sample because the

capital city is home to a unique range of public institutions that may affect opinion; these

effects may vary across different parts of the capital in ways that are impossible to identify

directly.

Table 2 includes estimates of the coefficients in the four different versions of the

model, along with the corresponding heteroscedasticity-robust t-ratios. These coefficients

have been estimated using the maximum likelihood estimator for spatial autocorrelation

(SAC) of Drukker et al. (2013), which allows for correlation between the error and errors

in nearby constituencies. The table also includes estimates of this correlation, which is

defined as where is a weight based on the inverse of the

distance between the centroids of constituencies j and k. The appendix includes alternative

estimates using either Ordinary Least Squares or Weighted Least Squares (with weights

based on constituency population sizes). These alternative estimates are generally quite

similar to those in Table 2.14 This variable is highly correlated with the proportion who are non-white and the proportion born outside

the UK; using one of these alternative measures makes little difference to the results.15 These variables are taken from Pippa Norris’s British General Election Constituency Results 5.0

(www.hks.harvard.edu/fs/pnorris/Data/Data.htm). Group 5 corresponds to the highest level of wealth and

group 1 to the lowest.

13

The first set of results in Table 2, based on a model including only the medieval

explanatory variables, shows that the Remain vote was significantly higher in constituencies

that once contained an archa or Saracen’s Head inn. Ceteris paribus, the ratio of Remain to

Leave votes was 15% higher in archa constituencies and 11% higher in Saracen’s Head

constituencies. None of the other medieval variables has an effect that is significant at the 5%

level. This is consistent with the conjecture that the medieval exposure of a particular

location to ethnic and religious diversity has had a lasting effect on opinion of the people who

live there, and this is reflected in greater sympathy for membership of the EU.

The second set of results in the table shows that adding log(1+libraries) to the model

does indeed reduce the size of the estimated coefficients on if-archa and if-saracen, although

the standard errors are too large to establish the statistical significance of this reduction. In

the second model, if-archa and if-saracen are jointly significant at the 10% level but not at

the 5% level. These results are certainly consistent with the hypothesis that the effects of

medieval exposure to diversity have been transmitted mainly through an education channel,

although our estimates are not precise enough to say exactly how important this channel is.

The estimated elasticity of the Remain / Leave ratio with respect to the number of libraries is

0.08, and this effect is significant at the 1% level, so the density of early educational

institutions is important in explaining modern voting patterns. As shown in Table 1, the

standard deviation of log(1+libraries) is 1.45, so a one standard deviation increase in the

number of libraries is associated with a Remain / Leave ratio that is 12% higher.

The third set of results in Table 2 shows the effect of adding students to the model.

This addition makes the estimated if-archa and if-saracen coefficients very much smaller and

statistically insignificant, but there is only a small reduction in the estimated log(1+libraries)

coefficient, which is still significant at the 1% level. Nevertheless, there is a large and

statistically significant coefficient on students: a one percentage point increase in the fraction

14

of students in the local population is associated with a Remain / Leave ratio that is 4% higher.

The standard deviation of students is four percentage points (see Table 1), so a one standard

deviation increase in student numbers is associated with a Remain / Leave ratio that is 16%

higher. However, the fourth set of results in Table 2, which is based on a model including all

of the additional covariates, shows that most of this effect is due to the correlation between

student numbers and other significant determinants of the voting share, including the

proportion of graduates in the constituency, the proportion of religious minorities, and the

proportion of pensioners.16 Controlling for these other characteristics, a one percentage point

increase in the fraction of students in the local population is associated with a Remain / Leave

ratio that is just 1% higher. The inclusion of the extra covariates also leads to a substantial

reduction in the size of the coefficient on log(1+libraries), which is only about a third as

large as before. Nevertheless, both the students and log(1+libraries) effects are still

significant at the 1% level.

Referring back to the hypotheses in the previous section, the results in Table 2 suggest

the following conclusions.

(i) The share of the Remain vote in the 2016 referendum was indeed higher in locations with

medieval exposure to diversity.

(ii) One channel for this effect does seem to have been through early educational institutions

such as libraries, although our estimates are not precise enough to say very much about the

relative importance of this channel.

(iii) Although the association between the number of libraries and the Remain vote is smaller

once we control for modern student numbers, the difference is small and statistically

insignificant. This suggests that modern student numbers do not capture all of the persistent

16 The strong positive (negative) association between the Remain / Leave ratio and the proportion of

graduates (proportion of pensioners) is consistent with the findings with respect to qualifications and age

reported by Goodwin and Heath (2016) and Manley et al. (2017).

15

effect of variation in the density of early educational institutions.

4. A Robustness Check

The results in Table 2 relate to voter choice on a single day: the 23 rd June 2016. In this

section, we present some results based on voter opinion five months later. In November-

December 2016, the BES conducted a survey of 30,319 individuals that included the

following question:17

‘How happy or disappointed are you that the UK voted to leave the EU?’

Responses to the question were given on a 0-10 Likert Scale, and in order to test the

robustness of the Table 2 results, we substitute log(remain / leave) with an alternative

dependent variable based on these responses. Let be the response of individual i

in constituency j, where zero indicates maximal satisfaction with Brexit and ten represents

maximal dissatisfaction:18 this measures the respondent’s position on a ‘Brexit unhappiness

scale’. Our alternative dependent variable is the constituency average

where is the number of respondents surveyed in

constituency j.19 Descriptive statistics for this variable appear in Table 1. The model to be

estimated is:

(4)

17 See www.britishelectionstudy.com/bes-resources/bes-wave-10-internet-panel-data-released/#.WbHfD2e

uUwU.18 773 out of the 30,319 individuals responded ‘don’t know’; these individuals are assigned to the median

group (unhappy = 5).19 Respondents came from all 650 mainland UK parliamentary constituencies, so the average number of

respondents per constituency is 46.6. Sixteen of the 460 English constituencies outside London had fewer

than 20 respondents; the smallest numbers were for Burnley (13), Dudley North (15), and Leicester East

(15). Fitting the model by Weighted Least Squares, with weights based on the within-constituency

standard deviation of unhappy, produces results very similar to those in Table 3.

16

Table 3 includes SAC estimates of the coefficients in this model, with the same four sets of

explanatory variables as in Table 2.

The first set of results in Table 3, based on a model including only the medieval

explanatory variables, shows that Brexit unhappiness is significantly higher in constituencies

that once contained a Saracen’s Head inn. Ceteris paribus, respondents in a Saracen’s Head

constituency are 0.29 points higher on the Brexit unhappiness scale than are respondents

elsewhere. The estimated effect of being in a constituency that contained an archa is almost

as large (0.22 points) but much less precisely estimated, and this effect is not significantly

different from zero. The variables if-archa and if-saracen are jointly significant at the 5%

level, so we have more evidence that medieval exposure to ethnic and religious diversity is

associated with greater sympathy for membership of the EU, but there is some uncertainty

around the individual contribution of proximity to a Jewish community.

The subsequent results in Table 3 are similar to those in Table 2. The inclusion of

log(1+libraries) in the model leads to a reduction in the estimated size of the if-archa and if-

saracen coefficients. The libraries effect is significant at the 1% level: the coefficient of 0.33

implies that a one standard deviation increase in log(1+libraries) is associated with an extra

0.48 points on the Brexit unhappiness scale. As in Table 2, the inclusion of students leads to a

small and statistically insignificant reduction in the estimated size of the libraries effect, but

student numbers do help to explain the variation in Brexit unhappiness: the coefficient of

8.61 implies that a one standard deviation increase in students is associated with an extra 0.35

points on the scale. Finally, as in Table 2, the inclusion of all of the additional covariates

leads to a large reduction in the estimated size of the coefficient on students, which in this

case becomes insignificantly different from zero. There is also a reduction in estimated size

of the log(1+libraries) coefficient, although this reduction is not so large as in Table 2.

5. Summary and Conclusion

17

Locations showing evidence of medieval exposure to ethnic and religious diversity (either

through proximity to a Jewish community or through local salience of the crusades) had a

significantly higher share of Remain votes in the 2016 referendum on EU membership than

did other locations; exposure is also associated with greater dissatisfaction with Brexit in a

subsequent BES survey. This suggests some inter-generational persistence in social norms,

and one channel for this persistence appears to be through regional variation in the density of

educational institutions. Estimates of the exposure effect are much smaller once we control

for variation in the number of libraries before 1850 an in the size of modern university

student populations. This is consistent with recent evidence that medieval exposure to

diversity is associated with a larger subsequent demand for educational institutions, and that

these institutions are associated with liberal values in the area around them (Fielding, 2017b).

Take the example of the Nottingham South constituency, which contains Nottingham

University’s two main campuses as well as the main Nottingham Trent University campus,

and is part of a city that was home to 65 libraries prior to 1850. The presence of two large

universities, and before that the large number of libraries, is consistent with the fact the

Nottingham was also home to a medieval Jewish community as well as having a Saracen’s

Head inn. The share of Remain votes in the constituency in the Hanretty (2017) dataset is

54%. This is very close to the fitted value from the final model in Table 2, which is 55%.

Using this model, a constituency which had no modern tertiary education institutions and

never had any libraries before 1850 (but which had all of Nottingham South’s other

characteristics) could be expected to have a Remain vote share of 45%.

It has been argued that the 2016 referendum was characterised by a distinct lack of

informed debate: see for example Galsworthy (2016). In this case, universities may have

helped to maintain a local culture in which liberal preferences predominate, but they have not

significantly influenced public opinion through greater understanding of the economic costs

18

and benefits of EU membership. Nevertheless, the regional variation in the density of

educational institutions reflects the fact that at certain times in the past and in certain places,

local communities have enthusiastically embraced new types of learning and new ideas: 65

libraries equates to a lot of books. The challenge for universities is to channel this latent

enthusiasm in the communities around them, so that informed debate about EU membership

is not restricted to economics graduates.

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22

Table 1. Descriptive Statistics (N = 460)

statistics for continuous variables proportion of binary

mean s.d. variables equal to one

log(remain/leave) –0.233 0.375 if-archa 0.080

unhappy 4.571 0.988 if-saracen 0.287

log(pop-med-town) 1.622 3.025 if-cathedral 0.030

log(1+libraries) 2.310 1.446 if-school 0.211

log(pop-1841-town) 8.325 3.189 if-coastal 0.141

students 0.039 0.041 if-med-town 0.226

no-qualifications 0.240 0.054 if-1841-town 0.891

graduates 0.260 0.073

unemployment 0.061 0.024

pop-density 0.015 0.015

minorities 0.059 0.084

pensioners 0.168 0.036

acorn1 0.315 0.196

acorn2 0.084 0.099

acorn3 0.282 0.082

acorn4 0.134 0.105

23

Table 2. Determinants of log(remain/leave): SAC Regression Coefficients and Standard Errors (N = 460)

coeff. s.e. coeff. s.e. coeff. s.e. coeff. s.e.if-archa 0.150* 0.076 0.124 0.074 0.070 0.062 0.018 0.026if-saracen 0.109** 0.034 0.054 0.035 0.005 0.030 0.029* 0.012if-cathedral 0.084 0.096 0.071 0.094 0.051 0.079 0.041 0.032if-school 0.074 0.044 0.063 0.043 0.061 0.036 0.003 0.015if-coastal –0.074 0.046 –0.090* 0.045 –0.119** 0.038 –0.012 0.016if-med-town 0.026 0.362 0.154 0.354 0.348 0.297 0.199 0.122log(pop-med-town) 0.000 0.052 –0.027 0.051 –0.058 0.043 –0.034 0.018log(1+libraries) 0.084** 0.024 0.068** 0.020 0.026** 0.008if-1841-town 0.023 0.174 0.362* 0.148 –0.118 0.065log(pop-1841-town) –0.013 0.023 –0.049* 0.020 0.008 0.008students 4.128** 0.296 1.013** 0.185no-qualifications –0.196 0.346graduates 4.232** 0.222unemployment 0.753 0.495pop-density –0.567 0.550minorities 0.748** 0.090pensioners –0.652* 0.295acorn1 –0.125 0.091acorn2 –0.011 0.104acorn3 0.075 0.105acorn4 0.031 0.094 0.893** 0.031 0.885** 0.033 0.900** 0.030 0.900** 0.027

* significantly different from zero at the 5% level; ** significantly different from zero at the 1% level. The regression equations also include region fixed effects.

24

25

Table 3. Determinants of unhappy: SAC Regression Coefficients and Standard Errors (N = 460)

coeff. s.e. coeff. s.e. coeff. s.e. coeff. s.e.if-archa 0.215 0.230 0.149 0.221 0.064 0.210 –0.246 0.186if-saracen 0.289** 0.107 0.116 0.107 0.054 0.102 0.065 0.091if-cathedral –0.170 0.290 –0.245 0.278 –0.302 0.262 –0.244 0.227if-school 0.290 0.136 0.260 0.132 0.242 0.124 0.257 0.112if-coastal –1.629 1.041 –1.169 1.006 –0.891 0.941 –1.035 0.813if-med-town 0.231 0.150 0.137 0.146 0.084 0.136 0.110 0.117log(pop-med-town) 0.005 0.140 –0.066 0.139 –0.205 0.124 0.063 0.111log(1+libraries) 0.333** 0.072 0.285** 0.071 0.270** 0.060if-1841-town 0.157 0.559 0.633 0.505 0.399 0.471log(pop-1841-town) –0.087 0.074 –0.129 0.067 –0.111 0.061students 8.605** 1.124 2.023 1.481no-qualifications 0.651 2.494graduates 7.309** 1.545unemployment 0.135 3.436pop-density 5.155 3.882minorities –1.426* 0.638pensioners –5.069* 2.023acorn1 –1.160 0.643acorn2 –1.146 0.784acorn3 –0.675 0.754acorn4 –1.379* 0.652 0.231** 0.020 0.198** 0.017 0.099 0.120 –0.300 0.162

* significantly different from zero at the 5% level; ** significantly different from zero at the 1% level. The regression equations also include region fixed effects.

26

27

Appendix

Tables A1-A2 include alternative estimates of the coefficients in equation (3): Table A1

shows Ordinary Least Squares estimates and Table A2 shows Weighted Least Squares

estimates, using weights based on the population of each parliamentary constituency in the

2011 census. The structure of each of these tables corresponds to the structure of Table 2,

with coefficient estimates from four alternative models.

The estimates in the appendix tables show the same general pattern as those in Table

2. The coefficients on if-archa and if-saracen in the first model are significantly greater than

zero: the point estimates are somewhat larger than in Table 2, but the magnitude of these

differences is only about one standard error. As in Table 2, the estimated size of the if-archa

and if-saracen coefficients falls as first log(1+libraries) and then students are added to the

model. However, unlike in Table 2, the if-archa coefficient remains significantly different

from zero. This significance corresponds to evidence that there is some other channel through

which the medieval exposure to diversity affects modern voting patterns, in addition to the

educational institution channel. However, this result should be treated with caution, because

the estimates in Tables A1-A2 do not allow for the large and significant spatial correlation in

the error term ( = 0.9: see Table 2). Finally, as in Table 2, the coefficients on

log(1+libraries) and students are significantly greater than zero. The estimated size of the

students coefficient in each model is very similar to that in Table 2, but the estimated size of

the log(1+libraries) coefficient is somewhat larger.

28

Table A1. Determinants of log(remain/leave): Ordinary Least Squares Regression Coefficients and Standard Errors (N = 460)

coeff. s.e. coeff. s.e. coeff. s.e. coeff. s.e.if-archa 0.261** 0.085 0.224** 0.078 0.149** 0.057 0.030 0.026if-saracen 0.150** 0.047 0.063 0.043 0.028 0.035 0.043** 0.016if-cathedral 0.005 0.098 –0.005 0.090 –0.005 0.061 0.044 0.029if-school 0.078 0.046 0.073 0.047 0.073 0.042 0.004 0.018if-coastal –0.111* 0.049 –0.144** 0.044 –0.176** 0.035 0.013 0.020if-med-town –0.347 0.401 –0.141 0.391 –0.031 0.281 0.082 0.127log(pop-med-town) 0.045 0.058 0.004 0.056 –0.015 0.041 –0.021 0.019log(1+libraries) 0.120** 0.024 0.099** 0.022 0.054** 0.010if-1841-town –0.052 0.173 0.372* 0.155 –0.136 0.078log(pop-1841-town) –0.014 0.022 –0.058** 0.020 0.002 0.010students 4.553** 0.392 1.150** 0.298no-qualifications –0.153 0.499graduates 4.341** 0.336unemployment 0.704 0.695pop-density 0.468 0.762minorities 0.646** 0.120pensioners –0.350 0.323acorn1 –0.158 0.117acorn2 –0.152 0.147acorn3 0.005 0.143acorn4 –0.122 0.136R2 0.215 0.299 0.473 0.898

* significantly different from zero at the 5% level; ** significantly different from zero at the 1% level. The regression equations also include region fixed effects.

29

30

31

Table A2. Determinants of log(remain/leave): Weighted Least Squares Regression Coefficients and Standard Errors (N = 460)

coeff. s.e. coeff. s.e. coeff. s.e. coeff. s.e.if-archa 0.277** 0.087 0.235** 0.079 0.150** 0.057 0.027 0.026if-saracen 0.154** 0.048 0.064 0.043 0.029 0.034 0.043** 0.016if-cathedral –0.008 0.101 –0.014 0.092 –0.012 0.062 0.047 0.029if-school 0.070 0.047 0.069 0.047 0.071 0.041 0.003 0.018if-coastal –0.105* 0.052 –0.141** 0.046 –0.180 0.035 0.012 0.021if-med-town –0.390 0.420 –0.187 0.409 –0.084 0.290 0.074 0.131log(pop-med-town) 0.051 0.060 0.010 0.059 –0.008 0.042 –0.020 0.019log(1+libraries) 0.119** 0.024 0.098** 0.021 0.054** 0.010if-1841-town –0.104 0.173 0.351* 0.153 –0.151* 0.079log(pop-1841-town) –0.008 0.022 –0.056** 0.020 0.004 0.010students 4.545** 0.391 1.136** 0.307no-qualifications –0.111 0.516graduates 4.328** 0.348unemployment 0.800 0.702pop-density 0.288 0.763minorities 0.672** 0.119pensioners –0.418 0.329acorn1 –0.126 0.121acorn2 –0.115 0.150acorn3 0.028 0.147acorn4 –0.104 0.141R2 0.214 0.304 0.485 0.900

* significantly different from zero at the 5% level; ** significantly different from zero at the 1% level. The regression equations also include region fixed effects.