# Online Appendix & Additional Results for “The Determinants of Social
Connectedness in Europe”

Michael Bailey Facebook. Email<EMAIL_ADDRESS>Drew Johnston Harvard
University. Email<EMAIL_ADDRESS>Theresa Kuchler New York
University, Stern School of Business; CEPR. Email<EMAIL_ADDRESS>Dominic Russel New York University, Stern School of Business. Email:
<EMAIL_ADDRESS>Bogdan State Facebook. Email<EMAIL_ADDRESS>Johannes Stroebel New York University, Stern School of Business; NBER; CEPR.
Email<EMAIL_ADDRESS>
(July 23, 2020)

##  1 Additional Case Studies of European Social Connectedness

#### Belgium.

Figure 1 maps the social network of two regions in Belgium: Limburg, in Panel
A, and Namur, in Panel B. Again, both regions are most strongly connected to
nearby regions within their own country. Yet, while the capitals of the two
regions (Hasslet and Namur, respectively) are less than 70km apart, the two
regions’ connections outside Belgium differ substantially. The official and
most commonly spoken language in Limburg is Dutch, whereas in Namur it is
French. Accordingly, Limburg is more strongly connected to the entire
Netherlands to the north and Namur is more strongly connected to areas
throughout all of France to the south.

#### Lake Geneva and Central Switzerland.

Figure 2 shows the social connectedness of two neighboring regions in
Switzerland, the Lake Geneva Region (Panel A) and Central Switzerland (Panel
B). The Lake Geneva Region includes the Swiss cantons of Geneva, Vaud, and
Valais, all of which are primarily French speaking. Central Switzerland and
includes the cantons of Lucerne, Uri, Schwyz, Obwalden, Nidwalden, and Zug,
all of which are primarily German speaking. Accordingly, the connections of
the Lake Geneva Region are strong throughout all of France to the west,
whereas the connections of Central Switzerland are stronger throughout all of
Germany to the north. These patterns provide more evidence of language playing
an important role in shaping social connectedness.

#### Aydın and Şanlıurfa.

Figure 3 shows the connections of two regions in Turkey, the Aydın Subregion
(Panel A) and the Şanlıurfa Subregion (Panel B). Aydın’s connections to
Germany appear similar to the Samsun Subregion (shown in the main paper), with
connectedness dropping-off at the former East German border. This pattern can
be seen in most Turkish regions, likely reflecting West Germany’s 1961-1973
labor recruitment agreement with Turkey. However, there is no similar drop-off
for the Şanlıurfa Subregion in Panel B, which shows relatively strong
connections to all of Germany. The Şanlıurfa Subregion is home to a sizeable
Kurdish population, including the largely Kurdish city of Diyarbakr. Armed
conflict between the Kurdish Workers’ Party and the Turkish government in the
1990s led many Kurds to seek refuge in Germany (Aydin, 2016). Much of this
migration happened after the reunification of East and West Germany, likely
explaining the Şanlıurfa’s connectedness to the entire country.

#### Picardy and Île-de-France.

Figure 4 shows the connections of two neighboring regions in France, Picardy
(Panel A) and Île-de-France (Panel B). Picardy is most strongly connected to
other regions in France, with connectedness dropping off sharply at country
borders. Indeed, only 6.2% of the region’s European connections are to
individuals located outside of France. By contrast, Île-de-France, which
includes Paris and its metropolitan area, shows fairly strong connections to a
number of regions outside of France—17.3% of the region’s European connections
are international. In particular, Île-de-France’s connections to regions with
other capital cities, including Madrid, Berlin, Prague, Warsaw, Oslo, and
Stockholm, appear comparatively much stronger. This is an example of the
differences in international connectedness between European regions.

#### Continental Croatia.

Figure 5 shows the connectedness of Continental Croatia, one of Croatia’s two
NUTS2 regions. The connections of the region are strongest with other
countries that were part of Yugoslavia—including Slovenia, Serbia, Montenegro,
and North Macedonia—suggestive of the importance of past country borders in
shaping connectedness. There are also strong connections between Croatia and
Ireland, likely related to a pattern of migration since Croatia’s 2013
accession into the European Union (see, for example, Thomas, 2019; Schmidt,
2017). Similarly, there are connections to central European countries, which
are geographically close and have also welcomed Croatian migrants.

Figure 1: Social Network Distributions in Belgium

Note: Figure shows the relative probability of connection, measured by
$SocialConnectedness_{ij}$, of all European regions $j$ with two regions $i$:
Limburg, BE (Panel A) and Namur, BE (Panel B). The measures are scaled from
the 20th percentile of all $i,j$ pairs in Europe. Darker regions have a higher
probability of connection.

Figure 2: Social Network Distributions in Switzerland

Note: Figure shows the relative probability of connection, measured by
$SocialConnectedness_{ij}$, of all European regions $j$ with two regions $i$:
Lake Geneva Region, CH (Panel A) and Central Switzerland, CH (Panel B). The
measures are scaled from the 20th percentile of all $i,j$ pairs in Europe.
Darker regions have a higher probability of connection.

Figure 3: Social Network Distributions in Turkey

Note: Figure shows the relative probability of connection, measured by
$SocialConnectedness_{ij}$, of all European regions $j$ with two regions $i$:
the Aydın Subregion, TR (Panel A) and the Şanlıurfa Subregion, TR (Panel B).
The measures are scaled from the 20th percentile of all $i,j$ pairs in Europe.
Darker regions have a higher probability of connection.

Figure 4: Social Network Distributions in France

Note: Figure shows the relative probability of connection, measured by
$SocialConnectedness_{ij}$, of all European regions $j$ with two regions $i$:
Picardy, FR (Panel A) and Île-de-France (Panel B). The measures are scaled
from the 20th percentile of all $i,j$ pairs in Europe. Darker regions have a
higher probability of connection.

Figure 5: Social Network Distributions in Continental Croatia (HR04)

Note: Figure shows the relative probability of connection, measured by
$SocialConnectedness_{ij}$, of all European regions with Continental Croatia.
The measures are scaled from the 20th percentile of all $i,j$ pairs in Europe.
Darker regions have a higher probability of connection.

##  2 Agglomerative Clustering Algorithm

The previous examples suggest that social connections are substantially
stronger within countries than between countries, but that there is also
substantial within-country variation based on certain regional
characteristics. We next seek to understand how these patterns would be
reflected if we created communities of regions with strong connections to each
other. To do so, we create clusters that maximize within-cluster pairwise
social connectedness using hierarchical agglomerative linkage
clustering.111There are a number of possible algorithms that could construct
such clusters. Conceptually, the agglomerative clustering algorithm starts by
considering each of the N countries as a separate community of size one. It
then iteratively combines clusters that are “closest” together. Here, our
measure of distance is $1/SocialConnectedness_{i,j}$. The distance between
clusters is the average of the pairwise distances between regions in the
clusters. Figure 6 shows the results when we use this algorithm to group
Europe into 20 and 50 communities, instead of the 37 existing countries.

Figure 6: Socially Connected Communities within Europe

Note: Figure shows European regions grouped together when we use hierarchical
agglomerative linkage clustering to create 20 (Panel A) and 50 (Panel B)
distinct groups of regions.

In Panel A, the 20 unit map, nearly all of the community borders (denoted by a
change in area color) line up with country borders (denoted by large black
lines), consistent with strong intra-country social connectedness. The only
exception is in the United Kingdom, where one region, Outer London West and
North West, is grouped together with Romania. This area of London has welcomed
a large number of Romanian immigrants in recent years and includes Burnt Oak,
a community nicknamed “Little Romania” (Harrison, 2018). Furthermore, the
borders of cross-country communities mostly line up with historical political
borders. For example, every region in the countries that made up Yugoslavia
until the early 1990s for which we have data—Slovenia, Croatia, Serbia, North
Macedonia, and Montenegro—are grouped together in one community. The same is
true of the regions in the Czech Republic and Slovakia, which were united as
Czechoslovakia until 1993. Other cross-country communities line up with even
older historical borders: the United Kingdom and Ireland, Denmark and Iceland,
Sweden and Norway, and Austria and Hungary all split from political unions in
the first half of the 20th century but remain grouped together by present-day
social connectedness.

While much of the alignment between country and community borders persists in
the 50 unit map in Panel B of Figure 6, countries begin to break apart
internally. Most of these resulting sub-country communities are spatially
contiguous, consistent with distance playing a strong role in social
connectedness within countries. One notable exception is Île-de-France, which
is home to Paris and accounts for nearly 30% of French GDP. The region is
grouped with France’s southeastern coast (often referred to as French Riviera)
and Corsica, popular vacation destinations for the well-heeled. We also see
linguistic communities form: Belgium splits into a French and a Dutch speaking
community, and Catalan and Andalusian Spanish communities emerge in Spain. The
southeastern corner of Turkey, which has a much higher concentration of Kurds
than the rest of the country, also forms its own community. Finally,
historical country borders remain influential: the regions of former
Czechoslovakia, former Yugoslavia, and Denmark and Iceland still form single
communities, while former East Germany forms its own community.

##  3 Social Connectedness and Travel

Social connectedness between two regions may be related to certain economic
and social interactions. For example, Bailey et al. (2018b) documents
correlations between social connectedness, trade flows, patent citations, and
migration patterns between U.S. counties. Bailey et al. (2019a) looks more
specifically at transportation, highlighting the relationship between
transportation infrastructure and urban social networks. In this section, we
look at the relationship between social connectedness and European passenger
train flows. Our unique data allow us to add a continental-level empirical
analysis to an existing literature of theoretical and survey-based studies
that explore the influence of social networks on travel decisions (for an
overview, see Kim et al., 2018).

#### Regional Data on European Travel.

For our dependent variable, we use the number of passenger train trips between
each regional pair $i$ and $j$. We discuss the availability of these data, as
well as our procedure for cleaning and standardization, in Appendix 5.4. We
also use information on rail and drive travel times between region geographic
centers. Rail travel times come from the European Transport Information System
2010 “observed” data.222Data available at:
http://ftp.demis.nl/outgoing/etisplus/datadeliverables/TextFiles/. Drive times
were generated using the Open Source Routing Machine, an OpenStreetMap-based
routing service.333OSRM relies on only open-source software and does not use
real-time data (e.g., traffic). Both of these factors increase replicability
compared to other online-mapping services. For more information, see:
http://project-osrm.org/.

#### Relationship between Travel and Connectedness.

As the outcome variable is non-negative and contains many zeros, we follow
Correia et al. (2019) and estimate a Poisson Pseudo-Maximum Likelihood
regression model. Specifically, our equation of interest is:

$\begin{split}PassengerTrain_{ij}=exp[\beta_{0}\log(SocialConnectedness_{ij})+X_{ij}+\alpha\log(D_{ij})+\psi_{i}+\psi_{j}]\cdot\epsilon_{ij}\end{split}$
(1)

Here, the definitions of log($SocialConnectedness_{ij}$), $X_{ij}$,
$\psi_{i}$, and $\psi_{j}$ remain unchanged from Equation 2 in the primary
paper. The variable $PassengerTrain_{ij}$ is the number of rail passengers
that travel from region $i$ to region $j$ in a given year. The vector for
“distance,” represented by $D_{ij}$, includes the geographic distance, as well
as the rail and driving travel times in minutes between the central points of
the regions.

Table 1 shows the results from Regression 1. Due to differences in data
availability between years, we present results using the most recent year for
which passenger train flows for a given $i,j$ pair is available (in columns
1-5) and, separately, results using 2015, 2010, and 2005 (columns 6, 7, and
8). Column 1 includes the log of $SocialConnectedness_{ij}$ and the NUTS2
region fixed effects as the only explanatory variables. We find that a 10%
increase in connectedness between a pair of regions is associated with a 17%
increase in passenger rail traffic between the two. Column 2 adds the log of
the geographic distance between the regions, which, intuitively, has a
negative relationship with the number of passenger train trips. In column 3,
we add controls for the travel time, separately by train and car, between the
central points of the regions. In general, we find that these travel times
have a stronger negative relationship with passenger rail traffic than
distance alone. Column 4 adds all country pair fixed effects; column 5 adds
all the demographic/soceioeconomic controls from Table 1 in the primary paper;
and columns 6, 7, and 8 repeat these analyses for the years 2015, 2010, and
2005, respectively. Adjusting for geographic distance, travel time by car and
train, and country fixed effects, we find that, on average, a pair of regions
with 10% higher social connectedness will have between 11.7 and 13.6% more
rail passengers travel between them. Overall, our results provide large-scale
empirical evidence to support existing models that suggest social networks
play an important role in individuals’ travel decisions (see e.g., Axhausen,
2008; Carrasco and Miller, 2009; Páez and Scott, 2007).

[h]

Table 1: Social Connectedness and Passenger Train Travel

| Dependent Variable: log(Passenger Rail Trips)
---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
|  Most recent |  Most recent |  Most recent |  Most recent |  Most recent |  2015 |  2010 |  2005
log(SocialConnectedness) |  1.711∗∗∗ |  1.506∗∗∗ |  1.416∗∗∗ |  1.290∗∗∗ |  1.364∗∗∗ |  1.241∗∗∗ |  1.167∗∗∗ |  1.236∗∗∗
|  (0.052) |  (0.080) |  (0.101) |  (0.112) |  (0.099) |  (0.056) |  (0.071) |  (0.093)
|  |  |  |  |  |  |  |
log(Distance in KM) |  |  -0.361∗∗ |  0.259 |  0.041 |  0.118 |  0.109 |  0.627∗∗∗ |  0.076
|  |  (0.180) |  (0.327) |  (0.354) |  (0.284) |  (0.172) |  (0.198) |  (0.252)
|  |  |  |  |  |  |  |
log(Rail Time in Mins) |  |  |  -0.493 |  -0.004 |  0.104 |  -1.096∗∗∗ |  -1.071∗∗∗ |  0.036
|  |  |  (0.442) |  (0.528) |  (0.543) |  (0.162) |  (0.251) |  (0.490)
|  |  |  |  |  |  |  |
log(Drive Time in Mins) |  |  |  -0.396∗∗ |  -0.446∗∗ |  -0.448∗∗∗ |  -0.013 |  -0.562∗∗∗ |  -0.409∗∗
|  |  |  (0.201) |  (0.198) |  (0.170) |  (0.110) |  (0.172) |  (0.165)
|  |  |  |  |  |  |  |
NUTS2 FEs |  Y |  Y |  Y |  Y |  Y |  Y |  Y |  Y
All Country Pair FEs |  |  |  |  Y |  Y |  Y |  Y |  Y
Table 1 Controls |  |  |  |  |  Y |  Y |  Y |  Y
pseudo-$R^{2}$ |  0.964 |  0.965 |  0.965 |  0.972 |  0.975 |  0.980 |  0.980 |  0.975
Number of Observations |  85,390 |  85,390 |  77,390 |  77,390 |  61,810 |  27,650 |  32,176 |  58,462
N Explained by FEs |  30,698 |  30,698 |  23,784 |  49,000 |  36,517 |  19,146 |  16,269 |  29,659

  * Note: Table shows results from Regression 1. The unit of observation is a NUTS2 region pair. The dependent variable in all columns is the number of passenger rail trips in 2015, 2010, or 2005 from region $i$ to region $j$. In columns 1-5, we use the most recent year for which these data are available for a given $i,j$ pair. In columns 6, 7, and 8, we use only the 2015, 2010, and 2005 data, respectively. Column 1 shows the results from using the log of $SocialConnectedness_{ij}$ and NUTS2 region fixed effects as the only explanatory variables. Column 2 adds the log of the geographic distance between the regions. Column 3 adds the log of the travel time, by train and car, between the central points of the regions. Column 4 adds all country-pair fixed effects. Columns 5-8 add the demographic/soceioeconomic controls from Table 1 in the primary paper. Observations that are fully explained by fixed effects are dropped before the PPML estimation. Standard errors are double clustered by each region $i$ and region $j$ in a region pair. Significance levels: *(p$<$0.10), **(p$<$0.05), ***(p$<$0.01).

##  4 International Connections and Views on European Union

A central goal of the European Union is to enhance cohesion and solidarity
across European countries, and a variety of programs, such as the Erasmus
exchange student program, explicitly exist to foster this connectivity
(European Union, ; European Commission, ). However, in recent years there has
been a decline in trust in the EU and a rise of anti-EU voting, that have lead
to, for example, the United Kingdom’s 2016 vote to exit the European Union. A
number of studies explore correlates with, and potential origins of, support
for Eurosceptic political parties (for example Algan et al., 2017; Becker et
al., 2017; Colantone and Stanig, 2018; Inglehart and Norris, 2016). While much
of this research emphasizes either economic insecurity or a cultural backlash
against multiculturalism/progressive values, a related strand of research
explores the role of personal connections in shaping political preferences.
Early evidence was provided by Lazarsfeld et al. (1944), who documents the
influence of friends on U.S. voters. More recently, McLaren (2003) finds that
intimate contact with members of minority groups can reduce individuals’
willingness to expel immigrants and Algan et al. (2019) finds that the
formation of friendships between students with different political opinions
causes their views to converge. Motivated by this literature, we explore
whether differences in how socially connected a region is to other European
countries is associated with higher trust in the European Union and a reduced
propensity to vote for Eurosceptic political parties.

European regions vary considerably on the share of social connections to
individuals living in foreign countries.444In our data, we focus on
connections between individuals living within Europe. Therefore, (as we
describe) our measure is the share of $European$ connections to individuals in
different $European$ countries, rather the the share of $all$ connections in
$any$ different country. For the median NUTS2 region, 9.8% of European
connections are to individuals in different countries. However, in the least
internationally connected decile of regions, less than 4.1% of connections are
to individuals living in different countries, compared to over 19.7% in the
most internationally connected decile. Figure 7 maps this share for every
region.

Figure 7: International Connectedness within Europe

Note: Figure shows the share of European friendship links from users in each
region to users in a different country. Darker orange regions have a lower
share of European friends in different countries and darker purple regions
have a higher share.

#### Regional Data on Euroscepticism.

We analyze two measures of Euroscepticism. Our first measure is a survey
measure of trust in the EU. As part of the “Eurobarometer” series of public
opinion surveys undertaken for the European Commission, the Flash
Eurobarometer 472: Public opinion in the EU regions asks respondents about
their trust in the EU. Specifically, the survey asks respondents “I would like
to ask you a question about how much trust you have in the European Union.
Could you tell me if you tend to trust it or tend not to trust it?” (European
Comission, b). The survey was conducted between October and November 2018, and
had 61,968 respondents (around 300 per region). Second, we use the vote share
for anti-EU parties. Regional-level data on parliamentary and presidential
elections in Europe from 2000 to 2017 are available from Algan et al.
(2017).555Data and programs are available at: https://www.brookings.edu/bpea-
articles/the-european-trust-crisis-and-the-rise-of-populism/. As described in
the paper, the determination of whether a particular party is “Anti-EU” is
primarily based on the Chapel Hill Expert Survey, with some additional
analysis of party platforms by the paper’s authors. From this data, we use the
average share of the electorate that voted for Anti-EU parties in elections
between 2009 and 2017. In addition to these two measures of Euroscepticism, we
use demographic and socioeconomic data from Eurostat, including the share of
each region’s population that was born in a different European country.

#### Relationship between Euroscepticism and Connectedness.

To systematically explore the relationship between regional views on the
European Union and international friendships, we use the equation:

$\begin{split}Share\\_EU\\_View_{i}=\beta_{0}*Share\\_Connections\\_Foreign_{i}+X_{i}+\zeta_{c(i)}+\epsilon_{i}\end{split}$
(2)

Here, $Share\\_EU\\_View_{i}$ is either the share of individuals responding
that they trust the European Union in the Eurobarometer survey or the average
share of the electorate that voted for Anti-EU parties between 2009 and 2017.
Our unit of observation for this regression is the level at which we observe
these two outcomes of interest. For both data sets, this includes a mixture of
NUTS1 and NUTS2 regions. The first explanatory variable,
$Share\\_Connections\\_Foreign_{i}$, is the share of the region’s European
connections that are to individuals in foreign countries. $X_{i}$ is a set of
regional socioeconomic characteristics. These are average income, unemployment
rate, and the shares of employment in manufacturing, construction, and
professional sectors. They may also include the share of residents living in
the region who are born in other European countries. The controls are
indicators based on the deciles of each measure, to capture any non-linear
relationships. Some specifications include country fixed effects, denoted here
by $\zeta_{c(i)}$.

Table 2 shows the results of this regression. Columns 1 and 5 show the
relationships between the outcomes of interest and the share of the region’s
European connections that are to individuals in foreign countries: a 1
percentage point increase in the share of connections that are to other
European countries is associated with a 0.50 percentage point increase in the
share of residents who trust the EU and a 0.76 percentage point decrease in
the share of votes for an Anti-EU political party. The second and fifth
columns show that when adding the socioeconomic controls listed above, the
magnitude of the relationship between the trust in the EU decreases and
becomes insignificant, but the magnitude of the relationship with anti-EU
voting slightly increases. When we also add the share of the population that
is born in other European countries (columns 3 and 7), the magnitude of the
relationship with trust increases and the magnitude of the relationship with
voting decreases. There is a complicated relationship between demographic and
socioeconomic factors, and views on the European Union; however, the
directionally persistent relationship between Euroscepticism and international
social connectedness at the regional level suggest foreign connections might
play a role in shaping views on the EU, a result that would be consistent with
the existing literature on personal connections impacting political
preferences. Columns 4 and 8 show that, after adding country-level fixed
effects, increases in a region’s foreign connections continue to imply a
decrease in the share of residents that trust the EU, while th effect on the
share that vote for anti-EU parties becomes insignificant. This suggests that
country-specific factors (e.g. the organizational resources of anti-EU
political parties within the country) dominate possible effects of
international connections in shaping Eurosceptic voting patterns, but not in
shaping individuals’ views.

[h]

Table 2: International Connections and Views on the European Union

| Share Trust in EU (%) | Share Vote for Anti-EU Parties (%)
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
Share of European friends |  0.499∗∗∗ |  0.161 |  0.254∗ |  0.326∗∗ |  -0.762∗∗∗ |  -0.819∗∗∗ |  -0.541∗∗ |  0.454
outside of country (%) |  (0.101) |  (0.127) |  (0.153) |  (0.155) |  (0.187) |  (0.230) |  (0.273) |  (0.303)
|  |  |  |  |  |  |  |
Socioeconomic Controls |  |  Y |  Y |  Y |  |  Y |  Y |  Y
Share Foregin Born Controls |  |  |  Y |  Y |  |  |  Y |  Y
Country Fixed Effects |  |  |  |  Y |  |  |  |  Y
$R^{2}$ |  0.109 |  0.503 |  0.571 |  0.891 |  0.071 |  0.493 |  0.568 |  0.818
Number of Observations |  201 |  201 |  201 |  201 |  221 |  221 |  221 |  221

  * Note: Table shows results from Regression 2. The dependent variable in columns 1-4 is the share of individuals responding that they trust the European Union in a survey conducted for the European Commission. The dependent variable in columns 5-8 is the average share of the electorate the voted for “Anti-EU” parties between 2009 and 2017. The unit of observation is the level at which we observe each dependent variable (either a NUTS2 or NUTS1 region). Columns 1 and 5 include only one explanatory variable: the share of the region’s European connections that are to individuals in foreign countries. Columns 2 and 6 add a set of demographic and economic controls. Columns 3 and 7 add controls for the share of the region’s population that was born in a different country. Columns 4 and 8 add country-level fixed effects. Significance levels: *(p$<$0.10), **(p$<$0.05), ***(p$<$0.01).

##  5 Additional Data Details

###  5.1 Facebook and Real World Connections.

Establishing a Facebook friendship link requires the consent of both
individuals, and the total number of friends for a person is limited to 5,000.
As a result, Facebook connections are primarily between real-world
acquaintances. Indeed, one independent survey of American Facebook users
revealed that only 39% of users reported being Facebook friends with someone
that they have never met in person (Duggan et al., 2015). In contrast,
Facebook users reported that they were generally Facebook friends with
individuals for which they have real-world connections: 93% said they were
Facebook friends with family other than parents or children, 91% said they
were connected with current friends, 87% said they were connected to past
friends (such as former classmates), and 58% said they were connected to work
colleagues (Duggan et al., 2015). As a result, networks formed on Facebook
more closely resemble real-world social networks than those on other online
platforms, such as Twitter, where uni-directional links to non-acquaintances,
such as celebrities, are common. In prior work Facebook friendships have been
shown to be useful to describe real world networks. For example, social
connectedness as measured through Facebook friendship links is strongly
related to patterns of COVID-19 spread (Kuchler et al., 2020b), international
trade (Bailey et al., 2020), and investment decisions (Kuchler et al., 2020a).
See Bailey et al. (2018c, a, b, 2019a, 2019b) for additional discussion of the
evidence that friendships observed on Facebook serve as a good proxy for real-
world social connections.

###  5.2 Subset of Friendships.

As one way of better understanding the connections underlying our measure of
Social Connectedness in the primary paper, we compare it to similar measures
constructed using restricted sets of friendships. Table 3 presents the cross-
correlation of measures limited to connections made during certain periods of
time (e.g., recent friendships vs old friendships) and to friendships between
individuals with certain shared characteristics (e.g., ages less than 5 years
apart). Each of these measures is highly correlated with the others. This
provides more evidence that Facebook connections resemble full real-world
networks and not, for example, primarily recently formed online-only
connections.

Table 3: Correlation of Social Connectedness Constructed from Select
Friendship Pairs

|  (1) |  (2) |  (3) |  (4) |  (5) |  (6) |  (7) |  (8)
---|---|---|---|---|---|---|---|---
(1) All |  1.000 |  |  |  |  |  |  |
(2) Added $<$1 Year Ago |  0.946 |  1.000 |  |  |  |  |  |
(3) Added 1-5 Years Ago |  0.991 |  0.967 |  1.000 |  |  |  |  |
(4) Added $<$5 Years Ago |  0.985 |  0.982 |  0.998 |  1.000 |  |  |  |
(5) Added $>$5 Years Ago |  0.970 |  0.848 |  0.932 |  0.915 |  1.000 |  |  |
(6) Both Female |  0.982 |  0.910 |  0.960 |  0.953 |  0.975 |  1.000 |  |
(7) Both Male |  0.986 |  0.940 |  0.982 |  0.977 |  0.949 |  0.955 |  1.000 |
(8) Ages Within 5 Years |  0.997 |  0.942 |  0.982 |  0.977 |  0.974 |  0.983 |  0.979 |  1.000

  * Note: Table presents correlations between social connectedness and similar measures constructed from restricted sets of connections. Rows and Columns 2-5 limit to connections made less than a year ago, between 1 and 5 years ago, less than five years ago, and more than 5 years ago. Rows and Columns 5-8 limit to connections between females, males, and individuals with ages 5 or fewer years apart.

###  5.3 Regional Demographic and Socioeconomic Data.

Information on demographic and socioeconomic characteristics of each region,
such as educational attainment, median age, average income, and unemployment
rate, is available from Eurostat. We calculate a measure of region-to-region
industrial composition similarity using employment data from Eurostat’s
Structural Business Statistics series. For regional data on language and
religion, we use the European Social Survey. In particular, the survey asks
respondents which language they speak most often at home and — if the
respondent considers him- or herself religious — their religious affiliation
(European Social Survey, ).666Most of our data come from Wave 8 of the survey,
which was conducted in 2016. However, when a country was not included in Wave
8, we use the most recent wave of the survey for which the country was
included. These countries are Denmark (Wave 7, 2014), Albania, Bulgaria,
Cyprus and Slovakia (Wave 6, 2012), Croatia and Greece (Wave 5, 2010), and
Latvia and Romania (Wave 4, 2008). In addition, Malta was not surveyed but is
comprised of a single NUTS2 region. According to other survey data, 97% of the
population considers Maltese their “mother tongue” (European Comission, a) and
95% identifies as Roman Catholic (Sansone, 2018). We include these as the most
common language and religion. Our analysis focuses on the regional pairs
within the smaller set of countries for which we have a full set of control
data.777We do not include Albania, Iceland, Lichtenstein, Luxembourg,
Montenegro, North Macedonia, Serbia, Switzerland, and Turkey; most of these
countries are excluded because they are not part of the European Union, and
therefore do not participate in many of the data collection efforts we use to
construct our data.

###  5.4 Passenger Train Travel Data

Our analyses in Section 3 use information on region-to-region passenger train
travel made available by Eurostat in the series “tran_r_rapa.” The data are
based on individual reports from Member States of the European Union or
European Free Trade Association, as well as European Union Candidate
Countries. For each observation, the reporting country is identified. The data
are reported for 2005, 2010, and 2015, in accordance with Regulation 91/2003
of the European Commission and subsequent regulatory updates. We take a number
of steps to clean the data to prepare it for our analyses. This process was
informed by both the “Reference Manual on Rail transport statistics”
(Eurostat, ) and correspondence with the Eurostat data providers.

We first restrict our data to observations at the NUTS2 level, removing any
country-level observations (we do, however, keep country-level data for
countries which have only a single NUTS2 region). We also exclude all pairs
that include a region with the unknown indicator “XX” or the extraregio
territory indicator “ZZ.” These pairs make up around 1.3% of passenger trips
in the data. From here, we are faced with four challenges: 1) As confirmed by
the authors’ correspondence with Eurostat, when the data appear as “non-
available” in a particular row this could mean either that there was no
traffic or that the relevant country did not provide the data.888In some
instances, countries report the data to Eurostat, but flag them as
confidential so that they are not included in the public release. We always
treat these data as missing in our final analysis. 2) There are a number of
hypothetical regional pairs missing, even between countries that did report
data elsewhere. 3) For some international regional pairs, there are data
reported from both countries on the same train flows, and often the number of
passengers does not match. 4) NUTS2 classifications changed in 2006, 2010, and
2013. Each year of data is reported using the NUTS2 classification that was
relevant at that particular point in time.

[h]

Table 4: Passenger train travel data availability

reporter |  domestic2015 |  international2015 |  domestic2010 |  international2010 |  domestic2005 |  international2005
---|---|---|---|---|---|---
AL |  0 |  0 |  0 |  0 |  0 |  0
AT |  0 |  0 |  0 |  0 |  0 |  0
BE |  0 |  0 |  1 |  1 |  1 |  1
BG |  1 |  1 |  1 |  1 |  0 |  0
CH |  1 |  1 |  1 |  1 |  0 |  0
CY |  0 |  0 |  0 |  0 |  0 |  0
CZ |  1 |  1 |  1 |  1 |  1 |  1
DE |  1 |  0 |  1 |  0 |  1 |  1
DK |  0 |  0 |  0 |  0 |  1 |  1
EE |  1 |  0 |  1 |  0 |  1 |  0
EL |  0 |  0 |  1 |  0 |  1 |  0
ES |  1 |  0 |  1 |  0 |  1 |  0
FI |  1 |  0 |  1 |  0 |  1 |  0
FR |  0 |  0 |  0 |  0 |  0 |  0
HR |  1 |  1 |  1 |  1 |  0 |  0
HU |  0 |  0 |  1 |  1 |  1 |  1
IE |  1 |  1 |  1 |  1 |  1 |  1
IS |  0 |  0 |  0 |  0 |  0 |  0
IT |  1 |  0 |  0 |  0 |  1 |  0
LI |  0 |  0 |  0 |  0 |  0 |  0
LT |  1 |  1 |  1 |  1 |  1 |  1
LU |  1 |  1 |  1 |  1 |  1 |  1
LV |  1 |  1 |  1 |  1 |  1 |  1
ME |  0 |  0 |  0 |  0 |  0 |  0
MK |  0 |  0 |  0 |  0 |  0 |  0
MT |  0 |  0 |  0 |  0 |  0 |  0
NL |  0 |  0 |  0 |  0 |  1 |  1
NO |  1 |  1 |  1 |  0 |  1 |  0
PL |  1 |  1 |  1 |  1 |  1 |  1
PT |  1 |  0 |  1 |  0 |  1 |  0
RO |  0 |  0 |  0 |  0 |  0 |  0
RS |  0 |  0 |  0 |  0 |  0 |  0
SE |  0 |  0 |  0 |  0 |  0 |  0
SI |  1 |  1 |  1 |  1 |  1 |  1
SK |  1 |  1 |  1 |  1 |  1 |  1
TR |  1 |  0 |  1 |  0 |  0 |  0
UK |  0 |  0 |  0 |  0 |  0 |  1

  * Note: Table shows the regional passenger train travel data availability by reporting country, year, and whether the travel is domestic or international. 0s indicate the data were not available and 1s indicate the data were available. Reporting country is given by the two-letter prefix of each country’s NUTS codes. The table only shows whether any data from a particular reporter were available, not whether any regions from this country are included in the final analysis. For example, although Austria did not report international data in 2015, pairs that include an Austrian region and a region in a country that did report international data in 2015 are included.

With respect to challenges 1 and 2, we found that each country reports data to
Eurostat in two intermediate data sets: one for domestic passenger travel and
another for international passenger travel. To identify countries that
submitted a particular set of data in a particular year, we group the data by
the reporting country, year, and whether the region pair is international or
domestic. We then generate a list of countries that had at least one non-
missing entry in each year/domestic-international group. These lists are
provided in Table 4. When “non-availble” values are reported by a country that
did not report data elsewhere in the year/domestic-international group, we
treat this as missing and exclude it. When “non-available” values are reported
by a country that did report data elsewhere in the group, we treat this value
as a zero (no traffic). Additionally, for countries that reported data in a
particular group, we fill any missing regional pairs (i.e. pairs that are not
in the data) in the group with zeros. Together, these assumptions handle
challenges 1 and 2.

For each international regional pair, there still remains two possible
reports: one from each of the regions’ home countries in the pair. In
instances when only one country reports the data, we take the non-missing
value from the reporting country. However, there are a number of instances
when each country reports data for the same international regional pair
(challenge 3). In these instances, we take the average of the two reports.
Finally, to update the data to the 2016 NUTS2 regions (challenge 4) we build a
crosswalk using the history of NUTS information provided by
Eurostat.999Available at: https://ec.europa.eu/eurostat/web/nuts/history In
instances when an older NUTS2 region split into multiple new regions, we set
the number of passengers in each row that includes a new region equal to the
corresponding old row’s number of passengers, multiplied by the new region’s
population share of the old region population (i.e. we assume that passenger
train travel in each of these regions is proportional to population).

##  6 Historical Europe Maps

Analyses in our primary paper use information on the country that each modern
NUTS2 region was a part of in the years 1900, 1930, 1960, and 1990. The maps
in this appendix show these country classifications for each year. The data
largely come from files provided by the Max Planck Institute for Demographic
Research Population History GIS Collection (MPIDR and CGG, ). In cases when a
modern region spans two historical countries, we classify the region as part
of the country for which it had a greater land area overlap.

Figure 8: 2019 countries of Europe

Note: Figure shows the 2019 country of each present-day NUTS2 region.

Figure 9: 1990 and 1960 countries of Europe

Note: Figure shows the 1990 country (Panel A) and 1960 country (Panel B) of
each present-day NUTS2 region.

Figure 10: 1930 and 1900 countries of Europe

Note: Figure shows the 1930 country (Panel A) and 1900 country (Panel B) of
each present-day NUTS2 region.

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