NATIONALISM AND INTERSTATE CONFLICT: A REGRESSION DISCONTINUITY ANALYSIS

NATIONALISM AND INTERSTATE CONFLICT:
A REGRESSION DISCONTINUITY ANALYSIS
ANDREW BERTOLI
21 JULY 2013
Abstract. Nationalism is widely viewed as a force for interstate violence, but does
it really have an important effect on state aggression that cannot be explained by
strategic concerns? I provide strong evidence that it does using regression discontinuity analysis. I take advantage of the fact that many countries experience a surge
of nationalism when they go to the World Cup, and the World Cup qualification
process from 1958-1998 produced a large number of countries that barely qualified
or barely missed. I show that these countries are well-balanced across a wide range
of factors, including past levels of aggression. However, the qualifiers experienced
a significant spike in aggression during the World Cup year. I also replicate the
analysis using the FIFA regional soccer championships and find similar results. In
both cases, the estimated treatment effect is larger for authoritarian states than
democracies, suggesting that democratic norms may help constrain nationalistic
aggression.
Scholars use nationalism to explain international conflicts from the Napoleonic
Wars to the U.S. invasion of Iraq (McCartney 2004; Cederman, Warren, and Sornette
2011). Nationalism is generally defined as the belief that a national group should
have the right to self-rule (Cottam 1979; Gellner 1983), but it often comes with
feelings of pride, superiority, and victimization (Anderson 1983; Mearsheimer 1990).
Scholars have argued that it can increase enmity between countries (Woodwell 2007),
undermine international cooperation (Rosato 2011), motivate societies to fight costly
wars (Posen 1993), and cause governments to overestimate their relative military
power (Schrock-Jacobson 2012).
This paper is a working draft. Any feedback or questions are welcome and can be e-mailed to
[email protected]. The data and replication code for this paper are available on my website at
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But does nationalism really have a powerful effect on state aggression that cannot be explained by security concerns? While wars are often preceded by strong
nationalist movements, this relationship is not necessarily causal (Posen 1993). For
instance, leaders who anticipate conflict might try to incite nationalistic fervor to
increase morale for a future war. Reverse causality is another concern, since disputes
at the international level often increase nationalism. Thus, it is difficult to know
whether nationalism can actually compel states to use force when they otherwise
would not do so, for reasons that are not entirely rational.
This paper uses international sporting events to test the effects of exogenous surges
of nationalism on state aggression. These events are an ideal place to look for boosts
of nationalism because they usually increase the salience of national identities. Scholars have shown that they lead to feelings of national unity and hatred towards other
countries (Orwell 1945; Billig 1995, Maguire and Poulton 1999; Tuck 2003; Vincent,
Kian, Kuntz, and Hill 2010). These sentiments have resulted in frequent outbreaks
of nationalistic violence at games (Markovits and Lars Rensmann 2010), and several
scholars have even argued that they played a contributing role in bringing about
some of the most destructive wars of the twentieth century (Reid 1999; Sack and
Suster 2000). The most famous examples are the 1936 Nazi Olympics and the 1969
Football War between El Salvador and Honduras.
I focus on the World Cup because its qualification process is ideal for regression
discontinuity analysis. In the past decade, this quasi-experimental approach has
become an increasingly accepted form of causal inference in observational studies
andrewbertoli.org. I would like to thank Jasjeet Sekhon, Allan Dafoe, John Henderson, Ron Hassner, Vinod Aggarwal, Steven Weber, Aila Matanock, Edward Miguel, Ernesto Dal Bó, Alexander
Theodoridis, David Broockman, David Dow, Jason Klocek, Benjamin Bartlett, Shinhye Choi, Tara
Buss, and Travis Johnson for helpful comments.
2
(Eggers and Hainmueller 2009; Crost, Felter, and Johnston 2010; Gerber, Kessler,
and Meredith 2011; Dunning 2012; Brollo and Nannicini 2012, Samii 2013). It can be
used when a treatment is given to units that surpass a certain cut-point in a scoring
system. The idea is to compare the units that scored just above and just below the
cut-point. These units should be similar except that the ones above the cut-point
received the treatment and the ones below did not.
While the World Cup qualification process can differ by continent and year, a
common format is that countries are grouped into regions, play a number of games
against the other countries in their region, and qualify by earning a top position in the
standings. It is therefore possible to compare the countries that barely qualified to
the ones that barely missed. These countries went to the World Cup or stayed home
based on small differences in their records after many games, so the data obtained
by this procedure should be similar to what would be expected in a randomized
experiment.
With this approach, I construct a treatment and control group, each consisting of
63 countries. Specifically, I select pairs of countries where one country qualified and
the other missed, provided that they were separated by no more than two points in
the standings and the qualifier scored at least five points. I show that these groups
are balanced across a wide range of political, economic, and demographic factors. I
also show that they are balanced on past levels of aggression, which I measure in the
standard way as the number of militarized interstate disputes that a state initiates.
The results show that going to the World Cup increases aggression substantially.
The countries that barely qualified experienced a significant spike in aggression during the World Cup year. The results hold under various statistical tests and other
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robustness checks. The estimated treatment effect is also much stronger for authoritarian states than democracies, suggesting that democratic norms may help reduce
nationalistic violence.
I also replicate the analysis using the FIFA regional soccer championships like the
European Football Championship and the African Cup of Nations. Using the same
design, I construct a new sample consisting of 45 pairs of countries that barely made
or missed qualification for their regional tournaments. Like before, these groups are
well-balanced on aggression levels prior to qualification, but the qualifiers become
significantly more aggressive following qualification.
This paper proceeds as follows: I first present a brief overview of the literature
related to this study. I then describe the research design in more depth and show
that the treatment and control groups are balanced on pre-treatment characteristics. Next, I present the findings from the World Cup analysis and establish the
robustness of the results. I then replicate the analysis using the FIFA regional soccer
championships. Lastly, I consider some of the mechanisms that could account for
these results.
Literature Review
This study supports the work of a number of scholars who argue that nationalism is
a key source of interstate violence. Most existing studies investigate how nationalism
played a role in specific cases, such as the Franco-Prussian War (Hall 1999), the
conquests of Imperial Germany and Japan (Snyder and Mansfield 1995), and the
Balkans Wars (Gagnon 1994). While these studies provide strong evidence that
nationalism leads to conflict and help identify the mechanisms through which this
process can unfold, their findings are limited to the cases they focus on and do not
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address the broader impact of nationalism on interstate conflict. Many of them also
fall short of clear causal identification. They focus on surges of nationalism that were
not exogenous and where the counterfactual world where the surge did not occur is
not always well-defined.
Quantitative evidence is also limited, in part because there is no comprehensive
dataset that tracks nationalism. Schrock-Jacobson (2012) recently published the first
large-N analysis that tests the effects of nationalistic surges on interstate war onset,
which uses a random sample of state years from 1816-1997. She uses regression to
show that countries that experienced an increase in nationalism over this period
tended to be more likely to go to war in the following year. This study provides
valuable evidence that nationalism leads to conflict, it is likely to suffer from omitted
variable bias. This problem is particularly concerning because countries usually
become more nationalistic in the lead-up to war, and the anticipation of future
conflict is an unobservable factor.
Cederman, Warren, and Sornette (2011) also provide an important contribution
to this research program by using quantitative analysis to show that the rise of
nationalism during the French Revolution coincided with a major increase in the destructiveness of war. While they control for population growth and industrialization,
they admit that omitted variable bias could be a problem. This is largely because
they are comparing wars from 1495-1789 to wars from 1790-1990, and their data
before 1790 is limited. To address this issue, they provide robustness checks that
suggest that omitted variables are not driving their results.
From a methodological standpoint, this paper contributes to the existing literature
in two ways. First, it considers surges of nationalism where there is a clearly defined
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counterfactual, since the surges would not have happened had the qualifiers failed
to advance to the World Cup. Second, it uses a quasi-experimental design that
controls for observable and unobservable factors through as-if randomization. Thus,
the design provides strong reason to believe that the results are not affected by
omitted variable bias.
This paper also adds to a growing body of scholarship on the effects of sports on
politics. Andrei Markovits and Lars Rensmann (2010) recently provided a valuable
contribution to this literature by investigating a number of ways that sports can affect
international relations, including their role in sparking nationalistic violence. Other
studies have linked the outcomes of sporting events to major changes in domestic
violence and heart attack rates (Carroll et al. 2002; Card and Dahl 2009; Brimicombe 2012), as well as significant fluctuations in stock prices (Edmans, Garcia, and
Norli 2007), national pride (Kavetsos 2012), and the incumbency advantage (Healy,
Malhotra, and Mo 2010). This study is the first to provide quantitative evidence
that sporting events have a significant impact on state aggression.
Design
Measuring Aggression. Similar to past studies (Leeds 2003; Oneal, Oneal, Maoz,
and Russett 1996), I measure aggression using the number of militarized interstate
disputes (MIDs) that a state initiates. These disputes are instances where states
explicitly threaten, display, or use force against other countries (Ghosn, Palmer,
Bremer 2004). This measure is commonly used in security studies, since wars happen
too infrequently to be a useful measure in most statistical tests. The Militarized
Interstate Dispute 3.10 dataset ends in 2001, so I do not include the 2002, 2006,
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or 2010 World Cups in this analysis. However, the World Cup qualification process
changed after 1998, and there are only two pairs of countries that would have been
included in the sample had this measure of aggression extended past 2001.1
Constructing the Treatment and Control Groups. Countries qualify for the
World Cup the winter before it. The first qualification round was held in 1934, and
it has been in place ever since. The host country and previous World Cup winner
qualify automatically, but all other teams must play their way in. They do so in one
of two ways: (1) by playing a round of games against some other countries in their
region and earning a certain position in the standings or (2) by winning a play-in
game or several playoff games. Either way, the format is set well in advance.
This study focuses on the standings format, comparing countries that barely made
and barely missed qualification. Whether these countries went to the World Cup or
stayed home was determined by small differences in their records after many games,
so there is little reason to expect much difference between countries on either side
of the qualification cut-point. Moreover, we can check that the qualifiers and nonqualifiers are balanced to verify that the design worked.
The playoff format is less easy to exploit with regression discontinuity analysis.
Countries make or miss the World Cup based on their performance in the final round,
which could be correlated with other factors that are related to their likelihood of
future aggression. This problem is particularly concerning because the last game is
often played between countries from different regions that have large disparities in
terms of GDP and population, along with many other factors.
1These
pairs were (1) Nigeria and Liberia in 2002 and (2) Senegal and Morocco in 2002.
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Figure 1. Example of the Final Standings from a 1982
Qualification Round
Rank
1
2
3
4
5
Country
West Germany
Austria
Bulgaria
Albania
Finland
Score
16
11
9
2
2
Qualified
Yes
Yes
No
No
No
Notes: The sample consists of pairs of countries like Austria and
Bulgaria that barely made and barely missed qualification.
Using data from the standings format from 1934-1998, I select pairs of countries
that were separated by no more than two points, provided that the winner scored
at least five points. These countries are listed in Table 1.2 There were seven pairs
before 1958 where the winner scored less than five points. In these cases, the teams
played only a small number games, typically three or less each. Since the goal was
to obtain two groups of countries that were assigned to treatment or control based
on small differences in their records after many games, these countries were dropped
from the analysis.
Seventeen of the 63 pairs tied in the standings. Nine of these ties were broken by a
playoff game, seven were decided by looking at which country had the larger average
margin of victory, and one was decided by which team scored more goals. I include
2I recognize that some countries appear in the sample more than once,
which could raise concerns
about non-interference (SUTVA) if the treatment effect does not wear off in four years. To address
this issue, I compute the estimated treatment effect for a large random sample of combinations of
these pairs of countries where no state is repeated. The estimated treatment effects for all of these
new samples are positive, and their mean is nearly equal to the estimated treatment effect of the
entire sample. A plot of the estimates is available in the online appendix.
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Table 1. Countries in the World Cup Sample
Qualifier
Non-qualifier
Year
Qualifier
Non-qualifier
Yugoslavia
Romania
1958
France
Bulgaria
France
Belgium
1958
Poland
Portugal
Austria
Netherlands 1958
Sweden
Norway
Soviet Union
Poland
1958
Spain
Romania
Hungary
Bulgaria
1958
Tunisia
Egypt
Britain
Ireland
1958
France
Ireland
Paraguay
Uruguay
1958
Austria
Bulgaria
Argentina
Bolivia
1958
Britain
Romania
Bulgaria
France
1962
Peru
Uruguay
Switzerland
Sweden
1962
El Salvador
Mexico
Portugal
Czechoslovakia 1966 New Zealand
China
Bulgaria
Belgium
1966
Portugal
Sweden
West Germany
Sweden
1966 Soviet Union
Switzerland
Chile
Ecuador
1966
Bulgaria
East Germany
Czechoslovakia
Hungary
1970
Romania
Denmark
Romania
Greece
1970
Austria
Turkey
Bulgaria
Poland
1970 Czechoslovakia
Portugal
Italy
East Germany 1970 United States
Trinidad
Sweden
France
1970
UAE
Qatar
Belgium
Yugoslavia
1970
Ireland
Denmark
Peru
Bolivia
1970
Switzerland
Portugal
Morocco
Nigeria
1970
Bulgaria
France
Sweden
Austria
1974 Netherlands
Britain
Netherlands
Belgium
1974
Bolivia
Uruguay
Yugoslavia
Spain
1974
Cameroon
Zimbabwe
East Germany
Romania
1974
Nigeria
Ivory Coast
Poland
Britain
1974
Morocco
Zambia
Uruguay
Colombia
1974 South Korea
Japan
Argentina
Paraguay
1974
Chile
Peru
Haiti
Trinidad
1974
Jamaica
Costa Rica
Italy
Britain
1978
Nigeria
Guinea
Austria
East Germany 1978
9
Year
1978
1978
1978
1978
1978
1982
1982
1982
1982
1982
1982
1986
1986
1986
1990
1990
1990
1990
1990
1994
1994
1994
1994
1994
1994
1994
1994
1994
1998
1998
1998
these pairs in the analysis. The playoff games were more like toss-ups because they
were played between teams of comparable skill. Also, there are not strong reasons to
believe that comparable teams would sort based on margin of victory or total goals
scored. Nevertheless, the results remain significant whether ties are included or not.
Lastly, I had to remove pairs where one of the teams did not represent a country.
I exclude Scotland, Northern Ireland, and Wales from this analysis, and I count
England as Britain. I also exclude the Representation of Czechs and Slovaks, which
was a union of players from the Czech Republic and Slovakia that played from 199293. No other changes were necessary.
Checking for Balance. The goal of the design was to achieve balance across observable and unobservable pre-treatment characteristics. The qualifiers and nonqualifiers should be similar except that the qualifiers went to the World Cup and the
non-qualifiers did not.
Of course, the two groups will not look exactly the same on pre-treatment characteristics. For example, one might expect a country that scored 13 points in the
standings to be slightly larger than a country that scored 12 points. Nonetheless,
given the randomness involved in soccer, the expected value of this difference should
be small. There will also be imbalances on some of the characteristics of the two
groups simply by chance. In randomized experiments, the p-values for pre-treatment
characteristics should be distributed uniformly between 0 and 1. Thus, there are
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statistically significant differences at the 5% level for about one out of every 20 pretreatment characteristics, simply because of chance variation. But the inferences
drawn from this study will be most credible if the qualifiers and non-qualifiers are
balanced on all observable characteristics that might influence future aggression.
Figure 2 provides a comparison between the two groups. Since qualification happens about six months prior to the summer of the World Cup, the data used in this
summary is taken from the year before the World Cup. Countries qualified at the
end of this year. For each variable, I plot the p-value from a two-tailed paired t-test
that evaluates the difference in means between the treatment and control groups. I
also plot the p-values for aggression levels in the years leading up to the World Cup.
Overall, the balance between the qualifiers and non-qualifiers looks similar to what
would be expected in a randomized experiment. The p-values seem to be distributed
uniformly between 0 and 1, which supports the idea that this data is like experimental data. The one potential concern is that the differences in means for military
expenditures and military personnel is large. However, the p-values indicate that
the qualifier and non-qualifier group are not imbalanced on these factors. The large
differences in means result from the fact that the Soviet Union and United States are
outliers and both appear in the qualifier group. While this issue might be concerning,
I show in the next section that the results remain significant at the 1% level when
the Soviet Union and United States are dropped from the sample. The non-qualifier
group also has higher levels of military expenditures and military personnel when the
Soviet Union and United States are dropped, which alleviates concerns that these
variables might be biasing the results.
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Figure 2: Balance Between the Qualifiers and Non−qualifiers
Variable
Name
Iron and Steel Production
Military Expenditures
Qualifier
Mean
10,627,400
Non−qualifier
Mean
6,784,490
12,081,400,000 3,861,670,000
Military Personnel
342,841
231,365
Total Population
32,107,100
35,358,600
Urban Population
9,126,860
8,617,980
Birth Rate
0.02
0.02
Death Rate
0.01
0.01
Infant Mortality
0.05
0.05
140,248,000
71,497,800
Energy Production
Imports
24,686,300,000 18,921,500,000
Exports
21,989,600,000 19,636,100,000
Land Area
481,127
189,375
Material Power Score
0.02
0.01
Level of Democracy
0.51
0.51
Great Power Status
0.05
00
Engaged in Civil War
0.02
0.02
Resolved Civil War
0.02
00
Year of State Formation
1871
1874
Sex Ratio
96
96
Life Expectancy
67
67
Median Age
29
29
Number of Alliances
12
12
U.S. Ally
0.46
0.52
Appearance at Previous World Cup
0.38
0.27
MIDs Initiated in the Year Before
0.17
0.11
MIDs Initiated in the 3 Years Before
0.70
0.6
MIDs Initiated in the 5 Years Before
1.1
0.89
0 .05.1
1
p−value
Notes: p-values are computed using two-tailed paired t-tests.
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Moreover, the qualifiers and non-qualifiers are comparable on past levels of aggression, so the data passes an important placebo test. It is unlikely that the balance
plot left out some factor that would make the qualifiers significantly more aggressive
than the non-qualifiers after qualification, since this factor should have also made
the qualifiers much more aggressive in the years leading up to qualification. So aside
from the treatment effect, there is little reason to suspect that the qualifiers would
behave much more aggressively after qualification.
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World Cup
Qualification
Qualifiers
Non−qualifiers
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10
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5
Militarized Interstate Disputes Initiated
15
Figure 2: Comparing Aggression Before and After the World Cup
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2
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0
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−5
−4
−3
−2
−1
0
3
4
Years Since Qualification
1. Findings
Comparing Trends in Aggression. Although the qualifiers and non-qualifiers
were comparable on past levels of aggression, the story is much different after qualification. Figure 3 tracks the aggression levels for the two groups over this period.
In the 6-months prior to qualification, the two groups had equal levels of aggression.
14
However, the qualifiers became significantly more aggressive following qualification,
and they remained so for about three years. This change in aggression between
the three years after and the three years before is significant at the 1% level for a
two-tailed paired t-test.
The smaller details of this graph also support the theory that international sporting
events cause aggression. Note that the two groups drop the year before qualification,
and then the qualifiers spike while the non-qualifiers stay low. This trend could be
explained by the fact that four years before qualification, some of the countries from
both groups went to the World Cup, and these effects do not fully wear off until Year
-1. The Summer Olympics also take place in Year 2.5, which could explain why the
treatment and control groups experience an increase in aggression at that time.
The qualifiers not only took military action more often than the non-qualifiers,
but the actions they took tended to be more violent. The Militarized Interstate
Dispute Dataset codes for the highest level of action taken by each country, with
one being the threat to use force and 20 being the start of interstate war. In the
three years following qualification, the median for the qualifiers was 15, whereas the
median for the non-qualifiers was 13. Similarly, the qualifiers initiated eight disputes
that resulted in fatalities, whereas the non-qualifiers initiated one.
Figure 4 provides another illustration of the treatment effect. For each graph,
the solid vertical line down the center is the qualification cut-point. The points
on the right represent the means for the countries that qualified, and the points
on the left represent the means for the countries that missed qualification, with
the vertical dotted lines marking the 2-point regression discontinuity window. The
graphs show that the qualifiers experienced a significant increase in aggression in
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Figure 3. Change in Aggression for the World Cup
2
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1
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1
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−2
0
2
Points Above/Below Cut−Point
4
−1
−2
−1
−2
−1
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−2
Militarized Interstate Disputes Initiated
Change in Aggression
2
3 Years After
2
3 Years Before
−4
−2
0
2
Points Above/Below Cut−Point
4
−4
−2
0
2
4
Points Above/Below Cut−Point
Notes: The solid lines through the data were estimated using local
linear regression. The shaded regions represent the 95% confidence
intervals, which were computed using bootstrapping. The bandwidth for each graph was selected using the optimal bandwidth
algorithm provided by Caughey (based on Imbens and Kalyanaraman 2009). The plots with the raw data are available in the online
appendix.
the three years following qualification. Although there is a small overlap in the
95% confidence intervals for the middle graph, the difference at the cut-point is
statistically significant for this graph (p ≈ 0.026) and the one on the right (p ≈ 0.004).
Lastly, Figure 5 shows the number of militarized interstate disputes that each of
the countries in the sample initiated in the three years following qualification. The
qualifier group not only includes more highly aggressive states, but it also has many
more aggressors in total. Moreover, the aggressors are well distributed geographically. The qualifier group has at least three aggressors from each continent, with the
16
Militarized Interstate Disputes Initiated
12
0
17
Soviet Union 1958
United States 1990
France 1958
Soviet Union 1986
Portugal 1966
France 1978
Cameroon 1994
Nigeria 1998
Britain 1958
Austria 1958
Argentina 1958
Tunisia 1978
France 1982
Paraguay 1958
Bulgaria 1966
Chile 1966
Czechoslovakia 1970
Bulgaria 1970
Peru 1970
Morocco 1970
Yugoslavia 1974
Argentina 1974
Italy 1978
Britain 1982
Peru 1982
Bulgaria 1986
Nigeria 1994
South Korea 1994
Yugoslavia 1958
Hungary 1958
Switzerland 1962
Bulgaria 1962
West Germany 1966
Romania 1970
Italy 1970
Sweden 1970
Belgium 1970
Sweden 1974
Netherlands 1974
Uruguay 1974
East Germany 1974
Poland 1974
Haiti 1974
Austria 1978
Poland 1978
Sweden 1978
Spain 1978
New Zealand 1982
El Salvador 1982
Austria 1982
Portugal 1986
Romania 1990
United Arab Emirates 1990
Austria 1990
Czechoslovakia 1990
Ireland 1994
Switzerland 1994
Bulgaria 1994
Bolivia 1994
Morocco 1994
Netherlands 1994
Chile 1998
Jamaica 1998
4
8
12
Figure 4: Aggression by Country in the Three Years Following World Cup Qualification
8
Qualifiers
Egypt 1978
Britain 1994
Belgium 1958
France 1962
Turkey 1990
Ecuador 1966
Hungary 1970
East Germany 1970
Spain 1974
Britain 1978
Norway 1978
Ireland 1982
Denmark 1990
Portugal 1990
Japan 1994
France 1994
Guinea 1998
Poland 1958
Romania 1958
Uruguay 1958
Ireland 1958
Bulgaria 1958
Netherlands 1958
Bolivia 1958
Sweden 1962
Belgium 1966
Sweden 1966
Czechoslovakia 1966
Greece 1970
Poland 1970
Bolivia 1970
Nigeria 1970
France 1970
Yugoslavia 1970
Austria 1974
Belgium 1974
Colombia 1974
Romania 1974
Britain 1974
Paraguay 1974
Trinidad 1974
East Germany 1978
Bulgaria 1978
Portugal 1978
Romania 1978
China 1982
Romania 1982
Mexico 1982
Bulgaria 1982
Uruguay 1982
Sweden 1986
East Germany 1986
Switzerland 1986
Qatar 1990
Trinidad 1990
Denmark 1994
Ivory Coast 1994
Portugal 1994
Uruguay 1994
Zambia 1994
Zimbabwe 1994
Peru 1998
Costa Rica 1998
4
Non−qualifiers
Note: The results remain significant at the 1% level when the Soviet Union and United States are dropped from the sample.
0
Militarized Interstate Disputes Initiated
exception of North America and Australia. It does not appear that any region is
driving the results.
It is concerning that the Soviet Union and United States account for a large number of the disputes for the qualifiers. However, they can be removed from the sample
without affecting the findings. In fact, the results for the change in aggression between the three years before and the three years after qualification remain significant
at the 1% level. This is because the low p-value is the result of a broad trend across
the data rather than a few outliers.
This point becomes clear if you calculate the change in aggression for each country
and count the number of pairs where the qualifier had a larger change in aggression
than the non-qualifier. Out of the 63 pairs, there are 27 pairs where the qualifier had
the larger change in aggression, compared to only 13 pairs where the non-qualifier
had the larger change, with the remaining pairs being ties. This result is highly
significant.
Robustness. There are two major concerns that must be addressed when verifying
the robustness of the results. First, how sensitive are the findings to the particular
statistical tests we are using? Second, how do the results change when features of
the design change?
In addition to the statistical tests used in the previous section, the results remain
significant for permutation inference using the mean as the test statistic and the
Wilcoxon rank sum and signed-rank tests, which are insensitive to outliers. The
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Table 2. OLS Estimates for the World Cup
3 Years Before
3 Years After
Qualification
Qualification
(Intercept)
0.223 (0.158)
0.191 (0.157)
World Cup Appearance
-0.116 (0.149)
0.312* (0.149)
Iron and Steal Production -6.61e-08*** (1.40e-08) -5.88e-08*** (1.39e-08)
Military Personnel
1.08e-06 (6.70e-07)
-6.92e-07 (6.66e-07)
Military Expenditures
6.86e-11*** (1.28e-11)
1.26e-11 (1.28e-11)
Energy Production
-6.31e-09*** (1.83e-09) -4.04e-09* (1.82e-09)
Total Population
-1.57e-08*** (3.32e-09) -9.06e-09** (3.30e-09)
Urban Population
3.33e-08* (1.51e-08)
2.67e-08 (1.50e-08)
Material Power Score
98.8*** (25.4)
112*** (25.3)
Great Power Status
-9.66*** (2.22)
0.741 (2.2)
Number of Alliances
7.87e-03 (0.0116)
-8.36e-04 (0.0115)
U.S. Ally
0.0787 (0.223)
0.146 (0.222)
Notes: I present the results for the 3 years before as a placebo test.
The outcome is militarized interstate disputes initiated over the
three year periods. This figure shows that the results are significant
for OLS in addition to being significant for a two-tailed paired ttest.
results are also significant for linear regression using the covariates from the balance
plot, which is a commonly used adjustment technique in regression discontinuity
analyses to reduce any possible bias (Lee 2008; Caughey and Sekhon 2011). The
results for a simpler model are presented in Table 2.
The findings are also robust to changes to the design. For instance, recall that
when constructing the sample, we only counted dyads where the advancing team
scored at least five points in the standings. This minimum score requirement can
be set anywhere between zero and six, and the results remain significant at the 5%
19
level. When the minimum score requirement is set at seven, the number of pairs of
countries in the sample drops to 37, and the results fall just out of significance at
the 5% level. However, this loss in significance should not raise concern given the
decreasing sample size.
The two-point regression discontinuity window can also be shifted without affecting
the findings in any alarming way. In fact, the results remain significant at the 5%
level if the window is set anywhere above two points. If the window is set at one
point, the number of pairs of countries in the sample drops to 39, and the results fall
slightly out of significance. But again, this should be expected given the shrinking
sample size.
Comparing Democracies to Non-Democracies. We can also examine how the
World Cup affects countries with different regime types. I divide the countries into
democracies and non-democracies using their POLITY IV Institutionalized Democracy Scores (Marshall, Gurr, and Jaggers 2013). Similar to past studies, I count
countries as democracies if their score was greater than or equal to six (SchrockJacobson 2012).
The estimated treatment effect is much larger for authoritarian states than democracies. Table 3 presents the estimates for both groups. After qualification, the
democracies appear to experience only a modest treatment effect, whereas the nondemocracies display a much larger one. Both the standard t-tests and the differencein-differences t-tests indicate that non-democracies become substantially more aggressive because of the World Cup, and these results hold even when the Soviet
Union is dropped from the sample.
20
Table 3. Estimates for Democracies and Non-Democracies
Qualifier Mean Non-qualifier Mean p-value***
Democracies (n=64)
3 Years Before
0.63
-0.75
3 Years After
0.75
-0.50
Dif-in-dif
0.13
-0.25
Non-Democracies (n=62)
Avg 3 Years Before
0.77
-0.45
Avg 3 Years After
1.23
-0.26
Dif-in-dif
0.45
-0.19
Non-Democracies (excluding Soviet Union) (n=60)
Avg 3 Years Before
0.38
-0.45
Avg 3 Years After
0.76
-0.26
Dif-in-dif
0.38
-0.19
0.722***
0.351***
0.201***
0.324***
0.030***
0.029***
0.677***
0.025***
0.005***
Notes: p-values are computed using standard two-tailed t-tests.
This finding is consistent with the work of several scholars who argue that democratic norms can create a shared sense of identity between the leaders and citizens
of different countries (Doyle 2005; Ikenberry 2009; Dafoe 2011; Tomz and Weeks
2013). It also supports the theory that nationalism has a different character in nondemocracies (Van Evera 1994). However, it casts doubt on previous observational
work that suggests that nationalism tends to have similar effects on democracies and
non-democracies (Schrock-Jacobson 2012).
In fact, there are many cases where authoritarian regimes tried to exploit nationalism from international sporting events for political purposes. Hitler described his
athletes as soldiers in track suits because he believed they could prove to the world
the superiority of Nazi society (Krüger 1998). In 1945, the Soviet Union tried to
21
politicize a series of soccer games against the United Kingdom to encourage Soviet
nationalism, and the British government was forced to cancel the games after it
concluded that they were harmful to Anglo-Soviet relations (Kowalskia and Portera
1997). World Cup wins also helped legitimize the fascists in Italy in 1934 and Argentina’s military junta in 1978 (Smith 2002; Gordon and London 2006).
This serious approach that non-democracies take to sports was also demonstrated
by the 2008 Beijing Olympics. China spent over twice as much as Britain did in 2012
and about 30 times as much as the United States did in 1996. The opening ceremony
was an incredible display of Chinese national power and unity, and differed greatly
from the light-hearted opening ceremony in London four years later. This example
provides further evidence that nationalism can be more intense and antagonistic in
states that lack a shared sense of identity with other countries.
Regional Championships
After observing the previous results, I collected additional data to test whether
regional soccer tournaments have a similar effect on state aggression. While these
competitions are smaller, they still attract enormous audiences, especially within the
participating countries. Moreover, the states that compete in them are closer to each
other geographically. Thus, they are more likely to have existing disputes that could
be exacerbated by competition on the playing field.
Fortunately, this hypothesis was not hard to test. These competitions are usually
held every four years in North America, South America, Europe, Africa, Asia, and
the Pacific Islands. Countries often must qualify for these tournaments in the same
way that they do for the World Cup, so I was able to use the same procedures as
22
Table 4. Countries in the Regional Championship Sample
Qualifier
Non-qualifier Year
Qualifier
Non-qualifier
Israel
Iran
1960
Spain
Romania
Trinidad
Jamaica
1967 Soviet Union East Germany
Congo
Tunisia
1968
Denmark
Czechoslovakia
Senegal
Guinea
1968
Ireland
Bulgaria
Taiwan
Japan
1968
China
North Yemen
Burma
Cambodia 1968
Japan
Jordan
Trinidad
Suriname
1971
Iran
North Korea
Saudi Arabia
Qatar
1976 Netherlands
Portugal
Thailand
South Korea 1976
Britain
Ireland
El Salvador
Costa Rica 1977
Egypt
Tunisia
Belgium
Austria
1980
Morocco
Niger
Spain
Yugoslavia 1980
Nigeria
Burkina Faso
Netherlands
Poland
1980
Zaire
Gabon
Czechoslovakia
France
1980 Cameroon
Malawi
Greece
Hungary
1980
Liberia
Senegal
Cuba
Suriname
1981 Mozambique
Guinea
Haiti
Trinidad
1981
Thailand
Singapore
Portugal
Soviet Union 1984
Kuwait
Lebanon
Denmark
Britain
1984
Angola
Zimbabwe
Romania
Sweden
1984
Namibia
Gabon
Spain
Netherlands 1984
Algeria
Mali
Syria
Indonesia
1984
Vanuata
Solomon Islands
India
Malaysia
1984
Year
1988
1988
1988
1988
1988
1988
1988
1992
1992
1992
1992
1992
1992
1996
1996
1996
1996
1996
1998
1998
1998
1998
before to construct a sample. This new sample of 45 pairs of countries, which are
listed in Table 4.
The qualifiers and non-qualifiers are well-balanced across the pre-treatment characteristics considered in the previous section. The results from two-tailed paired
t-tests for each factor are presented in Figure 6. Only two fall below the 10% significance level, which is about what would be expected in a randomized experiment.
The two groups are not quite as well-balanced on past levels of aggression, because
23
Figure 6. Balance Plot for Regional Championships
Variable
Name
Iron and Steel Production
Military Expenditures
Qualifier
Mean
Non−qualifier
Mean
9,803,020
7,161,840
9,654,280,000 7,493,170,000
Military Personnel
323,578
211,756
Total Population
67,194,600
22,560,600
Urban Population
14,585,500
6,842,710
Birth Rate
0.03
0.03
Death Rate
0.01
0.01
Infant Mortality
0.06
0.06
Energy Production
123,442,000
81,483,400
Imports
23,057,100,000 14,631,300,000
Exports
22,942,600,000 13,632,100,000
Land Area
478,166
305,060
Material Power Score
0.01
0.01
Level of Democracy
0.44
0.31
Great Power Status
0.02
0.02
Engaged in Civil War
0.04
0
Year of State Formation
1897
1919
Sex Ratio
100
100
Life Expectancy
63
63
Median Age
24
24
Number of Alliances
9.7
8.9
U.S. Ally
0.38
0.22
Appearance at Prev Regional Tournament
0.40
0.31
MIDs Initiated in the Year Before
0.82
0.24
MIDs Initiated in the 3 Years Before
1.7
0.64
MIDs Initiated in the 5 Years Before
2.5
1
0 .05.1
1
p−value
Notes: p-values are computed using two-tailed paired t-tests.
24
Soviet Union 1983
Guinea 1967
Indonesia 1984
Czechoslovakia 1987
Iran 1959
Cambodia 1967
South Korea 1975
France 1979
Britain 1983
Malaysia 1984
Bulgaria 1987
North Yemen 1988
North Korea 1988
Senegal 1995
Tunisia 1967
Jamaica 1967
Japan 1967
Suriname 1972
Qatar 1975
Costa Rica 1976
Austria 1979
Yugoslavia 1979
Poland 1979
Hungary 1979
Suriname 1980
Trinidad 1980
Sweden 1983
Netherlands 1983
Romania 1987
East Germany 1987
Jordan 1988
Portugal 1991
Ireland 1991
Tunisia 1991
Niger 1991
Burkina Faso 1991
Gabon 1991
Malawi 1995
Guinea 1995
Singapore 1996
Lebanon 1996
Zimbabwe 1997
Mali 1997
Gabon 1997
Solomon Islands 1998
0
15
30
Iran 1988
Israel 1959
Soviet Union 1987
Syria 1984
China 1988
India 1984
Egypt 1991
Cameroon 1995
Thailand 1996
Angola 1997
Saudi Arabia 1975
Cuba 1980
Spain 1987
Ireland 1987
Britain 1991
Kuwait 1996
Senegal 1967
Congo 1967
Trinidad 1967
Taiwan 1967
Burma 1967
Trinidad 1972
Thailand 1975
El Salvador 1976
Belgium 1979
Spain 1979
Netherlands 1979
Czechoslovakia 1979
Greece 1979
Haiti 1980
Portugal 1983
Denmark 1983
Romania 1983
Spain 1983
Denmark 1987
Japan 1988
Netherlands 1991
Morocco 1991
Nigeria 1991
DRC 1991
Liberia 1995
Mozambique 1995
Algeria 1997
Namibia 1997
Vanuatu 1998
45
Militarized Interstate Disputes Initiated
0
15
30
45
Militarized Interstate Disputes Initiated
Figure 7. Aggression by Country in the Three Years Before Regional Qualification
Qualifiers
Non−qualifiers
Notes: Iran gives the qualifiers a much larger average aggression
level before qualification, but aside from Iran the two groups look
very similar.
Iran appears in the qualifier group just after it fought the Iraq-Iran War (1980-88).
However, the overall difference is still far from statistically significant.
Figure 7 presents this issue more clearly. It shows the number of disputes initiated
by the countries in the sample in the three years leading up to qualification. Although
Iran gives the qualifiers a much higher average, the two groups are actually well-
balanced. Given that Iran appears in the sample, it had to be in either the qualifier
25
Soviet Union 1983
Cambodia 1967
France 1979
Guinea 1995
Iran 1959
South Korea 1975
Yugoslavia 1979
Britain 1983
Malaysia 1984
Jordan 1988
North Korea 1988
Portugal 1991
Zimbabwe 1997
Guinea 1967
Tunisia 1967
Jamaica 1967
Japan 1967
Suriname 1972
Qatar 1975
Costa Rica 1976
Austria 1979
Poland 1979
Hungary 1979
Suriname 1980
Trinidad 1980
Sweden 1983
Netherlands 1983
Indonesia 1984
Romania 1987
East Germany 1987
Czechoslovakia 1987
Bulgaria 1987
North Yemen 1988
Ireland 1991
Tunisia 1991
Niger 1991
Burkina Faso 1991
Gabon 1991
Malawi 1995
Senegal 1995
Singapore 1996
Lebanon 1996
Mali 1997
Gabon 1997
Solomon Islands 1998
0
2
4
6
8
India 1984
Iran 1988
Britain 1991
Kuwait 1996
Thailand 1975
DRC 1991
Syria 1984
Netherlands 1991
Cameroon 1995
Angola 1997
Israel 1959
Congo 1967
Taiwan 1967
Saudi Arabia 1975
Netherlands 1979
Cuba 1980
Spain 1983
China 1988
Egypt 1991
Nigeria 1991
Thailand 1996
Senegal 1967
Trinidad 1967
Burma 1967
Trinidad 1972
El Salvador 1976
Belgium 1979
Spain 1979
Czechoslovakia 1979
Greece 1979
Haiti 1980
Portugal 1983
Denmark 1983
Romania 1983
Spain 1987
Soviet Union 1987
Denmark 1987
Ireland 1987
Japan 1988
Morocco 1991
Liberia 1995
Mozambique 1995
Algeria 1997
Namibia 1997
Vanuatu 1998
Militarized Interstate Disputes Initiated
0
2
4
6
8
Militarized Interstate Disputes Initiated
or non-qualifier group, so where it ended up cannot alone indicate a systematic
difference between the two groups. This fact explains why the statistical tests do not
find a significant difference, even though the qualifiers have a much larger mean.
However, in the three years following qualification, the two groups look very differ-
ent. Figure 8 shows the number of disputes initiated by the countries in the sample
over this period. Here, there is a systematic difference between the two groups. The
Figure 5. Aggression by Country in the Three Years After Regional Qualification
Qualifiers
Non−qualifiers
26
Table 5. OLS Estimates for the Regional Championships
3 Years Before
3 Years After
Qualification
Qualification
(Intercept)
0.393* (0.210)
0.199 (0.202)
Regional Cup Appearance
0.224 (0.231)
0.495* (0.221)
Iron and Steel Production -4.59e-08* (2.27e-08) -8.19e-08*** (2.18e-08)
Military Personnel
1.04e-07 (8.37e-07)
-1.30e-06 (7.14e-07)
Military Expenditures
-3.63e-11 (2.52e-11)
3.68e-11 (2.42e-11)
Energy Production
-3.55e-09 (3.43e-09)
-6.89e-09* (3.29e-09)
Total Population
-8.74e-09* (3.74e-09)
-2.35e-09 (3.59e-09)
Urban Population
2.32e-08 (3.05e-08)
-1.16e-08 (2.92e-08)
Material Power Score
101* (60.6)
192** (58.1)
Great Power Status
8.73 (6.62)
-9.21* (6.35)
Number of Alliances
5.56e-03 (0.0152)
9.68e-03 (0.0146)
U.S. Ally
-0.631** (0.306)*
-0.180 (0.294)
Notes: I exclude the Iran and North Korea (1988) dyad from this
analysis, since Iran was an extreme outlier before qualification.
However, the results for the three years after remain significant
when this dyad is included.
difference is statistically significant over the the three year period at the 5% level for
a two-tailed paired t-test. It is also significant at the 5% level for a two-tailed standard t-test, the Wilcoxon signed-rank test, permutation inference using the mean as
the test statistic, and linear regression using the covariates from the balance plot. I
report the results for a simpler model in Table 5.
There are several similarities between these results and the ones presented in the
previous section. The disputes started by the qualifiers tended to be more violent
than the disputes started by the non-qualifiers. The qualifiers had a median highest
level of action of 15, whereas the non-qualifiers had a median of 9.5. The qualifiers
27
also started seven fatal disputes, while the non-qualifiers only started three. The
estimated treatment effect is again larger for authoritarian states than democracies.
In fact, the results are nearly significant at the 5% level for authoritarian states
alone, despite the decreased sample size. This finding reconfirms the argument that
democratic norms can play a role in constraining nationalistic aggression.
Lastly, I combined the regional championship sample with the World Cup sample
and set the regression discontinuity window at one point. My goal here was to reduce
any possible bias from using a window that was too wide, since countries that are two
points apart might not be comparable. I also dropped the Iran-North Korea (1988)
dyad, which would skew the pre-treatment balance tests regardless of whether Iran
ended up in the qualifier or non-qualifier group. This produced a new sample of 68
pairs of countries. As Figure 9 shows, the qualifier and non-qualifier groups are wellbalanced, and the results for a two-tailed paired t-test are significant at the 5% level
(p ≈ 0.038). Moreover, the results for a two-tailed difference in differences t-test are
significant at the 1% level (p ≈ 0.004).
.
28
Figure 9. Combined Sample with 1−Point Regression Discontinuity Window
Variable
Name
Iron and Steel Production
Military Expenditures
Qualifier
Mean
Non−qualifier
Mean
6,809,250
9,410,210
3,814,040,000 7,348,260,000
Military Personnel
239,324
270,000
Total Population
33,929,500
40,505,600
Urban Population
8,486,250
10,329,400
Birth Rate
0.02
0.03
Death Rate
0.01
0.01
Infant Mortality
0.05
0.05
Energy Production
72,028,200
98,681,600
Imports
20,498,200,000 19,433,900,000
Exports
20,040,600,000 20,504,300,000
Land Area
277,195
322,839
Material Power Score
0.009
0.01
Level of Democracy
0.50
0.44
Great Power Status
0.01
0.01
Engaged in Civil War
0.04
0.01
Resolved Civil War
0.01
00
Year of State Formation
1881
1887
Sex Ratio
98
98
Life Expectancy
66
66
Median Age
27
27
Number of Alliances
11
11
U.S. Ally
0.43
0.38
Appearance at Prev Regional Tournament
0.32
0.32
MIDs Initiated in the Year Before
0.21
0.21
MIDs Initiated in the 3 Years Before
0.56
0.71
MIDs Initiated in the 5 Years Before
0.81
1.1
MIDs Initiated in the 3 Years After
Change in Aggression
(3 Years After − 3 Years Before)
0.87
0.37
0.31
−0.338235
0 .05.1
1
p−value
Notes: p-values are computed using two-tailed paired t-tests.
N = 68 pairs of countries. Balance is even better than would
be expected in an experiment, and the results are significant.
29
Discussion
While the design helps us identify a clear treatment effect, it does not tell us
how nationalism made states more aggressive. However, some examples of violence
from sporting events reveal a number of mechanisms through which this process can
unfold.
First, nationalism can lead to conflict between the citizens of different countries,
which can escalate to violence at the international level. This spiral model was at
work in the Croatian War of Independence, where a violent riot between Serb and
Croatian soccer fans in May of 1990 worsened relations between the two sides and
helped lead to war a year later (Sack and Suster 2000). Many Serbs and Croats
consider this riot to be the unofficial beginning of the war. Another example is the
2009 Egypt-Algerian World Cup dispute. Egyptian fans attacked a bus carrying the
Algerian soccer team before a World Cup qualification game, and Algerians retaliated
against Egyptian media outlets and property in Algeria. The Egyptian government
called the attacks an insult and removed its ambassador from Algiers. In response
to this incident, the Arab League proposed that politicians from its member states
should not attend sensitive sports games because these events could make them more
hostile towards other countries (Belmary 2010).
This sort of violence may seem extreme, but it is fairly common at international
sporting events outside of North America. For instance, during the 1998 World
Cup in France, there was extensive fighting between British and Tunisian fans that
resulted in more than 50 hospitalizations. Several German fans also beat a French
police officer to the point of brain damage following a game. During the 2002 World
Cup held in South Korea and Japan, massive riots broke out in Beijing after Japan
30
defeated China. Chinese fans burned Japanese flags, and Japanese bystanders had to
be escorted to safety by the police. The 2006 World Cup in Germany featured clashes
involving fans from Germany, Poland, Argentina, and France. The 2010 World Cup
in South Africa also saw instances of nationalistic violence that resulted in severe
injury and death. These violent incidents can make leaders more nationalistic, since
they may feel that their citizens are threatened by fanatics from other countries.
Second, a surge in nationalism can lead to the repression of a minority ethnic
group, and if this group has ethnic brethren in control of a nearby state, it could
increase tensions between the two countries and potentially lead to conflict (Snyder
2000). This mechanism was at work in the 1969 Football War between Honduras and
El Salvador (Reid 1999). At the time, the two countries had a dispute over the land
rights of Salvadorans living in Honduras. After fans from El Salvador beat several
fans from Honduras and insulted their national anthem at an international soccer
game, the border between the countries was closed. Militias in Honduras began
torturing, killing, and expelling Salvadoran immigrants living in their country. In
response, El Salvador declared war on Honduras and invaded the country. The
resulting conflict caused about three-thousand casualties.
Third, nationalism can make leaders more hostile towards other states, even when
their citizens are not attacked. Many politicians are passionate fans of their national
sports teams and devote substantial resources to preparing them for competition
(Markovits and Rensmann 2010). In fact, leaders usually have more at stake in
these games than most fans do, because success on the playing field can increase
their domestic support. Every World Cup year, a number of articles are published
31
predicting how the results on the soccer field will affect elections around the world
(Oppenheimer 2010; Duerden 2013).
There are several notable cases where international sporting events led to conflict between governments directly. A series of boxing matches between Joe Louis
of the United States and Max Schmeling of Nazi Germany during the 1930s increased tension between the two countries, as both governments tried to use the
events to demonstrate their national strength (Margolick 2006). Similarly, a 2012
Argentinian Olympics commercial that claimed the Falkand Islands as Argentinian
territory sparked a heated debate between the Argentina and British governments
(Davies 2012). These countries have a long standing rivalry that has been exacerbated by competition on the playing field on several occasions, including the 1986
“Hand of God” goal that reignited tensions after the Falkands War (Reid 1999).
Fourth, nationalistic surges can lead to feelings of hostility towards traditional
rival states, even if they were not the initial target of the nationalist movement. For
instance, nationalism resulting from the 1936 Nazi Olympics increased feelings of
Aryan supremacy and victimization in Germany, and thus helped pave the way for
war with the Soviet Union despite the fact that the Soviets did not participate in
the games (Buckel 2008). More recently, Iraqi nationalism from the 2007 Asian Cup
increased feelings of resentment towards the United States (Street 2007).
Of course, international sporting events probably have many benefits that are not
considered here, like encouraging peace at the domestic level. In 2006, the civil
war in the Ivory Coast was suspended after the national soccer team qualified for
the World Cup (Mehler 2008). Although international sports do not create unity
between states, they may forge a common identity within them, and this domestic
32
stability could outweigh the costs at the international level. The point is not that
FIFA and other international sports organizations should stop holding competitions
between countries. However, these organizations should take more steps to reduce
the nationalistic fervor that surrounds their events.
Conclusion
This paper identifies the strong causal relationship between nationalism and state
aggression. The findings hold under various statistical tests and are not affected by
minor changes to the design. Moreover, the results can be replicated using the FIFA
regional soccer championships. As these international sporting events make clear,
nationalism remains a key source of interstate conflict. If democracy continues to
spread, it may help constrain the effects of nationalism in worsening state relations.
But unless the international system undergoes a revolutionary change, we should
expect nationalism to continue to be a divisive force in world politics.
33
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