When are politically irrelevant events relevant to election outcomes?

When are politically irrelevant events
relevant to election outcomes?∗
Raphael Corbi
This Version: November 2018
First Version: January 2017
Abstract
This paper analyzes the impact of transient emotional shocks induced by unexpected
soccer results on incumbent vote share in Brazilian elections. Conditioning on pregame
betting markets implied probabilities of each match outcome, I am able to interpret
the estimate of actual soccer results on voting behavior as a causal effect. The results
indicate that an increase of one s.d. in the share of people receiving a negative emotional
shock decreases the incumbent mayor vote share by 5 − 5.8 p.p. on average. This is
equivalent to flipping the result of 747 mayoral elections or 4.3% of the sample. The
effect is stronger for more intense emotional shocks and for games with higher stadium
attendance and local teams in the first division. Similar findings arise when I focus on
gubernatorial elections. These results cannot be explained by changes in turnout.
However I argue that such effect would not overturn the outcome of an election.
More specifically, I show that emotional shocks do not play a significant role in deter-
mining vote shares when elections are decided by a small margin. I also show that such
close election pattern is found in two different settings previously analyzed by the litera-
ture and provide complementary evidence from Google searches that individuals actively
seek more information about candidates in close electoral races. Overall these results
are consistent with a model in which voters’ preferences are affected by emotional cues
which may deviate their voting behavior from the forecasts of rational theory. Close
elections make information about candidates more salient in the media hence lowering
the attention cost to picking the best candidate, improving rational decision making of
limited attention voters and decreasing the bias induced by emotional shocks.
JEL codes: D03, D72.
Keywords: betting markets, soccer, behavioral voters, inattention, facebook, google
∗
A previous version of this paper circulated under the title "Emotional Voters". I would like to thank
Elias Papaioannou, Sergio Firpo, Leonardo Bursztyn, Filipe Campante, Marcos Nakaguma, Claudio Ferraz,
Eduardo Zilberman, Cezar Zucco, Daniela Campello, Bruno Giovannetti, Adriana Camacho, Rudi Rocha and
seminar participants for useful comments and suggestions. I also thank Tiago Ferraz and Cristiano Carvalho
for their excellent research assistance. Correspondence: University of São Paulo (rcorbi@usp.br).
1

1 Introduction
In recent years many countries have experienced high profile elections in which the winning
side does so by a small margin (e.g. Trump vs Clinton and Bush vs Gore in the US, Brexit
vote in the UK, Rousseff vs Neves in Brazil, constitutional referendum in Turkey). Many of
these close elections have led to major shifts in policy despite deeply divided and increasingly
polarized societies.1
Given the weak incentives to invest in rational decision-making that
prevail in the political arena, it is expected that insights from both rational theory and
behavioral sciences will be relevant in explaining voting behavior.
The literature of Political Economy relies on standard motivational assumptions from eco-
nomic theory to explain decision making in politics. Political actors are fully rational agents
that use the available information to determine actions so as to maximize utility, which is
affected only by their own payoff. Many empirical observations however are difficult to rec-
oncile with this simple but powerful model of behavior such as the paradox of voting (Downs,
1957; Brennan and Buchanan, 1984) and other types of group-based political participation
associated with the collective action problem (Olson, 1965). The emerging field of Behav-
ioral Political Economy borrows insights from psychology and political science in order to
enrich our understanding of the aspects of behavior in political settings that deviate from
the forecasts of rational theory.2
In particular, individuals’ decisions are affected by transient
emotions that are experienced at the time but are not part of the payoff from that particular
choice (Loewenstein and Lerner, 2003).3
This paper analyzes the impact of emotional shocks induced by soccer game outcomes
on voting behavior in Brazilian elections. More specifically, it investigates the link between
soccer results preceding an election and the incumbent vote share, conditional on the betting
markets pregame implied probabilities of each match outcome. Positive and negative unex-
1
Increasing political polarization has been attributed to import competition (Autor et al., 2016), financial
crises (Funke et al., 2016), media bias and political persuasion (Gentzkow and Shapiro, 2010; Enikolopov
et al., 2011; DellaVigna and Kaplan, 2007). The relevant dimensions of polarization and the extent of its
increase are debated in the literature. For a discussion, see Boxell, Levi and Gentzkow (2017).
2
It offers alternative explanations by introducing non-standard sources of utility from voting (Frey and
Stutzer, 2005; Olken, 2010 and Tyran, 2004), voters’ overconfidence regarding their own pivotal role (Quat-
trone and Tversky, 1988 and Dittmann et al., 2014), emotions and fairness (Passarelli and Tabellini, 2017).
See Schnellenbach and Schubert (2015) for a review.
3
For instance, immediate weather-induced mood fluctuations affect college choice (Simonsohn, 2009) and
stock returns (Saunders, 1993; Hirshleifer and Shumway, 2003), even though it is unlikely to affect college
quality or fundamentals. For a review of the literature in Psychology and Economics, see DellaVigna (2009).
2

pected emotional shocks driven by a soccer match only affect supporters of the two teams
involved. Thus I explore Facebook data on the geographical distribution of fans of each soc-
cer team across Brazilian municipalities in order to calculate the share of people that receive
an emotional shock relative to their ex-ante rational expectation towards the outcome of the
match and relate that to their voting patterns. The reason for focusing on Brazilian soccer
is threefold. First, Brazilians are strongly attached to this sport as suggested by the fact
that Sunday afternoon games attract over 40% of national TV audience.4
Second, interna-
tionally organized betting markets allows the analysis to be conditional on ex-ante predicted
outcomes, making it possible to interpret any differential effect as a causal effect of the game
result.5
Third, by exploiting the interaction of the distribution of supporters across geogra-
phy with observed and predicted outcomes, I am able to explore municipal cross-sectional
variation and hence circumvent the fact that elections are relatively rare events.
The results indicate that an increase of one standard deviation in the share of people
receiving a negative emotional shock decreases the incumbent mayor vote share by approxi-
mately 5 − 5.8 percentage points on average. Putting that into perspective, this is equivalent
to flipping the result of 747 mayoral elections or 4.3% of the sample. This effect is stronger
for games with higher stadium attendance, for local teams in the first division, and for more
intense emotional shocks. Moreover, a systematic placebo analysis provides evidence that the
results are not driven by an unknown specification error. Similar findings arise when I focus
on gubernatorial elections. These results cannot be explained by changes in turnout.
Should this evidence be taken as a challenge to voter competence? Could random polit-
ically irrelevant events overturn the outcome of an election? I aim to provide an answer to
these questions by showing that the effect of emotional shocks on incumbent vote share is
smaller for elections that are decided by a small margin. I argue that these elections comprise
the appropriate sub-sample on which we should test whether random shocks can change who
wins an election. It is reasonable to expect that a higher-information environment associated
with close elections will make voters less prone to deviate from the behavior predicted by
rational theory due to emotional shocks. Furthermore I show that a similar close election
pattern is also found in two different settings previously analyzed by the political science
literature using: (i) US football results data from Fowler and Montagnes (2015), and (ii)
4
TV audience data per game or across geography are not available for most of my sample period.
5
Coelho (2014) shows that soccer results in the week of the election correlate with election outcomes in
Brazil.
3

Spanish lottery data from Bagues and Esteve-Volart (2016). Finally I provide complemen-
tary evidence from Google searches that individuals actively seek more information about
candidates in close electoral races. Overall these results are consistent with a model in which
voters’ preferences are affected by emotional cues associated with unexpected wins and losses
by their favorite soccer teams which may deviate their voting behavior from the forecasts of
rational theory. Moreover, close elections make information about candidates more salient in
the media hence lowering the attention cost to picking the best candidate, improving rational
decision making of limited attention voters and decreasing the bias induced by emotional
shocks (Sims, 1998 and 2003).6
Sports can trigger particularly large emotional shocks. Professional cyclist and national
hero Gino Bartali allegedly helped save Italy from tumbling into civil war in the aftermath of
WWII when he unexpectedly won the 1948’s Tour de France at the age of 34.7
Indeed, losing
sport fans score significantly higher on post-game self-reported levels of unpleasant emotions
such as boredom, anger, humiliation and resentment, and lower on relaxation (Kerr et al.,
2005). Recent evidence shows that sports-induced emotional shocks affect stock returns (Ed-
mans et al., 2007), financial markets political expectations (Carvalho et al., 2017), domestic
violent behavior (Card and Dahl, 2011), juvenile court judge decisions (Eren and Mocan,
2016) and ethnic conflicts (Depetris-Chauvin and Durante, 2017).8
This paper contributes to a growing literature that associates seemingly unrelated factors
to voting behavior. For instance, election results are affected by current economic outcomes,
even if such outcomes seem independent from the politicians’ actions.9
These patterns are
consistent with (i) a model of voters exhibiting systematic attribution errors or with (ii)
rational voters learning from the additional information embodied in an exogenous shock (e.g.
a natural disaster) as the incumbents may plausibly be held responsible for disaster response
6
An alternative reasoning would be that a rational inattentive voter would consciously devote more scarce
attention effort to picking a candidate whenever she is more likely to be pivotal. However, even with a finite
number of voters the probability of being pivotal in a large election is so small that it cannot be taken as a
the main motivation for voting or paying costly attention (Matějka and Tabellini, 2017).
7
His obituary at the Daily Telegraph, UK, reads "just as it seemed the communists would stage a full-scale
revolt, a deputy ran into the chamber shouting ‘Bartali’s won the Tour de France!’ All differences were at
once forgotten as the feuding politicians applauded and congratulated each other on a cause for such national
pride. [...] All over the country political animosities were for the time being swept aside by the celebrations
and a looming crisis was averted."
8
Indeed, country leaders typically try to associate their image to successful national teams. This behavior
is consistent with marketing experimental evidence that preference for the status-quo is strengthened by
positive emotions (Yen and Chuang, 2008) and happy mood (Scheibehenne et al., 2014).
9
See Lewis-Beck and Stegmaier (2007) for a review of the literature on economic voting.
4

(Campello and Zucco, 2016; Ashworth et al., 2017).10
Bagues and Esteve-Volart (2016)
circumvent this ambiguity by exploring random prizes awarded by the Spanish Christmas
Lottery and find that the incumbent vote share is higher in prize-winning provinces.11
This
paper differs from the economic voting literature in two important aspects. First, it exploits
mood fluctuations triggered by unexpected sports outcomes that do not carry any information
regarding the incumbent’s ability. Second, soccer-induced emotional shocks are non-economic
by nature, especially when supporters live in distant geographical areas than their favorite
team, as opposed to monetary or weather shocks that may affect local economic prospects.
In a similar vein to this study, Healy et al. (2010, 2015a, 2015b) and Fowler and Montagnes
(2015a, 2015b) debate over a series of papers whether football results have an important
effect over US elections. Using data from Fowler and Montagnes (2015a), I show that both
college football and the NFL have a significant impact in electoral races that are decided by a
large margin. Once I focus on close elections such effect becomes small and less significant. A
similar patter occurs when I perform an equivalent analysis using Spanish lottery data from
Bagues and Esteve-Volart (2016). The positive effect of random lottery prizes on incumbent
vote share is higher in non-close elections.
This paper also relates to the literature in the intersection between attention, media and
politics. Recent evidence shows that the media coverage can affect the behavior of public
officials (Lim et al., 2015; Strömberg, 2004), political donors (Petrova et al., 2017), military
officials (Durante and Zhuravskaya, 2016), consumers (Bursztyn and Cantoni et al., 2016) and
voters (Larreguy et al., 2016; DellaVigna and Kaplan, 2007; Enikolopov et al., 2010; Bursztyn
et al., 2017). Informational salience also matters for behavior (Bordalo et al., 2012a and
2012b; Chetty et al., 2009). Thus information is a key element in shaping decision making,
especially in the political arena. Indeed Matějka and Tabellini (2017) propose a model of
electoral competition to study how voters allocate costly attention and show that voters’
selective ignorance can interact with policy design leading to important policy distortions.12
Hence understanding how individuals choose to pay attention to information provided by
10
Previous work associate election outcomes with rainfall (Meier et al. 2016), natural disasters and shark
attacks (Achen and Bartels, 2004, 2016; Healy and Malhotra, 2010). Fowler and Hall (2016) find the evidence
associating shark attacks and elections to be inconclusive.
11
The Spanish Christmas Lottery is a syndicate lottery whose top prize is awarded to several thousand
individuals typically clustered around the same geographical area. Thus it can be thought as a random
regional income shock.
12
For instance, they find that public goods are underfunded and that small groups and voters with extreme
preferences are more influential than under full information.
5

others is important to help circumventing these undesirable features.
Section 2.1 discusses Brazilian political institutions and election data. Section 2.2 presents
soccer data and associated betting market odds. Section 2.3 describes the Facebook support-
ers data. Section 3 presents the empirical strategy and main results. Section 4.1 argues
that the baseline effect of emotional shocks on voting behavior disappears in close elections.
Section 4.2 reports estimates that support the validity of this close election pattern in two
different settings using US football data from Fowler and Montagnes (2015a) and Spanish
lottery data from Bagues and Esteve-Volart (2016). Section 4.4 presents supporting evidence
using Google search volume data. Section 5 concludes.
2 Political Institutions and Soccer Data
In this section, I first describe how the Brazilian political system is organized around elections
every two years and present descriptive statistics. Then I introduce the main national soccer
competitions and compare match-level betting markets odds predictions to actual outcomes.
Finally I present the Facebook supporters data.
2.1 Electoral framework
The Federative Republic of Brazil is organized at three levels of government: the federal
union, 26 states and 1 federal district, and 5, 565 municipalities. The executive and legislative
powers are organized independently at all three levels, while the judiciary is organized at the
federal and state level. Municipal governments are managed by an elected mayor (Prefeito)
and legislature (Camara dos Vereadores), which are in charge of a significant portion of public
goods provision, related to education, health, and small-scale infrastructure.13
Each of the
26 states are semi-autonomous self-governing entities with an elected governor and legislative
assembly who fill the executive and legislative role, respectively. The President is both the
head of state and the head of the federal government.
The president and state governors are elected to a four-year term in an absolute majority
dual-ballot system. A second round runoff is required between the top two candidates if none
receives an absolute majority in the first round. In mayoral elections only municipalities with
13
For size and administrative organization, Brazilian municipalities are akin to U.S. counties
6

Figure 1: Election Timeline 2002-2016
more than 200, 000 registered voters may have a second round. The president, governors,
and mayors have their respective vice-president, vice-governors, and vice-mayors, who are
elected on unified slates. Figure 1 describes the timeline of major elections. All elections take
place in even years. Mayoral elections intercalate with simultaneously-held presidential and
gubernatorial races.14
Since 1998 first-round elections are held on the first Sunday of October
and runoff second rounds on the last Sunday of October. All executive-branch elected officials
start in office on January 1st
of the following year.
Voting is considered both a right and a duty in Brazil. Registration and voting are
compulsory for individuals between the ages of 18 and 70, and optional people between 16 to
18, above 70 and the illiterate. Around 86% of a total of 144 million registered voters in 2014
must vote and turnout have historically been above 80%. Even though voting is mandatory
for most, voters can chose to cast blank or void ballots which are typically seen as a form of
protest and do not count as valid votes towards the final result.
Politicians’ behavior in Brazil differs from their American counterparts’ in important
ways. Firstly, incumbent politicians among the two major parties in the U.S. exhibit high
rerunning rates (Lee, 2008) as opposed to Brazil. One reason behind such difference is that
reelection for executive office in Brazil has been allowed since 1997, while a third consecutive
term is not permitted.15
Second, party-switching is virtually non-existing in U.S. elections
while approximately 30% of incumbent mayors switch parties when attempting reelections.16
14
Municipal, State and Federal legislatures are elected simultaneously with but independently from their
executive branch counterparts.
15
Electoral accountability seems to affect the behavior of incumbent politicians. Using random audit reports
of municipal government as a measure of corruption, Ferraz and Finan (2011) find significantly less corruption
in municipalities where mayors can get reelected.
16
Throughout the paper I present the main results for incumbency at the party level as the 2-term limit
for executive seats by definition limit the number of incumbent candidates. However I show that the main
estimates are similar if I define incumbency at the candidate level. This is particularly reassuring as party-
switching whilst in office is a common phenomenon in Brazilian Politics and it is not uncommon for candidates
to run for reelection in different parties.
7

Election vote counts are retrieved from the Election Data Repository of TSE (Tribunal
Superior Eleitoral).17
TSE is an independent branch of the federal judiciary established by
the constitution of 1988 that regulates electoral procedures, including most administrative,
planning and normative tasks of the elections. Detailed data are available on personal char-
acteristics of each candidate and number of votes at the voting place level.18
I aggregate vote
share for incumbent candidates at the municipality level for gubernatorial elections in 2006,
2010 and 2014 and mayoral elections in 2004, 2008, 2012 and 2016.
Table 1 reports descriptive statistics for the elections included in the sample. Each obser-
vation is a candidate in a particular election. Mayoral elections in 2002-2016 had an average
of 3.4 candidates per municipality with 0.18 (0.15) of them as incumbent party (candidate)
running for reelection resulting in 49, 646 observations across four election years (Panel A).
Average vote share is 0.34 and there are approximately 4, 300 unique municipalities in the
sample out of 5, 565 total in the country. I exclude from the sample all municipalities with
less 5, 000 in 2017 due to Facebook data restrictions. 19
The three gubernatorial elections in
our sample (Panel B) had an average of 7.2 candidates per election with 0.12 (0.07) of them
as incumbent party (candidate) running for reelection. The resulting number of observations
is 86, 220 and the average vote share is 0.15.
2.2 Betting Odds and Soccer Results
This papers draws data from two main championships, namely Campeonato Brasileiro Série
A and Série B. The primary soccer competition in Brazil is Série A. During the course of a
season (from May to December) 20 teams plays each other twice (all-play-all system), once
at their home stadium and away, in a total of 38 matches.20
Teams receive three points for
a win and one point for a draw. At the end of each season, the top ranked team is declared
champion. Série B is the second tier of the Brazilian soccer league system. A system of
promotion and relegation exists between Série A and Série B. The last (top) four teams in
the Série A (B) are relegated (promoted) to Série B (A).21
17
Data were retrieved on November 2016 and are freely available at
http://http://www.tse.jus.br/eleicoes/estatisticas/repositorio-de-dados-eleitorais.
18
These include name, age, gender, marital status, ethnicity, place of birth, occupation, education, party,
declared campaign budget, declared income and tax returns.
19
Facebook data describe in section 2.3 is not available for these municipalities.
20
The exception is 2004 when 24 teams competed in a total of 46 matches per team.
21
Other important competitions include Copa do Brasil, a knockout football competition played by 86
teams representing all Brazilian states comparable to UK’s FA Cup and Spain’s Copa del Rey, and regional
8

Betting markets on Brazilian Soccer are organized by large international online exchanges
that are essentially order-driven markets in fixed-odds bets. Closely following a standard
financial exchange model, they allow individual customers to bet with each other directly
and typically charge a commission. Each game outcome (home team win, draw or loss) is
associated with an odds figure. For example, suppose A places a one-dollar bet on Brazil
against Argentina at odds of 1.65. If Brazil wins, A gets 1.65 dollars. Otherwise A loses 1.
The inverse of a betting odd is the implied probability of the underlying outcome.22
It is key to my identification strategy that betting markets produce unbiased predictions
of Brazilian soccer match outcomes. Sauer (1998) provides an extensive review of the sports
betting literature and concludes that standard definitions of market efficiency are generally
satisfied. Betting markets also exhibit high liquidity and trading volumes.23
They are effective
in absorbing publicly available information (Forrest, Goddard and Simmons, 2005) and do
so with very recent information up until the start of each game (Debnath et al., 2003).
Previous research suggests that betting odds are good predictors of outcomes in English
soccer (Croxson and Reade, 2014 and Nyberg, 2014).
Table 2: Actual and Predicted Soccer Results
Outcome Actual Result (s.d.) Implied Probability (s.d.)
home win .491 (.499) .468 (.141)
draw .251 (.434) .261 (.032)
away win .256 (.436) .269 (.124)
In order to verify whether these conclusions hold for Brazilian soccer, I collected available
data on average betting odds and results for 13,996 soccer matches from all major competi-
tions in the 2004-2016 period.24
Table 2 reports the frequency of actual outcomes and their
associated implied probability. Home teams have a clear advantage as they win half of all
state championships (played in January-April). Matches from these competitions are used in the placebo
analysis in section 3.4 but are not include the main sample as their season typically do not overlap with
elections.
22
The amount by which the sum of the implied probability of each outcome diverges from 100% is equivalent
to the bookmaker’s commission.
23
Online sports betting amounted to US$23.9 billion in 2002 and US$47.8 billion in 2008 (EPFL,
2012). Leading online sport exchange Betfair processed around seven million trades a day in 2005-
2006 - greater than the number of daily trades on all the European Stock Exchanges combined. See
http://corporate.betfair.com/media/press-releases/2012/29-06-2012.aspx?p=1 for more information.
24
Averaging odds over many different bookmakers has the advantage of cancelling out strategic and un-
9

-8-4048
RealizedScoreDifferential
-.9 0 .9
Win Probability (home - away)
Figure 2: Score Differential and Implied Probabilities
games while the other half is split evenly between away teams and draws. The implied proba-
bility of each outcome closely matches its observed frequency. Figure 2 shows the relationship
between realized score differential (home team’s final score minus away’s) and implied win
probability differentials (probability of winning minus probability of losing). The two mea-
sures are clearly correlated and the coefficient of a regression of score on implied probabilities
yields a coefficient of 2.35 with standard deviation of 0.048 and R2
of 0.14. This is suggestive
that international betting market odds are informative about actual Brazilian soccer match
outcomes. The blue dots on the right (left) represent observations in which the home team
is predicted to win (to lose). A team is defined to be predicted to win (lose) if its implied
probability of winning is higher (lower) than 0.5 and a match is defined as close if neither
team is predicted to win.25
Table 3 - Panel A reports summary statistics of the soccer matches in the main sample.
I focus on the last soccer match of each team in the days preceding each election. Only
intentional inefficiencies of individual bookmakers. For a discussion about why different bookmakers’ odds
may vary, see Vlastakis et al. (2009). Hvattum and Arntzen (2010) and Leitner, Zeileis and Hornik (2010)
analyze the performance of aggregated odds to forecast soccer match results.
25
For the precise description of the definition, see Section 3. The results are robust to this particular
classification.
10

Série A odds data are available before 2010 and hence only 10 or 12 matches and 20 or 24
teams are used as seen in Panel A. From 2010 Série B data are also available increasing the
number of matches and teams to 20 and 40, respectively. The average number of days before
an election is 2.6 in the sample and longest period of time between any single match and
its corresponding election is 9 days. The average number of goals scored in a match is 2.6
and stadium attendance is just below 13, 000. Table 3 - Panel B reports observed results
and the associated probabilities of match outcomes implied by betting markets. Home teams
exhibit a clear advantage as they win approximately 0.59 of all matches while draws or away
wins sum up each to 0.2. The implied probabilities follow a similar pattern. Home teams
are predicted to win one in every two matches, while draws or away wins are predicted in
approximately one quarter of games each.
2.3 Facebook Supporters Data
Data on supporters of the 65 main soccer teams in Brazil were retrieved from Mapa das Cur-
tidas - a joint effort between Facebook and sports news provider globoesporte.com.26
Based on
60 million ‘likes’ on official Facebook pages of each team, it provides the share of supporters
of a team in all Brazilian municipalities above 5, 000 inhabitants.27
Even though not officially
designed as a representative survey, Facebook counted 125 million users in Brazil in 2017,
out of approximately 200 million inhabitants and 140 million of people with internet access.28
Moreover it correlates well with data from in-person nationally representative surveys. Ap-
pendix Table 1 reports the share of supporters for the top teams in Brazil estimated by three
different survey companies. Both team rankings and support shares strongly correlate with
26
These are ABC, América-RN, ASA, Atlético-MG, Atlético-PR, Avaí, Bahia, Botafogo, Botafogo-PB,
Campinense, Ceará, Chapecoense, Corinthians, Coritiba, CRB, Criciúma, Cruzeiro, CSA, Figueirense, Fla-
mengo, Fluminense, Fortaleza, Goiás, Grêmio, Guarani, Internacional, Joinville, Náutico, Palmeiras, Paraná,
Paysandu, Ponte Preta, Portuguesa, Remo, Sampaio Corrêa, Santa Cruz, Santos, S˜eo Paulo, Sport, Treze,
Vasco, Vila Nova, Vitória. Notable absent teams are Pelotas, Brasil de Pelotas, Botafogo-RP, Comercial-RP,
Caxias and Juventude.
27
Data include all facebook ‘likes’ as of May 2017 except Brazilians resident abroad. Although users
may ‘like’ more than one team, this behavior is rare and hence irrelevant to the estimates. All teams’
Facebook pages either are ‘verified by Facebook’ to be truly managed by their owners or have been manually
confirmed by Facebook staff. Due to inconsistencies in determining the precise location of users in small
towns, these data were discarded by globoesporte.com and hence were not used in this paper. For more
details, visit https://globoesporte.globo.com/futebol/noticia/como-foi-feito-o-mapa-de-curtidas-das-torcidas-
do-brasil-no-facebook.ghtml
28
Similar mapping efforts have been done by Facebook on NBA basketball and MLB baseball supporters
jointly with The New York Times , as well as NFL supporters with the Atlantic. In Europe, similar studies
have been done by Twitter and the Guardian on UK Premier League supporters.
11

Figure 3: Geographical Distribution of Soccer Fanbase
the facebook measure (ρ > 0.95).
An important feature of Brazilian soccer fandom is its widespread geographical distribu-
tion. The two most popular teams, Flamengo and Corinthians, have more than 40% share
of all soccer fans and top 12 teams have 87%. Comparatively the top 2 NFL teams have
less than 20% (Dallas Cowboys and Pittsburgh Steelers).29
Figure 3 provides visual evidence
of such prominence. Thicker white lines represent state borders and thinner lines represent
municipalities. Flamengo and Corinthians are the top teams in 2639 and 1489 out of 5570
Brazilian municipalities, respectively. Flamengo is most popular in most of the Northern
and Northeastern states and costal Southeastern states of Rio de Janeiro and Espírito Santo.
Corinthians leads in most of São Paulo, Paraná, Mato Grosso and Mato Grosso do Sul.30
Apart from the these 2 dominant forces, others important teams have their fanbase spread
across the country. Figure 4 shows the geographical distribution of supporter per team, with
29
See http://deadspin.com/5980852/who-is-americas-favorite-nﬂ-team-facebook-data-oﬀer-a-clear-winner
30
More regional teams Cruzeiro and Grêmio respectively dominate their home states of Minas Gerais and
Rio Grande do Sul, while Bahia and Sport are strong around their respective home state-capital cities of
Salvador and Recife.
12

(a) Corinthians (b) Palmeiras (c) São Paulo
(d) Flamengo (e) Vasco (f) Santos
(g) Grêmio (h) Cruzeiro (i) Fluminense
Figure 4: Fanbase Geographical Distribution per Team
13

darker colors indicating larger shares. Palmeiras, São Paulo, Vasco and Santos have fans scat-
tered around the country. More regional teams such as Grêmio, Fluminense and Cruzeiro
have very a strong fanbase at home but little support elsewhere.31
3 Empirical Strategy and Baseline Results
In this section, I first describe how I combine soccer results, betting odds and Facebook
supporters data in one single dataset in order to estimate the causal impact of emotional cues
associated with wins and losses by professional soccer teams on mayoral election outcomes.
Then I present the main results followed by a placebo analysis. I also show how heterogeneous
the baseline estimates are according to emotional salience and shock intensity. Finally, I
present results on gubernatorial elections and conclude by analyzing whether the baseline
effects can be explained by voter turnout or invalid votes.
3.1 Matching Soccer Results to Election Outcomes
Given the features of soccer fandom and political institutions in Brazil, I specify a regression
model for vote share that exploits the interaction between the team-specific time-invariant
geographical distribution of supporters and unexpected soccer results in the days preceding
each election. Specifically I assume that
νc,i,t = β
κ∈K
sκ,i shockκ,t +βinc
κ∈K
sκ,i shockκ,t x incc,i,t +γ incc,i,t +θXc,i,t +µi +δs,t +εi,t (1)
where νc,i,t is the share of votes received by the candidate c in municipality i at time t, sκ,i
denotes the share of supporters of team κ in municipality i, shockκ,t represents emotional
cues that reflect mood-induced gain/loss utility around a rational reference point and incc,i,t
is an indicator whether candidate c is the incumbent.32
By construction shockκ,t ranges from
−1 to 1 and can be decomposed into positive and negative shocks, with positiveκ,t capturing
situations when better-than-expected soccer outcomes take place, such as if team κ (i) draws
31
This is consistent with TV broadcasting from São Paulo and Rio de Janeiro having spread historically
to other regions.
32
Throughout the paper I present the main results for incumbency at the party level as the 2-term limit
for executive seats by definition limit the number of incumbent candidates. However I show that the main
estimates are similar if I define incumbency at the candidate level. This is particularly reassuring as party-
switching whilst in office is a common phenomenon in Brazilian Politics and it is not uncommon for candidates
to run for reelection in different parties.
14

or wins a match it is predicted to lose, or (ii) wins a match it is predicted to draw at time t
and negativeκ,t being defined analogously. Formally:
shockκ,t = positiveκ,t − negativeκ,t (2)
positiveκ,t = [winκ,t + drawκ,t] plose
κ,t + winκ,t pclose
κ,t (3)
negativeκ,t = [loseκ,t + drawκ,t] pwin
κ,t + loseκ,t pclose
κ,t (4)
with winκ,t , drawκ,t and loseκ,t indicating whether team κ wins, draws or loses a match
and pwin
κ,t , pclose
κ,t and plose
κ,t indicating whether team κ is ex-ante predicted to win, draw or
lose.33
Hence sκ,i positiveκ,t and sκ,i negativeκ,t can be interpreted as the share of people
that receive a positive/negative emotional shock relative to their ex-ante rational expectation
towards the outcome and sκ,i shockκ,t = sκ,i positiveκ,t − sκ,i negativeκ,t.34
A team is predicted to win if its probability of winning implied by pre-game betting
markets odds is greater than 0.5.35
Hence:
pwin
κ,t =
1, if Prob(win) > 0.5
0, otherwise
(5)
plose
κ,t =
1, if Prob(lose) > 0.5
0, otherwise
(6)
and pclose
κ,t = 1 − max{pwin
κ,t , plose
κ,t }, that is, a match is defined as close if neither team is
predicted to win.
Municipal fixed-effects µi account for time-invariant factors determining election outcomes
related to local preferences, geography, culture, local institutional quality, corruption, etc.36
33
The share of people that receive a neutral shock is 1− sκ,ipositiveκ,t− sκ,inegativeκ,t by construction.
Neutral shocks can be subdivided into winκ,t pwin
κ,t , drawκ,t pclose
κ,t and loseκ,t plose
κ,t . Any of the three
possible neutral shocks can be used as a base category and thus be excluded from the regression. Throughout
the paper, I treat drawκ,t pclose
κ,t as the excluded base category and include winκ,t pwin
κ,t and loseκ,t plose
κ,t
as controls.
34
These emotional shock variables can be thought as Bartik instruments (Bartik, 1991; Goldsmith-Pinkham
et al., 2018). The Bartik instrument is formed by interacting local industry shares and national industry
growth rates.
35
This is equivalent to saying that a team is predicted to win if its betting-markets implied probability of
winning is higher than probability of not winning. The results are robust to defining pwin
κ,t = 1 if Prob(win)>c
for at least any c ∈ [0.5, 0.6].
36
Naritomi, Soares, and Assuncao (2012) show that there are sizeable differences across Brazilian munici-
palities on institutional quality that are related to the type of colonization and local geographic features.
15

Xc,i,t represents municipality-level time-varying controls such as election-day local rainfall
controls, employment level and candidate political alignment. δst are state-year dummies
that capture aggregate developments (national and state level) such as public policy and
regional business cycles.
Using Facebook data regarding the distribution pattern of soccer supporters across mu-
nicipalities is key to the empirical strategy in two distinct ways. First, it deals with the fact
that the main soccer teams in Brazil have supporters in all regions and thus their soccer
results should be thought as a national shock (rather than local) that affects different regions
with different intensities. Second, by taking advantage of such team-specific cross-sectional
municipal variation I am able to circumvent the fact that elections are relatively rare events.
The primary interest is in the effect of match results that precede an election. Assuming
betting odds provide non-biased forecasts of Brazilian soccer game outcomes, conditional
actual results can be thought as equivalent to random experiments so that βinc
and β in
specification (1) yield unbiased estimates of the causal effect of emotional shocks on voting
behavior.
Table 4 summarizes the aggregate emotional shock variables regarding mayoral and gu-
bernatorial 1st
round elections. Rows (1) shows that positive and negative emotional shocks
affect on average 0.16 − 0.20 and 0.21 − 0.29 of soccer fans, respectively. This implies that
over one half of all games are associated with a neutral shock, that is, the actual outcome is
the same as the predicted by the betting market odds. Rows (2)-(7) split shock according to
shock intensity, stadium attendance, whether team is local or is in Série A. A high intensity
negative shock is defined as a loss when it is predicted to win. A low intensity negative shock
is defined as a draw when it is predicted to win or a loss when it is predicted to have a close
game. High intensity positive shocks are defined accordingly. Low (high) attendance games
have stadium attendance below (above) 10,000 people (sample median). A game has a ’local’
team if it involves a team based within the same state where it is held and Série A is the first
division of Campeonato Brasileiro.
I report heteroskedasticity-robust standard errors clustered at the municipality level in
mayoral elections and state-year level for gubernatorial elections.
16

3.2 Baseline Results
This section presents baseline estimates on the average effect of emotional shocks on vote
shares. I begin by reporting estimates that associate unexpected soccer results with mayoral
elections held in 2002, 2006, 2010 and 2014. This serves two purposes. First, it establishes
that the link between conditional match outcomes and elections results are stable across
specifications, as would be expected if outcomes are orthogonal to other covariates. Second,
it provides a benchmark against which I can evaluate the effect of soccer outcomes on state-
level gubernatorial elections.
Table 5 reports the baseline OLS estimates. All specifications include municipality and
state-year fixed effects. Columns (2)-(5) sequentially add controls for election-day rainfall,
candidate’s political alignment with governor and president, candidate personal characteris-
tics such as gender, place of birth, age, marital status and schooling, and local employment
rate.37
The coefficients associated with emotional shocks are stable across all specifications, con-
sistent with the assumption that game outcomes are orthogonal to covariates conditional on
ex-ante betting market predictions. The estimates associated with shocks interacted with
incparty suggest that the effect of emotional shocks on incumbent vote share is positive in the
range of 5 − 5.8 percentage points. Putting that into perspective, a one-standard-deviation
emotional shock would be equivalent to flipping the result of 747 municipal elections or 4.3%
of the mayoral elections in the sample. Such striking result is consistent with other findings
in the literature that relate sports-induced emotional cues and elections (Healy et al., 2010;
Busby et al., 2016). The effect of such politically irrelevant event suggests that mood can
play a significant role in opinion formation processes, especially regarding preferences for the
status-quo.38
In section 4, I argue that such effect is not generalizable across elections. More
specifically, I show that emotional shocks do not play a significant role in determining vote
shares in elections that are decided by a particularly small margin and that such close election
pattern is also found in the context of other countries.
Table 6 reestimates the baseline regressions allowing the impact of negative and posi-
37
Previous works show that election outcomes are affected by weather (Gomez et al., 2007), economic
conditions (Wolfers, 2007; Ashworth et al., 2017; Brunner et al., 2011) and political alignment (Brollo and
Nannicini, 2012).
38
Experimental evidence in marketing and behavioral science literature show that the preference for the
status-quo is strengthened by positive emotions (Yen and Chuang, 2008) and happy mood (Scheibehenne et
al., 2014).
17

Figure 5: Empirical Distribution of Estimated Coefficients from Placebo Weeks
tive emotional shocks on voting behavior to differ. The estimates on negative and positive
shocks are very similar and change little across specifications 1 and 2. Once I control for
mayoral political alignment, the effect of negative emotions become greater than the posi-
tive in specifications 3-5. Such asymmetry is also found in the estimated effect of emotional
cues on domestic violent behavior (Card and Dahl, 2011) and juvenile court judge decisions
(Eren and Mocan, 2016). Non-incumbent mayoral candidates are not significantly affected
by emotional shocks and incumbent parties systematically receive around 0.13 extra votes.
I also check whether the results are robust to the definition of incumbency at the party
level. As discussed in the previous sections, a high party-switching rate, as well as two-term
limits and other characteristics of the Brazilian electoral system cause party and candidate
incumbency to be quite different from each other. Table 7 replicates the baseline estimates in
Tables 5 and 6 with incumbency defined at the candidate level. All estimates are very close
to the ones discussed above.
3.3 Timing of Soccer Matches around Elections
The baseline specifications focus on the last soccer match of each team in the days preceding
each election. Using soccer data from all 4 matches before and after an election, I am able to
18

complement the analysis in two distinct ways. Firstly, by exploring whether election outcomes
respond to shocks associated with matches preceding the last I can explicitly test how durable
the estimated effects are. Second, a significant association between vote share and shocks
arising from games after the elections would falsify a causal interpretation. Figure 5 plots the
estimates of 8 separate regressions against emotional shocks from the fourth-to-last (-4) game
before an election until the fourth (4) game after an election. All shocks before the election
have a small, positive and insignificant impact on vote share except (-1) which correspond to
the baseline reported in column (5) - Table 5. All shocks after the election fluctuate around
zero.
In sum, the data suggests that the incumbent vote share reacts only to the very last
match of a team. Short-lived effects from emotional shocks are also reported in the case of
domestic violent behavior (Card and Dahl, 2011), juvenile court judge decisions (Eren and
Mocan, 2016) and ethnic conflicts (Depetris-Chauvin and Durante, 2017).
3.4 Placebo Analysis
An interesting way to test the validity of the identification strategy is to check whether
past and future soccer results are associated with election results. This section provides a
systematic placebo analysis in order to address the concern that results may be driven by
an unknown misspecification error. Here I estimate a series of placebo regressions similar to
equation 1 with, as emotional shock variables, team-specific soccer results in day d denoted
by shockκ,d with d = t ± s and s ∈ [1, 200] for a total of 400 placebo regressions during soccer
season.
Figure 6 shows the empirical c.d.f. of the placebo estimates compared to the benchmark
estimates from column (5) of Table 5 (the vertical line). In all but 10 regressions I obtain
coefficients smaller than benchmark estimates of β = 0.0579. This is consistent with the view
that the baseline estimated effects are not spurious.
3.5 Emotionally Salient Games and Treatment Intensity
I proceed by testing additional hypotheses that should hold if professional soccer games
indeed influence voting. Specifically, I check whether the effect of soccer results are different
according to game salience and whether more intense emotional shocks yield larger estimates.
These results are reported in Table 8.
19

Figure 6: Empirical Distribution of Estimated Coefficients from Placebo Weeks
If the link between soccer outcomes and voting behavior arises through the impact of
emotional cues, one might expect more ‘emotionally salient’ games to have larger effects on
the incumbent vote share. I define salient games in three alternative ways as: (i) games that
attract stadium attendance larger than 10, 000 (sample median); (ii) games that involve a
local team based within the same state; and (iii) games in the first division (Campeonato
Brasileiro Serie A). I reproduce in column (1) of Table 5 estimates from column (1) in Table 5
as a benchmark for exposition purposes. In columns (2)-(4) I allow the coefficient on negative
emotional shocks to vary according to salience. The effect of emotional shocks on incumbent
vote share is higher in high attendance (0.056) relative to low attendance (0.041). On the
same vein, games that involve local teams and belong to Serie A have relatively larger effects.
An interesting check to further investigate the impact of emotional cues on voting behavior
is to exploit how the estimated effect varies with the intensity of shocks given the ex-ante
expectations regarding the outcome of the game. More specifically, the effect of worse-than-
expected soccer outcomes can be divided into (i) high intensity or (ii) low intensity. For a
given team, a high intensity shock is defined as a loss when it is predicted to win. A low
intensity shock is defined as a draw when it is predicted to win or a loss when it is predicted to
have a close game. Estimates reported in column (5) indicate that the effect of high intensity
emotional shocks is larger (0.061) than low intensity shocks (0.051).
20

3.6 Gubernatorial Elections
Using the estimates of the impact of soccer results on mayoral elections as a benchmark, I
now turn to investigate whether the link between emotional cues and incumbent vote share
are also found in other elections in different years. In particular, I focus on state-level
gubernatorial electoral races in 2006, 2010 and 2014. Focusing on these elections enriches
the analysis for two different reasons. First, they provide an interesting way to test how
general the mayoral elections results are in elections for office in different spheres of the
public administration. Second, as elections for governor are held in different years than for
mayor, the shocks triggered by recent soccer results are different and independent than the
ones explored in the previous results.
Table 9 - Panel A reports estimates for the effect of emotional shocks on the 1st
round
of gubernatorial elections. Columns (1)-(4) and (5)-(8) define incumbency at the party and
candidate level, respectively. Emotional shocks have an large estimated effect in the range of
0.20-0.35 on incumbent vote share albeit not always statistically significant. The magnitudes
are around four to six times larger than for mayoral elections estimates presented in Table
5.39
Panel B allows the effect to be asymmetric. The effect of positive shocks are positive
but smaller and statistically insignificant while negative shocks are associated with a large
and more significant effect.
3.7 Electorate Turnout
Previous literature found that rainfall decreases turnout and benefits republicans in US elec-
tions (Gomez et al., 2007). It is conceivable that a negative emotional shock might decrease
the incumbent vote share through direct punishment at the ballot or indirectly through lower
turnout.
Table 10 - Panel A reports estimates that associate soccer outcomes with voter turnout in
mayoral elections. As a mirror to Table 5, columns (1)-(5) record OLS regression coefficients
of positive and negative aggregate emotional shocks with municipality fixed-effects and state-
year dummies. All coefficients are stable across specifications, consistent with game outcomes
being orthogonal to covariates. The estimated effect is insignificant and very close to zero.
39
The literature in political science suggests that when faced with multiple elections in a single ballot voters
tend to focus their attention in the higher office, decreasing the information on candidates for the lower office.
See Andersen (2011).
21

This pattern is not very surprising as voting is compulsory in Brazil. Panel B reports similar
estimates for on invalid votes, which include all blank and void votes as a share of the total.
Again all results are virtually zero.
4 Close Elections and Voter Attention
In the previous section I provide systematic evidence that unexpected soccer results before
an election affect how individuals vote. The estimated effect is of considerable magnitude
and holds for different spheres of government. In the case of gubernatorial elections, it is
important to point out that they take place at the same time as presidential elections, with
potentially different incumbent parties. Further analysis shows that the effect of emotional
shocks on incumbent vote share is greater for games with higher stadium attendance, for
local teams in the first division, and for more intense emotional shocks. Should this evidence
be taken as a challenge to voter competence? Could random politically irrelevant events
overturn the outcome of an election?
In this section, I aim to provide an answer to these questions by investigating whether the
effect of emotional shocks on incumbent vote share is found for elections that are decided by a
small margin. I argue that these elections comprise the appropriate sub-sample on which we
should test whether random shocks can change who wins an election. Close elections differ
from other elections in two ways. First, the probability of being pivotal increases for voters in
elections with small winning margins. Second, close elections tend to make information about
candidates more salient in the media and among people in general. Voters who receive more
information have more accurate perceptions towards candidates (Andersen, 2011) and rely
less frequently on heuristics or information shortcuts when making their decisions of whom
to support (McDermott, 2005). Thus it is reasonable to expect that a higher-information
environment associated with close elections will make voters less prone to deviate from the
behavior predicted by rational theory due to emotional shocks.
I begin by allowing the psychological effects of soccer results to vary according to the
degree of election closeness in mayoral and gubernatorial elections. Table 11 presents the
results for mayoral elections. Column (1) in Panel A replicates the baseline effect in column
(1) of Table 5 for exposition purposes. Panel B - column (1) reestimate the full sample
baseline specification and interacts the emotional shock variable with the absolute winning
22

margin. A clear pattern emerges. The effect of the emotional shock is small and insignificant
for non-incumbent while the coefficients interacted with margin are very significant. This
implies that the marginal effect of emotional shocks is positive for incumbents and negative
for non-incumbents and increase with margin.
Columns (2)-(7) restrict the sample for elections with the difference between the vote
shares of the top 2 candidates is less/more than 0.15, 0.125, 0.10, 0.075, 0.05 and 0.025,
respectively, in Panels A and B. All estimates in Panel B (non-close elections) are in the [0.07-
0.11] range larger than the baseline of 0.05 while all those in panel A in the [0-0.02] range and
mostly insignificant.40
Table 12 follow a similar approach for gubernatorial elections. Even
though variation across election closeness is much more restricted in gubernatorial elections
(only 27 states in Brazil), a similar pattern emerges. The effect of negative emotional shocks
are stronger in non-close elections across specifications and for both incumbency defined at
the party or candidate levels.
4.1 Close Elections in Two Different Setting
In this section, I investigate whether the relationship discussed above between the effect of
politically irrelevant events on voting behavior and close elections is also found in two different
settings previously analyzed in the literature.
Healy et al. (2010) and Fowler and Montagnes (2015) A well known paper by
Healy et al. (2010) investigates whether college football results affect elections in the US.
Victories within 2 weeks of an election reportedly increase the success of the incumbent party
in presidential, senatorial, and gubernatorial elections in the home county of the team. Fowler
and Montagnes (2015) replicate their results for college football but fail to find a similar effect
when focusing on NFL games. As their treatment variable football, they assign a value of 1 if
the team wins, 0 if the team loses, and 0.5 if the team draws, and take the average of these
values for the two games preceding an election.
I employ the same type of close election analysis from last section to the Fowler and
Montagnes (2015) data. The results are reported in Table 13. Columns (1) and (5) in
Panel A replicates the college football and NFL baseline estimates in Fowler and Montagnes
(2015) for exposition purposes. Panel B - column (1) and (5) reestimate the full sample
40
Allowing positive shocks to vary with election closeness yield similar results.
23

baseline specifications and interacts the emotional shock variable with the absolute winning
margin. On both cases, the baseline effects mask considerable heterogeneity across election
closeness. The coefficient on football x incparty becomes negative. The interaction with margin
is significant and positive, implying that the effect of college football on incumbent vote share
increases with winning margin. Columns (2)-(4) and (6)-(8) restrict the sample to close and
non-close elections. While most coefficients are non-significant probably due to sample size,
the estimates for non-close elections in Panel B are always greater in magnitude than in Panel
A, both for college and professional football.
Bagues and Esteve-Volart (2016) In a recent paper, Bagues and Esteve-Volart ex-
plore the relationship between Spanish Christmas Lottery prizes and voting behavior across
Spanish provinces. The Spanish Christmas Lottery is a syndicate lottery whose top prize
is awarded to several thousand individuals typically clustered around the same geographical
area. Thus it can be thought as a random regional income shock. They find that, although
winning the lottery is independent from politicians’ actions, incumbents receive significantly
more votes in winning provinces and argue that such effect may be explained by personal
well-being influencing voting decisions on a subconscious level.
I use their data to check whether the estimated impact of random lottery prizes varies with
the degree of election closeness. Table 14 reports estimates from the baseline specification in
Bagues and Esteve-Volart (2016) which links incumbent vote share to lottery prizes awarded
in a given province during the term as a percentage of GDP, controlling for lottery expenditure
and local economic conditions. Column (1) simply replicates column (2) from Table (3) in
their paper. Columns (2)-(4) restrict the sample according to close and non-close elections
in Panels A and B. Receiving 1 percent of GDP in the form of lottery winnings during the
electoral term increases the votes received by the incumbent by approximately 0.21 percentage
points, relative to the votes obtained by the incumbent in losing provinces. The effect is
significant across specification and systematically greater for non-close elections.
4.2 Google Trends
A clear pattern emerges from the results discussed above. While voters are systematically
influenced by unexpected shocks associated with politically irrelevant events such as sport
results and random lottery prizes, they are less so in elections that are decided by small
24

0204060
popularityongoogletrends
-10 -5 0 5 10
weeks around election
governor (close <5%) soccer
governor (non-close >5%)
Figure 7: Popularity on Google Trends: Governor vs Soccer
margins. Understanding why this is so can be instructive about the mechanism of ratio-
nal decision making in politics. A natural place to start regards voters’ ability to gather
information about candidates and make informed decisions.
This section aims to provide additional insights into the relationship between election
closeness and voter attention. In order to analyze individuals’ demand for information re-
garding candidates, I collected Google search volume data on terms related to soccer and
elections. Google Trends provides a relative index of the volume of Google queries by geo-
graphic location and category. Index measures are scaled separately for each state in each
year, so that the popularity of a topic relative to another is comparable across states and
time.41
Figure 7 plots weekly average state-level indices for Google queries on two speciﬁc topics,
namely soccer and governor.42
It covers a 21-week period centered around election week
41
For example, if one data point is 50 and another data point is 100, this means that the number of searches
was half as large for the ﬁrst data point as for the second data point. The maximum value of the index is set to
be 100 and the scaling is done separately for each request, or state-year in this case. See Stephens-Davidowitz
and Varian (2014) for a primer on using Google data in academic research.
42
Topics from Google Trends are a group of terms that share the same concept. A search on topic governor
includes results for queries such as gubernatorial election, election candidates and gubernatorial election poll.
25

102030405060
popularityongoogletrends
-10 -5 0 5 10
weeks around election
president (gubernatorial close <5%) soccer
president (gubernatorial non-close >5%)
Figure 8: Populartogle Trends: President vs Soccer
(first round) for all gubernatorial election years in the sample (2006, 2010 and 2014). For
analytical purposes, I split governor queries into close (winning margin smaller than 5%) and
non-close electoral races. At a first glance, three patterns stand out. First, soccer queries
(blue) are an order of magnitude more popular than queries related to governor both in close
(red) and non-close elections (green). Second, governor queries become more popular around
election time as expected, with the few days previous to the election experiencing a spike in
search volume. Third, the surge is visibly greater in close elections. In fact, the popularity
of governor surpasses that of soccer only at election week for the close election sample.43
In an effort akin to a falsification test, Figure 8 plots search volume for soccer and pres-
ident. This is particularly interesting because presidential and gubernatorial elections are
simultaneously held across the country. In a similar fashion to Figure 7, I split president
queries according to closeness in gubernatorial elections, hence maintaining the same sample
split as before. Searches for soccer remain overwhelmingly more popular at most weeks except
election time. The popularity of term president in close and non-close elections is strikingly
similar across virtually all weeks. This indicates that the patterns observed in Figure 7 are not
43
Search volume related to governor remains higher for close elections during the 3 weeks following the
first-round vote as close elections are more likely to have a second-round.
26

.2.3.4.5.6.7
relativepopularityongoogletrends
0 20 40 60 80
margin of victory in %
Figure 9: Relative Popularity on Google Trends: Mayor vs Soccer
spurious. Indeed voters seem to actively seek more information about candidates competing
in close elections.
Google Trends does not report weekly queries indices at the municipality level so I am
unable to exactly replicate the analysis above for mayoral elections. However it reports
municipal yearly average search volume in 2016.44
Thus I am able to compare the popularity
of mayor relative to soccer and relate that to the winning margin in each city during a
mayoral election year. Figure 9 plots average municipality-level search volume indices for
mayor divided by soccer. Albeit signiﬁcant heterogeneity, google searches on mayor relative
to soccer are proportionally higher in municipalities with close elections. Indeed the share
of observations with relative popularity of mayor above the sample median (0.42) is 0.56,
considering those with winning margin smaller than 20%, and 0.38 for the remaining.
44
This is because of anonymity reasons as municipalities have a limited number of searches. By expanding
the analysis from weekly to yearly, Google provides data on 150 cities in 25 out of the 27 states in the country.
27

5 Conclusion
The importance of understanding what drives voting behavior has been pushed to the fore-
front of the public debate in recent years. Given the weak incentives to invest in rational
decision-making that prevail in the political arena, it is expected that insights from both
rational theory and behavioral science to be relevant in determining election outcomes.
This paper analyzes the impact of emotional shocks induced by unexpected soccer results
on voting behavior in Brazilian elections. The main results indicate that an increase of
one standard deviation in the share of people receiving an emotional shock decreases the
incumbent mayor vote share by approximately 5 − 5.8 percentage points on average. Putting
that into perspective, this is equivalent to flipping the result of 747 mayoral elections or 4.3%
of the sample. This effect is stronger for games with higher stadium attendance, for local
teams in the first division, and for more intense emotional shocks.
Could random politically irrelevant events overturn the outcome of an election? In order to
provide an answer to this question, I show that the effect of emotional shocks is systematically
lower for elections that are decided by a small margin. Furthermore I find that a similar close
election pattern is also found in two different settings previously analyzed by the literature and
provide complementary evidence from Google searches that individuals actively seek more
information about candidates in close electoral races. Overall these results are consistent
with a political economy model in which voters’ preferences are affected by emotional cues
which may deviate their voting behavior from the forecasts of rational theory. Moreover, close
elections make information about candidates more salient in the media hence lowering the
attention cost to picking the best candidate, improving rational decision making of limited
attention voters and decreasing the bias induced by emotional shocks.45
45
See Sims (1998 and 2003) for an overview of rational innatention models. An alternative reasoning
would be that a rational inattentive voter would consciously devote more scarce attention effort to picking
a candidate whenever she is more likely to be pivotal. However, even with a finite number of voters the
probability of being pivotal in a large election is so small that it cannot be taken as a the main motivation
for voting or paying costly attention (Matějka and Tabellini, 2017).
28

References
[1] Abramowitz, Alan I. and Kyle L. Saunders. (2008). “Is polarization a myth?”The Journal
of Politics. 70(2): 542-555.
[2] Achen, C. H. and Bartels, L. M. (2004). “Blind Retrospection: Electoral Responses to
Drought, Flu, and Shark Attacks.”Unpublished paper.
[3] Achen, Christopher H. and Larry M. Bartels. (2016). “Democracy for Realists: Why Elec-
tions Do Not Produce Responsive Government”, Princeton University Press, Princeton,
NJ.
[4] Alesina, A. and F. Passarelli (2015). “Loss Aversion in Politics”, Tech. rep., National
Bureau of Economic Research, Cambridge, MA.
[5] Andersen, David J. (2011). “Pushing the Limits of Democracy: Concurrent Election and
Cognitive Limitations of Voters.”PhD Dissertation. New Jersey: The State University of
New Jersey
[6] Ashworth, S., Bueno de Mesquita, Ethan and Friedenberg, A. (2017). “Learning About
Voter Rationality”, Working paper
[7] Bagues, Manuel and Esteve-Volart, Berta (2016). “Politicians’ Luck of the Draw: Ev-
idence from the Spanish Christmas Lottery”Journal of Political Economy 2016 124:5,
1269-1294
[8] Bartik, Timothy (1991). “Who Benefits from State and Local Economic Development
Policies? ”W.E. Upjohn Institute.
[9] Bischoff, I., and Siemers, L.H.R. (2013). “Biased beliefs and retrospective voting: why
democracies choose mediocre policies,” Public Choice, 156, 163-180.
[10] Bloom, H. S. and H. D. Price (1975): “Voter response to short-run economic conditions:
The asymmetric effect of prosperity and recession,” American Political Science Review,
69, 1240-1254.
29

[11] Boxell, Levi and Gentzkow, Matthew and Shapiro, Jesse M., (2017). “Is the Internet
Causing Political Polarization? Evidence from Demographics”NBER Working Paper
No. w23258.
[12] Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. (2012a). “Salience and Experimen-
tal Tests of the Endowment Eﬀect.”American Economic Review Papers and Proceedings
102 (3): 47-52.
[13] Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. (2012b). “Salience Theory of
Choice Under Risk.”Quarterly Journal of Economics 127 (3): 1243-1285.
[14] Brennan, G., Buchanan, J.M., (1984). “Voter choice - evaluating political alterna-
tives.”American Behavioral Science 28, 185-201.
[15] Brollo, F., and Nannicini, T. (2012). “Tying Your Enemy’s Hands in Close Races: The
Politics of Federal Transfers in Brazil.”American Political Science Review, 106(4), 742-
761
[16] Brunner, Eric J., Stephen L. Ross, and Ebonya L. Washington. (2011). “Economics and
Policy Preferences: Causal Evidence of the Impact of Economic Conditions on Support
for Redistribution and Other Proposals.”Reviws of Economics and Statistics, 93 (3):
888-906.
[17] Bursztyn, L. and Cantoni, D. (2016). “A Tear in the Iron Curtain: The Impact of Western
Television on Consumption Behavior.”Review of Economics and Statistics, 98(1): 25-41
[18] Bursztyn, L., Cantoni, D., Funk, P. and Yuchtman, N. (2017) “Polls, the Press, and
Political Participation: The Eﬀects of Anticipated Election Closeness on Voter Turnout
”NBER Working Paper No. w23490.
[19] Busby, E., Druckman, J. and Fredendall, A. (2017) “The Political Relevance of Irrelevant
Events”The Journal of Politics 79:1, 346-350.
[20] Campello, Daniela and Cesar Zucco Jr., (2016). “Presidential Success and the World
Economy,”The Journal of Politics 78:2, 589-602.
[21] Carvalho, Carlos, Ribeiro, Ruy and Zilberman, Eduardo (2017) “Sentiment, Electoral
Uncertainty and Stock Returns ”Texto para DiscussÃ£o PUC-Rio No. 655
30

[22] Coelho, Thiago de Lucena (2014) “Futebol e politica: uso politico do futebol nas eleições
locais. ”Dissertação (Mestrado em Pós Graduação Mestrado Acadêmico e Doutorado) -
Fundação Getulio Vargas.
[23] Chetty, Raj, Adam Looney, and Kory Kroft. (2009). “Salience and Taxation: Theory
and Evidence.”American Economic Review, 99(4): 1145-77.
[24] Croxson, K. and James Reade, J. (2014). “Information and Efficiency: Goal Arrival in
Soccer Betting. ”Economics Journal, 124:62-91.
[25] Debnath, Sandip; David M. Pennock; C. Lee Giles and Steve Lawrence (2003). “Infor-
mation incorporation in online in-Game sports betting markets”, Proceedings of the 4th
ACM conference on Electronic commerce, June 09-12, 2003, San Diego, CA, USA
[26] DellaVigna, Stefano. (2009). “Psychology and Economics: Evidence from the
Field.”Journal of Economic Literature, 47(2): 315-72.
[27] DellaVigna, Stefano and Kaplan, Ethan (2007). “The Fox News Effect: Media Bias and
Voting.”Quarterly Journal of Economics; 122 (3): 1187-1234.
[28] Depetris-Chauvin, Emilioand Durante, Ruben (2017) “One Team, One Nation: Football,
Ethnic Identity, and Conflict in Africa, ”working paper
[29] Dittmann, I., Kubler, D., Maug, E., Mechtenberg, L., (2014). “Why votes have value:
instrumental voting with overconfidence and overestimation of others’ errors.”Games and
Economic Behavior 84, 17-38.
[30] Downs, A., (1957). “An Economic Theory of Democracy.”Harper & Brothers, New York.
[31] Durante, Ruben and Zhuravskaya, Ekaterina, (2016). “Attack When the World Is Not
Watching? U.S. News and the Israeli-Palestinian Conflict ”Journal of Political Economy,
Forthcoming
[32] Edmans, Alex, Diego Garcia, and Oyvind Norli. (2007). “Sports Sentiment and Stock
Returns.“Journal of Finance, 62(4): 1967-98.
[33] Enikolopov, Ruben, Maria Petrova, and Ekaterina Zhuravskaya. (2011). “Media and
Political Persuasion: Evidence from Russia.”American Economic Review, 101(7): 3253-
85.
31

[34] EPFL (2012). “Football Betting Manifesto.” Strategic proposal on Sport Betting
[35] Ferraz, Claudio; Finan, Frederico (2011). “Electoral Accountability and Corruption:
Evidence from the Audit Reports of Local Governments,”American Economic Review,
Vol.101:1274-1311.
[36] Fiorina, Morris P. and Samuel J. Abrams. (2008). “Political polarization in the American
public.”Annual Review of Political Science. 11: 563-588.
[37] Fowler, Anthony and B. Pablo Montagnes. (2015a). “College Football, Elections, and
False-Positive Results in Observational Research.”Proceedings of the National Academy
of Sciences, 112(45):13800-13804
[38] Fowler, Anthony and B. Pablo Montagnes. (2015b). “Reply to Healy et al.’s discussion
of auxiliary tests .”Accessed October 19, 2015.
[39] Fowler, Anthony and Hall, Andrew (2016).“Do Shark Attacks Influence Presidential
Elections? ”Tech. rep., Working Paper
[40] Forrest, D., Goddard, J., Simmons, R. (2005). “Odds-setters as forecasters: The case of
the football betting market.”International Journal of Forecasting, 21, 551-564
[41] Franck, E., Verbeek, E., and Nuesch, S. (2010). “Prediction accuracy of different market
structures - bookmarks versus a betting exchange.”International Journal of Forecasting,
26, 448-459.
[42] Frey, B.S., Stutzer, A., (2005). “Beyond outcomes: measuring procedural utility.”Oxford
Economics Papers. 57, 90-111.
[43] Fujiwara, Thomas (2015). “Voting Technology, Political Responsiveness, and Infant
Health: Evidence from Brazil”, Econometrica, 83(2): 423-464.
[44] Funke, M., Schularick, M. and Trebesch, C. (2016). “Going to extremes: Politics after
financial crises, 1870-2014, ”European Economic Review, Volume 88, Pages 227-260
[45] Gentzkow, M. and Shapiro, J. M. (2010), “What Drives Media Slant? Evidence From
U.S. Daily Newspapers.” Econometrica, 78: 35-71
32

[46] Goldsmith-Pinkham, P.; Sorkin, I. and Swift, H. (2018 )“Bartik Instruments: What,
When, Why, and How ”Working paper.
[47] Gomez, Brad T., Thomas G. Hansford, and George A. Krause. (2007). “The Republi-
cans Should Pray for Rain: Weather, Turnout, and Voting in U.S. Presidential Elec-
tions.”Journal of Politics 69: 649-663.
[48] Healy, Andrew J., Neil Malhotra, and Cecilia Hyunjung Mo. (2010). “Irrelevant Events
Aﬀect Voters? Evaluations of Government Performance.”Proceedings of the National
Academy of Sciences 107 (29): 12804:9.
[49] Healy, Andrew J., and Neil Malhotra. (2010). “Random Events, Economic Losses, and
Retrospective Voting: Implications for Democratic Competence.”International Quarterly
Journal of Political Science, 5 (2): 193-208.
[50] Healy, Andrew J., Neil Malhotra, and Cecilia Hyunjung Mo. (2015a). “Determining
False-Positives Requires Considering the Totality of the Evidence. ”Proceedings of the
National Academy of Sciences 112 (45):E6591.
[51] Healy, Andrew J., Neil Malhotra, and Cecilia Hyunjung Mo. (2015b). “Reply
to Fowler and Montagnes (2015): Discussion of auxiliary tests. ”Available at
dx.doi.org/10.7910/DVN/FKEZH6.
[52] Hirshleifer, David, and Tyler Shumway. (2003). “Good Day Sunshine: Stock Returns and
the Weather.”Journal of Finance, 58(3): 1009-32.
[53] Hvattum, L. M., and Arntzen, H. (2010).“Using ELO ratings for match result prediction
in association football.”International Journal of Forecasting, 26, 460-470
[54] Kamenica, Emir, and Matthew Gentzkow. (2011). “Bayesian Persuasion.”American Eco-
nomic Review, 101(6): 2590-2615.
[55] Kerr, J., Wilson, G. V., Nakamura, I. and Sudo, Y. (2005). “Emotional dynamics of soccer
fans at winning and losing games,”Personality and Individual Diﬀerences, Volume 38,
Issue 8, Pages 1855-1866, ISSN 0191-8869
33

[56] Klasnja, Marko and Titiunik, Rocio (2017). “The Incumbency Curse: Weak Parties,
Term Limits, and Unfulfilled Accountability”, American Political Science Review, 111
(1), 129-148
[57] Klein, J. G. (1991): “Negativity Effects in Impression Formation: A Test in the Political
Arena,”Personality and Social Psychology Bulletin, 17, 412?418.
[58] Larreguy, Horacio A., Marshall,John and Snyder,James M., Jr. (2016) “Leveling the
Playing Field: How Campaign Advertising Can Help Non-Dominant Parties, ”NBER
Working Paper Series No. 22949
[59] Lau, R. R. (1985): “Two Explanations for Negativity Effects in Political Behav-
ior,”American Journal of Political Science, 29, 119.
[60] Lee, David S. (2008). “Randomized experiments from non-random selection in US House
elections.”Journal of Econometrics, 142(2), 675-697
[61] Lelkes, Ypthach. (2016).“Mass polarization: Manifestations and measurements.”Public
Opinion Quarterly. 80(S1): 392-410.
[62] Lewis-Beck, Michael S., and Mary Stegmaier. (2007). “Economic Models of Vot-
ing.”Oxford Handbook of Political Behavior, edited by Russell Dalton and Hans-Dieter
Klingemann, 518-37. Oxford: Oxford Univ. Press
[63] Lim, Claire S. H., James M. Snyder Jr., and David Strömberg (2015). “The Judge, the
Politician, and the Press: Newspaper Coverage and Criminal Sentencing across Electoral
Systems, ”American Economic Journal: Applied Economics, 7(4): 103-35.
[64] Lockwood, Ben and James Rockey, 2015. “Negative Voters: Electoral Competition with
Loss-Aversion,”The Warwick Economics Research Paper Series (TWERPS) 1063.
[65] Loewenstein, George, and Jennifer S. Lerner. (2003). “The Role of Affect in Decision
Making.”in Handbook of Affective Sciences, ed. Richard J. Davidson, Klaus R. Scherer,
and H. Hill Goldsmith, 619-42. Oxford and New York: Oxford University Press
[66] McDermott, M. L. (2005). “Candidate Occupations and Voter Information Short-
cuts.”Journal of Politics, 67: 201?219.
34

[67] Magalhaes, Leandro de (2015). “Incumbency Effects in a Comparative perspective: Ev-
idence from Brazilian mayoral Elections”, Political Analysis, 23:133-136
[68] Magalhaes, Leandro de; Hirvonen, Salomo (2015). “Multi-Office Incumbency Advantage:
Political Careers in Brazil”, working paper
[69] Meier, Armando and Schmid, Lukas and Stutzer, Alois, (2016). “Rain, Emotions and
Voting for the Status Quo.”IZA Discussion Paper No. 10350.
[70] Nyberg, Henri (2014). “A Statistical Test of Association Football Betting Market Effi-
ciency ”, working paper
[71] Oliveira, Carlos and Turgeon, Mathieu (2015). “Ideologia e comportamento politico no
eleitorado brasileiro”, Opiniao Publica, vol.21, n.3
[72] Olken, B.A., (2010). “Direct democracy and local public goods: evidence from a field
experiment in Indonesia.”American Political Science Review 104, 243-267.
[73] Olson, Mancur (1965), “The Logic of Collective Action,”Cambridge, MA, Harvard Uni-
versity Press
[74] Pankoff, Lyn D. (1968), “Market Efficiency and Football Betting”, Journal of Business,
41, 103-114.
[75] Passarelli, F. and Tabellini, G. (2017). “Emotions and Political Unrest,”Journal of Po-
litical Economy, 125:3, 903-946
[76] Petrova, M, Sen, A, Yildirim, P. (2017). “Social Media and Political Donations: New
Technology and Incumbency Advantage in the United States, ”London, Centre for Eco-
nomic Policy Research.
[77] Prior, Markus. 2013. “Media and political polarization.”Annual Review of Political Sci-
ence. 16:101-127.
[78] Quattrone, G.A., Tversky, A., (1988). “Contrasting rational and psychological analyses
of political choice.”American Polititical Science Review 82, 719-736.
[79] Sauer, R. D. (1998). “The economics of wagering markets. ”Journal of Economic Liter-
ature, 36, 2021-2064.
35

[80] Saunders, Edward M., Jr. (1993). “Stock Prices and Wall Street Weather.”American
Economic Review, 83(5): 1337-45.
[81] Scheibehenne, B., Von Helversen, B., and Shevchenko, Y. (2014). “Change and status
quo in decisions with defaults: The eﬀect of incidental emotions depends on the type of
default.”Judgment and Decision Making, 9(3), 287-296.
[82] Schnellenbach, J. and Schubert, C. (2015). “Behavioral political economy: A survey”,
European Journal of Political Economy, Volume 40, Pages 395-417
[83] Simonsohn, U. (2010), “Weather To Go To College.”The Economic Journal, 120: 270-
280.
[84] Sims, C.A. 1998. “Stickiness.”Carnegie-Rochester Conference Series on Public Policy 49,
317-356.
[85] Sims, C.A. 2003. “Implications of Rational Inattention.”Journal of Monetary Economics
50(3), 665-690.
[86] Stephens-Davidowitz, Seth, and Varian, Hal. (2014). “A Hands-on Guide to Google
Data.”Tech. Rep., Google.
[87] Stevenson, J. (2010). “The story of the 1978 World Cup.”BBC Jonathan Stevenson’s
blog, accessed on 31st May 2017.
[88] Soroka, S. and S. McAdams (2015): “News, Politics, and Negativity,”Political Commu-
nication, 32, 1?22.
[89] Soroka, S. N. (2006): “Good news and bad news: Asymmetric responses to economic
information,”Journal of Politics, 68, 372?385.
[90] Strömberg, David, (2004). “Radio’s Impact on Public Spending, ”The Quarterly Journal
of Economics, ”119, issue 1, p. 189-221.
[91] Tyran, J. R. (2004). “Voting when Money and Morals Conﬂict. An Experimental Test
of Expressive Voting.”Journal of Public Economics, 88, 1645-64.
36

[92] Vlastakis, N., Dotsis, G., and Markellos, R. N. (2009). “How efficient is the European
football betting market? Evidence from arbitrage and trading strategies.”Journal of
Forecasting, 28, 426-444.
[93] Wolfers, Justin. (2007). “Are Voters Rational? Evidence from Gubernatorial Elec-
tions.”Manuscript, Wharton School, Univ. Pennsylvania.
[94] Yen, H.R. and Chuang, SC. (2008). “The effect of incidental affect on preference for
the status quo.”Journal of the Academy of Marketing Science, Volume 36, Issue 4, pp
522-537
37

year election
election date (Oct)
1st / 2nd round
number of
candidates
2
n
d
incumbent
party
incumbent
candidate
number of
municipalities
vote
share
number of
obs
2004 mayor 3rd / 31st 3.4 0.17 0.13 4,293 0.33 12,696
2008 mayor 5th / 26th 3.2 0.17 0.19 4,292 0.35 11,912
2012 mayor 7th / 28th 3.3 0.21 0.14 4,296 0.35 12,105
2016 mayor 2nd / 30th 3.6 0.18 0.15 4,292 0.33 12,933
Total - - 3.38 0.18 0.15 17,173 0.34 49,646
year election
election date (Oct)
1st / 2nd round
number of
candidates
2
n
d
incumbent
party
incumbent
candidate
number of
municipalities
vote
share
number of
obs
2006 governor 1st / 29th 8.3 0.11 0.08 4,292 0.13 32,215
2010 governor 3rd / 31st 6.4 0.12 0.07 4,291 0.16 26,261
2014 governor 5th / 26th 6.7 0.13 0.07 4,295 0.15 27,744
Total - - 7.21 0.12 0.072 12,878 0.15 86,220
Table 1 - Election Descriptive Statistics
PANELA: Mayoral Elections
The Table reports summary statistics of the mayoral and gubernatorial elections included in the analysis. The sample includes all municipality-year observations covering all Brazilian municipalities with more than 5,000
inhabitants with an incumbent candidate in the race, over seven consecutive elections (2004,2006, 2008, 2010, 2012, 2014 and 2016).
PANEL B: Gubernatorial Elections
38

year election
soccer
competition
number of
matches
number of
teams
mean(days)
before election
max(days)
before election number of goals
stadium
attendance
2004 mayor serie A 12 24 1 1 3.2 10,983
2006 governor serie A 12 20 6.1 8 2.4 14,013
2008 mayor serie A 10 20 1.7 4 3.6 16,470
2010 governor serie A / B 20 20+20 1.3 2 3.1 10,705
2012 mayor serie A / B 20 20+20 2.1 5 2.7 11,777
2014 governor serie A / B 20 20+20 2.1 5 2.4 15,055
2016 mayor serie A / B 20 20+20 3.1 9 2.5 10,340
Total 16.3 32.6 2.6 4.9 2.6 12,763
year election champioship home wins draw away wins home wins draw away wins
2004 mayor serie A 0.75 0.00 0.25 0.46 0.27 0.26
2006 governor serie A 0.50 0.25 0.25 0.46 0.27 0.27
2008 mayor serie A 0.60 0.10 0.30 0.50 0.26 0.24
2010 governor serie A / B 0.35 0.45 0.20 0.50 0.26 0.24
2012 mayor serie A / B 0.70 0.25 0.05 0.50 0.26 0.25
2014 governor serie A / B 0.55 0.25 0.20 0.51 0.26 0.22
2016 mayor serie A / B 0.77 0.08 0.15 0.50 0.27 0.23
Total 0.60 0.20 0.20 0.49 0.27 0.24
The Table shows summary statistics of the soccer matches and that precede the each election. Panel A reports statistics on the matches included in the sample, giving the number of matches and teams, number of days before the election, average goals
score and stadium attendance. Betting odds data are not available for serie B before 2010. Panel B shows average actual results and the associated betting market implied probabilities. The implied probability is simply the inverse of the betting odd for each
outcome (normalized to eliminate the commission of the betting house).
Table 3 - Soccer Match Data Description
PANELA: Summary Statistics
PANEL B: Actual Results and Implied Probabilities
odds market implied probabilityactual results
39

mean s.d. mean s.d. mean s.d. mean s.d.
all games 0.200 0.142 0.290 0.193 0.158 0.117 0.214 0.190
high intensity 0.001 0.008 0.057 0.098 0.037 0.063 0.021 0.064
low intensity 0.199 0.141 0.233 0.166 0.121 0.110 0.193 0.187
high attendance 0.094 0.124 0.149 0.146 0.028 0.046 0.004 0.016
low attendance 0.106 0.121 0.141 0.153 0.130 0.125 0.211 0.188
local team 0.052 0.104 0.070 0.154 0.078 0.123 0.071 0.142
non-local team 0.148 0.138 0.220 0.188 0.080 0.069 0.143 0.160
Serie A team 0.197 0.144 0.220 0.188 0.150 0.115 0.194 0.167
Not Serie A team 0.003 0.014 0.018 0.040 0.008 0.022 0.020 0.042
The Table shows summary statistics of the positive and negative emotional shocks variables for mayoral, gubernatorial and presidential elections. Σ sk,i positivek,t can be interpreted as the share of people in a
municipality that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of a soccer match. Negative shocks are defined accordingly. Section 3 describes how to
construct these variables in more detail. A high intensity negative shock is defined as a loss when it is predicted to win. A low intensity negative shock is defined as a draw when it is predicted to win or a loss
when it is predicted to have a close game. Positive shocks according to intensity are defined accordingly. Low (high) attendance games have stadium attendance below (above) 5,000 people (sample median).
A game has a 'local' team if it involves a team based within the same state where it is held. A team is predicted to win (lose) if its betting markets implied probability of winning is higher (lower) than 0.50. A
match is predicted to be close if neither teams are predicted to win.
Table 4 - Emotional Shocks Variables
Mayoral Elections (2004, 2008, 2012, 2016) Gubernatorial Elections (2006, 2010, 2014)
Σ sk,i positivek,t Σ sk,i negativek,tΣ sk,i positivek,t Σ sk,i negativek,t
40

(1) (2) (3) (4) (5)
Σ sk,i shockk,t x incparty 0.0506*** 0.0509*** 0.0562*** 0.0565*** 0.0579***
(0.013) (0.013) (0.012) (0.012) (0.012)
Σ sk,i shockk,t x (1-incparty) -0.011 -0.011 -0.009 -0.008 -0.007
(0.010) (0.010) (0.010) (0.010) (0.010)
incumbent party 0.132*** 0.132*** 0.125*** 0.123*** 0.123***
(0.002) (0.002) (0.002) (0.002) (0.002)
Observations 49561 49561 49561 49556 49547
R-square 0.166 0.166 0.185 0.192 0.192
Municipality Fixed-Effect Yes Yes Yes Yes Yes
State-year dummies Yes Yes Yes Yes Yes
Rainfall control No Yes Yes Yes Yes
Political Alignment No No Yes Yes Yes
Candidate characteristics No No No Yes Yes
Local Employment No No No No Yes
Table 5 - Emotional Shocks in Mayoral Elections
Incumbent Party Vote Share
The table reports regression estimates associating emotional shocks from unexpected soccer results and vote share in mayoral elections. Σsk,i shockk,t = Σsk,i positivek,t - Σsk,i
negativek,t with Σsk,i positivek,t defined as the share of people that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match
(see section 3 for a precise definition). Σsk,i negativek,t is defined accordingly. incparty is a dummy variable for incumbent party. All specifications include municipality and state-
year and fixed-effects (constants not reported). Columns (2)-(5) sequentially add controls for election-day rainfall, candidate’s political alignment with governor and president,
candidate personal characteristics such as gender, place of birth, age, marital status and schooling, and local employment rate. The unit of observation is a candidate in an election.
Heteroskedasticity-adjusted standard errors clustered at the state-year level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95%
(**) and 90% (*) confidence level.
emotional shock
41

(1) (2) (3) (4) (5)
Σ sk,i negativek,t x incparty -0.0493*** -0.0502*** -0.0663*** -0.0670*** -0.0669***
(0.018) (0.018) (0.018) (0.018) (0.018)
Σ sk,i positivek,t x incparty 0.0537*** 0.0534*** 0.0400* 0.0401* 0.0430**
(0.020) (0.020) (0.021) (0.021) (0.021)
Σ sk,i negativek,t x (1-incparty) 0.009 0.008 0.006 0.006 0.006
(0.015) (0.015) (0.015) (0.015) (0.015)
Σ sk,i positivek,t x (1-incparty) -0.013 -0.013 -0.011 -0.011 -0.008
(0.017) (0.017) (0.017) (0.017) (0.017)
incumbent party 0.130*** 0.130*** 0.130*** 0.128*** 0.128***
(0.005) (0.005) (0.005) (0.005) (0.005)
Observations 49561 49561 49561 49556 49547
R-square 0.166 0.166 0.185 0.192 0.192
Table 6 - Asymmetric Emotional Shocks in Mayoral Elections
Incumbent Party Vote Share
emotional shock
The table reports regression estimates associating emotional shocks from unexpected soccer results and vote share in mayoral elections. Σsk,i positivek,t is defined as the share of people
that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match (see section 3 for a precise definition). Σsk,i negativek,t is defined
accordingly. incparty is a dummy variable for incumbent party. All specifications include municipality and state-year and fixed-effects (constants not reported). Columns (2)-(5)
sequentially add controls for election-day rainfall, candidate’s political alignment with governor and president, candidate personal characteristics such as gender, place of birth, age,
marital status and schooling, and local employment rate. The unit of observation is a candidate in an election. Heteroskedasticity-adjusted standard errors clustered at the state-year
level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95% (**) and 90% (*) confidence level.
42

(1) (2) (3) (4) (5)
Σ sk,i shockk,t x inccand 0.0504*** 0.0508*** 0.0554*** 0.0551*** 0.0561***
(0.012) (0.012) (0.012) (0.012) (0.012)
Σ sk,i shockk,t x (1-inccand) -0.0216** -0.0213** -0.0186* -0.0181* -0.0170*
(0.010) (0.010) (0.010) (0.010) (0.010)
incumbent candidate 0.154*** 0.154*** 0.148*** 0.146*** 0.146***
(0.002) (0.002) (0.002) (0.002) (0.002)
Σ sk,i negativek,t x inccand -0.0458** -0.0470*** -0.0626*** -0.0623*** -0.0621***
(0.018) (0.018) (0.018) (0.018) (0.018)
Σ sk,i positivek,t x inccand 0.0596*** 0.0592*** 0.0481** 0.0479** 0.0503**
(0.020) (0.020) (0.020) (0.020) (0.020)
Σ sk,i negativek,t x (1-inccand) 0.014 0.013 0.010 0.009 0.009
(0.015) (0.015) (0.015) (0.015) (0.015)
Σ sk,i positivek,t x (1-inccand) -0.0313* -0.0317* -0.0290* -0.0291* -0.027
(0.017) (0.017) (0.017) (0.017) (0.017)
incumbent candidate 0.147*** 0.147*** 0.147*** 0.144*** 0.145***
(0.006) (0.006) (0.006) (0.006) (0.006)
Observations 49561 49561 49561 49556 49547
Incumbent Candidate Vote Share
emotional shock
The table reports regression estimates associating emotional shocks from unexpected soccer results and vote share in mayoral elections. Σsk,i shockk,t = Σsk,i positivek,t - Σsk,i negativek,t
with Σsk,i positivek,t defined as the share of people that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match (see section 3
for a precise definition). Σsk,i negativek,t is defined accordingly. inccand is a dummy variable for incumbent candidate. Panel A reports baseline estimates as in Table 5 and Panel B allows
the effect of positive and negative shocks to be asymmetric as in Table 6. All specifications include municipality and state-year and fixed-effects (constants not reported). Columns (2)-
(5) sequentially add controls for election-day rainfall, candidate’s political alignment with governor and president, candidate personal characteristics such as gender, place of birth, age,
marital status and schooling, and local employment rate. The unit of observation is a candidate in an election. Heteroskedasticity-adjusted standard errors clustered at the state-year
level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95% (**) and 90% (*) confidence level.
PANELA: Baseline Effect
PANEL B: Asymmetric Effect
43

baseline
High/Low
Stadium
Attendance
Local or Non-
local Team
First/Second
Championship
Division
High/Low
Shock Intensity
(1) (2) (3) (4) (5)
incumbent party 0.132*** 0.132*** 0.132*** 0.132*** 0.130***
(0.002) (0.002) (0.002) (0.003) (0.003)
Σ sk,i shockk,t x (1-incparty) -0.011 -‐ -‐ -‐ -‐
(0.010)
Σ sk,i shockk,t x incparty 0.0506*** -‐ -‐ -‐ -‐
(0.013)
Σ sk,i shockk,t x (1-incparty) x -‐ -‐0.0127 -0.0187 -0.0447 -0.013
LOW / NON-LOCAL / NOT-INTENSE / 2nd
(0.014) (0.013) (0.044) (0.010)
Σ sk,i shockk,t x incparty x -‐ 0.0416** 0.0458*** 0.007 0.0513***
LOW / NON-LOCAL / NOT-INTENSE / 2nd
(0.018) (0.015) (0.065) (0.012)
Σ sk,i shockk,t x (1-incparty) x -‐ -‐0.0104 0.0165 -0.00918 0.0345
BIG / LOCAL / INTENSE / 1st
(0.011) (0.023) (0.010) (0.023)
Σ sk,i shockk,t x incparty x -‐ 0.0560*** 0.0716*** 0.0505*** 0.0609**
BIG / LOCAL / INTENSE / 1st
(0.014) (0.026) (0.013) (0.028)
Observations 49561 49561 49561 49561 49561
The table reports hetrogeneous estimates associating emotional shocks from unexpected soccer results and vote share in mayoral elections. Σsk,i shockk,t = Σsk,i positivek,t - Σsk,i negativek,t
with Σsk,i positivek,t defined as the share of people that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match (see section 3 for
a precise definition). Σsk,i negativek,t is defined accordingly. incparty is a dummy variable for incumbent party. All specifications include municipality and state-year and fixed-effects
(constants not reported). Column (1) report the same baseline estimates as in column (1) in table 5 for exposition purposes. Columns (2)-(5) sequentially allows the effect of shocks to
differ according to (i) stadium attendance (below/above sample median of 10,000), (ii) whether or not a team belongs to same state, (iii) first or second division match, and (iv) whether
shock is of high or low intensity. A high intensity positive shock occurs when a team wins a match it is predicted to lose, and a low intensity positive shock occurs when a team draws a
match it is predicted to lose or wins a match predicted to be close. High/low negative shocks are defined analogously. The unit of observation is a candidate in an election.
Heteroskedasticity-adjusted standard errors clustered at the state-year level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95% (**)
and 90% (*) confidence level.
Heterogeneity Analysis
emotional shock
44

(1) (2) (3) (4) (5) (6) (7) (8)
Σ sk,i shockk,t x inc 0.196 0.196 0.244* 0.244* 0.218 0.218 0.357* 0.357*
(0.130) (0.130) (0.136) (0.136) (0.197) (0.197) -0.199 -0.199
Σ sk,i shockk,t x (1-inc) -0.0308 -0.031 -0.0391* -0.0391* -0.017 -0.017 -0.0265* -0.0266*
(0.020) (0.020) (0.021) (0.021) (0.013) (0.013) (0.014) (0.014)
incumbent 0.436*** 0.436*** 0.411*** 0.411*** 0.445*** 0.445*** 0.411*** 0.411***
(0.039) (0.039) (0.041) (0.041) (0.044) (0.044) (0.046) (0.046)
Σ sk,i negativek,t x inc -0.269* -0.270* -0.273* -0.273* -0.360** -0.361** -0.393** -0.393**
(0.137) (0.137) (0.140) (0.140) (0.160) (0.160) (0.186) (0.186)
Σ sk,i positivek,t x inc 0.037 0.037 0.179 0.179 0.005 0.005 0.302 0.302
(0.287) (0.287) (0.278) (0.278) (0.373) (0.374) (0.357) (0.357)
Σ sk,i negativek,t x (1-inc) 0.0414* 0.0412* 0.0448* 0.0449* 0.0202* 0.0199* 0.0266* 0.0266*
(0.022) (0.022) (0.023) (0.023) (0.012) (0.012) (0.014) (0.014)
Σ sk,i positivek,t x (1-inc) -0.008 -0.009 -0.028 -0.028 -0.006 -0.006 -0.025 -0.025
(0.038) (0.038) (0.037) (0.037) (0.022) (0.022) (0.022) (0.022)
incumbent 0.484*** 0.484*** 0.431*** 0.431*** 0.510*** 0.510*** 0.427*** 0.427***
(0.059) (0.059) (0.058) (0.058) (0.072) (0.072) (0.072) (0.072)
Observations 85875 85875 85875 85875 85875 85875 85875 85875
R-square 0.348 0.348 0.43 0.43 0.23 0.23 0.331 0.331
Municipality Fixed-Effect Yes Yes Yes Yes Yes Yes Yes Yes
State-year dummies Yes Yes Yes Yes Yes Yes Yes Yes
Rainfall control No Yes Yes Yes No Yes Yes Yes
Political Alignment No No Yes Yes No No Yes Yes
Local Employment No No No Yes No No No Yes
The table reports regression estimates associating emotional shocks from unexpected soccer results and vote share in gubernatorial elections. Σ sk,i positivek,t is defined as the share of
people that receive a positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match (see section 3 for a precise definition). Σ sk,i negativek,t
similarly captures negative emtional shocks. All specifications include municipality and state-year and fixed-effects (constants not reported). Columns (1)-(4) and (5)-(8) define
incumbency at the party and candidate level, respectively. Controls include dummies for election-day rainfall, candidate’s political alignment with governor and president, candidate
personal characteristics such as gender, place of birth, age, marital status and schooling, and local employment rate. Panel A reports baseline estimates as in Table 5 and Panel B allows
the effect of positive and negative shocks to be asymmetric as in Table 6. The unit of observation is a candidate in an election. Heteroskedasticity-adjusted standard errors clustered at
the state-year level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95% (**) and 90% (*) confidence level.
incumbent party incumbent candidate
Table 9 - Emotional Shocks in Gubernatorial Elections
Incumbent Vote Share
PANELA: Baseline Effect
PANEL B: Asymmetric Effect
emotional shock
45

(1) (2) (3) (4) (5)
Σ sk,i shockk,t -0.003 -0.003 -0.003 -0.003 -0.003
(0.003) (0.003) (0.003) (0.003) (0.003)
Σ sk,i shockk,t 0.006 0.006 0.006 0.006 0.006
(0.007) (0.007) (0.007) (0.007) (0.007)
Observations 17145 17145 17145 17145 17141
Candidate characteristics No No Yes Yes Yes
The table reports regression estimates associating emotional shocks from unexpected soccer results and other election outcomes. Panel A reports estimates with turnout rate as
dependent variables and Panel B with invalid votes (blank or null votes).Σsk,i shockk,t = Σsk,i positivek,t - Σsk,i negativek,t with Σsk,i positivek,t defined as the share of people that receive a
positive emotional shock relative to their ex-ante rational expectation towards the outcome of the match (see section 3 for a precise definition). Σsk,i negativek,t is defined accordingly.
All specifications include municipality and state-year and fixed-effects (constants not reported). The unit of observation is municipality-year pair. Heteroskedasticity-adjusted standard
errors clustered at the state-year level are reported in parentheses below the coefficients. Significantly different from zero at 99% (***), 95% (**) and 90% (*) confidence level.
Turnout and Invalid Votes
emotional shock
Panel A: Turnout Rate
Panel B: Invalid Votes (Blank and Null)
46

When are politically irrelevant events relevant to election outcomes?

When are politically irrelevant events relevant to election outcomes?

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to When are politically irrelevant events relevant to election outcomes?

Similar to When are politically irrelevant events relevant to election outcomes? (20)

More from Stockholm Institute of Transition Economics

More from Stockholm Institute of Transition Economics (20)

Recently uploaded

Recently uploaded (20)

When are politically irrelevant events relevant to election outcomes?