OWN-NATIONALITY BIAS: EVIDENCE FROM UEFA …pope/soccer_paper.pdfOWN-NATIONALITY BIAS: EVIDENCE FROM UEFA CHAMPIONS LEAGUE FOOTBALL REFEREES BRYSON R. POPE and NOLAN G. POPE∗ Weexaminetheexistenceandmagnitudeofown-nationalitybias

OWN-NATIONALITY BIAS: EVIDENCE FROM UEFA CHAMPIONS LEAGUEFOOTBALL REFEREES

BRYSON R. POPE and NOLAN G. POPE∗

We examine the existence and magnitude of own-nationality bias. Using player-match level data from 12 seasons of the UEFA Champions League (UCL) and refereeassignment policies that pair players and referees from the same country, we determinethe bias that referees exhibit toward players from their native country. Players officiatedby a referee from the same country receive a 10% increase in beneficial foul calls.Referees’ own-nationality bias is more pronounced for national team players, playersat home, and in later stages of the tournament. Elite referees exhibit as much, or more,own-nationality bias as their less experienced counterparts. (JEL L83, J15)

I. INTRODUCTION

Athletes continually search for ways to obtaineven the slightest advantage over their oppo-nents. It is often these slim margins that makethe difference in competitive games and leagues.League officials have the responsibility to orga-nize and monitor leagues such that no unfairadvantage is gained by any particular playeror team. Similarly, referees in football, and inmost sports, are hired precisely for the purposeof officiating matches with objectivity, and toprovide a fair playing environment. However,sometimes referees, whether consciously orsubconsciously, exhibit a systematic bias foror against players due to their race, gender,nationality, or other characteristics.

In this paper, we address whether own-nationality bias is exhibited by referees towardprofessional football (soccer) players in theUEFA Champions League (UCL). We alsoexplore how different types of players and set-tings affect the magnitude of the own-nationalitybias. These players and settings include nationalteam players, players at home, matches officiatedby the most elite referees, and in later stages of

∗We would like to thank Brent Hickman, Grant Gann-away, Joseph Price, Ryan Hill, Steven Levitt, Nathan Petek,and Mary Li for their helpful comments and discussion. Wealso thank Henrik Kleven, Camille Ladais, and EmmanuelSaez for use of their European football player database.Pope, B. R.: Department of Economics, Brigham Young Uni-

versity, Provo, UT 84601. Phone 801-854-8260, Fax 805-893-8830, E-mail [email protected]

Pope, N. G.: Department of Economics, University of Chic-ago, Chicago, IL 60615. Phone 801-995-9184, [email protected]

the tournament. Lastly, we look at how much, ifany, of the identified own-nationality bias is dueto cultural or linguistic similarities.

The data consist of 12 seasons of player-matchspecific information from the UCL, which is oneof the most prestigious professional club foot-ball tournaments in the world and brings in over€1.1 billion in annual revenue. The UCL tourna-ment consists of 10 months of matches with 76of the best club teams in Europe. It provides awindow into high stakes, competitive, and scruti-nized interactions between 402 of the most highlytrained football referees and 4,294 of the bestfootball players in the world, all from a total of105 countries.

UEFA assigns referee squads composed of4–6 referees from the same country to tour-nament matches such that a referee squad’snationality is different than that of the national-ity of either of the two teams being officiated.This method of assignment allows for our iden-tification strategy to detect whether refereesexhibit favoritism toward players from theirsame country, or in other words, own-nationalitybias. Although no referee squad and team arefrom the same country, oftentimes there are

ABBREVIATIONS

CAGE: Cultural, Administrative, Geographic and Eco-nomicGDP: Gross Domestic ProductNBA: National Basketball AssociationOLS: Ordinary Least SquaresUCL: UEFA Champions LeagueUEFA: Union of European Football Associations

1

Economic Inquiry(ISSN 0095-2583)

doi:10.1111/ecin.12180© 2014 Western Economic Association International

2 ECONOMIC INQUIRY

individual players who are from the same coun-try as the referee squad. These same countrypairings of referee squads and individual playersallow for a within-player analysis of referees’own-nationality bias. For a specific player acrossgames, we compare the fouls called by a refereesquad from his home country to the fouls calledby a referee squad not from his home country.

We find that players in a same country referee-player pairing receive more favorable foul callsthan when they are not in a same country referee-player pairing. On average, having a refereesquad from the same country increases the num-ber of beneficial foul calls received by a playerby about 10%. The amount of own-nationalitybias exhibited by referees is more pronounced(15–20%) for national team members, whenplayers are playing at their home venue, forthe highest qualified referees, and during thelater stages of the tournament. We also find thatthe own-national bias found cannot be solelyattributed to similarities in language or culture ofreferees and players from the same country.

There are two particularly pertinent studiesthat analyze racial bias in athletics. Price andWolfers (2010) show that in the National Basket-ball Association (NBA), more personal fouls arecalled against players when they are officiated byan opposite-race refereeing crew than when theyare officiated by an own-race refereeing crew.Also, Parsons et al. (2011) find that an umpirecalls fewer strikes when the pitcher is of a differ-ent race than the umpire. They also show that thisis particularly true when there is low scrutiny ofthe officiating. When a computerized camera sys-tem is in place, which allows pitches to be shownelectronically on television and online, this biasis nearly eliminated. The bias also decreases withhigh attendance and later in the game. It appearsthat baseball pitchers attempt to compensate forthe bias by throwing less subjective pitches suchas fast-balls over home plate in order to give theumpire less of an opportunity to discriminate.

As so much research has been done on racialbias in many different settings, it is notable thatlittle research has been done on own-nationalitybias. With the intense patriotism exhibited bypassionate fans, football is a likely place forown-nationality bias to be demonstrated. Foot-ball hooligans are, in fact, stereotypically knownfor their ardent support of their team of choice.The frequent violent after-match events suchas killings, beatings, and taunting of opposingteam supporters indicate that at least the fans,at times, act on these biases. The ubiquitous

nature of football riots and on-the-pitch inter-player displays of discrimination, such as play-ers being fined for shouting racially deroga-tory remarks toward opposing players, raisesthe question of whether referees also show par-ticular forms of bias toward or against certainplayers. We hypothesize that just as basketballreferees and baseball umpires exhibit own-racebias in their calls, we will observe professionalfootball referees exhibiting an own-nationalitybias toward players with the same nationality asthemselves by awarding them with more bene-ficial calls.

Although racial bias has been given muchwider attention by the literature than that ofown-nationality bias, Zitzewitz’s (2006) researchon Olympic events is a notable exception. Hefinds that Olympic figure skating and ski jump-ing judges display biases in favor of athletes fromtheir same country. He notes that these own-nationality biases can be quite large. Using datafrom the 2002 Winter Olympics he finds thatboth figure skating and ski jumping judges scoretheir compatriots about 0.13 standard deviationshigher than other judges.

The rest of the paper will proceed as follows.Section II gives a detailed description of the UCLdataset as well as the specifics of the UCL tour-nament. Section III gives an in-depth discussionof our identification strategy and model. SectionIV reports the results along with a discussion ofwhy such own-nationality biases occur. SectionV concludes.

II. DATA AND INSTITUTIONAL BACKGROUND

Our main dataset is player-match informa-tion drawn from the Union of European Foot-ball Associations’ (UEFA) official website.1 Thedataset consists of matches from the UCL whichis the most lucrative club football competition inthe world with an estimated gross commercialrevenue of €1.1 billion.2 Different from interna-tional football in which each team consists ofplayers all from the same country, club foot-ball teams consist of players from all around theglobe. In the UCL, nearly half of all players arenot from the country in which the club team theyplay for is located. In addition to on-site spec-tators, the most recent 2012–2013 UCL tourna-ment attracted more than 360 million television

1. http://www.uefa.com/uefachampionsleague2. http://www.uefa.org/management/finance/news/news

id=1840934.html

POPE & POPE: OWN-NATIONALITY BIAS 3

viewers.3 The tournament has been held annuallysince 1955 for the top European football clubsfrom 54 countries.4 The UCL lasts the better partof a year (from July to May), and consists of qual-ification rounds, a 32-team group stage, and a 16-team knockout stage.

The formal tournament begins with 32 teams.Of these teams, 22 earn an “automatic entrant”status by their high ranking in their home coun-try league. The remaining 10 spots in the tourna-ment are selected from 54 teams that participatein three qualifying rounds, with the lower seededleague teams entering in the first round and thehigher seeded league teams entering in the laterrounds. These 32 teams are divided into eightgroups of four to begin the group stage. The groupstage proceeds in a round-robin format with everyteam playing the other three teams twice, bothat home and away. Each win is awarded threepoints, each draw is awarded one point, and eachloss is awarded zero points. The two teams withthe most points awarded advance to the knock-out stage where the top seed from each group ismatched against a second-seed team from anothergroup.5 The round of 16, quarterfinals, and semi-finals are identical in setup with two matchesplayed between the two teams, one home gameand one away game for each team. The teamwith the highest aggregated score from the twomatches moves to the next round. In contrast, thefinals match is a one-match affair played at a pre-determined neutral location.

Teams have strong incentives to do well in theUCL tournament. For one, the prestige and evenfame that comes from winning this tournamentmay result in greater ability to recruit the top foot-ballers.6 The financial incentives are also signif-icant. In 2012, teams in the 32-team group stagewere guaranteed at least €7.2 million. For eachadditional advanced round, teams are guaranteedto earn an additional €3–€9 million with the win-ner receiving a guaranteed total of €26.7 mil-lion. Besides each team’s guaranteed allotment,each team earns additional money depending onthe proportion of the broadcasting revenue from

3. Faisal Chishti. “Champions League final at Wemb-ley drew TV audience of 360 million” (May 30, 2013).Sportskeeda. Absolute Sports Private Limited.

4. http://www.uefa.com/memberassociations5. The one exception is that no team will play a runner up

from their own country.6. Pope and Pope (2009) show that at the collegiate level,

universities receive more applications the years immediatelyfollowing a strong showing in the NCAA March Madnesstournament.

within the territory of their respective nationalassociations. In 2012, teams in the 32-team groupstage earned between €8.6 and €62.9 million withan average of €24.8 million per team.7 In general,the more games a team plays on television andwins, the more prize money the team will earnthroughout the tournament.

The dataset from UEFA’s websites providesdetailed match and player-specific data for everymatch in the UEFA Champions League for the2001–2002 to 2012–2013 seasons. For eachmatch, each team lists 18 players on their ros-ter who are eligible to play in that match. Foreach 90-minute match, 11 starters begin on thepitch and each team is allowed a maximum ofthree player substitutions. The dataset containsinformation for all players on the roster for eachmatch. Player information includes whether theplayer started or was a substitution and how manyminutes the player was on the pitch. These dataalso include individual statistics on the number offouls committed, fouls suffered (fouls committedagainst the player by the opposing team), cau-tionary cards, offside calls, goals scored, assists,shots on target, and shots off target. Columns 1and 2 of Table 1 give the individual field play-ers’ (excluding goalkeepers) summary statisticsfor those who played at least 30 minutes. The firstvariable in Table 1, the difference in fouls suf-fered and fouls committed, shows the net benefi-ciary calls obtained by a player. A positive fouldifference means the player received more bene-ficial foul calls than disadvantageous calls. Overthe 12-year period of these data, 2,563 matcheswere played with over 4,000 different footballplayers. All statistics are also available at theteam level.

Besides player and team information for eachmatch, the UCL data also include the name andnationality of the center and sideline referees. Allreferees in a referee squad assigned to a matchare from the same country. For example, thecenter referee, both sideline referees, and a fourthreferee would all be from Norway. Figure 1 showsthe fraction of matches with referee squads froma given country. German referee squads are themost common with Italian, Spanish, French, andEnglish referee squads following close behind.

An additional dataset provides further playerinformation by combining Kleven, Landais, andSaez’s (2013) European football player database

7. http://www.uefa.org/management/finance/news/newsid=1840934.html

4 ECONOMIC INQUIRY

TABLE 1Summary Statistics

Paired Players

All Observations Paired Matches Unpaired Matches Difference in Means

Variable MeanStandardDeviation Mean

StandardDeviation Mean

StandardDeviation

Paired- All

Paired -Unpaired

Suffered − committed −0.06 1.62 0.13 1.80 −0.06 1.80 0.19 0.19Fouls suffered 1.06 1.36 1.31 1.47 1.29 1.40 0.25 0.02Fouls committed 1.12 1.34 1.19 1.31 1.34 1.43 0.07 −0.15Yellow cards 0.16 0.38 0.14 0.37 0.16 0.38 −0.02 −0.02Red cards 0.01 0.09 0.01 0.08 0.01 0.08 0.00 0.00Offsides 0.21 0.64 0.26 0.77 0.31 0.79 0.05 −0.05Goals scored 0.12 0.37 0.12 0.36 0.15 0.42 0.00 −0.03Assists 0.07 0.27 0.11 0.33 0.10 0.32 0.04 0.01Shots on target 0.42 0.80 0.49 0.87 0.56 0.95 0.07 −0.07Shots off target 0.40 0.76 0.52 0.85 0.53 0.87 0.12 −0.01Minutes played 83.16 13.91 83.34 13.21 83.91 12.93 0.18 −0.57UCL games played 10.58 12.71 23.20 19.89 23.20 19.89 12.62 0.00Number of players 4,294 259 259Number of matches 2,563 328 1,816Player-match obs 45,459 435 5,573

Notes: All observations include all player-match observations from 2001 to 2013. Paired players are players who have playedin at least one match with a referee squad from same country. Paired matches are matches in which the player has a referee squadfrom the same country. Suffered minus committed are the number of fouls committed against a player by the opposing teamminus the number of fouls the player commits against the opposing team.

FIGURE 1Fraction of Player Observations from Top 25 Countries

with UEFA’s football player database. This com-bined data provide crucial information on indi-vidual player’s nationality along with their ageand whether they played for their national team ornot. Figure 2 shows the fraction of players from agiven country for the 25 countries with the most

players in the UCL. Of the top 25 countries, allbut Brazil and Argentina are European countries.

Lastly, we use the Cultural, Administrative,Geographic and Economic (CAGE) similarityindex designed by Pankaj Ghemawat.8 The

8. http://www.ghemawat.com/cage/


FIGURE 2Fraction of Referee Observations from Each Country

CAGE index was designed to measure how sim-ilar two countries are with the goal of informingbusiness managers how easy or difficult it is toconduct business between the two countries. TheCAGE index uses 16 factors such as language,religion, trade, distance, and gross domesticproduct (GDP) to relate 162 countries with eachother. This index gives a measure of more than99% of the referees’ and players’ countries inour UCL dataset. The individual factors that theindex takes into account to relate referees’ andplayers’ countries and the index itself allow us todetermine if the identified own-nationality biasis being driven by cultural, linguistic, or othersimilarities between countries.

III. METHODOLOGY

Our empirical strategy relies heavily on howreferee squads are assigned to matches. Foreach stage of the tournament an administrativecommittee known as the UEFA Refereeing Unitcreates a proposal of referee assignments for eachmatch. This proposal is then discussed, revised,and finalized by the UEFA Referee Committee.The two committees use observer marks, per-formance, personality, availability, development,administrative factors, and successful comple-tion of the relevant written and fitness tests todetermine referee assignment.9 In addition to the

9. http://www.uefa.org/MultimediaFiles/Download/Tech/uefaorg/General/01/89/25/77/1892577_DOWNLOAD.pdf

two committees, there are two guiding policiesfollowed by UEFA when assigning referees.First, to discourage bias, no referee squad canbe from the same country as either of the clubs.For example neither an English or Spanish ref-eree squad would be assigned to a ManchesterUnited vs. Barcelona match. Second, refereeassignments remain confidential up to 2 daysbefore the match for the purpose of minimizingpublic influence.

Our identification strategy exploits the factthat a referee squad is never assigned to officiatea club team from the same country as the ref-eree squad. However, while officiating clubs fromcountries other than their own, referee squadsmay at times officiate a team with a player fromtheir home country. That is to say there are nosame country referee-team pairings but there arefrequent same country referee-player pairings.Our basic strategy compares a player who is offi-ciated by a referee squad from his home coun-try to himself when he is officiated by a refereesquad not from his home country. For example,if a Portuguese player playing on a team based inSpain had a match against a team in Germany, it ispossible that a referee squad from Portugal couldbe assigned to officiate the match. That specificplayer would then have a referee squad from hisown country, whereas all other non-Portugueseplayers would not. In this example, we desig-nate this player as a “paired player” since hehad at least one match with a referee squad fromhis home country and designate the match as a

6 ECONOMIC INQUIRY

FIGURE 3Fraction of Player-Referee Pairings from Each Country

“paired match” since the referee squad is fromhis home country. Figure 3 gives the proportionof referee-player pairings from each country. Thelargest portion of referee-player pairings consistsof a player and referee squad both of whom arefrom France.

The ideal identification strategy would be ifreferees were assigned randomly to matches. Ifreferee assignments were random, then referees’own-nationality bias could easily be identified bytaking the difference in beneficial calls betweenmatches when a player is assigned an own-nationreferee and matches when the same player is notassigned an own-nation referee. Although refereeassignment is not strictly random, the informa-tion used by UEFA to make referee assignmentis unrelated to pairing players and referees fromthe same nation but rather uses information onother factors such as referees’ availability, fit-ness tests, and performance level. Although ref-eree assignment is not random, it is likely randomwith regard to how it assigns referees to play-ers from their same nation. In addition, becausewe compare players with a same nation refereeto themselves without a same nation referee byusing individual fixed effects, it would take a spe-cial type of selection in the referee assignmentprocess to explain the results of referees exhibit-ing own-nationality bias. For example, in orderfor the referee assignment process to explainthe results, referees would have to be assignedin a very particular and unlikely manner. For a

selection bias to exist referees would have to beassigned to matches with a paired player in whichthe paired player was well behaved and com-mitted less fouls and players on the opposingteam were ill behaved and committed more foulsagainst the paired player. This type of selectionin the referee assignment process would requirethe UEFA committees to know ex ante in whichgames a player would be well behaved and com-mit fewer fouls and the other team would be rel-atively ill behaved and commit more fouls. Inaddition to knowing these two things ex ante, theUEFA committees would also need to make ref-eree assignments according to this information.Although this type of referee assignment is pos-sible, it seems unlikely to be the case.

Table 1 shows a simple outline of the identifi-cation strategy to be used along with results fromdifferences in group means. Columns 3 and 4 arethe player-match summary statistics for playersin matches in which the player and referee squadare from the same country (i.e., paired playersin paired matches). Columns 5 and 6 are theplayer-match summary statistics for players whohad at least one match with a same country ref-eree squad, but in matches in which the playerand referee squad are not from the same country(i.e., paired players in unpaired matches). Col-umn 7 displays the difference between variablemeans for paired players in paired matches andall player-match observations. The paired play-ers’ foul difference (suffered minus committed)


is 0.19 fouls higher than the foul difference forall observations. This corresponds to roughly an8% increase in beneficial foul calls for playerswith a referee squad from the same country. Thereis no such benefit for cautionary cards or off-side calls. This difference between paired playersin paired matches and all player-match observa-tions in column 7 could be driven by both thebeneficial effect of having an own-nation ref-eree and by any underlying differences betweenpaired and unpaired players. Column 8 showsthe difference between the mean of each vari-able for paired players in paired matches andpaired players in unpaired matches. The resultsof this comparison are nearly identical to col-umn 7, with an 8% increase in beneficial foulcalls and no change in cautionary cards or offsidecalls. The difference between paired players inpaired matches and unpaired matches is no longerdriven by any underlying differences betweenpaired and unpaired players and is just the effectof having an own nation referee. Because thedifferences in columns 7 and 8 were so sim-ilar it appears that the underlying differencesbetween paired and unpaired players are small.However, the difference shown in column 8 isunbalanced and therefore the below analysis willimplement a within-player analysis using playerfixed effects.

Formally, the model to be used is as follows:

Yi = α + βsamecountryi + δXi + γMi(1)

+ φHi + πAi + μi + ηi + νi

+ θi + εi

where Yi is the outcome of interest (e.g., fouldifference, fouls committed, fouls suffered, dis-ciplinary cards, etc.) for a specific match-playerobservation i. Our main outcome of interest isfoul difference, which is calculated as the num-ber of fouls suffered by a player minus the num-ber of fouls committed by that player. A big-ger foul difference implies a larger number ofbeneficial foul calls for that player. The vari-able samecountryi is a binary variable equal toone if the player and referee squad are from thesame country and zero if not. Additionally, Xi isa vector of player characteristics including age,age squared, minutes played, and whether theplayer started the match. The vector Mi containsmatch characteristics including year of matchfixed effects, month of match fixed effects, andtournament round fixed effects. The vector Hicontains the player’s own-team match statisticsof the number of goals scored, shots on target,

shots off target, and corner kicks. The vector Aicontains similar opposing-team match statistics.The variables μi, ηi, νi, and θi are player, ref-eree, own-team, and opposing-team fixed effects,respectively. No measures of player aggressive-ness, such as tackles, are available in these datato allow us to analyze how playing style similar-ities between players and referees from the samecountry might affect the results.

IV. RESULTS

Table 2 shows the estimates of the amountof own-nationality bias in referees’ foul callsusing various specifications of the model inEquation (1). The outcome variable for Table 2is the foul difference (fouls suffered minus foulscommitted). For all results, unless otherwisespecified, estimates are for players who playedat least 30 minutes in the match excluding goal-keepers. Column 1 has no controls and is simplythe difference between paired players in pairedmatches to all players in unpaired matches.This is the same value as the “paired minus all”column in Table 1 of 0.187. This implies thatplayers from the same country as the refereesquad receive roughly one-fifth more of a bene-ficial foul call per match. Compared to the baseof 2.18 foul calls per player per match this isan 8.5% increase in beneficial foul calls. This isequivalent to a 0.12 standard deviation increasein beneficial foul calls. This estimate, however,could be bias due to systematic differences inpaired players and unpaired players. Column2 adds player fixed effects so the estimate isthe difference in beneficial foul calls between aplayer who is officiated by a referee squad fromhis/her same home country and himself/herselfwhen he/she is officiated by a referee squad notfrom his/her home country. With player fixedeffects, the estimate of the amount of own-nationality bias in foul calls increases to 0.23and is statistically significant at the 0.01 level.This is again roughly a 9% increase in beneficialfoul calls.

Column 3 adds controls for player characteris-tics, own team statistics, opposing team statistics,and match information. Adding these controlscauses virtually no change in the own-nationalitybias in foul calls. Column 4 adds own team,opposing team, and referee fixed effects. Againthere is little change in the point estimates. Col-umn 5 includes a match fixed effect. In column6 the sample is restricted to only those play-ers who started the match. Column 7 uses the

8 ECONOMIC INQUIRY

TABLE 2The Effect of a Same Country Referee on Foul Difference

Foul Difference (Suffered − Committed)

Variable (1) (2) (3) (4) (5) (6) (7)

Same country 0.187** 0.230*** 0.224** 0.215** 0.220** 0.206** 0.234**[0.086] [0.088] [0.088] [0.089] [0.095] [0.093] [0.097]

Started — — −0.05 −0.04 −0.04 — −0.10[0.042] [0.043] [0.045] [0.073]

Minutes played — — 0.002*** 0.002*** 0.002*** 0.002*** 0.004***[0.001] [0.001] [0.001] [0.001] [0.001]

Age — — 0.05 0.04 0.04 0.04 0.03[0.045] [0.047] [0.051] [0.049] [0.053]

Age2 — — 0.00 0.00 0.00 0.00 0.00[0.001] [0.001] [0.001] [0.001] [0.001]

Own teamGoals — — 0.00 0.00 0.108** 0.00 0.00

[0.008] [0.008] [0.052] [0.008] [0.009]Shots on target — — 0.00 0.00 0.01 0.00 0.00

[0.004] [0.004] [0.026] [0.004] [0.005]Shots off target — — 0.014*** 0.014*** 0.047** 0.013*** 0.016***

[0.004] [0.004] [0.020] [0.004] [0.004]Corners — — 0.01 0.00 −0.067** 0.01 0.00

[0.004] [0.004] [0.028] [0.004] [0.004]Opposing team

Goals — — 0.021*** 0.026*** 0.136*** 0.027*** 0.028***[0.008] [0.008] [0.051] [0.008] [0.009]

Shots on target — — −0.021*** −0.022*** −0.02 −0.022*** −0.022***[0.004] [0.004] [0.026] [0.004] [0.005]

Shots off target — — 0.00 0.00 0.03 0.00 0.00[0.004] [0.004] [0.020] [0.004] [0.004]

Corners — — −0.013*** −0.014*** −0.085*** −0.014*** −0.015***[0.004] [0.004] [0.028] [0.004] [0.004]

Fixed effectsPlayer FE X X X X X XTournament stage FE X X X X XYear FE X X X X XMonth FE X X X X XOwn team FE X X X XOpposing team FE X X X XReferee FE X X X XMatch FE XObservations 45,455 45,455 45,401 45,401 45,401 43,204 45,401R2 0.00 0.18 0.18 0.20 0.21 0.21 0.20

Notes: For all specifications the outcome variable of interest is fouls suffered minus fouls committed (number of beneficialfoul calls). The model used is shown in Equation (1). The sample is restricted to players who played at least 30 minutes forcolumns 1–5 and to starters for column 6. Column 7 weights players’ foul difference by the number of minutes played. Standarderrors are clustered at the match level and are reported in brackets.

***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level.

outcomes variable of foul difference weighted by90 divided by the number of minutes played bythe player so that the point estimates are to beinterpreted as the number of beneficial foul callsper full match played for players of the samenationality as the referee. For example if a playeronly played half of the match and has a fouldifference of 2, his/her weighted foul differencewould be doubled to 4. Regardless of which spec-ification is used, a player being officiated by acorresponding same home country referee squad

receives an approximately 0.22 increase in ben-eficial foul calls during the match. Given thatthe average number of combined fouls commit-ted and suffered per player per match is 2.2 forall players and 2.5 for paired players, this resultsin roughly a 9% increase in beneficial foul calls.Owing to the consistent nature of the estimatesregardless of the specification chosen, the spec-ification in column 4 will be used for all furtheranalysis performed in this section. We denote thisspecification as the full model.


The 0.22 foul benefit in the foul differenceconsists of two parts: fewer fouls committed andmore fouls suffered. Table 3 uses the full modelto look at other outcomes besides foul differenceincluding fouls committed and fouls suffered.Columns 1 and 2 display the estimates of thecoefficient on the same country binary variablewith fouls committed and fouls suffered as theoutcome variable. The 0.22 foul benefits from aplayer being from the same country as the ref-eree squad appears roughly equally split betweenthe two parts. In each case the signs are in theexpected direction, but are at best marginallysignificant. Columns 3 and 4 show that thereis no own-nationality bias for offside calls orfor cautionary (yellow or red) cards given. Thiscould be the case for a number of reasons. Onepossibility is that there are fewer offside calls andsubstantially fewer cards given to an individualplayer each match compared to normal fouls.Another plausible reason is illustrated in thefindings of Parsons et al. (2011) with regard tobaseball umpires and pitchers. In evaluating samerace match ups between pitchers and umpires,they find that when umpires are monitoredunder a system of computerized cameras used toevaluate the umpires ball/strike calls, evidenceof any race or ethnicity preference disappearsentirely. Under such explicit evaluation, umpireshave strong incentives to suppress any bias thatcould be detrimental to their own career. Withmodern day replay it is usually clear whether thelinesman made the correct offside call or not.Even with modern day replay, however, foulsand other infractions are far more subjective.Similar to umpires, football referees may beaware that offside calls will place them underexplicit evaluation and give them a strongerincentive to suppress any own-nationality bias. Itis also interesting to note that typically linesmenare less experienced referees whereas the mostelite referees are assigned more frequently to bethe center referee position.

In addition to looking at offside calls and cau-tionary cards, columns 5 and 6 look at whetherteam managers adjust their coaching decisionsdue to player-referee pairings and play pairedplayers more. Again, Parsons et al. (2011) findthat pitchers, either consciously or subcon-sciously, recognize the bias being exhibited byumpires and as a result, change their behavior inorder to compensate. When bias is being exhib-ited the pitcher will throw the less subjectivefastball over home plate more often than usual.It may be the case that football managers exhibit

a similar change in behavior in order to compen-sate. Column 5 shows that there is no statisticalincrease in the number of minutes played when aplayer is paired with the referee squad. Column6 also shows that players paired with the refereesquad are no more likely to enter the match. Itappears there is no substantial shift in coachingstrategy by allowing a player who creates aplayer-referee pairing to play more minutes or beon the pitch in that particular match. This findingthat managers do not change their coachingbehavior due to player-referee pairings has atleast two possible explanations. First, managersmight be unaware of the beneficial foul callsreceived by paired players and therefore do notchange their behavior. Second, managers maybe aware of the beneficial foul calls receivedby paired players and are in fact behaving opti-mally, but believe that the benefit of gettingmore beneficial foul calls from a paired playeris outweighed by the cost of playing a less ableplayer. As paired players receive more beneficialfoul calls but do not receive more beneficial callson the more important card decisions, it is likelythat having a more able player on the field ismore influential to the outcome of the match thanhaving a less able paired player. Later in Table 6,we consider the impact of having a paired playeron match outcomes.

Table 4 shows estimates of own-nationalitybias in beneficial foul calls using the full modelfor different types of players, matches, andreferees. Column 1 restricts the sample to onlythose players who play for their home coun-try’s national team.10 The national team is thetop squad for any given country which usuallyconsists of 18–22 players. These players are typi-cally widely recognized by citizens of their coun-try and are high-profile figures. When restrictedto only national players, the own-nationalitybias increases to 0.28 and is highly signifi-cant. Column 2 shows that the own-nationalitybias completely disappears when the sample isrestricted to players who are not national teammembers. This difference could be due to refereesrecognizing that national team players are fromthe same country as themselves, whereas they failto recognize the players not on the national teamto be from the same country as themselves. Alter-natively, referees might recognize that all same

10. The national team consists of only those players whoplayed on the adult national team. For example, the USA’s U-21 team is technically a national team, however it would notclassify as the national team in this context.

10 ECONOMIC INQUIRY

TABLE 3The Effect of a Same Country Referee on Other Variables

Variable Fouls Committed Fouls Suffered Offsides Card Minutes Played Enters Match

Same country −0.122* 0.093 0.001 −0.004 0.853 0.005[0.065] [0.064] [0.035] [0.018] [1.129] [0.015]

Observations 45,401 45,401 45,401 45,401 53,469 67,132R2 0.374 0.403 0.378 0.155 0.359 0.328

Notes: The top of each column indicates the outcome variable of interest. The model used is the same as column 4 of Table 2and is shown in Equation (1). The sample is restricted to players who played at least 30 minutes in the match. Standard errors areclustered at the match level and are reported in brackets.


country players are from their home country,but there exists a closer nationality associationbetween the referees and high-profile nationalteam players than with nonnational team players.

A number of studies have shown that refer-ees can be pressured by crowds to favor the hometeam (Buraimo, Forrest, and Simmons 2010; Gar-icano, Palacios-Huerta, and Prendergast 2005).Scoppa (2008) found that professional Italian ref-erees added more injury time if home teams werelosing. Sutter and Kocher (2004) and Dohmen(2008) found that, for the German Bundesliga,referees added more time in matches where thehome team was behind. Columns 3 and 4 ofTable 4 restrict the sample to players in matchesat home and matches away from home. We findthat the own-nationality bias is also larger forplayers at home than for players away from home.It appears that referees are more willing to act ontheir own-nationality favoritism when the homecrowd is supportive of their decision.

Biases are often thought to attenuate or disap-pear in high stakes, competitive, and scrutinizedsituations or with highly trained individuals(Parsons et al. 2011). The UCL setting is a highstakes, competitive, and scrutinized situation.To assure the employment of the highest qualityreferees, UEFA systematically reviews how eachreferee performs. For every UEFA match playedin the tournament, an observer is assigned toevaluate the performance of the referee. Allobservers are experienced former Europeanreferees. According to UEFA guidelines theymust conduct an oral debriefing with the ref-eree after the match and then produce a writtenreport along with a score or mark between 0and 10 to rate their performance. These refereeobservers’ reports are kept confidential withinthe referee committee. The UCL referees arealso highly trained and experienced. UEFA hasfour main categories of referees ranging from

third class to Elite. Each increase in level isaccompanied by a wage increase per matchranging from €100 to €3,000. To achieve Elitestatus a referee must be very experienced andhave consistently performed well in the past. Forexample, for the 2012/2013 season there were atotal of 36 elite referees. Among these referees,they averaged roughly 13 UEFA champion’sleague appearances each. They also averagedover 22 years as a professional referee and over7 years with the FIFA badge which is requiredto be eligible to referee UEFA championshipgames. Using UEFA’s referee categories, we areable to vertically differentiate referees. Typicallyit is assumed that better and more experiencedreferees show fewer biases. The results in column5 and 6 of Table 4 show that the elite refereesin fact exhibit as much or more own-nationalitybias as non-elite referees. The own-nationalitybias in foul calls appears to persist despite, orperhaps because of, the high stakes, competitive,and scrutinized situation of the UCL even amongthe most highly trained and experienced referees.

Club teams have large incentives to do wellin the UCL. Teams that made it to the 32-teamgroup stage of the tournament in 2012 on averageearned €24.8 million and gained the prestige andrecruiting power that comes with a strong show-ing in the tournament. Each club is rewarded withmore and more money as they progress throughthe tournament. The benefit of performing wellin each match for a team and player increases asthe tournament progresses. The monetary bene-fit is nonlinear and the benefit in terms of pres-tige and recruiting power is also likely highlynonlinear with a much larger benefit toward theend of the tournament than the beginning. With agood performance later in the tournament beingmuch more valuable than at the beginning, ref-erees might demonstrate more own-nationalitybias as the benefit of each advantageous call


TABLE 4The Effect of a Same Country Referee by National Team, Home, and Elite Referee

Variable National Team Not National Team Home Away Elite Ref Not Elite Ref

Same country 0.285*** −0.005 0.326** 0.092 0.464*** 0.183[0.102] [0.204] [0.131] [0.135] [0.165] [0.112]

Observations 32,478 5,511 22,150 23,251 11,683 33,638R2 0.194 0.318 0.275 0.267 0.314 0.224

Notes: For all specifications the outcome variable of interest is fouls suffered minus fouls committed (number of beneficialfoul calls). The model used is the same as column 4 of Table 2 and is shown in Equation (1). The sample is restricted to playerswho played at least 30 minutes in the match. The title at the top of each column indicates the subsample of the data on which theanalysis is performed. Standard errors are clustered at the match level and are reported in brackets.


TABLE 5The Effect of a Same Country Referee by Stage

in Tournament

VariableQualification

MatchesGroup Stage

MatchesKnockoutMatches

Same country −0.008 0.177 0.428**[0.131] [0.122] [0.189]

Observations 13,914 23,140 8,347R2 0.347 0.25 0.289

Notes: For all specifications the outcome variable of inter-est is fouls suffered minus fouls committed (number of bene-ficial foul calls). The model used is the same as column 4 ofTable 2 and is shown in Equation (1). The sample is restrictedto players who played at least 30 minutes in the match. Thetitle at the top of each column indicates the stage of the tourna-ment on which the sub-analysis is performed. Standard errorsare clustered at the match level and are reported in brackets.

***Significant at the 1% level; **significant at the 5%level; *significant at the 10% level.

increases. In Table 5, the sample is restricted tothree different stages of the tournament: quali-fication matches, the 32-team group stage, andthe 16-team knockout stage. As the tournamentprogresses we observe increasingly strong evi-dence for the existence of own-nationality bias.This bias goes from nonexistent in the qualifi-cation matches to almost one-half of a benefi-cial foul call per match for paired players in thelater knockout matches. This is roughly a 20%increase in beneficial foul calls.

Although we find significant evidence of refer-ees exhibiting own-nationality bias, it is unclearwhether this favoritism translates into more vic-tories for the team with more paired players. It isalso unclear if team managers should be adjust-ing their strategy owing to the own-nationalitybias exhibited by referees. In order to determine ifown-nationality bias influences match outcomes,we analyze match level data for all away teams

TABLE 6The Effect of a Same Country Referee on Not

Losing

Not Lose

Variable (1) (2) (3) (4)

Number of more pairs 0.094*** 0.042** 0.026 0.042[0.016] [0.018] [0.027] [0.066]

Own team FE XOpposing team FE XOwn team-year FE XOpposing team-year FE XMatchup FE XObservations 2,254 2,254 2,254 2,254R2 0.011 0.341 0.767 0.855

Notes: The observation level is at the team-match level forall away teams. The number of more pairs indicates how manymore same country player-referee parings the away team hascompared to the home team. Not lose is a binary variableequal to one if the away team wins or ties. Standard errorsare clustered at the match level and are reported in brackets.


for each match in the dataset. In Table 6 theoutcome variable of interest is a binary variableequal to one if the away team wins or draws.The independent variable of interest is the num-ber of paired players that the away team has morethan the home team. It is important to note that insome games both teams have one or more referee-player pairs which could potentially give them anadvantage. Of the 2,564 matches in our dataset,250 matches have only one referee-player pairwhereas 56 matches have two pairs, 1 match hasthree pairs, 5 matches have four pairs, and 1match has five pairs.

Column 1 of Table 6 is a simple ordinary leastsquares (OLS) regression of whether the awayteam did not lose (win or tie) on the number ofmore paired players that the away team had com-pared to the home team. Column 1 implies a team

12 ECONOMIC INQUIRY

with one more paired player is 9.4 percentagepoints more likely to win or draw. Besides theestimate being implausibly high, it is clear thatthis estimate is biased upward due to the factthat the number of paired players on a team andteam quality is likely positively correlated. Thereason for this positive correlation is becauseonly foreign players on a team can possiblybe a paired player and richer and higher qual-ity teams buy more foreign players. Specifica-tions used in columns 2–4 help to mitigate someof this bias, but may or may not fully controlfor it. Column 2 includes own and opposing-team fixed effects and the estimate is cut in half.The correlation between the number of foreignplayers on a team and how rich and high qual-ity a team is, is also likely to exist within ateam over different years. Column 3 includesown and opposing-team-year fixed effects. Theestimate is reduced to a 2.6 percentage pointincrease in the likelihood of not losing, butis statistically insignificant. Column 4 includesmatchup fixed effects, which includes a fixedeffect for each pair of two teams that play eachother. Again the estimate is positive but notstatistically significant.

In addition to looking at the own-nationalitybias of referees toward players, in Table 7 welook at how cultural and language similaritiesbetween referees and players, and referees andteams, affect beneficial foul calls. For example,referees could be beneficially biased toward play-ers who speak the same language as themselvesor perhaps toward players from countries withsimilar cultures as themselves. For example, areferee from Germany may favor Swiss playersor teams, or a referee from Norway perhaps mightfavor Swedish players or teams given the similar-ities in language and culture. Alternatively, com-monality in language in referee-player pairingsplausibly could be the driving force behind thebias that referees exhibit. Perhaps players withouta language barrier can more effectively disputecalls or persuade the referee to be more lenient.For the majority of referees the data contain thenative language and second languages spoken byeach referee.

With a large enough dataset, and by using asimilar methodology as above, one could poten-tially create an index of how much bias refereesfrom any given country have toward playersfrom any other specific country. In addition, onecould then start to determine what characteristicsare driving the bias between the two countriesinvolved. Owing to limited statistical power this

TABLE 7The Effect of Linguistic and Cultural

Similarities of Referees and Players or Teams

Foul Difference

Variable Player Level Team Level

Same language 0.037 0.534[0.047] [0.626]

Same native language 0.105 −0.138[0.065] [0.659]

Cultural distance −0.054* 0.26[0.031] [0.975]

Geographic distance −0.018 0.144[0.015] [0.285]

Border −0.047 −0.432[0.033] [0.461]

Notes: Each cell reports the coefficient on the indi-cated variable when estimating the full model version ofEquation (1) and replacing the same country binary variablewith the indicated variable. For the team level, Equation (1)is used, but with observations at the team-match level insteadof the player-match level. Standard errors are clustered at thematch level and are reported in brackets.


is not feasible with the currently available data.However, Table 7 provides some informationregarding which characteristics affect the amountof bias between two countries.

Column 1 of Table 7 uses the full model spec-ification of Equation (1) and replaces the samecountry variable with one of the five language orcultural variables listed in Table 7. The first tworows show that there is a positive, but not sta-tistically significant, increase in beneficial foulcalls for players who speak the same languageor the same native language as the referee. Notethat the most commonly spoken national lan-guage in the players’ home country determinesthe players’ spoken language. Row 3 uses thestandardized CAGE index in which higher scoresrepresent more cultural differences between thetwo countries. Row 3 shows that there is a smalland marginally significant decrease in beneficialfoul calls for every standard deviation increasein the CAGE index. This implies that there maybe some effect of differing cultures between ref-erees and players on foul calls; however, whenusing two alternative measures of cultural dis-tance between referees and players no such rela-tionship exists. Row 4 uses geographical distancemeasured in thousands of kilometers between thecountries’ largest cities to calculate cultural dis-tance measure between countries. Row 5 useswhether or not the two countries being comparedborder each other as a cultural distance measure.


It does not appear that having a referee squad thatspeaks the same language or that has a similarcultural background significantly affects benefi-cial foul calls.

Column 2 of Table 7 reports a similar analysisto column 1 except at the team level. Column 2shows that a team playing a match with a refereesquad that speaks the same language or has asimilar cultural background as the club’s countryof origin has no effect on the number of teambeneficial foul calls.

V. CONCLUSION

Professional UEFA Champions League foot-ball matches provide a competitive, scrutinized,and high-stakes situation to analyze the existenceand amount of own-nationality bias in highlytrained referees. Taking advantage of the pairingsof referees and players from the same country asset up by UEFA referee assignments allows usto identify an own-nationality bias exhibited byreferees. The own-nationality bias found amongprofessional UCL referees increases the num-ber of beneficial foul calls for players from thereferee’s home country by 8–10%. This bias isconcentrated on national team members espe-cially while playing at home or in later stagesof the tournament. When vertically differentiat-ing referees, own-nationality bias is as strong orstronger for higher qualified referees. We alsofind that language and cultural similarities havelittle impact on referees’ foul calls. Althoughdirect application of these results to other mar-kets may be tenuous at present, further research

may continue to give insights and a better under-standing of own-nationality bias in other con-texts involving subjective assessments whetheron the football pitch, in the classroom, or in ajob interview.

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Documents

OWN-NATIONALITY BIAS: EVIDENCE FROM UEFA …pope/soccer_paper.pdfOWN-NATIONALITY BIAS: EVIDENCE FROM UEFA CHAMPIONS LEAGUE FOOTBALL REFEREES BRYSON R. POPE and NOLAN G. POPE∗ Weexaminetheexistenceandmagnitudeofown-nationalitybias