Winning Isn’t Everything: Corruption in Sumo Wrestling
By MARK D UGGAN AND STEVEN D. LEVITT*
There is a growing appreciation among economists of the need to better understand the rolethat corruption plays in real-world economies.
Although some have argued that it can be welfare enhancing (Nathaniel Leff, 1964), mostcommentators believe that a willingness to accept bribes (or similar forms of corruption) ineither the public or the private sector reduces
economic ef ciency (Andrei Shleifer andRobert W. Vishny, 1993). As a result, governments and  rms often create incentives to motivate their employees to be honest (Gary S.
Becker and George J. Stigler, 1974).
While it is generally agreed that corruption is
widespread, there is little rigorous empiricalresearch on the subject. Because of corruption’s
illicit nature, those who engage in corruption
attempt not to leave a trail. As a consequence,much of the existing evidence on corruption isanecdotal in nature. More systematic empiricalsubstantiation of corrupt practices is unlikely toappear in typical data sources. Rather, researchers must adopt nonstandard approaches in an
attempt to ferret out indirect evidence ofcorruption.
To date, there have been only a handful ofstudies that attempt to systematically document
the impact of corruption on economic outcomes. The  rst empirical study of corruption
dates to 1846 when Quetelet documented thatthe height distribution among French males
based on measurements taken at conscription
was normally distributed except for a puzzling
shortage of men measuring 1.57–1.597 meters(roughly 5 feet 2 inches to 5 feet 3 inches) andan excess number of men below 1.57 meters.
Not coincidentally, the minimum height forconscription into the Imperial army was 1.57
meters (Stephen Stigler, 1986). More recent empirical work on corruption includes Robert H.
Porter and J. Douglas Zona (1993), which  nds
evidence that construction companies collude
when bidding for state highway contracts bymeeting before the auction, designating a serious bidder, and having other cartel members
submit correspondingly higher bids. R. Preston
McAfee (1992) details a wide variety of bidrigging schemes. Paulo Mauro (1995) uses subjective indices of corruption across countries todemonstrate a correlation between corrupt governments and lower rates of economic growth,
although the relationship may not be causal.
Ray Fisman (2001) analyzes how stock prices
of Indonesian  rms  uctuate with changes informer Prime Minister Suharto’s health status.
Firms with close connections to Suharto, which
presumably bene t from corruption within theregime, decline substantially more than otherIndonesian  rms when Suharto’s health weakens. Whether the rents accruing to those close to
Suharto are due to corruption or simply badpolicy, however, is hard to determine. Rafael Di
Tella and Ernesto Schargrodsky (2000) document that the prices paid for basic inputs at
public hospitals in Buenos Aires fall by 10 –20
percent after a corruption crackdown.
In this paper we look for corruption among
Japan’s elite sumo wrestlers. While acknowledging that sumo wrestling is not itself a subjectof direct interest to economists, we believe that
this case study nonetheless provides potentiallyvaluable insights. First, if corrupt practices
thrive here, one might suspect that no institution
is safe. Sumo wrestling is the national sport ofJapan, with a 2,000-year tradition and a focus
on honor, ritual, and history that may be unparalleled in athletics.1 Moreover, Japan is generally* Duggan: Department of Economics, University of Chicago, 1126 East 59th Street, Chicago, IL 60637, and National
Bureau of Economic Research (e-mail: mduggan@midway.uchicago.edu); Levitt: American Bar Foundation and Department of Economics, University of Chicago, 1126 East
59th Street, Chicago, IL 60637 (e-mail: slevitt@midway.uchicago.edu). We would like to thank Gary Becker, Casey
Mulligan, Andrei Shleifer, Stephen Stigler, Mark West, twoanonymous referees, seminar participants, and especially
Serguey Braguinsky for helpful comments. Kyung-HongPark provided truly outstanding research assistance. Levitt
gratefully acknowledges the research support of the National Science Foundation and Sloan Foundation.

1
See Mark West (1997) for an examination of the legal
rules and informal norms that govern sumo wrestling in Japan.

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VOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLING

found to have relatively low rates of corruption
in cross-country comparisons (Transparency International, 2000).2 Second, because of the simplicity of the institutional framework, it is easy
to understand and model the incentives facing
participants. Third, data to test for match rigging are readily available. While the exact techniques we utilize for studying corrupt behavior
in sumo wrestling will require modi cation before being applied to more substantive applications, our analysis may nonetheless aid future
researchers in that task.
The key institutional feature of sumo wrestling that makes it ripe for corruption is theexistence of a sharp nonlinearity in the payoff
function for competitors. A sumo tournament(basho) involves 66 wrestlers (rikishi) participating in 15 bouts each. A wrestler who
achieves a winning record (eight wins or more,
known as kachi-koshi) is guaranteed to rise up
the of cial ranking (banzuke); a wrestler with alosing record (make-koshi) falls in the rankings.
A wrestler’s rank is a source of prestige, thebasis for salary determination, and also in uences the perks that he enjoys.3
Figure 1 demonstrates empirically the importance of an eighth win to a wrestler. The horizontal axis of the  gure is the number of wins awrestler achieves in a tournament; the verticalaxis is the average change in rank as a consequence of the tournament. The change in rank is
a positive function of the number of wins. With
the exception of the eighth win, the relationshipis nearly linear: each additional victory is worth
approximately three spots in the rankings. The
critical eighth win—which results in a substantial promotion in rank rather than a demotion—
garners a wrestler approximately 11 spots in the
ranking, or roughly four times the value of the

FIGURE 1. PAYOFF TO TOURNAMENT WINS

typical victory. Consequently, a wrestler entering the  nal match of a tournament with a 7-7
record has far more to gain from a victory thanan opponent with a record of, say, 8-6 has tolose. There will also be incentives for forwardlooking wrestlers to rig matches on earlier daysof the tournament.4 According to our roughcalculations, moving up a single spot in the
rankings is worth on average approximately$3,000 a year to a wrestler, so the potential
gains to trade may be substantial if the wrestlers
 x a match.5 Of course, no legally binding
contract can be written.
In this paper, we examine more than a decadeof data for Japan’s sumo elite in search ofevidence demonstrating or refuting claims ofmatch rigging. We uncover overwhelming evidence that match rigging occurs in the  nal daysof sumo tournaments. Wrestlers who are on themargin for attaining their eighth victory win far
more often than would be expected. High winning percentages by themselves, however, are42
Nevertheless, in recent years, sumo wrestling has been
dogged by allegations of rigged matches, none of whichhave been substantiated. Of cials from the Japanese Sumo
Association dismiss these complaints as fabrications on thepart of disgraced former wrestlers. Ultimately, despite yearsof allegations, no formal disciplinary actions have beentaken towards any wrestler.
3
For instance, the lowest-ranked wrestlers in the heyamust rise early each morning to clean the building andprepare the food for the main meal of the day. When awrestler reaches the rank of juryo, placing him among the
top 66 sumo wrestlers in Japan, he no longer is required to
do chores for other rikishi. Those in the top 40 with ranks ofmaegashira or better have their own servants.

1595

An earlier version of this paper, Duggan and Levitt(2001), derive a formal model of the incentives facing participants. For wrestlers on the bubble, the incentive to rig matches
increases monotonically over the course of a tournament.
5Wrestlers ranked between  fth and tenth earn an annual
income—including only of cial wages, bonuses, and prize
money—of roughly $250,000 per year. The fortieth-ranked
wrestler earns approximately $170,000. The seventiethranked wrestler receives only about $15,000 per year (noof cial salary, just a small stipend to cover tournament
expenses, some prize money, and room and board). All
information on annual salaries are based on the authors’calculations and information provided in Mina Hall (1997).
Unof cial sources of income such as endorsements would
likely increase the disparity between the top and bottom
wrestlers.

1596

THE AMERICAN ECONOMIC REVIEW

far from conclusive proof of match rigging.
Those wrestlers who are on the margin forachieving the eighth win may exert greater effort because their reward for winning is larger.
We offer a number of pieces of evidence against
this alternative hypothesis. First, whereas the
wrestler who is on the margin for an eighth win
is victorious with a surprisingly high frequency,
the next time that those same two wrestlers faceeach other, it is the opponent who has an unusually high win percentage.6 This result suggests that at least part of the currency used inmatch rigging is promises of throwing future
matches in return for taking a fall today. Second, win rates for wrestlers on the bubble varyin accordance with factors predicted by theory
to support implicit collusion. For example, success rates for wrestlers on the bubble risethroughout the career (consistent with the development of reputation), but fall in the last yearof a wrestler’s career. Third, match rigging disappears during times of increased media scrutiny. Fourth, some wrestling stables (known asheya) appear to have worked out reciprocity
agreements with other stables such that wrestlers from either stable do exceptionally well on
the bubble against one another.7 Finally, wrestlers identi ed as “not corrupt” by two former
sumo wrestlers who have alleged match rigging
do no better in matches on the bubble than in
typical matches, whereas those accused of being
corrupt are extremely successful on the bubble.
It is dif cult to reconcile any of these  ndings
with effort as the primary explanation.
The remainder of this paper is organized asfollows. Section I introduces the data used in
the analysis and presents the empirical evidence
documenting the strong performance of wrestlers on the bubble. Section II attempts to distinguish between increased effort and match
rigging as an explanation for the observed patterns in the data and also considers the way inwhich the market for rigged matches operates,e.g., how contracts are enforced, the use of cash
payments versus promises of future thrownmatches, and individuals versus stables as thelevel at which deals are brokered. Section III

6

By the second subsequent meeting, the winning percentages revert back to the expected levels, suggesting that
deals between individual wrestlers span only two matches.
7
Wrestlers in the same stable do not wrestle each other.

DECEMBER 2002concludes with a discussion of the broader economic implications of our analysis.
I. Evidence of Strong Performanceon the Bubble

Our data set consists of almost every of cialsumo match that took place in the top rank
(Sekitori) of Japanese sumo wrestling between
January 1989 and January 2000. Six tournaments are held a year, with nearly 70 wrestlers
per tournament, and 15 bouts per wrestler.
Thus, our initial data set consists of over 64,000
wrestler-matches representing over 32,000 total
bouts (since there are two wrestlers per bout). A
small number of observations (less than 5 percent of the total data set) are discarded due tomissing data, coding errors, or early withdrawal
from the tournament due to injury. A total of
281 wrestlers appear in our data, with the average number of observations per wrestler in arandomly selected match equal to 554, and amaximum of 990. The average number of total
matches between the same two wrestlers competing in a randomly selected match is 10; thus
we often have many observations involving thesame two wrestlers at different points in time.
For each observation, we know the identity ofthe two competitors, who wins, the month andyear of the tournament, and the day of the match(tournaments last 15 days with one match per
wrestler per day). For roughly 98 percent of thesample, we also know what wrestling stable thewrestlers belong to; information is missing forsome wrestlers in the early part of our sample
who had only a short stint in the top ranks.
We begin by looking at the distribution ofwins across wrestlers at the end of tournaments.
We expect that a disproportionate number ofwrestlers should  nish with eight wins becauseof the high payoff associated with the eighthwin (see Figure 1). To the extent that wrestlers
are rigging matches not only on the  nal day,but also in the days immediately preceding theend of the tournament, extra weight should also
be observed on nine wins, as wrestlers who areclose to the margin on days 13 or 14 may buy
wins that ultimately were not needed to reacheight wins due to subsequent victories. Figure
2 presents a histogram of  nal wins for the
60,000 wrestler-tournament observations inwhich a wrestler completes exactly 15 matches.
For purposes of comparison, we also present theVOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLINGFIGURE 2. WINS IN A SUMO TOURNAMENT(ACTUAL VS. BINOMIAL)

expected pattern of results assuming that all
wrestlers are identical and that match outcomes
are independently distributed.
Figure 2 provides clear visual evidence insupport of the model’s prediction. Approximately 26.0 percent of all wrestlers  nish withexactly eight wins, compared to only 12.2 percent with seven wins. The binomial distribution
predicts that these two outcomes should occur
with an equal frequency of 19.6 percent. The
null hypothesis that the probability of seven andeight wins is equal can be rejected at resounding
levels of statistical signi cance. Nine victories
also appears more often than would be expected. Although this distortion is far less pronounced visually, nine victories is signi cantly
more likely than six (16.2 percent versus 13.9
percent).
Further evidence that wrestlers on the bubble
win far more often than would be expectedcomes from estimating regressions of the general form
(1)

Win ijtd 5 b Bubble ijtd 1 g Rankdiff ijt
1 l ij 1 d it 1 e ijtd

where i and j represent the two wrestlers, tcorresponds to a particular tournament, and d isthe day of the tournament. The unit of observation is a wrestler-match. Bubble is a vector ofindicator variables capturing whether wrestler ior j is on the margin for reaching eight wins in
the bout in question. The Bubble variables arecoded 1 if only the wrestler is on the margin,
21 if only the opponent is on the margin, and 0
if neither or both of the combatants are on the1597

margin in the match. Rankdiff is the gap between the of cial ranking of wrestlers i and jentering tournament t. In some regressions, we
include  xed effects for each wrestler and each
opponent; in other speci cations we include
wrestler-opponent interactions. In all cases, weestimate linear probability models, with standard errors corrected to take into account thefact that a match is included in the data set
twice—once for wrestler i and once for wrestler j.
Table 1 reports the excess win percentages
for wrestlers on the margin, by day, for the last ve days of the tournament. On day 15, only
wrestlers with exactly seven wins are on themargin; on day 14, wrestlers with either six orseven wins are on the margin, etc. The six
columns correspond to different regression
speci cations. The even columns include thedifferences in ranks for the two wrestlers entering this tournament. The  rst two columns haveno wrestler  xed effects; columns (3) and (4)include both wrestler and opponent  xed effects.
The  nal two columns add wrestler-opponent
interactions so that the identi cation comes
only from deviations in this match relative toother matches involving the same two wrestlers.
The results in Table 1 are quite similar acrossspeci cations.8 Wrestlers on the bubble on day
15 are victorious roughly 25 percent more often
than would be expected. Win percentages areelevated about 15 percent on day 14, 11 percent
on day 13, and 5 percent on day 12 for wrestlers
on the margin. There is no statistically signi cant evidence of elevated win rates for wrestlers
on the bubble on day 11. The pattern of coef cients is consistent with the hypothesis that thefrequency of match rigging will rise as the tournament comes to a close. If all of the excess
wins are due to rigging, then the results imply
that on day 15 half of the bubble matches arecrooked. For days 14, 13, and 12 respectively,
the estimated percentage of rigged matches isroughly 28, 22, and 10 percent respectively.

8

Adding the difference in current rank between wrestlers has little impact on the other coef cients in the regression. The difference in ranks is an important predictor ofmatch outcomes when wrestler  xed effects are excluded
from the model. The top-ranked wrestler facing an average
wrestler would be expected to win about 70 percent of thetime. Once we control for wrestler  xed effects, however,
the explanatory power of the difference in ranks disappears.

1598

THE AMERICAN ECONOMIC REVIEWDECEMBER 2002

TABLE 1—EXCESS WIN PERCENTAGES FOR WRESTLERS ON THE MARGIN FOR ACHIEVING AN EIGHTH WIN,BY DAY OF THE MATCH
On the Margin on:

(1)

(2)

(3)

(4)

(5)

(6)

Day 150.244(0.019)0.249(0.019)0.249(0.018)0.255(0.019)0.260(0.022)0.264(0.022)

Day 140.150(0.016)0.155(0.016)0.152(0.016)0.157(0.016)0.168(0.019)0.171(0.019)Day 130.096(0.016)0.107(0.016)0.110(0.016)0.118(0.016)0.116(0.019)0.125(0.019)Day 120.038(0.017)0.061(0.018)0.064(0.017)0.082(0.018)0.073(0.020)0.076(0.021)

Day 110.000(0.018)0.018(0.018)0.015(0.018)0.025(0.018)0.010(0.021)

0.012(0.021)—0.0053(0.0003)—

0.0020(0.0003)—

20.0020(0.0004)

Constant

0.500(0.000)0.500(0.000)

—

—

—

—

R2

0.008

0.018

0.030

0.031

0.06340.0653

Number of observations

64,27262,70864,27262,70864,27262,708Wrestler and opponent  xed effects

No

No

Yes

Yes

Yes

Yes

Wrestler-opponent interactions

No

No

No

No

Yes

Yes

Rank difference

Notes: The dependent variable in all regressions is an indicator variable corresponding to whether or not a wrestler wins thematch. The unit of observation is a wrestler-match. Values reported in the table are coef cients associated with an indicatorvariable taking the value 1 if only the wrestler is on the margin for achieving eight wins, 21 if only the opponent is on themargin for achieving eight wins, and 0 otherwise. On day 15, only wrestlers with seven wins are on the margin. On day 14,
wrestlers with six or seven wins are on the margin. On day 13, wrestlers with  ve, six, or seven wins are on the margin, andso on. The omitted category in all regressions is all wrestlers who are not on the margin for achieving eight wins, as well as
wrestlers in matches in which both participants are on the margin for eight wins. When a full set of wrestler and opponent xed effects are included, the constant is omitted. In all cases, standard errors are corrected to account for the fact that there
are two observations per bout (one for each wrestler). The differences in the wrestler rank variable is the numerical rank orderof the wrestler minus that of his opponent, based on of cial rankings published prior to each tournament. This variable ismissing for part of our sample.

II. Distinguishing Between Match Rigging
and Effort

of analyses that appear to con rm the corruption
story and rule out effort as the explanation.

The empirical results presented thus far are
consistent with a model in which opponents
throw matches to allow wrestlers on the margin
to achieve an eighth victory. The results, however, are also consistent with a scenario in
which effort is an important determinant of thematch outcome, and wrestlers on the bubble,
having more to gain from a win, exert greater
effort.9 In this section, we present a wide variety

A. What Characteristics In uence theLikelihood of Winning When on the Bubble?
We begin by analyzing the personal and situational characteristics that in uence the success rate of the wrestler on the bubble. One
potentially important determinant of the frequency of match rigging is the probability thatthe collusive behavior will be detected. During
our sample, there have been two periods in

9

The results are also consistent with a model in which
wrestlers show altruism for one another, sacri cing theirown outcomes to help an opponent who has more to gain.
Given the nature of athletics and the nontrivial cost to thewrestler of losing the match (roughly $10,000), the altruism
story strikes us as an unlikely explanation.

VOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLING

which media attention has focused on match
rigging. The  rst of these was in April and May
of 1996. A former sumo wrestler who had become a stable-master came forward with allegations of match rigging. At the same time,
another former wrestler also came forward to
decry rigged matches. Ironically, both of thesemen died a few weeks later, just hours apart, inthe same hospital. This fueled speculation
among the media of foul play, although a subsequent police investigation revealed no evidence for this. The second period of media
scrutiny took place in late 1999– early 2000. A
former sumo wrestler named Itai raised allegations of match rigging that were widely coveredby the media, even in the United States. The
three tournaments in our sample that are most
likely to be affected by the media attention arethose held in May 1996, November 1999, andJanuary 2000.
The literature on repeated-play games (e.g.,
Drew Fudenberg and Jean Tirole, 1991) suggests that the ability to sustain collusion should
be positively related to the frequency withwhich two wrestlers expect to meet in the futuresince more future meetings imply the availability of more severe punishments for wrestlers
who do not cooperate. Empirically, we proxy
the expected frequency of future matches using
two variables: (1) the number of meetings between the two wrestlers that took place in thepreceding year, and (2) whether the wrestler isin the last year of his career. Although theprecise ending of a wrestler’s career is notknown in advance to the participants, it is likelythat signals of retirement are available (e.g.,
declining performance, injuries, etc.).10 If it
takes time to establish a reputation as a wrestler
who is willing to collude and who can betrusted, then one might predict that the longer a
wrestler has been active in the top ranks ofsumo, the better he will do when he is on the
bubble, and also, the worse he will perform
when the opponent is on the bubble.
Because there are a series of monetary prizes
given to wrestlers who have good records in a10
In order to minimize endogeneity, we exclude the very
last tournament of a wrestler’s career. It is possible that aloss on the bubble may drop the wrestler out of the top levelof wrestlers, inducing retirement. Including the last tournament of a wrestler’s career slightly increased the magnitude
and statistical signi cance of the coef cient.

1599

given tournament, wrestlers in the running forsuch prizes are unlikely to be willing to throw
matches.11 The overall tournament champion
wins $100,000; the juryo champion wins
$20,000. In addition, $20,000 awards for “ ghting spirit” and “outstanding technique.” In order
to win those prizes, wrestlers must compile verystrong records. The potential value of a victory
for a wrestler in the running for such prizes islikely to be at least as great as the value to a
wrestler on the margin for an eighth win.
A  nal determinant of match rigging that we
consider is the possibility of coordinated match
rigging among stables of wrestlers. The lives ofsumo wrestlers center around the stable withwhich they are associated. Stable-masters exert
a tremendous in uence over both the wrestlingcareer of wrestlers and their lives more generally.12 Given the important role of the stable,
and the fact that stable-masters bene t fromhaving highly ranked wrestlers, it would not be
surprising if corruption were coordinated at thestable level. For example, stables might havecollective reputations, with stable-masters enforcing punishments on wrestlers who pursue
their individual best interests at the expense of
the stable. Some stable-masters, on the other
hand, may not condone match rigging becauseof ethical concerns or risk aversion.
We empirically examine one particular formof stable-level collusion: the presence of reciprocity agreements across stables. If such deals
existed, one would expect both that wrestlers
from stable A will have very high win rates
when on the bubble facing wrestlers from stable
B, and vice versa.13 It is dif cult to tell analternative story that would account for such apattern in the data. For instance, if effort is the11

Although we do not directly observe which wrestlers
might be under consideration for these prizes, having one ofthe  ve best records (plus ties) up until that point in thetournament is an excellent predictor. Wrestlers with one ofthe  ve best records in the tournament entering days 13, 14,
or 15 win a prize 50 percent of the time. Less than  ve
percent of wrestlers with records outside the top  ve ondays 13–15 eventually win a prize.
12
For instance, it is expected that the foremost sumo wrestler in a stable will marry the daughter of the stable-master.
13
The variable we use to test this empirically is theoverall win rate for wrestlers on the bubble in matches
involving both a wrestler from stable A and a wrestler fromstable B (excluding the current match). This variable re ects
both stable A’s success on the bubble against stable B andstable B’s success against stable A.

1600

THE AMERICAN ECONOMIC REVIEW

story, then stables with wrestlers capable ofexerting particularly high effort when they needa win might also be expected to have wrestlers
who rise to the occasion when faced with theopportunity to beat a very motivated opponent,
leading to a zero or negative correlation. Similarly, if one stable has developed speci c techniques that provide it with an advantage over aparticular stable, success rates on the bubble
will again be negatively related.
These various hypotheses are tested in Table 2.
Because our interest is in how these factors
in uence success on the bubble, the table reports only the coef cients on the interactions
between these various factors and the outcomeof bubble matches, i.e., any incremental impactthat these factors have on bubble matches aboveand beyond their impact in nonbubble matches.
Also included in the speci cations, but notshown in the table, are the main effects. Column(1) is the baseline speci cation. Column (2)adds both wrestler and opponent  xed effects.
Column (3) also includes wrestler-opponent interactions. The results are generally quite similar across the three columns of the table.
The top row of the table is the main effect of
a wrestler being on the bubble, which leads toan excess win likelihood of roughly 12–16 percentage points. In those tournaments with a highlevel of media scrutiny, however, these excess
wins completely disappear.14 In two of the three
speci cations, the performance of wrestlers on
the bubble in high and low media scrutiny tournaments is statistically signi cant at the 0.05
level. In none of the three columns can one
reject the null hypothesis that wrestlers on the
bubble in high-scrutiny tournaments do any better than chance. In the words of Supreme Court
Justice Louis D. Brandeis, “Sunlight is said tobe the best disinfectant.”15
When the opponent is in the running for
winning one of the special prizes awarded, anybene t of being on the bubble also disappears.
This result is consistent with opponents in therunning for prizes being unwilling to throw amatch.
The variables designed to capture factors in14
To calculate the overall excess win percent for a
wrestler on the bubble in a period of high media scrutiny,
the coef cients in rows 1 and 2 are added together.
15
We thank an anonymous referee for bringing this very
appropriate quote to our attention.

DECEMBER 2002

TABLE 2—DETERMINANTS OF EXCESS WIN LIKELIHOODS
FOR W RESTLERS ON THE BUBBLE
Variable

(1)

(2)

(3)

0.126
(0.026)0.117(0.026)0.155(0.029)High media
scrutiny

20.188(0.071)

20.177(0.071)

20.146(0.080)

Opponent inrunning for a
prize thistournament

20.149(0.047)

20.129(0.046)20.156(0.052)

Number ofmeetings
between two
opponents inthe last year

20.0048(0.0082)

20.0031(0.0081)20.0024(0.0096)

Wrestler on
bubblein his last yearof competing

20.0361

20.0195

20.0346(0.0398)(0.0395)(0.0493)

Years in sumo for
wrestler on
bubble0.0077(0.0036)

0.0077(0.0036)0.0091(0.0043)

Winning
percentage inother bubble
matches
between these
two stables

0.272(0.059)0.293(0.058)

—

R2

0.016

0.074

0.246

Wrestler andopponent  xed
effects?

No

Yes

Yes

Wrestler-opponent
interactions?

No

No

Yes

Wrestler on bubble
Wrestler on bubble
interacted with:Notes: The dependent variable in all three regressions is anindicator variable corresponding to whether or not a wrestler wins the match. In addition to the listed interaction
terms, all main effects are also included in the speci cations. Wrestler on bubble is an indicator variable that equals
1 (21) if the wrestler (opponent) is on the bubble on days
13, 14, or 15 (record of 7-7, 7-6, 6-7, 7-5, 6-6, or 5-7) but
the opponent (wrestler) is not, and 0 otherwise. Matchesfrom 1989, the  rst year of our sample, are excluded because the variable for the number of matches between thetwo wrestlers in the previous year cannot be computed.
Observations where stables are unknown or no two wrestlers from the stables ever meet on the bubble in our data set
are also excluded from the sample. The number of observations is consequently 42,788. Standard errors are corrected to account for the fact that there are two observations
per bout (one for each wrestler).

VOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLING uencing implicit collusion achieve mixed results. The number of matches between two
wrestlers in the preceding year has an unexpected negative impact on a wrestler’s likelihood of winning a match on the bubble,
although the coef cient is substantively small
and carries a t-statistic of far less than 1 in all of
the speci cations. Wrestlers in the last year of
their career do slightly worse than expected on
the bubble, although again the result is notstatistically signi cant. With the exception of
the last year, however, success on the bubble
increases over the career. A  ve-year veteran isabout 4 percentage points (off a baseline of 13
percentage points) more likely to win on the
bubble than a rookie. This last result is consistent with the importance of developing a
reputation.
Finally, the bottom row of coef cients inTable 2 measures the extent to which a wrestler’s success on the bubble today is in uencedby overall success rates (excluding this match)when wrestlers from that stable meet wrestlers
from the opponent’s stable and one of the wrestlers is on the bubble.16 The coef cient isstrongly positive and statistically signi cant. As
noted above, that result that is dif cult to reconcile with any hypothesis other than stablecoordinated collusion. Moreover, the size of thecoef cient is large: for each 10-percentagepoint increase in success in other bubble
matches between these two stables, the wrestler
on the bubble is 3 percentage points more likely
to win today, controlling for other factors.
B. What Happens When Wrestlers Meet
Again in the Future?
If collusion is the reason that wrestlers on the
bubble perform well, then the opponent must becompensated in some way for losing the match.
It is possible that such payments are made in
cash, or in promises to return the favor in the
future. The likelihood that two wrestlers willmeet again soon is high: in our data 74 percentof the wrestlers who meet when one is on themargin for eight wins will face one anotheragain within a year.
16

Because wrestlers do not change stables over thecourse of their careers, there is no usable variation in thisvariable when wrestler-opponent interactions are included
in column (3).

1601

Table 3 explores the pattern of match outcomes over time for wrestlers who meet when
one is on the margin. The even columns include
wrestler-opponent interactions so identi cationof the parameters comes only from variation in
outcomes involving the same two opponents;
the odd columns do not. The regression speci cations include indicator variables categorizing the timing of the meetings between two
wrestlers relative to the match where one wrestler is on the bubble. The omitted category ismatches preceding a bubble meeting by at least
three matches.17
Focusing  rst on columns (1) and (2), whichcorrespond to all meetings on the bubble, there
are no systematic differences in outcomes in thetwo matches preceding the match on the bubble,
as re ected in the statistically insigni cant coef cients in the top row. When the wrestler ison the bubble (second row), he is much more
likely to win, consistent with the earlier tables.
The parameter of greatest interest is the negative coef cient for the  rst meeting between thetwo wrestlers after the match in which the wrestler is on the bubble. The wrestler who was on
the margin in the last meeting is approximately
seven percent less likely to win than would
otherwise be predicted. This  nding is consistent with part of the compensation for throwing
a match being the promise of the opponent
returning the favor in the next meeting. There isno evidence that any return of favors extends
beyond the next match, as two and threematches out the winning percentages return tonormal.
The  nal four columns of Table 3 replicate
the  rst two columns, except that the sample isdivided into cases where the wrestler wins whenon the margin versus instances when the wrestler loses when on the margin.18 One would not
expect a wrestler to intentionally lose in future
meetings to an opponent who does not throw thematch on the margin. Thus, breaking down the17
We have experimented with other groupings ofmatches, for instance allowing a different coef cient for
matches preceding/following the bubble match by morethan three meetings. The parameters of interest are notsensitive to alternative speci cations.
18
In columns (3)–(6), the actual matches taking place on
the bubble are excluded from the regression since there is novariation. In columns (3) and (4), all bubble matches arewon by the wrestler on the bubble; in columns (5) and (6),no matches are won by the wrestler on the bubble.

1602

THE AMERICAN ECONOMIC REVIEWDECEMBER 2002

TABLE 3—WIN PERCENTAGES IN PRECEDING AND SUBSEQUENT MATCHES(For Two Wrestlers Who Meet When One is on the Margin in the Final Three Days of a Tournament)
Only Matches inWhich the Wrestler on
the Margin Wins

All Matches on the
MarginVariable

Only Matches inWhich the Wrestler on
the Margin Loses

(1)

(2)

(3)

(4)

(5)

(6)

One or two matches prior to the
bubble match

20.002(0.009)0.005(0.012)0.020(0.011)0.019(0.017)20.041(0.016)20.035(0.022)

Bubble match

0.151(0.010)0.164(0.014)

—

—

—

—

First meeting after bubble match

20.073(0.011)20.062(0.015)20.082(0.015)20.079(0.020)20.056(0.020)20.040(0.027)

Second meeting after bubble match

20.002(0.013)0.005(0.016)0.031(0.017)0.028(0.022)20.061(0.023)20.039(0.030)Three or more meetings after
bubble match

20.010(0.006)0.012(0.011)

0.013(0.007)0.022(0.014)20.045(0.008)20.013(0.017)

Constant

0.500(0.000)—0.500(0.000)—0.500(0.000)—Wrestler-opponent interactions?

No

Yes

No

Yes

No

Yes

R2

0.008

0.271

0.002

0.279

0.002

0.279

Notes: Entries in the table are regression estimates of the outcomes of matches between, after, and contemporaneous withthese two wrestlers meeting when one wrestler is on the margin for achieving eight wins on the last three days of thetournament. The dependent variable in all regressions is an indicator variable for whether the wrestler wins a match. The unitof observation is a wrestler match. The  rst two columns correspond to all wrestlers who meet when one is on the margin.
In columns (3) and (4), the coef cients reported correspond only to those cases where the wrestler on the bubble wins thematch. Columns (5) and (6) report coef cients only for those wrestlers on the bubble who lose the match. Columns (3) and(5) are estimated jointly, as are columns (4) and (6). Except for columns (1) and (2), bubble matches are excluded from theregressions. The excluded category in all regressions are matches occurring more than two matches prior to a bubble matchand not falling into any of the other categories named. When a full set of wrestler and opponent  xed effects are included,
the constant is omitted. In all cases, standard errors are corrected to account for the fact that there are two observations perbout (one for each wrestler). Number of observations is equal to 64,273.

data in this way provides a natural test of the
hypothesis that the poor performance in the next
match is due to a deferred payoff to an opponent
who threw a match. The data provide clear
support for the collusion hypothesis. Wrestlers
who win on the bubble tend to do slightly better
than expected leading up to the bubble match,then do much worse in the next meeting withthe same opponent. Relative to the surrounding
matches, the  rst post-bubble match sees the
wrestler losing approximately 10 percentage
points more frequently than would be expected(i.e., row 3 minus row 1).
The pattern for wrestlers who lose to the opponent when on the margin for achieving eight wins
is very different. In the matches just prior to the
bubble match, the wrestler is slightly underperforming. This continues unaffected through thepost-bubble matches as well. Unlike columns (1)–(4), there is no downward spike in wins following
the bubble match. The  nding that winners on the
bubble fare badly the next time they face the same
opponent, but losers do not, is consistent with thematch-rigging hypothesis, but not with an effort
story. If increased effort is responsible for strong
performances in matches on the margin, there is
no reason to expect systematic underperformancethe next time the two wrestlers meet, and certainly

19
When two wrestlers meet and both are on the bubble,
there is similarly no evidence that the wrestler who wins
fares more poorly the next time the two wrestlers meet. Thisis consistent with no match rigging occurring when both
wrestlers are on the bubble, as predicted by the model.
More generally, there is no evidence of negative serial
correlation in match outcomes. When multiple lags of past
match outcomes between the two wrestlers are added to thespeci cations, the coef cients are positive.

VOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLING

not exclusively among those who won on the
bubble.19
The payment-in-kind story suggested by the
results above is unlikely to be the only form ofcompensation for wrestlers who throw matches.
Based on Table 3, roughly two-thirds of theexcess wins garnered on the bubble are returned
the next time the two wrestlers meet. This
price—two-thirds of a match down the road in
return for throwing a match today—is too low
to represent the only form of payment. Whenrumors circulate about match rigging in sumo,
they often suggest the presence of cash transfers, although we are able to provide no evidence about this channel.
C. Do the Data Con rm Public Allegations ofCheating by Sumo Insiders?
Two former sumo wrestlers have made public the names of 29 wrestlers who they allege tobe corrupt and 14 wrestlers who they claim
refuse to rig matches (Keisuke Itai, 2000; Onaruto, 2000; Shukan Post, 2000).20 In this section, we investigate whether the performance on
the bubble of these two groups of wrestlers
systematically differs. If strong performance on
the bubble is due to match rigging and theallegations are true, then one would expect thecorrupt wrestlers to do extremely well on the
bubble, whereas the honest wrestlers would do
no better in bubble matches than on any othermatch.
To test this hypothesis, we estimate a regression identical to equation (1), but including a full set of interactions between whether
a match is on the bubble and the classi cationof each wrestler and his opponent as either“corrupt,” “clean,” or “status unknown.” Only
43 of the 281 wrestlers (15 percent) in our
sample are speci cally identi ed as either
corrupt or clean. The remainder are classi ed
as status unknown. The whistle-blowers weremore likely to identify prominent wrestlers
with long careers, however, so over 60 percent of the matches in our sample involve atleast one wrestler identi ed by name as either
clean or corrupt.
20

The books that we cite are published only in Japanese.
We thank Serguey Braguinsky both for bringing the existence of this information to our attention and for translating
the relevant material for us.

1603

Table 4 reports the results of the estimation.
All of the coef cients in the table come from asingle regression. The values in the table represent excess win percentages on the bubble, i.e.,
how much better a wrestler does when on the
bubble relative to matches in which neither
wrestler is on the bubble. The columns of thetable identify the status of the wrestler on the
bubble—the wrestler who wants to buy a victory. The rows correspond to the status of the
opponent of the wrestler on the bubble—thewrestler who might want to sell a win. When
two wrestlers identi ed as corrupt meet on the
bubble, the one who needs the victory is 26
percentage points more likely to win the match
than if those two wrestlers met with neither on
the bubble. This excess win percentage is highly
statistically signi cant. When a corrupt wrestler
meets a wrestler classi ed as “status unknown,”
the results are very similar. Win percentages forthe wrestler needing the victory when both
wrestlers are classi ed as “status unknown” arealso highly elevated (18.1 percentage points
higher), but not quite as extreme. In stark contrast, none of the  ve coef cients involving
wrestlers identi ed as clean are statistically signi cant from zero. This implies that the outcomes of bubble matches involving a clean
wrestler are no different than the results when
the same two wrestlers meet, but neither is on
the bubble. This result holds true regardless ofwhether the clean wrestler is himself on the
bubble or facing a wrestler who is on the bubble. Thus, Table 4 provides strong con rmation
not only of the claim that elevated win percentages on the bubble are due to match rigging, butalso that the allegations made by the two sumo
insiders appear to be truthful. Moreover, it appears that most of the wrestlers not speci cally
named by the whistle-blowers are corrupt, since
their outcomes differ only slightly from those
wrestlers named as corrupt.
III. Conclusion

This paper provides strong statistical analysis
documenting match rigging in sumo wrestling.
The incentive structure of promotion leads togains from trade between wrestlers on the margin for achieving a winning record and theiropponents. We show that wrestlers win a disproportionate share of the matches when they
are on the margin. Increased effort cannot

1604

THE AMERICAN ECONOMIC REVIEWDECEMBER 2002

TABLE 4—EXCESS WIN PERCENTAGES ON THE BUBBLE FOR WRESTLERS LABELED BY SUMOINSIDERS AS “CORRUPT” OR “CLEAN”
Wrestler on the Bubble Is Identi ed as:

Corrupt
Opponent ofwrestler on the
bubble is
identi ed as:

Statusunknown
Clean

Corrupt

Status Unknown

Clean

0.260
(0.037)0.271(0.021)

0.270(0.021)0.181(0.019)20.010(0.038)0.041(0.031)

0.036(0.027)20.033(0.035)0.022(0.074)

Notes: Entries in the table are coef cients from a regression with full set of interactions
between whether a match is on the bubble and the classi cation of a wrestler and his opponent
as clean, corrupt, or status unknown by two sumo insiders (Itai, 2000; Onaruto, 2000; Shukan
Post, 2000). The regression is identical to equation (1) in the text, except for the inclusion of
the aforementioned interactions. Twenty-nine wrestlers are categorized as corrupt, 14 areclassi ed as clean. The remainder of wrestlers are not speci cally named and are categorized
as status unknown. Standard errors are corrected to take into account that there are two
observations per bout (one for each wrestler). Number of observations is equal to 64,273.

explain the  ndings. Match rigging disappears
in times of increased media scrutiny. Wrestlers
who are victorious when on the bubble lose more
frequently than would be expected the next timethey meet that opponent, suggesting that part ofthe payment for throwing a match is future payment in-kind. Reciprocity agreements between
stables of wrestlers appear to exist, suggesting thatcollusive behavior is not carried out solely by
individual actors. Allegations by sumo insiders aredemonstrated to be veri ed in our data.
While sumo wrestling per se is not of directinterest to economists, the case study that itprovides is potentially of usefulness to the economic analysis of corruption. Anecdotal allegations of corrupt practices among sumo wrestlers
have occasionally surfaced, but have been dismissed as impossible to substantiate. In thispaper, we demonstrate that the combination of aclear understanding of the incentives facing participants combined with creative uses of data
can reveal overwhelming statistical evidence ofcorruption. Details of the corrupt practices, thedata sources, and the telltale patterns in the data
will all vary from one application to the next.
Nonetheless, the success of our study in documenting the predicted patterns of corruption in
one context raises the hope that parallel studies
with more substantive economic focus may
yield similar results.
Moreover, our analysis provides insight into
how to combat corruption. First, the match rigging we identify can be directly linked to thearti cially imposed nonlinearity in incentives
for wrestlers who achieve a winning record.21
Removing this distortion to incentives would
eliminate the bene ts of corruption. Second,match rigging appears to be sensitive to thecosts of detection. Increased media scrutiny
alone is suf cient to eliminate the collusive
behavior. Presumably, other approaches to raising the expected punishment would likewise beeffective. Third, at least in the sumo context,
insiders appear to have good information about
who is corrupt. Providing strong incentives forwhistle-blowers, particularly when such accusations can be corroborated by objective data
analysis, may prove effective in restraining corrupt behavior.
While perhaps beyond the scope of this paper, a question of interest is why those in chargeof the sumo wrestling have not attempted toeliminate corruption, either by eliminating thenonlinearity or by increasing expected punishments. A partial answer is that there are barriers
to entry for a second sumo league, so the competitive pressure exerted on the current sumo
association is limited.22 A second possibility is
21
Nonlinearities of this sort have been shown to distortbehavior in many other contexts as well, including Robert
Topel (1983) and Judith Chevalier and Glenn Ellison(1997).
22
It is worth noting that the popularity of sumo wrestlinghas declined substantially over the last two decades, suggesting that other forms of recreation are substitutes.

VOL. 92 NO. 5

DUGGAN AND LEVITT: CORRUPTION IN SUMO WRESTLING

that the nonlinear payoff structure generates
interest in otherwise unimportant matches on the nal days of the tournament. For the same reason that wrestlers want to rig the matches on the
bubble, the fans are interested in the outcome.
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