PERFORMANCE IN MIXED-SEX AND SINGLE-SEX COMPETITIONS:
WHAT WE CAN LEARN FROM SPEEDBOAT RACES IN JAPAN
Alison Booth and Eiji Yamamura*
Abstract—In speedboat racing in Japan, men and women compete under the
same conditions and are randomly assigned to mixed-sex or single-sex
groups for each race. We use a sample of over 140,000 individual-level
records to examine how male-dominated circumstances affect women’s
racing performance. Our fixed-effects estimates reveal that women’s race
time is slower in mixed-sex than all-women races, whereas men’s race time
is faster in mixed-sex than men-only races. The same result is found for
place in race. Inoltre, in mixed-sex races, men are more aggressive, COME
proxied by lane changing, than women in spite of the risk of being penalized
for rule infringement.
IO.
introduzione
Agrowing literature investigates whether gender gaps in
economic outcomes might be due to differences in
male and female attitudes to competition or to risk.1 Some
experimental studies have found the competitive choices
men and women make differ according to whether they
compete against men or women in competitive environ-
menti (see Gneezy, Niederle, & Rustichini, 2003; Gneezy
& Rustichini, 2004; Niederle & Vesterlund, 2011; E
Booth & Nolen, 2012). Inoltre, the actual performance
can vary, as in Gneezy et al., (2003), who used experimen-
tal data to show that women’s performance in competitions
differs depending on the gender of their competitors.
in questo documento, we adopt a different but complementary
approach to these experiments by analyzing unique perfor-
mance data from a real-world activity that is by its very nat-
ure competitive and where potential payoffs from winning
are high. Women have been competing in this activity since
the early 1950s under exactly the same conditions as men,
and all participants are randomly allocated to either single-
sex or mixed-sex groups for the competition. The activity is
Received for publication January 3, 2017. Revision accepted for publi-
cation August 30, 2017. Editor: Rohini Pande.
* Booth: Australian National University; Yamamura: Seinan Gakuin
Università.
For their helpful comments we thank the editor, Rohini Pande, and four
anonymous referees, Kyohei Yoneda, Yoshihide Ari, Takumi Nishi,
Ryohei Hayashi, Shoko Yamane, Yoshiro Tsutsui, Fumio Ohtake, Tim
Hatton, Aki Asano, participants at the 2016 Japanese Behavioural Eco-
nomics Conference, and seminar participants at the Australian National
Università.
1 Studies investigating gender differences in performance in competi-
tive environments include Gneezy et al. (2003), Niederle and Vesterlund
(2007, 2011), Booth (2009), Dreber, von Essen, and Ranehill, (2011),
Ca´rdenas et al. (2012), and Niederle (2014). Studies exploring gender dif-
ferences in preference to enter a competition include Gneezy, Leonard,
and List (2009), Booth and Nolen (2012), Apicella and Dreber (2015),
and Buser, Dreber, and Mo¨llerstro¨m (2017), while studies analyzing atti-
tudes toward, risk include Booth, Sosa, and Nolen (2014), Dreber, van
Essen, and Ranehill (2014), and Khachatryan et al. (2015), and Buser,
Niederle, and Oosterbeek (2014) explore how preference for competition
across genders affects academic task choice. In a study that is closest to
ours, Backus et al. (2016) find that the gender composition of chess tour-
naments affects the behavior of men and women in ways that are detri-
mental to female performance.
speedboat
racing. In this occupation, women represent
approximately 13% of all racers, and they are treated as
the equals of men. The rules of the race are strictly monitored,
and any breaching of the rules results in disqualification. IL
potential payoffs are very high, but severe sanctions on dis-
qualified racers mean they cannot participate, resulting in a
fall in annual revenue. Consequently racers have a strong
incentive to follow the rules in order to win the race. But they
also face trade-offs because in order to win, they may have to
engage in risky lane changing to improve their position.
Using data from these races, we explore how female and
male performance and strategies in the mixed-sex races dif-
fer from the single-sex races. Our data are in panel form,
where we have information for each racer’s performance
time and strategy across all the races in which they have
competed. Così, we have a total of over 15,000 women-
race observations and over 127,000 men-race observations.
After controlling for unobservable individual-specific
effects and other performance-relevant factors we describe
subsequently, we find the following. (UN) The performance
of female racers is slower in the mixed-sex races than the
all-female races, while men’s time is faster in the mixed-
sex races than in the all-male races. (B) Men adopt a more
confident or aggressive strategy to obtain advantageous
positions in the mixed-sex races than in the men-only races,
whereas women adopt a less aggressive strategy in the
mixed-sex races than in the women-only races. (C) There
are no gender differences in disqualifications across the
mixed-sex and the single-sex races.
The first above is of particular interest. It shows that
female competitive performance, even for women who have
chosen a competitive career and are very good at it, È
enhanced by being in a single-sex environment rather than
in a mixed-sex environment in which they are a minority.
Our two other findings are also of great interest, since they
follow from our investigation of the mechanisms through
which our first finding operates.
The remainder of the paper is set out as follows. In section
II, we describe the institutional background of the Japanese
Professional Motor Boat Race, and in section III we provide
an overview of the data and a brief discussion of strategies.
In section IV, we explain the estimation approach. Sezione
V presents the estimation results and interprets the major
findings. Section VI summarizes our conclusions and draws
out some implications for future research.
II.
Speedboat Racing in Japan
Speedboat racing in Japan takes the form of tournaments
that are tightly controlled by a central federation, the Japanese
The Review of Economics and Statistics, ottobre 2018, 100(4): 581–593
(cid:2) 2018 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0
Internazionale (CC BY 4.0) licenza.
doi:10.1162/rest_a_00715
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582
THE REVIEW OF ECONOMICS AND STATISTICS
Speedboat Racing Association. Male and female racers
receive exactly the same intensive training, and there is
only one training school, the Yamato Boat School. Not only
do women train with the men under the same conditions, Essi
also participate and compete with men in the races under
the same conditions. Well before a race day, individuals are
randomly assigned to mixed-sex or single-sex races.
To qualify as a professional speedboat racer, individuals
between the ages of 15 E 29 years must train for one year
and pass a final examination at Yamato Kyotei Gakko
(Yamato Boat School). 2 Because of this wide age window,
individuals who gain entry are from a variety of back-
grounds, ranging from individuals who have completed
only junior high school, up to individuals whose highest
educational qualification is from a university. Inoltre,
some of the entrants have also had a subsequent career after
completing their education. Così, time since graduation
from the boat school is not just picking up age.
In this section, we describe the institution of speedboat
racing in some detail, since an understanding of this is impor-
tant for interpreting the data and estimates. Unless otherwise
noted, our principal source of information is Himura (2015).
In speedboat racing in Japan, there are about 1,600 racers,
aged between 18 E 70 years,3 of whom around 1,400 are
men and 200 women. There are twenty-four speedboat racing
stadiums throughout Japan, and boat races are randomly held
about four days a week in each stadium. Racers go to many
different stadiums to compete. The racing circuit is a large
artificial pond or sectioned-off body of water that is 600
meters in length. Competitors race around it three times, Guida-
ing to a total race distance of 1,800 meters. In each racing
meet, there are twelve races, and six racers compete in any
given race. The prizes offered are considerable.
On race meeting day and before each race, each racer’s
name is announced. That individual then drives the boat
(randomly allocated to him or her for that day) a distance of
150 meters along a straight section of the circuit. His or her
performance time is immediately reported, and this pro-
vides a public measure of the racer’s condition. A short
exhibition time is held to indicate good condition, since the
time depends not only on physical and mental factors (Quello
may or may not vary across days) but also on the boat and
its engine randomly allocated to the racer for that day.4
2 There were 1,435 applicants for the 2015 entrance exam to the
Yamato Boat School. Of these, only 35 were admitted (27 men and
8 women) E 25 graduated. Training covers driving techniques and
inspection and maintenance of the engine and boat.
3 The youngest racer is 16 years old. There is no compulsory retire-
ment age. While students can enter the Yamato Kyotei Gakko (Yamato
Boat School) from 15 years of age, it takes a year to graduate and become
a racer, and hence the rule is that the age of the youngest racer is 16 years.
Tuttavia, this is the exceptional case. In the dataset used in this paper, IL
youngest racer is 18 years old.
4 Speedboat racing is financed from betting. The sport is run by local
governments (the principal), that deputize tasks to the Japanese Motor-
boat Association (the agent). Local governments also own the boats, Ma
the Japanese Motorboat Association is in charge of them. The boats used
for racing at a particular stadium are always kept at that stadium.
FIGURE 1.—THE PREMATURE START SYSTEM
Fonte: Japan Boat Race Association, http://www.boatrace.jp/en.html (accesso ottobre 7, 2016).
Racers are obliged to inspect and mechanically maintain
the boat and engine allocated to them and have no assis-
tance in this task. They cannot reject either the boat or the
engine that has been randomly assigned to them. Così,
racers are motivated to aim for a good exhibition time in
order to understand the boat’s condition and its match to
their talents, and then to adopt a racing strategy dependent
on that. Così, racers use performance times in the exhibi-
tion run to obtain information not only about competitors
but about their own performance. This information is also
used by bettors.
Speedboat racing uses the premature start system, In
which boats must pass the starting line within 1 second after
the starting clock reaches 0. ‘‘Standby warm-up’’ refers to
the period from the time racers receive the signal to leave
the docks (pit) to the moment they cross the starting line.
Racers’ initial pits—and therefore lanes—are determined
prior to the race by the committee of the association. How-
ever, racers can strategically change their lane during the
initial period of turnaround as illustrated in figure 1 E
may thus end up in a different position for the start of the
race. Following in a position behind another boat is judged
as a violation.
UN. Racers and Gender
Japanese speedboat racing is characterized by an open-
ness to age and gender. Hence, a woman can compete with
men and win if her performance time is the fastest of the
racers in the mixed-sex race. For female racers, the differ-
ence between the women-only race and mixed-sex race is
come segue; all five competitors are the same sex (female) In
the women-only race, whereas in the mixed-sex race,
almost all five competitors are the opposite sex. Conse-
quently, we are able to examine how the gender of competi-
tors influences women’s performance. Tuttavia, mixed-sex
races are very different for men and women, since men
always outnumber women in the mixed races.
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PERFORMANCE IN MIXED-SEX AND SINGLE-SEX COMPETITIONS
583
FIGURE 2.—COMPOSITION OF RACES ACCORDING TO NUMBER OF MEN RACERS
Reflecting the gender ratio, there are only one or two
women racers among six racers in most cases of mixed-sex
races. Figura 2 breaks down races according to the number
of men participating. In slightly fewer than 80% of races,
all six racers are men, while in 7% percent of races, all six
racers are women. The remainder—about 15%—are mixed-
sex races. Almost half of the mixed-sex races have only one
woman competing with men racers. Tuttavia, among dif-
ferent types of races, rules and condition are equivalent.5
Therefore, even in the mixed-sex races, women racers are
treated like men racers on an equal basis6. There is no dif-
ference in prize money between genders in the mixed-sex
races or the all-male and all-female races.
B. Race Grade, Racer’s Grade, Prizes, and Penalties
Race participants win prize money according to whether
they finish first, second, or third in each race. Inoltre, Tutto
racers receive a fee for racing on race day even if they are
not placed. We define the order in which participants cross
the finishing line as their place in that race. Races are also
classified into five grades: super grade (SG), grade I (GI),
grade II (GII), grade III (GIII), and ‘‘usual’’ races. Nel
higher-grade races, the number of points that winners earn
is greater (see Himura 2015). Any racers can participate in
the ‘‘usual’’ race, which is the bottom rank. In GIII races,
racers under 30 years old with high winning rates are
selected to participate. The criteria for being selected to
participate in GII and GI races are stricter. In SG, racers are
selected from top-ranked racers on the basis of prior perfor-
mance. Within a year, the number of races is eight in the
SG; around 40 in the GI eight in the GII around fifty in the
GIII and almost every day for the ‘‘usual’’ races.
Prize money for race winners is considerable: $300,000 (SG), $100,000 (GI), $40,000 (GII), $10,000 (GIII), E
5 An exception is minimum weight: men have to weigh more than 50 kg,
and women have to be over 47.5 kg.
6 For all races, boats and motors are the same model and make and are
used for only one year. Tuttavia, individual performance may vary across
boats and motors due to differences in deterioration and maintenance. A
avoid unfairness across racers, allocation of machines is decided by draw-
ing lots.
under $10,000 (‘‘usual’’ racers). There are also other mone- tary prizes. In SG, Per esempio, prize winnings are around $150,000 (second place), $50,000 (third), $20,000 (fourth),
$10,000 (the fifth), and under $10,000 (the sixth).
The Japanese Motorboat Association selects race partici-
pants. Various status racers, from the top to the bottom
levels, are evenly and randomly assigned to participate in
the ‘‘usual races’’. Di conseguenza, the top-class racers partici-
pate not only in the high-grade races such as SG and GI but
also in the ‘‘usual’’ races.
As noted, a racer obtains points according to his or her
order in the race. Per esempio, in the bottom-grade race
(‘‘usual’’ race) and the next-to-bottom race (GIII), points
accumulated in first, second, third, fourth, fifth, and sixth
places are 10, 8, 6, 4, 2, E 1, rispettivamente (Himura, 2015).
In the case of GI and GII (SG), 1 point (2 points) is added to
each of the points listed above. But penalties are also possi-
ble. For instance, participants navigating poorly and break-
ing rules in the race or the turnaround period lose 7 points.
Individuals’ aggregated points in a season are subse-
quently used to select participants in the top-grade (SG)
race. Racers disqualified for interrupting other racers are
automatically excluded from SG races. There is an extra
element to point accumulation: each individual’s points are
aggregated for three years, and the total then determines
racers’ grades, known as A1, A2, B1, and B2. (We use this
as a measure of ability.) Participants disqualified for inter-
rupting others during a race lose 15 points. If they break the
rules—for the actual race or in the turnaround period—they
lose 2 points. Hence, racers have a considerable incentive
not only to win the race but also to avoid rule breaking and
potentially losing their grade classification.
For the four grades of racer, average annual earnings
associated with each grade are as follows: A1 grade (top
grade): $330,000 $; A2 grade: $190,000 $; B1 grade:
$80,000 $; and B2 grade: $50,000 $. Higher-grade racers
are allowed to participate in more races. Even on a day when
there are no high-grade races, A1 racers can take part in the
‘‘usual’’ race and so can earn something. Inoltre,
higher-grade racers can also participate in higher-grade
races with greater rewards. Percentages of women racers for
A1, A2, B1, and B2 are about 11%, 19%, 46%, E 23%,
rispettivamente, while for men, they are around 21%, 20%,
43%, E 14%, rispettivamente. Therefore, as a whole, compo-
sition of ranks of racers for women is lower than men.
Racing in an inner lane confers an advantage. While
racers are allowed to change lane during the race, they are
disqualified and face severe penalties if they interrupt other
racers’ runs. Così, changing to an inner lane requires a
highly skilled technique in order not to interrupt others. In
the case of disqualification, apart from losing points, racers
are penalized by being prohibited from racing for one
month and banned from participating in GI and SG races
for a year. Disqualification thus reduces aggregated points
and lowers the chance of shifting to a higher grade, inevita-
bly reducing annual revenue. All in all, top-class racers are
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584
THE REVIEW OF ECONOMICS AND STATISTICS
skilled enough to change to a better lane while not interupt-
ing other racers to avoid disqualification.
C. Strategies
In speedboat racing, contestants can choose a number of
ways to boost their performance as well as adversely affect
the performance of their immediate competitors. These
activities involve costs, and contestants therefore face sim-
ple trade-offs when making decisions. By increasing effort
and other performance-enhancing activities, a racer in-
creases her probability of winning, but this extra effort is
costly. The bigger the prize spread, the greater the expected
gain from winning, and hence the more worthwhile it may
be to boost ones’s own performance.
Strategies to improve own performance include not only
effort in the actual race but also fine-tuning the engine of
the randomly allocated boat and dieting before race day to
be at optimal weight. Strategies that adversely affect the
performance of
immediate competitors include seizing
command of an inner lane as well as insulting or otherwise
intimidating competitors (known in cricket as ‘‘sledging’’).
While lane changing is easily observable, sledging is not.
And yet it is a potent way to weaken opponents’ concentra-
zione, causing them to underperform.
Psicologico
factors affect own performance and
responses to the activities of other contestants. In our data,
we have mixed-sex and single-sex races, so we can explore
how the performance of men and women differs across
these environments. The literature shows that women prefer
not to compete against men (see Apicella & Dreber 2015;
Ca´rdenas et al., 2012; Khachatryan et al., 2015). But in
speedboat racing, women are sometimes compelled to
through their random allocation to boat race groups. Questo
allocation is known several months before the actual races.
Racing a speedboat against others involves skill not only
at maneuvering the boat but also at jockeying for a desir-
able position, since the inner lanes confer an advantage.
Tuttavia, while lane changing can bring benefits, it can also
bring costs, because the rules are strict and breaking them
leads to serious penalties. Owing to male characteristics of
‘‘over confidence’’ or a greater tendency to take risk (found,
Per esempio, in Dreber et al., 2014; Almenberg & Dreber
2015), male speedboat racers may be more likely than
women to adopt an aggressive strategy—or to be successful
at it, for it is possible that women are less confident in
mixed races, and as a result, aggressive male behavior is
more successful. Within our data set, this is proxied by lane
changing. Our prediction is that women racers follow a less
aggressive or confident strategy than men and are less suc-
cessful at lane changing. (Unfortunately, we do not have
information on the number of attempted infractions relative
to the number a person is actually charged with. Hence, we
cannot test the hypothesis that even if women racers are
found to be less aggressive, that they are less likely to be
penalized than men in the mixed-sex race.)
We now consider individual performance in the solo exhi-
bition race. It is hard to separate out strategic and psycholo-
gical factors within our data. Tuttavia, both affect perfor-
mance in the actual race, in which peer effects as well as
own decisions play a part. In contrasto, strategy plays a much
smaller role in the exhibition run. This is because, in the
exhibition run, participants run solo and do not compete
directly with the other race participants. Thus jockeying for
position is not relevant. But there are other ways in which
competitors can sabotage performance in an exhibition
run. Chowdhury and Gurtler (2015) extensively surveyed
studies investigating sabotage in competitions. They report
widespread evidence of sabotage, defined as an activity con-
ducted to damage others and driven by material benefits for
the saboteur. In the context of speedboat racing, it would be
easy for a participant to take a subtly menacing attitude
toward a competitor. One example might be glaring, Quale
may so unnerve the recipient that his or her performance is
affected, in both the exhibition run and the actual race. Such
behavior is likely to be very hard to observe by the race
organizers. There is also a possibility that a subset of racers
might collude to slow down another racer. Again we have
no data on this and simply mention it as a possibility.
III. Data and Descriptive Statistics
Our data are individual records for the period April 2014
to October 2015 from Boat Advisor, the database of Japa-
nese speedboat racing.7 Of the 24 boat race stadiums in
Japan, only seven provide all racers’ records, and we used
these to construct a panel data set for the number of racers
and the races in which they participated. The seven sta-
diums cover a wide variety of locations and statuses.8 In a
racing meet, racers participate in two or three races. For the
period studied, there are 202 female and 1,430 male racers.
The average number of races in which each of these indivi-
duals participated was 250, resulting in 400,000 person-race
observations. Our estimating subsample comprises all those
races with complete information about racers’ records,
which yields approximately 140,000 person-race observa-
zioni. This is a far larger sample than other data sets used to
consider gender differences in preferences and behavior
that are obtained from experiments (Dreber et al., 2011
2014; Ca´rdenas et al., 2014) and from survey data (Buser
et al., 2014; Almenberg & Dreber, 2015). Inoltre, COME
7 While a racer’s engine is randomly assigned by lot, the starting lane or
dock position is assigned in a more complicated way. The committee aims
to reduce disparity in racers’ winning probabilities, and states publicly that
lanes are assigned to equalize the condition of racers to run a close race. In
this sense, the assignment of lanes has been done in a quasi-random way.
8 Our seven stadiums (at Suminoe, Marugame, Kiryu, Miyajima,
Biwako, Karatsu, and Amagasaki) are representative of the other sta-
diums. To check this, we compared average characteristics of our sta-
diums with the average for the rest with regard to each of the following:
days of racing meets in a year; total number of visitors; average purchase
per visitor; average revenue per day; and days of meets for SG and GI
races. There was no statistically significant difference between our seven
and all the others.
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PERFORMANCE IN MIXED-SEX AND SINGLE-SEX COMPETITIONS
585
FIGURE 3.—MEAN OF PLACE IN THE RACE ACCORDING TO LANES
TABLE 1.—BASIC STATISTICS AND DEFINITION OF VARIABLES USED IN ESTIMATION
Race time (seconds)
Place in the race
Exhibition time (seconds)
Weight (kg)
Number of lanes changed down
toward the first lane
Number of lanes changed up
toward the sixth lane
Poor navigation
Disqualification
Mixed-sex race
Number of opposite-sex racers
Number of higher-grade racers
Number of lower-grade racers
Number of more experienced racers
Number of less experienced racers
Number of heavyweight racers
Number of lightweight racers
Exhibition rank_1 (1st place)
Exhibition rank_2
Exhibition rank_3
Exhibition ranka_4
Exhibition rank_5
Exhibition rank_6 (last place)
Race grade_1 (SG)
Race grade_2 (GI)
Race grade_3 (GII)
Race grade_4 (GIII)
Race grade_5 (Usual)
Lane_1 (inner lane)
Lane_2
Lane_3
Lane_4
Lane_5
Lane_6 (outer lane)
Number of racers
Observations
Women
(1)
Men
(2)
Difference
(1) – (2)
113.2
3.68
6.72
47.5
0.07
112.7
3.43
6.73
52.0
0.12
0.05***
0.25***
(cid:2)0.01***
(cid:2)4.5***
(cid:2)0.05***
0.09
0.09
(cid:2)0.005**
0.0003
0.0010
0.31
1.25
1.77
1.36
2.54
2.21
1.51
0.14
0.182
0.172
0.167
0.166
0.161
0.152
0.002
0.048
0
0.342
0.608
0.154
0.159
0.160
0.165
0.175
0.186
202
15,472
0.0004 (cid:2)0.0001
0.0012 (cid:2)0.0002
0.19***
0.12
1.10***
0.15
0.24***
1.53
(cid:2)0.10***
1.47
0.17***
2.36
(cid:2)0.15***
2.37
(cid:2)2.93***
4.45
(cid:2)3.40***
3.54
0.017***
0.165
0.06*
0.166
0.001
0.166
0.167 (cid:2)0.001***
0.167 (cid:2)0.006***
0.167 (cid:2)0.015***
0.001 (cid:2)0.008***
0.055 (cid:2)0.007***
(cid:2)0.01***
0.01
0.047
0.295
0.878 (cid:2)0.270
0.168 (cid:2)0.014***
0.168 (cid:2)0.009***
0.167 (cid:2)0.007**
0.167 (cid:2)0.002
0.166
0.166
1,430
127,020
0.009***
0.020***
Statistically significant at ***1%, **5%, E 1%. Number of competitors whose status is higher
than the racer is used to capture other racers’ skill and techniques. Number of competitors whose status
is lower than the racer also included. Inoltre, we obtain the graduation period (from Yamato speed-
boat racers school where boys and girls must graduate to get a racer license). Time passed since gradua-
tion is considered to be the degree of experience. From this, we construct the number of competitors who
have experience higher (or lower) than the racer and include it as a control. Heavyweight racers are
defined as equivalent to or heavier than 48.2 kg, which is the 75th percentile for a male racer’s weight.
The number of heavyweight racers is the number of heavyweight racers participating in the race. Light-
weight racers are defined as equivalent to or heavier than 50.2 kg, which is the 25 percentile for female
racers’ weight. The number of lightweight racers is the number of lightweight racers participating the
race. Poor navigation is defined as unfairly interfering with other racers, although not to a serious degree.
If its degree is serious, the racer is disqualified. Racers attempt to avoid poor navigation and disqualifica-
zioni, so their occurrence is very rare. Therefore, mean values of poor navigation and disqualifications
are very low.
fourth place, with the difference between genders being
0.25 and statistically significant. The weight differences
between women and men (reported in the fourth row) are as
expected: men are significantly heavier.
The fifth row of table 1 shows that in the turnaround per-
iod before the formal start of the race, 5% of female racers
changed their initial lane down toward the first lane, COME
compared with 8% of men, a statistically significant differ-
ence. This suggests that male racers have a more aggressive
strategy than women racers. (This is consistent with exist-
ing studies—for example, Gneezy et al, 2009, and Apicella
& Dreber, 2015.) One might expect aggressive lane chan-
ging to increase the probability of being caught for poor
navigation and being disqualified. Tuttavia, dummies for
poor navigation and disqualification show that only 0.04%
well as in formmation about racer’s performance measured
by the race time, time in exhibition run, and whether a
penalty was received, we have detailed information about
the characteristics of the race: place and day of the week,
the grade of the race, gender composition of the race,
and the condition of racers as captured, for examples, by
their weight on the day of the race and their lane in the race.
With regard to weight, although individuals are randomly
allocated to single-sex and mixed-sex races, this occurs sev-
eral months before the races in which they are to partici-
pate. Così, racers might conceivably alter their weight in
response to these allocations.
We use this rich data set to explore gender differences in
performance in competitive circumstances. The subsample
of women-race observations
slightly over 15,000,
whereas that for men is over 120,000. Figura 3 shows the
relation between place in the race and race lane. As is well
known, racers gain an advantage if they are given an inner
lane (Himura, 2015), and figure 3 illustrates this.
È
Racers are classified, as already noted, into five grades:
SG, GI, GII, GIII, and ‘‘usual’’ races. To participate in the
higher-grade races with greater prize money, racers are
required to have performed well in races so far. Therefore,
the higher the grade of race, the faster the participants
should be.
Tavolo 1 presents means, disaggregated by gender, del
main variables used in our analysis. (There are also some
additional dummy variables included in estimation but not
reported; these are presented in the table notes.) The means
are calculated from person-race observations, and the num-
ber of these is reported in the bottom row of the table.
Tavolo 1 shows that average male race times are faster than
those of women, and this difference is statistically significant.
Tuttavia, the difference in exhibition time between male and
female racers is considerably smaller. This difference in race
times may be due to male racers having better strategies in
competitive circumstances, whereas in less competitive cir-
circostanze (exhibition run), men and women racers’ abilities
are almost equivalent.
The second row of table 1 reports the race rank. Così, for
all race observations, the average was between third and
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586
THE REVIEW OF ECONOMICS AND STATISTICS
of men are caught for poor navigation and 0.12% disquali-
fied, percentages only slightly higher than the 0.03% E
0.10%, rispettivamente, found for women, and the differences
are not statistically significant. Così, while male racers
appear to be more aggressive in terms of lane changing,
they are no more likely to be caught for risky navigation.
The proportions of women and men in mixed-sex races are
31% E 12%, rispettivamente. Across all races, women face
an average of 1.25 opposite-sex racers, while men face only
0.15.
Since boat racing ability, as measured by race time and
place in race, differs slightly across the sexes, we need to
control for ability when estimating the determinants of race
time and race place for the mixed-sex and single-sex races.
We construct two variables using the information we have
for racers’ grades (A1, A2, B1, and B2): the number of
higher-grade and the number of lower-grade racers an indi-
vidual faces in a race. Women typically face 1.77 higher-
grade and 1.36 lower-grade racers, while the comparable
figures for men are 1.53 E 1.47, rispettivamente. To calculate
racers’ experience, we obtained each racer’s graduation
date and use time elapsed since then as our proxy for
experience. We find that averaging across races, women
typically face 2.54 more experienced racers and 2.21 less
experienced racers, while the comparable figures for men
are 2.36 E 2.37, rispettivamente.
There is a considerable difference in the gender composi-
tion of race grades. Primo, consider the gender proportions of
participants in race grade 5 (the ‘‘usual’’ races). Here we
see that 60% of woman-race observations are found in the
lowest-grade race as compared with 88% of men-race
observations. Tuttavia, in race grade 4, there are only 5%
of all male observations, which is lower than the 34% for
female. Aggregating race grade 4 and race grade 5 indicates
that almost 95% of male observations, as well as of female
observations, are found here. There is little difference in the
gender rates for race grades 1, 2, E 3, although it should
be noted that race grade 3 È 0 for women and so women
racers did not participate in GII races at all. As noted in sec-
tion IIB, there are only eight races for GII in a year; così,
it is unsurprising that no women were observed here. In
table 1 we also include means for randomly assigned start-
ing lane (the lane or pit from which a participant starts at
the very beginning of the race, before the turnaround per-
iod, as illustrated in figure 1).
Table A.1 in the online appendix shows differences in
key variables between single-sex and mixed-sex races and
also between women and men racers in each of these group
types. For our purposes here, the most interesting compari-
son is female racer time between the single-sex and mixed-
sex races: women run about 1.4 seconds faster in single-sex
races than in mixed-sex races, a difference that is statisti-
cally significant at the 1% level. For men, there is no statis-
tically significant difference in race times between the
single-sex and mixed-sex races. Accordingly, the raw data
show that women racers’ performance is influenced by the
gender composition of the race, but this does not hold for
men racers. With regard to the single-sex races, we find that
the gender difference in race time is only 0.1 second—a
tiny amount, although statistically significant. In the mixed-
gender races, male racer time is significantly faster by 1.6
seconds than women’s. Therefore, the gender difference in
the mixed-sex races is sixteen times larger than in single-
sex races.
While in this section we explored correlations in the raw
dati, in section V, we will use fixed-effects regression tech-
niques to control for other factors affecting our variables of
interesse. Before presenting these results, we outline our
econometric model.
IV. The Econometric Model
Our randomization is key to enabling us to document our
basic stylized fact: that the same woman performs relatively
worse in terms of her race time in mixed-sex races as com-
pared with single-sex races, while for the average male
racer, the opposite is true. We report in our tables of regres-
sion results for place in race and race time, a baseline model
with the minimum of controls. We estimate this separately
for the subsamples of male and female racers, as well as for
the pooled sample of men and women. We also report esti-
mates from an expanded specification with additional con-
trols in order to see if the direct effect of the mixed-sex
variables alters once we control for the ability, experience,
and weight of competitors in each race. If male competitors
are of higher ability or have more experience, the estimated
coefficient to the treatment variables in the baseline model
might be an overestimate of the actual
true effect on
females of being in a mixed-sex race. We also include in
the expanded specification the weight of competitors, since
heavier racers run more slowly. Later in the paper, we will
investigate if women might reduce their effort in mixed
races, and men might increase their effort, by engaging in
strategic behavior with regard to the outcome variables of
lane changing or weight.
Our expanded specification for various outcome mea-
sures is
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Ritk ¼ a0 þ a1Mitk þ X
itB þ Y
itC þ ei þ mtk þ uitk:
0
0
(1)
The dependent variable R denotes the performance of
individual i on race day t at stadium k. These include place
in race, the natural log of race time in seconds, lane chan-
ging, poor navigation, disqualification, and exhibition time.
In equation (1), the constant is denoted by a0, while a1 is the
marginal effect of the independent variable of interest, M.
(In some specifications, M will be the mixed-sex dummy,
while in others, it will be the number of opposite-sex racers.)
Other controls are captured by the row vector Xit.
As illustrated in figure 3, place in race depends on lane.
Inoltre, on a race meeting day, there are twelve races
in a stadium. Superior-graded racers tend to participate in
PERFORMANCE IN MIXED-SEX AND SINGLE-SEX COMPETITIONS
587
TABLE 2.—DEPENDENT VARIABLE: PLACE IN THE RACE (FIXED-EFFECTS ESTIMATES)
Linea di base
With Control Variables for Ability
(1) Tutto
(2) Women
(3) Men
Mixed-sex dummy*
Women racer dummy
Mixed-sex dummy
Number of opposite-sex
racers
Number of higher-grade
racers
Number of lower-grade
racers
Number of more
experienced racers
Number of less
experienced racers
Number of heavyweight
racers
Number of lightweight
racers
Lane_1
Lane_2
Lane_3
Lane_4
Lane_5
Lane_6
Control group for
Mixed-sex dummy (cid:3)
Women racer dummy
Control group for
mixed-sex dummy
Control group for
women racer dummy
Groups
Observations
0.93***
(0.04)
(cid:2)0.28***
(0.01)
[2.31]
0.81***
(0.01)
0.92***
(0.02)
1.05***
(0.02)
1.38***
(0.02)
1.63***
(0.02)
[3.44]
[3.46]
[3.43]
1,632
139,929
0.19***
(0.01)
(cid:2)0.23***
(0.01)
[2.56]
0.57***
(0.05)
0.71***
(0.05)
0.88***
(0.05)
1.26***
(0.05)
1.48***
(0.05)
[2.28]
0.84***
(0.02)
0.95***
(0.02)
1.07***
(0.02)
1.40***
(0.02)
1.64***
(0.02)
202
15,210
1,430
124,719
(4) Tutto
0.74***
(0.04)
(cid:2)0.19***
(0.01)
0.18***
(0.004)
(cid:2)0.17***
(0.004)
0.06***
(0.01)
0.06***
(0.01)
(cid:2)0.03***
(0.002)
(cid:2)0.02***
(0.005)
[2.31]
0.78***
(0.01)
0.90***
(0.01)
1.01***
(0.02)
1.31***
(0.02)
1.54***
(0.02)
[3.44]
[3.46]
[3.43]
1,632
139,929
(5) Women
(6) Men
0.19***
(0.01)
0.16***
(0.01)
(cid:2)0.16***
(0.01)
0.03
(0.03)
0.01
(0.03)
(cid:2)0.06***
(0.01)
0.03***
(0.01)
[2.56]
0.54***
(0.05)
0.68***
(0.05)
0.83***
(0.05)
1.16***
(0.05)
1.36***
(0.05)
(cid:2)0.17***
(0.01)
0.18***
(0.004)
(cid:2)0.17***
(0.004)
0.06***
(0.01)
0.06***
(0.01)
(cid:2)0.03***
(0.002)
0.01
(0.01)
[2.28]
0.81***
(0.02)
0.92***
(0.02)
1.03***
(0.02)
1.33***
(0.02)
1.56***
(0.02)
202
15,210
1,430
124,719
Statistically significant at ***1%. Robust standard errors clustered on races are shown in parentheses. Values within brackets are mean values of the base group (control group) for dummy variables. Dummies for
race grade, location dummies, and interaction dummies between locations and days are included but not reported. Number of interaction dummies between locations and days are 630.
the tenth to twelveth races among them even if there are
only ‘‘usual’’ races in the day. In equation (1), these other
factors are incorporated in the vector Yit. C is the column
vector of coefficients to be estimated. As explained in the
previous section, we have data for seven racing stadiums
and the races that occurred almost every day for around one
and half years. The conditions of races and racers vary by
place and day because of weather and the random allocation
of engine and boat. To control for conditions, we include
dummies for place and days of the race and their interac-
zioni, as represented in equation (1) by mtk. Unobservable
individual time-invariant characteristics, ei are controlled
for through fixed-effects estimation.
V. Results
UN. Place in Race
Tavolo 2 reports determinants of place in race, Quale
ranges from first to sixth. Fixed-effects estimates of the par-
simonious baseline model are presented in the first three
columns of table 2. We control for the randomly assigned
starting lane as well as additional controls listed in the table
notes. For all tables of estimates, we report in parentheses
robust standard errors clustered on races. In brackets in
each table, we provide means for the outcome variable for
the relevant control group.
The first column of table 2, estimated for the sample of
all men and women, shows that a woman places worse (Quello
È, further from first place) when she is in an opposite-sex
race, while a man places better (closer to first place) In
mixed-sex races. Per esempio, a woman randomly allocated
to a mixed-sex race performs almost one place worse (IL
coefficient is 0.93) than if she were in a single-sex race,
ceteris paribus. Men randomly assigned to mixed-sex races
do significantly better than they do in single-sex races (IL
coefficient is (cid:2)0.28). This implies that men racers’s place
in the race improves by 0.28 point on the 6 point scale when
they run in the mixed-sex races than in the single-sex races.
The second and third columns of table 2 are estimated on
the subsamples of women and men, rispettivamente, and the
treatment variable is now the number of opposite-sex racers
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588
THE REVIEW OF ECONOMICS AND STATISTICS
(rather than the simple dummy variable used in the first col-
umn). For women, the estimated coefficient to the number
of opposite-sex competitors is 0.19 and is precisely esti-
mated, meaning that a woman’s place is worsened by 0.19
on the 6 point scale with the number of opposite-sex com-
petitors. In contrast for men, we find that the estimated
coefficient to the number of opposite-sex competitors is
(cid:2)0.23, implying that a man’s place is improving by 0.23 SU
IL 6 point scale in the number of opposite-sex competitors.
These effects are statistically significant at the 1% level.
We now turn to another control variable of interest: IL
randomly assigned starting lane (from which a participant
starts at the very beginning of the race, before the turn-
around period). The innermost lane is the base in the regres-
sion tables. Estimated coefficients for dummy variables for
the other lanes are statistically significant and positive. IL
magnitude of the coefficients is monotonically increasing
across lanes; racers randomly allocated to outer lanes are
less likely to win than those allocated to the inner lane.
Our randomization is key to establishing a basic stylized
fatto: that the same woman performs relatively worse in
terms of her place in race in mixed-sex races as compared
with single-sex races, while for the average male racer, IL
opposite is true. Given this basic fact, can we say any more
about how to interpret this finding? One candidate mechan-
ism is that women who face higher-ability competitors
choose to exert less effort, and this is why they do worse in
mixed-sex races. To explore this potential mechanism, we
include additional variables. Così, in the last three columns
of table 2, we include proxies for relative ability and experi-
ence (the numbers of higher-grade and lower-grade racers
that an individual competes against in a race, as well as the
numbers of more experienced racers and less experienced
racers.)9 In appendix table A.4, we show the randomization
balance of our ability measures across mixed-sex and
single-sex races.
We also include variables indicating the number of heavy-
weight racers and the number of lighter-weight racers an
individual competes against. Lighter racers can race faster
(less resistance in water) than a heavyweight one, and they
can maneuver more quickly and have an advantage in
invading an inner lane.10 The greater the number of compe-
titors who are lighter than a given racer, the less likely it is
that that racer will win. Since women racers are lighter on
average than men,
the inclusion of this variable might
reduce the estimated coefficient to gender.
Estimates reported in the last three columns of table 2
show that even controlling for relative ability, experience,
and weight, women are more likely to place lower when
racing against mixed-sex racers than they are when racing
9 In this, we follow Yamane and Hayashi (2015), who observe peer
effects among competitors in swimming races.
10 Tuttavia, there are weight limits (51 kg for men, 47 kg for women).
The difference in weight limit is the only advantage for women in the
race. A racer whose weight is lower than the limit, is obliged to wear
weights in his or her jacket.
against all-female racers, while men place better (closer to
first place) with mixed-sex competitors than they are with
single-sex. The magnitude of the coefficients to the treat-
ment variables differs slightly in this expanded specifica-
zione, but it is still the case that women in mixed-sex races
are slightly less likely to be poorly placed and men are
slightly less likely to be well placed, as compared to the
estimates with no ability controls. These effects remain sta-
tistically significant at the 1% level.
We now turn to the estimated impact of the other con-
trols. Tavolo 2 shows that across all specifications in col-
umns 3 A 6, more higher-grade (higher-ability) participants
in a race reduce the likelihood of being well placed, while
more lower-grade racers increase the likelihood of being
well placed. This is as expected. Turning to experience, we
see the estimated coefficients to these variables are positive
for both more experienced and less experienced partici-
pants, but this effect is small and imprecisely estimated for
women.
The estimated coefficient to the number of heavyweight
racers is negative and statistically significant. The more
heavyweight racers there are in a race, the more likely it is
that a woman will place, a finding consistent with weight-
reducing maneuverability and speed. The more lightweight
racers there are in a race, the less likely is a woman to
place.
Our basic stylized fact from the baseline model was that
the same woman performs relatively worse in terms of her
place in race in mixed-sex races as compared with single-
sex races, while for the average male racer, the opposite is
VERO. Our expanded specification shows that this remains a
stylized fact even after controlling for the ability, experi-
ence, and weight of the other racers. But can we say any
more about how to interpret this stylized fact? An addi-
tional candidate mechanism is that men might be more suc-
cessful than women at changing lanes. Before investigating
this in section IVC, we first report the impact of the treat-
ment variables on racers’ recorded time in seconds.
B. Time in Race in the Baseline Model
Race time and place in race are both relevant information
for bettors. Although place matters more to the individual
since it translates directly into winning, a participant wants
to travel faster in order to place. Table 3A reports fixed-
effects estimates of the log of recorded race time in sec-
onds, with the baseline and the expanded specifications
including the same sets of controls as in Table 2. Column 1
shows that women run more slowly in the mixed-sex race,
while men’s time is faster. The second and third columns of
Table 3A are estimated on the subsamples of women and
men, rispettivamente, and the treatment variable for each is
now the number of opposite-sex racers. For women, we find
that the estimated coefficient to the number of opposite-sex
competitors is 0.002 and is precisely estimated, Senso
that women’s time is increasing with the number of oppo-
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PERFORMANCE IN MIXED-SEX AND SINGLE-SEX COMPETITIONS
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UN. Dependent Variable: Log of Time Record in Races (Fixed-Effects Estimates)
TABLE 3.—FE ESTIMATES OF RACE TIME AND PLACE IN RACE, WITH ADDITIONAL CONTROLS
Linea di base
With Control Variables for Ability
(1) Tutto
(2) Women
(3) Men
(4) Tutto
(5) Women
(6) Men
Mixed-sex dummy (cid:3)
Women racer dummy
Mixed-sex dummy
Number of opposite-sex
racers
Control group for
Mixed-sex dummy (cid:3)
Women racer dummy
Control group for
mixed-sex dummy
Control group for
women racer dummy
Groups
Observations
0.009***
(0.001)
(cid:2)0.002**
(0.0004)
[4.72]
[4.72]
[4.72]
0.002***
(0.0003)
(cid:2)0.001**
(0.0002)
0.005***
(0.001)
(cid:2)0.0005
(0.0004)
[4.72]
[4.72]
[4.72]
0.001***
(0.0004)
(cid:2)0.0006*
(0.0004)
1,632
139,929
202
15,210
1,430
124,719
1,632
139,929
202
15,210
1,430
124,719
B. Dependent Variable: Place in the Race and Log of Time Record in Races (Fixed-Effects Estimates):
Examination of Opposite Gender Racers’ Influence on Inner Lane (Advantageous) Racers’ Performance
Number of opposite sex racers (cid:3)
Inner lane dummy
Number of opposite-sex racers
Control group for inner lane dummy
Groups
Observations
Place in the Race
Log of Time Record in Races
(1) Women
0.04***
(0.01)
0.18***
(0.01)
[4.22]
202
15,210
(2) Men
0.02
(0.02)
(cid:2)0.18***
(0.02)
[3.91]
1,430
124,664
(3) Women
0.001***
(0.002)
0.001***
(0.0004)
[4.74]
202
15,210
(4) Men
0.0002
(0.0003)
(cid:2)0.001*
((cid:2)0.0004)
[4.73]
1,430
124,664
Statistically significant at ***1%, **5%, E 1%. Robust standard errors clustered on races are shown in value in parentheses. Values within brackets are mean values of base group (control group) for dummy vari-
ables. All control variables included in columns 4 A 6 of Table 2 are included but not reported. Inner lane dummy is 1 if the racer’s lane is Lane_1, Lane_2 and Lane 3; otherwise, 0.
site-sex competitors. In contrast for men, we find that the
estimated coefficient to the number of opposite-sex compe-
titors is is (cid:2)0.001, implying that a man’s time falls with the
number of opposite-sex competitors; note, Anche se, that for
men, the effect is less precisely estimated and it is statisti-
cally significant only at the 5%. Così, we show once again
that women’s and men’s performance in these competitions
differs depending on the gender of their competitors.
Controlling for ability, experience, and the weight of
competitors in columns 4 A 6 of Table 3A, we find that the
magnitude of the treatment effect is reduced in column 4,
where we use only dummy variables for whether the race is
mixed sex, though it is still very precisely estimated. In col-
umn 5 (women only), a woman’s time is increasing with
the number of male competitors, while a man’s time is
reduced with the number of female competitors, ceteris
paribus.
C. Lane-changing
Are men more successful than women at lane changing,
and might this help explain our stylized fact? In section II,
we hypothesized that in mixed-sex races, women are less
likely to change lanes either because they are less willing to
engage in aggressive behavior or they are less confident in
mixed settings. This would imply that not only are they less
likely to squeeze their opponents out of their allocated lanes
but are also more likely themselves to be blocked from
retaining an advantageous lane.
Racers randomly allocated to the inner lane enjoy that
advantage only if they retain that position. Our initial exam-
ination of lane changing is shown in table 3B, dove noi
interact the number of opposite-sex racers with the inner
lane dummy variable (taking the value 1 if the racer’s lane
is Lane_1, Lane_2, and Lane_3, otherwise 0). The first two
columns present estimates of the determinants of place in
race, and the last two columns display estimates of recorded
race time in seconds. For women in an inner lane, their
place in race and their time worsen in the mixed-sex races.
Inoltre, if they are initially randomly allocated to the
inner lane, their performance worsens even more (the com-
bined effect is 0.19 þ 0.03 ¼ 0.22).
To investigate further gender differences in lane chan-
ging, we next estimate the determinants of the number of
lanes changed down, in the turnaround period, toward the
first lane (the inner lane), excluding observations of those
who were initially randomly allocated to an inner lane. IL
estimated coefficients for the variables of interest are
reported in columns 1 A 3 of table 4. The dependent vari-
able takes the value of 0 if racers do not change their lane
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THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 4.—DEPENDENT VARIABLE: NUMBER OF LANES CHANGED DOWN TOWARD THE FIRST LANE AND TOWARD THE SIXTH LANE
Towards the First lane (excluding 1 lane racers)
Towards the Sixth lane (excluding 6 lane racers)
Mixed-sex dummy (cid:3)
Women racer dummy
Mixed-sex dummy
Number of opposite-sex
racers
Number of higher-grade
racers
Number of lower-grade
racers
Number of more
experienced racers
Number of less
experienced racers
Number of heavy weight
racers
Number of light weight
racers
Control group for
Mixed-sex dummy (cid:3)
Women racer dummy
Control group for
mixed-sex dummy
Control group for
women racer dummy
Groups
Observations
Tutto
(1)
(cid:2)0.06***
(0.02)
0.04***
(0.01)
(cid:2)0.01***
(0.002)
0.03***
(0.002)
(cid:2)0.02***
(0.003)
(cid:2)0.001
(0.003)
0.001
(0.002)
0.0003
(0.003)
[0.15]
[0.14]
[0.15]
1,632
116,531
Women
(2)
Men
(3)
Tutto
(4)
(cid:2)0.02***
(0.005)
(cid:2)0.01***
(0.004)
0.01**
(0.004)
(cid:2)0.02**
(0.007)
0.002
(0.007)
0.01***
(0.005)
(cid:2)0.004
(0.004)
0.03***
(0.008)
(cid:2)0.01***
(0.002)
0.03***
(0.002)
(cid:2)0.02***
(0.003)
(cid:2)0.001
(0.003)
0.001
(0.002)
(cid:2)0.004
(0.008)
202
12,854
1,430
103,677
(cid:2)0.03***
(0.008)
0.02***
(0.002)
(cid:2)0.01***
(0.001)
0.02***
(0.003)
(cid:2)0.0002
(0.003)
(cid:2)0.01***
(0.002)
(cid:2)0.004
(0.004)
[0.14]
[0.14]
[0.45]
1,632
116,558
Women
(5)
0.18***
(0.02)
0.04***
(0.007)
0.03***
(0.005)
0.01*
(0.004)
0.02**
(0.009)
(cid:2)0.01
(0.01)
(cid:2)0.01
(0.01)
0.003
(0.005)
Men
(6)
(cid:2)0.03***
(0.006)
0.02***
(0.002)
(cid:2)0.01***
(0.001)
0.02***
(0.003)
0.003
(0.003)
(cid:2)0.01***
(0.002)
0.01
(0.01)
202
12,383
1,430
104,175
Statistically significant at ***1%. Robust standard errors clustered on races are shown in values in parentheses. Values within brackets are mean values of base group (control group) for dummy variables. Control
variables included in table 2 are included but not reported here.
Guidance for dependent variable: In columns 1 A 3, the dependent variable is 0 if racers do not change their lane or change up the lane (toward 6) during the initial period of turnaround. The variable is a positive
value if racers change down (toward 1). For instance, the variable is 5 (considered the most aggressive behavior) if racers change from lane 6 to lane 1 during the period. The variable is 1 if racers change from lane 6
to lane 5. In this way, the variable is considered a proxy for the aggressiveness of the strategy, which ranges between 0 E 5. In columns 4 A 6, the dependent variable is 0 if racers do not change their lane or change
down the lane (toward 1) during the initial period of turn around. The variable is a positive value if racers change up (toward 6). Racers changed their lane toward 6 only if competitors behaved aggressively to intrude
their lane because racers do not have incentive to change up. For instance, the variable is positive (considered as less aggressive to blocking competitors). The variable is 1 if racers change from lane 5 to lane 6. In
Da questa parte, we make the variable a proxy for the degree of aggressiveness to block, which ranges between 0 E 5.
and is positive if they do, with the value increasing in the
number of lanes changed.
We also estimate the number of lanes changed down
toward the sixth (outer) lane excluding observations of
those who were randomly allocated lane 6, with results
reported in columns 4 A 6 of table 4. Here the dependent
variable takes the value 1 if racers do not change their lane
in the turnaround period and is positive if they do, with the
value increasing in the number of lanes changed to a less
advantageous position. This can therefore be thought of as a
measure of inability to block more aggressive racers. For
esempio, the dependent variable will take the value 1 if a
racer has shifted one lane away from her allocated lane and
into a less advantageous position.
We see from the first three columns that for men, IL
treatment variables (either the mixed-sex dummy or the
number of opposite-sex racers) significantly increase the
probability of shifting toward the most advantageous lane,
whereas they reduce it for women. The last three columns
of table 4 show that for men, the treatment variables signifi-
cantly reduce the probability of shifting toward the less
advantageous lane, whereas they increase it for women.
Taken together, these results suggest that women are less
inclined to adopt strategically aggressive behavior—or are
less successful at blocking it—during the turnaround period
when men take part in the race. In contrasto, men are more
inclined to follow and succeed at strategically aggressive
behavior during the turnaround period in the mixed-sex
race. These results suggest that women in our data set are
less aggressive than men and less able to block aggressive
competitors and that this tendency is more pronounced
when competing against men.
D. The Determinants of Rule Breaking
Next we turn to the determinants of breaking rules. Col-
umns 1 A 3 in table 5 report fixed-effects estimates of dis-
qualification, while columns 4 A 6 report the fixed-effects
results for being penalized for poor navigation. The esti-
mates show that neither of the treatment variables is statisti-
cally significant. Therefore, competing with opposite-sex
racers does not have any effect on the likelihood of being
caught for poor navigation or for being disqualified. In sum,
the probability of losing points and grade by disqualifica-
tion is the same regardless of gender, and this holds in spite
of the fact that male racers are distinctly more active in lane
changing. Since lane changing involves some risk of foul-
ing, this suggests that males are able to develop aggres-
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591
TABLE 5.—DEPENDENT VARIABLE: DUMMIES FOR DISQUALIFICATION AND FOR POOR NAVIGATION (FIXED-EFFECTS ESTIMATIONS)
Mixed-sex dummy (cid:3)
Women racer dummy
Mixed-sex dummy
Number of opposite-sex
racers
Control group for
Mixed-sex dummy (cid:3)
Women racer dummy
Control group for
mixed-sex dummy
Control group for
women racer dummy
Groups
Observations
(1) Tutto
(cid:2)0.001
(0.002)
(cid:2)0.0003
(0.0004)
[0.001]
[0.001]
[0.001]
1,633
142,492
Disqualification
(2) Women
(3) Men
(cid:2)0.0003
(0.0004)
(0.0005)
202
15,472
1,431
127,020
(4) Tutto
(cid:2)0.001
(0.001)
0.0004
0.00002
[0.0004]
[0.0004]
[0.0004]
1,633
142,492
Poor Navigation
(5) Women
(6) Men
(0.0003)
0.0001
(0.0002)
0.0005
(0.0004)
202
15,472
1,431
127,020
Statistically significant at ***1%. Robust standard errors clustered on races are shown in values in parentheses. Values within brackets are mean values of base group (control group) for dummy variables. All con-
trol variables included in columns 4 A 6 of table 2 are included but not reported.
sively strategic skills without being caught for rule break-
ing. Clearly there is a trade-off between the improved likeli-
hood of winning if a racer changes lanes, on one hand, E
the greater probability of being caught for fouling while
changing lanes, on the other hand. It is possible that less
risk-averse and more confident racers are able to perfect
their lane-changing techniques without penalties or disqua-
lification. The gender differences we have observed are
consistent with the experimental literature on gender differ-
ence in over confidence and risk aversion (see Niederle &
Vesterlund, 2011; Eckel & Grossman, 2008). If on average
men exhibit these traits more than women, they may have
become well practiced in lane changing without being pena-
lized. In contrasto, more risk-averse or less confident women
may run safely to avoid the penalty and keep grade and
revenue.
E. Estimating the Correlates of Weight
Table 6A reports fixed-effects estimates of the correlates
of weight in kilograms as measured on race day. Since
racers receive their schedule several months before an
event, it is possible for them to adjust their weight in
advance according to the types of races to which they have
been randomly allocated in order to run faster. To the extent
that individuals feel threatened by running against the oppo-
site sex, they may exert extra effort by losing weight for the
mixed-sex races. Our estimates in panel (UN) show that for
women, measured weight
is negatively associated with
being in a mixed-sex race, and these coefficients are statisti-
cally significant at the 10% level. In contrasto, for men, mea-
sured weight is positively associated with being in a mixed-
sex race, and these coefficients are statistically significant at
IL 1% level. In our interpretation, a woman reduces her
weight to prepare for competing with males. A male racer
may fail to maintain a light weight because he does not take
the female competitor seriously. But there are other factors
that work to his advantage, including lane changing.
TABLE 6.—DEPENDENT VARIABLE: LOG OF WEIGHT ON RACE DAY
AND LOG OF RECORDED EXHIBITION TIME (SECONDS) BEFORE RACES
(FIXED-EFFECTS ESTIMATIONS)
UN. Dependent Variable
Log of Weight on the Race Day
(1)
(2)
Women Women
(3)
Men
(4)
Men
(cid:2)0.106*
(0.061)
[3.86]
0.041***
(0.015)
[3.95]
202
15,472
202
15,472
1,431
127,020
1,431
127,020
Log of Recorded Exhibition
Time (seconds) before Races
0.095*
(0.061)
0.025
(0.022)
Mixed-sex dummy
Control group for
mixed-sex dummy
Groups
Observations
B. Dependent Variable
Mixed-sex dummy
Number of
opposite-sex racers
racers
Number of lower-grade
Number of higher-grade (cid:2)0.008
(0.009)
0.011
(0.009)
[1.90]
Control group for
racers
0.036*
(0.017)
0.020
(0.014)
(cid:2)0.008 (cid:2)0.012*** (cid:2)0.012***
(0.009)
0.012
(0.009)
(0.003)
0.010***
(0.003)
(0.003)
0.010***
(0.003)
[1.91]
mixed-sex dummy
Groups
Observations
202
15,472
202
15,472
1,431
127,020
1,431
127,020
Statistically significant at *10% E ***1%. Robust standard errors clustered on races are shown in
value in parentheses. In panel A, where the dependent variable is the log of weight, the set of control
variables is equivalent to that in columns 1 A 3 of table 2, but its results are not reported. In panel B,
where the dependent variable is the log of exhibition time, the set of control variables is equivalent to
that in columns 4 A 6 of table 2, but its results are not reported. Estimated coefficients and standard
errors are multiplied by 100 for ease of presentation and interpretation.
F. Performance in the Exhibition Run
While strategy likely plays a smaller role in the exhibi-
tion run than in the race, because participants run solo in
the exhibition run, there are ways for competitors to poten-
tially affect their own performance. Per esempio, competi-
tors with a preoccupied or distant demeanor immediately
before the exhibition might give different impressions to
men and women because of gender differences in percep-
tions of it. Ovviamente, such behavior is unobservable with
our data, and we offer it only as a potential mechanism.
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THE REVIEW OF ECONOMICS AND STATISTICS
Panel (B) of table 6 reports fixed-effects estimates of
exhibition run times. Here we see that both the mixed-sex
race dummy and number of the opposite sex racers are posi-
tive but imprecisely estimated. The positive coefficients
suggest that racers exhibit more slowly before the mixed-
sex race regardless of gender. Tuttavia, the reason is likely
to differ between men and women. Though we cannot test
for this, we hypothesize here that male racers may not make
a full effort in the mixed-sex races because they discount
female competition, whereas a woman racer may not bring
her ability into full play because of perceived pressure from
male racers.
Coefficients of number of higher-grade racers and num-
ber of lower-grade racers show the negative and the positive
signs, rispettivamente. It is interesting that these signs differ
from those in table 2. What is more, they are significant for
men but not for women. Men have greater incentives to run
faster when competing with racers with better records. UN
woman’s incentive, Tuttavia, is influenced by her competi-
tors’ gender but not their skill and ability. All in all, table 2
and panel A of table 6 suggest that the effect of a competi-
tor’s skill and ability depends on whether strategic interac-
tion is absent (panel A of table 6) or present (table 2).
VI. Conclusione
Speedboat racing in Japan takes the form of tightly con-
trolled tournaments for which women and men racers
receive the same intensive training. Women racers partici-
pate and compete in races under the same conditions as men,
and all individuals are randomly assigned to mixed-sex or
single-sex groups for each race. in questo documento, we used a sam-
ple of over 140,000 observations of individual-level racing
records obtained from the Japanese Speedboat Racing Asso-
ciation to examine how male-dominated circumstances
affect women’s and men’s racing performance. We con-
trolled for individual fixed effects plus a host of other factors
affecting performance, including the ability of competitors
within a race. Our estimates revealed that women are less
likely to be placed in mixed-sex races than in all-women
races, whereas men are more likely to be placed in mixed-
sex races than men-only races. We found the same results
when we used the dependent variable time in a race. More-
Sopra, in mixed-sex races, male racers tend to be more aggres-
sive, as proxied by lane changing, in spite of the risk of being
penalized if they contravene the rules, whereas women’s
strategies are less aggressive. We find no difference in dis-
qualification rates between genders. We suggest that gender
differences in risk attitudes and confidence may result in dif-
ferent responses to the competitive environment and that
gender identity is also likely to play a role.
The first finding above is of particular interest. It shows
that female competitive performance, even for women who
have chosen a competitive career and are very good at it, È
enhanced by being in a single-sex environment rather than
in a mixed-sex environment in which they are a minority.
Our other findings are also of great interest, since they fol-
low from our investigation of the mechanisms through
which our first finding operates. In particular, we have
argued that male racers are aggressive but not imprudent by
taking into account competitors’ condition, as well as the
risk of disqualification when jockeying for position.
The gender proportion in the mixed-sex speedboat races
is skewed toward men. Women racers assigned by lot to a
mixed-sex race will typically face five male competitors or,
rather infrequently, four. We suggest that this gender imbal-
ance may trigger awareness of gender identity for both men
and women and that this might go some way to explaining
observed differences in behavior across mixed-sex and sin-
gle-sex groups.11 For example, a man’s gender identity may
lead him to consider being defeated by women to be more
dishonorable than by men, and he will try to avoid it.
Our findings may well have implications for other activ-
ities in which men and women compete with one another
and where the gender balance is skewed in favor of men.
One example is in the STEM disciplines, where being in a
minority may well affect the performance of the women in
that situation.
Finalmente, we point out that sportspeople are likely to be
particularly selected on willingness to compete, and to that
extent, our effects of mixed-sex treatments may be rela-
tively muted compared to other settings where selection is
not as competitive. Alternatively, behavior in repeated
(daily) interactions may differ from that in a short race. Noi
hope that future research will explore these issues further.
11 According to the gender-identity hypothesis, a society’s prescriptions
about appropriate modes of behavior for each gender might result in indi-
viduals’ experiencing a loss of identity should they deviate from the rele-
vant code.
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