GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.205

Overconfidence Increases Productivity

Yusuke Kinari, Noriko Mizutani, Fumio Ohtake and Hiroko Okudaira

July 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Overconfidence Increases Productivity*

Yusuke Kinaria
Graduate School of Economics, Kyushu University

Noriko Mizutanib
Institute for Research on Household Economics

Fumio Ohtakec
Institute of Social and Economic Research, Osaka University

Hiroko Okudairad
Graduate School of Economics, Okayama University*

We thank Kan Takeuchi, Hideaki Kato, and participants at the fourth annual meeting of the Associationof Behavioral Economics and Finance. This work was financially supported by a Grant-in-Aid for
Challenging Exploratory Research from the Japan Society for the Promotion of Science and theCollaborative Program of the Institute of Social and Economic Research, Osaka University.
aCorresponding author: Graduate School of Economics, Kyushu University, 6-19-1, Hakozaki,
Higashi-ku, Fukuoka, 812-8581, Japan. Tel: +81-92-642-2493; e-mail: kinari@en.kyushu-u.ac.jp.
bInstitute for Research on Household Economics, 2-3-7, Kudan-kita, Chiyoda-ku, Tokyo, 102-0073,
Japan. E-mail: mizutani@kakeiken.or.jp.
cInstitute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, 567-0047, Japan.
E-mail: ohtake@iser.osaka-u.ac.jp.
dGraduate School of Economics, Okayama University, 3-1-1, Tsushima-naka, Kita-ku, Okayama
700-8530, Japan. E-mail: okudaira@e.okayama-u.ac.jp.

Overconfidence Increases Productivity

Abstract
Recent studies report that productivity increases under tournament reward structures
than under piece rate reward structures. We conduct maze-solving experiments underboth reward structures and reveal that overconfidence is a significant factor inincreasing productivity. Specifically, subjects exhibiting progressively higher degrees ofoverconfidence solve more mazes. This result shows a positive aspect ofoverconfidence, which usually has been examined in its negative aspect as an
expectation bias.

JEL: C9, D03, J33
Keywords: Expectation, Overconfidence, Reward structure, Productivity, Experiment1. Introduction
There are many ways of paying rewards. Under a piece rate reward structure, thereward people earn depends solely on their individual productivity, whereas under atournament reward structure, it depends on not only their productivity but also theircompetitors’ productivities. It is important to know how different reward structures
affect productivity. In the field of labor economics and business, such knowledge could
guide firms in setting employee rewards to increase profits, and in the context of
education, it helps educators understand how to motivate children toward their best
efforts.
Recent studies examine the relationship between reward structures and productivity
and report that productivity increases under the tournament structure compared with thepiece rate structure (Gneezy et al., 2003; Freeman and Gelber, 2010; Mizutani et al.,2009; Niederle and Vesterlund, 2007).1 Freeman and Gelber (2010), whose research ismost relevant for this study, conducted maze-solving experiments and investigated theeffects of the tournament prize structure and the disclosure of information about othersubjects’ skills. They asked groups of six subjects to participate in two rounds ofmaze-solving problems. In Round 1, subjects were rewarded by piece rate. After thegroups completed Round 1, researchers informed subjects in half of the groups about
the distribution of mazes solved in Round 1 in their groups, but they withheld that
information from subjects in the remaining half. In Round 2, subjects solved mazes
under three different reward structures: fixed, single-prize tournament, andmultiple-prize tournament. Using the number of mazes solved as the measure of1

Although Mizutani et al. (2009) and Niederle and Vesterlund (2007) do not focus on productivity, theirexperimental results demonstrate that productivity increases under the tournament structure comparedwith the piece rate structure.

performance, they found that performance was lowest under the fixed-reward structure,higher under the single-prize tournament structure, and highest under the multiple-prize
tournament structure. They also found that revealing information on performance ofother group members affected productivity. When this information was disclosed tosubjects who scored highly in Round 1, their subsequent performance was greater thanwhen the information was withheld. In contrast, performance among subjects whoscored low in Round 1, decreased when the information was disclosed and increased
when it was withheld.
Freeman and Gelber (2010) interpret their results as demonstrating the effect of
incentives on effort. Under the multiple-prize tournament structure, all subjects have an
incentive to increase their rewards, whereas the fixed-prize structure provides no suchincentive. Under the single-prize structure, subjects with a high chance of winning earn
the incentive, and those with a low chance of winning lose the incentive.
This study examines the changes in productivity that occur when subjects receive no
information about the distribution of competitors’ skills. We conduct maze-solving
experiments under the piece rate and tournament reward structures and explore theeffects of subjective expectations and overconfidence on productivity. Subjects are
divided into groups of four individuals and are asked to anticipate their rankings within
their groups. If we offer information about other subjects’ skills, it would act as anincentive; if we withhold information about other subjects’ skills, subjective
expectations would act as the incentive. That is, if subjects regard themselves as good
performers, they will try harder to win the tournament. If they do not regard themselves
as good performers, they will give up the attempt to win the tournament.
These subjective expectations are based on two factors: subjects’ actual ability andtheir confidence levels. We refer to the latter as “overconfidence” or “underconfidence.”
The results of Freeman and Gelber (2010) suggest that overconfidence may affectproductivity. They report that the performance of subjects who scored low in Round 1
increases when the information regarding other subjects’ performance is withheld. Thismay occur because subjects do not realize that their skills are less than theircompetitors’—that is, they are overconfident. Moreover, it is considered that theperformance of subjects who scored highly in Round 1 decreases more when theinformation is withheld because they are underconfident.
We have other grounds for concluding that overconfidence increases productivity.
Gneezy et al. (2003) conduct maze-solving experiments and observe a gender difference
in increases in performance from the piece rate to the tournament reward structure. In
particular, female subjects perform better in all-female groups, whereas theirperformance does not increase in mixed-gender groups. Mizutani et al. (2009) conduct
computational experiments and report that women become overconfident in all-female
groups, whereas they are under-confident in mixed-gender groups. These results imply
that overconfidence increases productivity and that a gender difference inoverconfidence causes the gender difference in increased productivity. We test thishypothesis using actual overconfidence measures constructed from participants’subjective estimates of their rankings within their groups and the number of mazes
solved.
The remainder of this paper is organized as follows. Section 2 describes our
experiment. Section 3 presents our results and reveals that subjective expectations andoverconfidence affect subjects’ performance significantly. Specifically, subjects who
expect worse rankings increase their performance more than those who expect betterrankings, and overconfident subjects increase their performance. This is evidence that
overconfidence has a positive aspect, which previously has been studied mainly in its
negative aspect as an expectation bias. Section 4 concludes.

2. Method
We conducted the experiment at Osaka University on October 25, November 15 and29, and December 6, 2009. Subjects included 132 students (67 males and 65 females)affiliated with Osaka University. We assigned subjects to groups of four participants
and asked them to solve mazes. Subjects were not aware of which members were
included in their groups, but knew their groups’ gender ratios. We organized groups
according to five gender ratios: nine groups of four males; one group of three males andone female; 13 groups of two males and two females; two groups of one male and threefemales; and eight groups of four females each.2 Our subjects’ tasks included solving as
many mazes as possible within three minutes, and the time remaining always appearedwith the maze on their computer screens. Subjects moved cursors from the start to thegoal point using arrow keys on their keyboards. They could skip a maze by clicking aradio button on their screens and move to the next maze whenever they were willing to
do so. The number of skips was unlimited.
The experiment consisted of three tasks—Task 1, Task 2, and Task 3. For Task 1, we
rewarded subjects by piece rate; their reward of ¥100 per maze solved depended solely
on their individual performance. Task 2 was completed under the tournament reward
structure; only the winner in each group received a reward of ¥400 per maze solved. For

2

We initially intended to conduct our experiments using three gender groups (all-male, all-female, andtwo males-two females). However, two additional groups (one male-three females and three males-one
female) emerged as we recruited subjects hastily to replace those who cancelled at the last minute.

Task 3, subjects could choose the piece rate or tournament reward structure before
solving mazes, and rewards were paid on the basis of their choices. To accustom
subjects to maze-solving using a computer, we conducted one-minute training sessions
before Task 1.
In addition, subjects were asked to anticipate their ranking within their groups after
completing each task. We asked them not only their expected rankings but also theirprobability distribution forecasts. In estimating the expected ranking, subjects pick arank between 1 and 4. In estimating the probability distribution, subjects were required
to give their expected probabilities of each rank by percentages. We also asked them tocomplete a questionnaire at the end of the experiment to measure their attitudes towardrisk and competition.
All subjects received a ¥3,000 show-up fee for participation. At the end of theexperiment, we randomly selected one task among the three, for which we actually paid
the reward. Subjects were paid on the basis of their performance in the tasks plus theshow-up fee.

3. Results
3.1. Gender differences in performance
Table 1 reports the mean number of mazes solved and the standard errors for Tasks 1
and 2. For both tasks, the mean number of mazes solved by males was greater than thatby females. The gender differences in mean mazes solved are approximately 2.5 in Task
1 and 1.9 in Task 2 and are significant at 1%. Niederle and Vesturland (2007) and
Mizutani et al. (2009) conduct computational experiments under the piece rate andtournament structures and find no gender differences in performance, perhaps indicatinggender differences in the ability to solve mazes.3
Table 2 presents the change in performance from Task 1 to Task 2. Both males andfemales increase their performance significantly. For male subjects, the mean
performance increases about 0.9 (9.4%), and for female subjects, it increases about 1.5(18.5%). The gender difference in increase in performance, approximately 0.6 (9.1%), issignificant at 1%. Our result is inconsistent with that of Gneezy et al. (2003), who reportthat performance of males improve under the tournament compared with that under thepiece rate, but female performance do not. However, this result does not reject our
hypothesis that the gender difference in overconfidence causes the gender difference inincreased performance. This is because females were more overconfident than males inour experiment, as reported in 3-3.

3.2. Expected ranking and performance
Table 3 demonstrates subjects’ performance in Tasks 1 and 2 and compares thechange in performance with subjects’ expected rankings following Task 1. While nearly
half the subjects estimated that they ranked second within their groups, only threesubjects believed they ranked fourth. Subjects’ actual performance tends to be lower
when their estimated ranking is lower, indicating that subjects to some extent ranked
themselves reasonably. However, given that more than half estimated their rankings asabove average, the result resembles that of Svenson (1981), who reports that a majorityof subjects regarded themselves as more skillful and less risky than the average driver.
On the other hand, performance in Task 2 tends to increase when subjects’ expected
rankings following completion of Task 1 were lower. That is, the increase in3

Perhaps gender stereotyping produces the result. In fact, Steele and Ambady (2006) and Shih, Pittinsky,
and Ambady (1999) report that gender stereotypes influenced performance.

performance of subjects who ranked themselves lower was greater than that of subjects
who ranked themselves higher. This result differs slightly from Freeman and Gelber(2010), who report that the increase in performance among highly-skilled subjects isgreater than the increase among lowly-skilled subjects, even when subjects have no
information about other subjects’ skills. Our result may indicate that individuals nevergive up trying to win, even when they ranked themselves low.

3.3. Overconfidence and performance
We asked subjects to estimate their rankings within their groups after each task. This
expected ranking is based on two factors: subject’s actual ability and their confidence
level. We refer to the latter as “overconfidence” or “underconfidence.” Many studies
document gender differences in overconfidence.4
We define overconfidence numerically by deducting expected ranking from subject’s
actual ranking after completing both the tasks. As a proxy for each subject’s actual
ranking, we use his or her average ranking within the group generated by 1 million
times random grouping. As a proxy for each subject’s expected ranking, we use both themean of the expected probability distribution (Overconfidence 1) and the point forecast(Overconfidence 2). For both measures, a positive (negative) value implies that subjects
are overconfident (underconfident) because their expected rankings were higher (lower)
compared with their actual ranking.
Table 4 shows the means of the overconfidence measures and their gender differences.
Means of overconfidence measures for all subjects are 0.266 for Overconfidence 1 and
0.391 for Overconfidence 2. Both are significant at 1%, which implies that our subjects

4

Croson and Gneezy (2009) introduced many studies on the gender difference in overconfidence.

are generally overconfident. Furthermore, women are more overconfident than men inboth measures. Gender differences are significant at 1% for Overconfidence 1 and at
10% for Overconfidence 2. Previous literature reports that men are more overconfident
than women (Barber and Odean, 2001). Mizutani et al. (2009) conduct almost the same
experiment employing computational rather than maze-solving tasks, and they reportthat male subjects are more overconfident than female subjects. These results indicatethat gender differences in overconfidence depend on the task assigned.
Figure 1 shows the means of Overconfidence 1 by the change in performance from
Task 1 to Task 2. The horizontal and vertical axes present the change in performancefrom Task 1 to Task 2 and the degree of overconfidence, respectively. We find apositive correlation between the increase in performance and degree of overconfidence.
Subjects whose performance increased are generally overconfident, as we define it here,
and those whose performance decreased are generally underconfident. Further, greater
increases in performance are associated with greater degrees of overconfidence. It isnoteworthy that subjects whose performance does not change are neither overconfident
nor underconfident.
Overconfidence has been studied mainly in its negative aspect as an expectation bias.
Barber and Odean (2001) report that overconfidence increases frequency of stock
trading, but returns decrease because of high transaction costs. Mizutani et al. (2009)
and Niederle and Vesturland (2007) report that overconfidence leads subjects tooverestimate their capabilities and enter competitions, which consequently decreases
their returns. While these studies emphasize how overconfidence decreases returns, ourexperiment demonstrates that overconfidence can improve subjects’ performance, even
if their performance does not improve sufficiently to earn or increase a reward. This isevidence that overconfidence has a positive aspect.5

3.4. Gender and performance
Table 5 presents the change in performance from Task 1 to Task 2 by gender andgender grouping, although one group of three males-one female and two groups of one
male-three females are excluded because the number of observation is few. Male
subjects in two male-two female groups improve their performance compared withthose in all-male groups, although the difference is not significant. The gendercomposition of groups did not affect female subjects’ performance. Women improvetheir performance from Task 1 to Task 2 irrespective of whether men belong to theirgroup. This finding is inconsistent with that of Gneezy et al. (2003), suggesting that theeffect of gender on performance may differ by country or culture.
Table 6 shows the means of overconfidence measures by gender and gender grouping,although one group of three males-one female and two groups of one male-threefemales are excluded because the number of observations is few. Male subjects tend to
be more overconfident when grouped with females, and female subjects tend to be more
overconfident in all-female groups. These results are consistent with those of Mizutani
et al. (2009), although differences in overconfidence measures between groups are
insignificant.
Results from Tables 5 and 6 are inconsistent with those of Gneezy et al. (2003) and
Mizutani et al. (2009) in that differences in the changes in performance andoverconfidence between groups are not significant. However, these results still support
5

Several recent studies demonstrate the positive roles of overconfidence. Galasso and Simcoe (2010) and
Hirshleifer et al. (2010) reveal that CEO overconfidence encourages innovation. The latter also find that
CEO overconfidence increases the firm’s profitability. Anderson and Brion (2010) reveal that
overconfidence helps individuals gain status.

our hypothesis that overconfidence increases performance. Tables 5 and 6 demonstratethat overconfidence and performance tend to correlate positively, although thecorrelation for women is ambiguous.

3.5. Regression analysis
We run the OLS regression to evaluate quantitatively the effect of expected rankingsand overconfidence on increase in performance. We use the increase in performanceand the rate of increase as dependent variables. As independent variables, we use
dummy variables constructed by subjects’ point forecasts for Task 1 andOverconfidence 1 plus a female dummy and a constant term.6
Table 7 presents the regression results. In Eq.1, the coefficient of the constant term issignificantly positive at 1%, indicating that productivity increases under the tournament
reward structure. This result is consistent with previous literature. The coefficient of thefemale dummy is also significantly positive at 1%, which is inconsistent with the resultof Gneezy et al. (2003). However, as noted above, this result does not reject our
hypothesis that the gender difference in overconfidence causes the gender difference inincreased performance, for women are more overconfident than men in our experiment.
In Eq.2, we added Overconfidence 1 as an independent variable in Eq.1. The
coefficient of Overconfidence 1 is significantly positive at 1%, but the coefficient of thefemale dummy is no longer significant. These results support our hypothesis that thegender difference in overconfidence causes gender difference in the increase inperformance.

6

We use expected rankings on Task 1 rather than Task 2 because we wish to investigate how subjects’
expectations affect subsequent performance. To use expected rankings of Task 1 is useful in that we canignore the endogeneity problem that subjects ranked themselves low because they did not perform well.

In Eq.3, we added dummies for subjects’ expected rankings as independent variables
to Eq.1. Coefficients of all dummies are positive, and dummies for second- andfourth-place rankings are significant at 10%. This result indicates that subjects who
ranked themselves second and fourth improved their performance more than subjects
who ranked themselves first. The significance of the constant term weakened comparedwith Eq.1, indicating that performance increases under the tournament reward structure
are engendered by the incentive to earn higher rankings or greater rewards. Our results
are consistent with that of Freeman and Gelber (2010) in suggesting that subjects try
harder to win and elevate their performance. However, they differ from Freeman andGelber (2010) in that the increase in performance of subjects who ranked themselves
lower within their groups exceeded that of subjects who ranked themselves higher.
In Eq.4, we added Overconfidence 1 and the expected ranking dummies to Eq.1.
Coefficients of the constant term and the female dummy are not significant, whereas
coefficients are significantly positive for Overconfidence 1 and the expected ranking
dummies. Therefore, we retain our result that the increase in performance and its genderdifference are brought about by incentives for higher rankings or larger rewards andoverconfidence.
Table 8 shows the results of OLS regressions using the rate of increase inperformance from Task 1 to Task 2 as the dependent variable. The results robustly
support our hypothesis. Coefficients of the expected ranking dummies andOverconfidence 1 are significantly positive. Subjects who ranked themselves lower tend
to increase performance more than subjects who ranked themselves higher. Furthermore,
the significance of the constant term and the female dummy are weakened by adding
Overconfidence 1 and the expected ranking dummies to the regression. Our resultsremained unchanged when we added the variance of the expected probability
distribution, risk aversion, the number of skips in solving mazes, dummy variables forgender ratio, and attitudes toward competition. Our results are also retained when we
substituted Overconfidence 2 for Overconfidence 1 as an independent variable.

3.6. Learning and reward structure
To accustom themselves to maze-solving on a computer, subjects attended
one-minute training sessions before Task 1. However, we cannot rule out the possibility
that one minute was too brief for subjects to master their tasks and that the increase inperformance from Task 1 to Task 2 indicates a learning effect.
To measure the effects of overconfidence and expected rankings while controlling forthe learning effect, we run the OLS regressions after restricting the sample to subjects
who chose the tournament reward structure for Task 3—the same reward structure as
Task 2. Thus, we can control for both the learning effect and attitudes toward
competition. Dependent variables are the increase in performance from Task 2 to Task 3
and its rate of increase. Independent variables are the dummies constructed by subjects’point forecasts in Task 2. We also use Overconfidence 1 as an independent variable,
which is calculated on the basis of subjects’ performance and their expected rankings
for Task 2.
Table 9 presents the results of OLS regression. In Eqs. 1 and 3, the constant terms are
not significant, indicating we can dismiss the learning effect and the effect of the reward
structure. On the other hand, we find the gender difference in increase in performanceeven from tournament to tournament. Although insignificant in Eq.1, the female dummy
is significant at 10% in Eq.3. However, its significance weakens when we add

Overconfidence 1 and the expected ranking dummies as independent variables,
supporting our hypothesis that the gender difference in overconfidence causes thegender difference in increased performance. Overconfidence remains significant forincreased performance: its coefficient is significantly positive at 1%. Conversely, it isunclear whether performance of subjects, who ranked themselves lower, increases morethan performance of subjects who ranked themselves higher. The coefficient of thesecond-rank dummy is significant at 5%, but that of the third-rank dummy isinsignificant. 7 However, subjects who expected their rankings second within theirgroups increased their performance more than those who expected their rankings first.
Accordingly, our results are robust with regard to the learning effect and subjects’
attitudes toward competition.

3.7. Upper half and lower halfFreeman and Gelber (2010) report that subjects who ranked poorly in ability do notincrease their performance, whereas those who ranked highly increase theirperformance when informed about the distribution of mazes solved in their group.
Based on our finding that overconfidence increases performance, we could re-interpretthe result of Freeman and Gelber (2010). We might conclude that overconfidence
among lesser-performing subjects is corrected by providing them objective information
and as a result their performance does not increase. If this is the case, lower-performing
subjects might increase their performance when information about the distribution ofmazes solved is withheld.
Table 10 presents the results of OLS regressions by subjects’ skills. We divided the7

No subjects in the restricted sample expected their rankings to be the lowest.

sample into upper and lower halves based on their average rankings in a group of four
subjects generated by 1 million times random grouping. Subjects in the lower half who
ranked themselves fourth in their groups increased their performance, implying that we
could improve their performance by withholding the objective information—that is, by
not correcting overconfidence. It is noteworthy that the effect of overconfidence for theupper half is stronger than for the bottom half. It may imply that the effect ofoverconfidence on increased performance depends on subjects’ skill.

4. Conclusion
This study has clarified the effect of subjective expectations and overconfidence on
productivity by conducting experiments in which subjects solved as many mazes as
possible in three minutes. Subjects were divided into groups of four. In Task 1, subjects
solved mazes under a piece rate reward structure that paid ¥100 per correct answer. In
Task 2, they solved mazes under a tournament reward structure in which only thewinner received ¥400 per correct answer. In Task 3, subjects could choose either reward
structure and were paid based on their choices. They were asked to anticipate theirrankings within their groups after completing each task.
Previous literature reveals that performance increases under the tournament reward
structure. Freeman and Gelber (2010), the research most relevant for this study,
conclude that incentives contribute substantially to improving performance, and they
investigate how performance changes by informing subjects’ about the performance ofother group members. However, we focused on subjective expectations, which serve as
an incentive when information about other subjects’ performance is withheld. In
addition, Gneezy et al. (2003) report a gender difference in increased performance underthe tournament reward structure. We focus on the gender difference in overconfidence
as a cause of the gender difference in higher performance, and examine the effect ofoverconfidence on productivity.
We found that subjective expectations and overconfidence significantly affectproductivity. Subjects who ranked themselves lower than other group members increase
their performance more than subjects who ranked themselves higher, and overconfident
subjects increase their performance. This reveals the positive aspect of overconfidence,
which previously has been examined mainly in its negative aspect as an expectation
bias.

References

Anderson, Cameron and Sebastien Brion (2010), “Overconfidence and the attainment of
status in groups,” Working Paper Series, Institute for Research on Labor and
Employment, UC Berkeley.

Barber, Brad M. and Terrance Odean (2001), “Boys will be Boys: Gender,
Overconfidence, and Common Stock Investment,” Quarterly Journal of
Economics, 116(1), pp. 261-292.

Croson, Rachel and Uri Gneezy (2009), “Gender Differences in Preferences,” Journal of
Economic Literature, 47(2), pp.448-474.

Freeman, Richard B. and Alexander M. Gelber (2010), “Prize Structure and Informationin Tournaments: Experimental Evidence,” American Economic Journal:
Applied Economics, 2(1), pp.149-164.

Galasso, Alberto and Timothy S. Simcoe (2010), “CEO Overconfidence andInnovation,” NBER Working paper No. 16041.

Gneezy, Uri, Muriel Niederle, and Aldo Rustichini (2003), “Performance inCompetitive Environments: Gender Differences,” Quarterly Journal of
Economics, 118(3), pp. 1049-1074.

Hirshleifer, David A., Angie Low, and Siew H. Teoh (2010), “Are Overconfident CEOs
Better Innovators?” MPRA Paper No. 22425.

Mizutani, Noriko, Hiroko Okudaira, Yusuke Kinari, and Fumio Ohtake (2009),“Overconfidence Makes Men Compete More,” Journal of Behavioral
Economics and Finance, 2(1), pp.1-26, (in Japanese).

Niederle, Muriel and Lise Vesturland (2007), “Do Women Shy Away from
Competition? Do Men Compete Too Much?,” Quarterly Journal of
Economics, 122(3), pp.1067-1101.

Shih, Margaret, Todd L. Pittinsky, and Nalini Ambady (1999), “Stereotype
Susceptibility: Identity Salience and Shifts in Quantitative Performance,”
Psychological Science, 10(1), pp.80-83.

Steele, Jennifer R. and Nalini Ambady (2006), “Math is Hard! The effect of genderpriming on women’s attitudes,” Journal of Experimental Social Psychology,
42, pp.428-436.

Svenson, Ola (1981), “Are we all less risky and more skillful than our fellow drivers?”
Acta Psychologica, 47(2), pp.143-148.

Table 1. Gender difference in performance
Task 1 (piece rate)

Task 2 (tournament)

	
 

Obs.

Mean

S.E.

Obs.

Mean

S.E.

Male

67

12.2090.291

67

13.1340.240

Female

64

9.719

0.280

64

11.250

0.268

All

131

10.9920.229

13112.2140.197

	
 2.490***

0.405

1.884***

0.359

GenderDifference
Notes: *** measure significance at 1%. Because of system-related issues, one
observation is missing.

Table 2.	
  Change in performance (from Task 1 to Task 2)
Increase in performance

Rate of increase

	
 

Obs.

Mean

S.E.

Obs.

Mean

S.E.

Male

67

0.925***

0.203

67

0.094***

0.018

Female64

1.531***

0.208

64

0.185***

0.027

All

131

1.221***

0.147

131

0.139***

0.017−0.606**0.291−0.091***

0.032

GenderDifference
Notes: ** and *** measure significance at 5% and 1% confidence levels. Because of
system-related issues, one observation is missing.

Table 3.	
  Performance by expected rankings on Task 1
Expected ranking
on task 1

Obs.

First

28

SecondThird

FourthTask 1

Task 2

Increase inRate of(piece rate)(tournament)

performanceincrease

12.71413.3210.607**

0.060**(2.386)(2.161)(1.524)(0.127)10.50011.9841.484***

0.167***(2.455)(2.229)(1.667)(0.192)10.711

11.842

1.132***

0.130***(2.535)(2.236)(1.663)(0.177)8.667

11.3332.667*

0.403(3.055)(0.577)(2.517)(0.417)62

38

3

Notes: The table shows subjects’ performance by expected rankings on Task 1. *, **,and *** measure significance at 10%, 5%, and 1%, respectively. Because of
system-related issues, one observation is missing.

Table 4.	
  Gender differences in overconfidence
Overconfidence 1

Overconfidence 2

	
 

Obs.

Mean

Obs.

Mean

Male

66

0.043

66

0.265**

Female620.502***

62

0.524***

All

128

0.266***

128

0.391***

Gender Difference-0.459***-0.259*Notes: The table shows means of two overconfidence measures and gender differences
between them. In both measures, a positive (negative) sign implies that subjects are
overconfident (under-confident). *, **, and *** measure significance at 10%, 5%, and
1%, respectively. Because of computer trouble, one observation is missing. In addition
to it, we exclude 3 observations that belong to the same group with her because ourmethod requires sample size by a factor of four when we calculate overconfidence
measures.

Table 5.	
  Gender and performance	 Gender

4 males

Increase inRate ofperformanceincreaseObs.
0.694***

0.071***(1.527)(0.138)0.923***

0.098***(1.742)(0.160)1.500***

0.174***(1.666)(0.185)1.400***

0.177***(1.708)(0.229)36

Male
2 males and26
2 females
4 females

32

Female2 males and25
2 females

Notes: *** measure significance at 1%. Because of computer trouble, one observation is
missing.

Table 6. Gender and overconfidence	 GenderObs.

4 male36

Overconfidence 1

Overconfidence 2−0.0270.226(0.833)(1.007)

0.043

0.235

(0.764)(0.894)0.606***

0.731***(0.726)(0.933)0.437**

0.371*(0.965)(0.981)Male
2 males and24
2 females
4 female

32

Female2 males and24
2 females

Notes: The table shows means of two overconfidence measures by gender and gender.
In both measures, a positive (negative) sign means that people are overconfident(under-confident). *, **, and *** measure significance at 10%, 5%, and 1%,
respectively. Because of system-related issues, one observation is missing. In addition
to it, we exclude 3 observations that belong to the same group with her because ourmethod requires sample size by a factor of four when we calculate overconfidence
measures.

Table 7. Effects of expected rankings and overconfidence on increase in performance
Eq.1

Eq.2

Eq.3

Eq.4

0.925***

0.992***

0.552*

0.449(0.203)(0.187)(0.315)(0.288)0.606**

0.120

0.385

−0.270(0.291)(0.279)(0.312)(0.300)Constant

Female dummy
0.843***

0.974***(0.166)(0.173)

Overconfidence 1
0.715*

0.742**(0.398)(0.359)0.357

1.015**(0.433)(0.411)1.730*

2.447**(1.037)(0.936)

SecondThird

Fourth
Obs.

131

128

131

128

R2

0.033

0.193

0.069

0.254

Notes: The table shows the estimation results by OLS regressions. The dependent
variable is increase in performance from Task 1 (piece rate) to Task 2 (tournament). *,**, and *** measure significance at 10%, 5%, and 1%, respectively. Because of
system-related issues, one observation is missing. In addition to it, in estimating Eq.2
and Eq.4, we exclude 3 observations that belong to the same group with her because ourmethod requires sample size by a factor of four when we calculate overconfidence
measures.

Table 8. Effects of expected rankings and overconfidence on rate of increase inperformance
	
 

Eq.1

Eq.2

Eq.3

Eq.4

0.094***

0.099***

0.051

0.030

(0.022)(0.020)(0.034)(0.030)0.091***

0.031

0.061*-0.024(0.032)(0.030)(0.034)(0.031)

Constant

Female dummy
0.112***

0.132***(0.018)(0.018)Overconfidence 1
0.082*

0.091**(0.044)(0.037)0.043

0.136***(0.047)(0.042)0.291**

0.392***(0.113)(0.096)

SecondThird

Fourth
Obs.

131

128

131

128

R2

0.058

0.283

0.117

0.387

Notes: The table shows the result by OLS regressions. The dependent variable is therate of increase in performance from Task 1 (piece rate) to Task 2 (tournament). *, **,and *** measure significance at 10%, 5%, and 1%, respectively. Because of
system-related issues, one observation is missing. In addition to it, in estimating Eq.2
and Eq.4, we exclude 3 observations that belong to the same group with her because ourmethod requires sample size by a factor of four when we calculate overconfidence
measures.

Table 9. Effects of expected rankings and overconfidence on increase in performancefrom Task 2 (tournament) to Task 3 (tournament)
Increase in performance	 Rate of increase

Eq.1

Eq.2

Eq.3

Eq.4

0.122

−0.6250.014−0.062(0.296)(0.414)(0.029)(0.041)

0.489

0.169

0.089*

0.060(0.536)(0.576)(0.053)(0.057)

Constant

Female dummy
0.802***

0.086***(0.294)(0.029)1.085**

0.113**(0.512)(0.050)

0.399

0.008

(1.026)(0.101)

Overconfidence 1

SecondThird
Observations

59

57

59

57

R2

0.014

0.178

0.047

0.236

Notes: The table shows the result by OLS regressions. The dependent variables are
increases in performance from Task 2 to Task 3 in Eq.1 and Eq.2 and the rate ofincrease in performance from Task 2 to Task 3 in Eq.3 and Eq.4. ** and *** measuresignificance at 5% and 1%.

Table 10. Effects of expected rankings and overconfidence on increase in performanceby subjects’ maze-solving skills
Increase in performance

Rate of increase

Bottom half

Upper half

Bottom half

Upper half

−0.102−0.838−0.009−0.073(1.344)(1.027)(0.169)(0.084)−0.971**

0.053-0.0700.011(0.426)(0.481)(0.053)(0.039)0.357

2.019***

0.097**

0.160***(0.292)(0.497)(0.037)(0.041)1.280*

1.038**

0.160*

0.088**(0.681)(0.516)(0.085)(0.042)

1.252

1.576**

0.177*

0.126**(0.777)(0.679)(0.098)(0.056)

3.577***

3.211*

0.579***

0.249(1.209)(1.885)(0.152)(0.155)Observations

64

64

64

64

R2

0.225

0.291

0.305

0.286

	
 
Constant

Female dummy

Overconfidence 1

SecondThird

FourthNotes: The table presents results from OLS regression. The dependent variables are theincrease in performance and rate of increase in performance from Task 1 to Task 2.
Standard errors are reported in parentheses. *, **, and *** measure significance at 10%,5%, and 1%, respectively. “Bottom half” (“Upper half”) refers to subjects whose
average ranking in a group of four subjects generated by 1 million times random
grouping is above (below) median (2.165). Because of system-related issues, one
observation is missing. In addition to it, we exclude 3 observations that belong to thesame group with her because our method requires sample size by a factor of four when
we calculate overconfidence measures.

2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2

-1

0

1
2
3
Increase in Performance	
 4

5

Figure 1. Positive correlation between overconfidence and increase in performance

Notes: Figure 1 shows the arithmetic means and 95% confidence intervals of
Overconfidence 1 by the change in performance from Task 1 to Task 2. The horizontal
and vertical axes represent the change in performance from Task 1 to Task 2 and thedegree of overconfidence, respectively.