Discussion Paper No. 1025

THE SUPERIOR PEER
IMPROVES ME:
EVIDENCE FROM SWIMMING DATA

Shoko Yamane
Ryohei Hayashi

March 2018

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

The Superior Peer Improves Me: Evidence from Swimming Data

Shoko Yamane† and Ryohei Hayashi‡
March 2018

Abstract
This study examined the peer effects of newcomer on the performance of existing members ofteams, based on comparing the performances of swimming team members before and after thearrival of a newcomer. The identification strategy was similar to a natural experimental setting.
This study found that the performance of an existing member of a team improves when a newcomer
joins the team and that this effect is larger when the newcomer is a superior to the original teammembers.

Keywords: Peer effect, Sports data
JEL classification: D62, L83†

Faculty of Economics, Kindai University, 3-4-1 Kowakae, Higashi-Osaka, Osaka, 577-8502, JAPAN,
E-mail: syamane@kindai.ac.jp, Tel: +81-6-6721-2332‡
School of Political Science and Economics, Tokai University
We thank Hiroki Shimoi and Tatsuki Miyata for their help in data cleansing. We are deeply grateful to
Fumio Ohtake. An earlier version of this paper was presented at the Kyoto Summer Workshop on Applied
Economics, Kansai Labor Seminar, and the CEE and RISS Seminar Series on Experimental Economics.
We would like to express our gratitude to all seminar participants for their valuable comments. This work
was supported by JSPS KAKENHI Grant Number JP15K17029, and a grant from the MEXT Joint Usage/
Research Center at the Institute of Social and Economic Research, Osaka University.

11. Introduction
People spend a large amount of their lives in classrooms and workplaces where their classmatesand colleagues are assumed to influence them in many ways: for instance, most people have had theexperience of starting something new because of a colleague’s example or aspiring to something as aresult of a classmate’s influence. Few studies, however, have measured such long-term social effects.
Several interesting topics in this field have remained unexplored. For example, does an individualwith greater skill enhance or inhibit his/her peers? Is the influence of a talented individual limited tothose around him/her or does it expand to cover all members? This study attempts to answer thesequestions.
The social effects from classmates and colleagues would have two pathways, namely, learning
effect and motivation effect. In the learning effect, learning a better way from others is effective in
improving one’s performance. Motivation effect means that the existence of peers itself enhances
one’s performance. In either pathway, the performances of others influence one’s productivity. This
externality is called “peer effect” in economics, studied extensively in the areas of educational orlabor economics.
Examining peer effects in a real-world setting requires data on the performance of each individual
and those of peers assigned randomly. If the peer assignment is non-random, the estimated peer
effect becomes biased because the error term is correlated with the characteristics of the peers1.
Further, to reveal peer effects, these effects must not be reflected and overlapped. Social effects arealways endogenous; in other words, a person simultaneously influences and is influenced by a group.
This endogeneity makes it difficult to estimate the causal impact of peer effects, as Manski (1993)
discussed. It is not easy to find micro data that meet these conditions, and more so for individual
panel data. It has resulted in a scarcity of empirical literature on long-term peer effects.
To address these identification problems, research can focus on newcomers. For original members,
the appearance of a newcomer is exogeneous and may be characterized as a random shock2.
Therefore, by comparing performance before and after transfer, the peer effects by a newcomer
could be revealed clearly 3 . The comparison requires that transfer timings on the panel datacontaining the individuals’ and their peers’ performances be identified. The appropriate empirical
data are hard to find; however, we found that this gap could be bridged by swimming data.
1

Other identification problems, such as common shock, require resolution. Sacerdote (2011)
summarized these problems and the methods for their resolution.
2 Although the newcomer’s performance itself seems open to modification, the transfer could not be
exogenous for a transfer. For example, they might transfer schools because they were in trouble withthe teacher or classmates, or because their grades were not improving. Therefore, the changes innewcomers’ performances were not focused upon.
3 The impact of transference may be examined by comparing the teams with respect to the presence
and absence of transfers. However, the swimming teams that had never imported or exported
members are biased. For example, such teams could be weak, small, have decided to be closed, or
isolated. Therefore, this mode of comparison was not adopted.
2

The swimming data in this study, collected from an open online database, include nine-year
individual panel data that comprise the records and team names for each competition and swimmer.
In tracking the changes of one team, transfer swimmers need to be identified, along with theirtransfer timings to new teams. Our data also include the characteristics of the transferees (e.g.,school, age, gender, personal best record, and previous team’s name). As such, differences in theimpact from superior and inferior peers can be revealed. Another advantage is that swimmingrecords provide a constant and objective index of individual performance without any room for luck.
In contrast with using examination scores, for instance, the examination difficulty would becontrolled, but this control is difficult to achieve. Meanwhile, swimming records are free from thisproblem and are evaluated by the same standards regardless of time and place. Measurement errors
can be ignored because swimming records in official competitions are measured electronically. In
addition, to swim fast is a common aim for every swimmer, compared with the complex aims related
to school education: not only to get high examination scores but to increase scholastic abilities.
Swimming is a sport of speed, and every swimmer in the competition intends to swim as fast as
he/she can.
Swimming is a highly popular after-school activity in Japan. In a survey by Recruit Life Style(2015), swimming accounted for 43.7% of all after-school culture lessons among elementary school
students4. In Japan, swimming is a compulsory activity from elementary to high school. Most
parents send their children to swimming clubs to prevent them from developing an inferiority
complex, so there are many swimming clubs, both large and small, all across Japan and mostchildren attend them once or twice a week. As swimmers spend the most time with their teammates,
there is a mutual exchange and learning of better techniques. They are also mutually motivated infriendly rivalry.
In the context of peer effect, especially focusing on learning effect, many scholars have conductedstudies. Burke and Sass (2013) and Cooley (2009) indicated that classmates influence one anotherusing test scores. While most studies did indicate a significant effect from classmates, Zimmerman(2003) and Hayashi (2016) denied the influences of roommates, who spend more time with one
another than classmates, on academic achievements. Both studies used randomly assigned
roommates. In the classroom, an important question is how the spillover works. Hoxby andWeingarth (2005) summarized a number of spillover models, such as the Bad Apple model, whichstates that the presence of a low-ability student harms and “spoils” many other students. In theShining Light model, a high-ability student improves all others. Further, the Invidious Comparison
model suggests that high-ability students are negatively affect the performances of lower-ranking
students, whereas the Boutique model means that students’ performances are enhanced when they4

Respondents to this survey were 927 mothers of elementary school kids who attended after-school
lessons.
3

are surrounded by others like themselves. The present work can suggest which spillover models areobserved in the afterschool club activity among elementary and junior high school students. The
spillover effect has been reported in the workplace as well. For example, Kato and Shu (2016)
collected rejection rates as measurements of performance at a large textile firm in China, and noted aspillover effect from high-ability to low-ability weavers, but not vice versa.
Some economic studies have focused on the effects of transfers. Moser et al. (2014) used Jewishémigrés from Nazi Germany to the U.S. and found that the number of patents in fields that acceptedémigrés increased by 31%. Contrary to this, from similar studies focused on exporters, Azoulay et al.
(2010) showed that when so-called academic superstars died, the publications of their collaborators
declined by 5% to 8%. Waldinger (2012) found no influence by local peers among researchers, using
data on the dismissal of scientists in Nazi Germany.
Finally, scholars have investigated peer effects using sports data. Gneezy and Rustichini (2004)
revealed, using elementary school running data, that the existence of competitors improves boys’
performances but not girls’. Yamane and Hayashi (2015) reported a relatively clean test of peer
effects using swimming data and revealed that peers influence individuals’ performance. They also
found that being chased improves one’s performance, and that observability is an important
determinant of peer effects. Both studies used data from short-term competitions, which cannot
provide information on learning effects. Other studies have demonstrated the influence of muchsuperior peers, which might be called the superstar effect. Brown (2011) investigated the effect of
the superstar, Tiger Woods, on his peers using PGA tournament data. He found that the existence of asuperstar diminished the performance of competitors. Gould and Kaplan (2011), using baseball data,
showed the effects of Jose Canseco, a star baseball player later arrested for using steroids: thenumber of homeruns scored by Canseco’s team significantly increased after playing with Canseco.
They concluded that Canseco taught his teammates how to play well but also influenced them on
how to use steroids, which is a negative learning effect.
While numerous studies already exist related to peer effects, the present work may be the first to
examine and demonstrate clearly the learning and motivation effects from peers using exogenous
shock. The rest of this paper is organized as follows: Section 2 presents the dataset and estimation
model, the estimation results are explained in Section 3 and, finally, the discussion and conclusion
are given in Section 4.

2. Methods
2.1 Data source
We have collected data from Swim-Record.com (http://www.swim-record.com/index.html), whichis the official website of the Japan Swimming Federation (JSF). The site contains records fromapproximately 1,500 official competitions, which are held by every JSF member organization in4each prefecture. Members are obliged to release all official JSF competition records to the public.
Because the official records contained only JSF-registered swimmers in competitions that aregoverned by international rules, the data excluded unofficial records (e.g., citizens’ competitions).
The database covered records of swimming competitions for individuals, from first-year elementary
to university students and working adults, from April 1, 2007 to March 31, 20155.

2.2 Dataset
First, transfer swimmers were identified. A transfer swimmer was defined as an individual whose
team name changed6. We only considered transfers in which the time gap between the last record
with the previous team and the first record with the new team was within one year, and where thoseconcerned had records of more than one year in both their former and later teams. This was toeliminate possible errors whereby a swimmer could be misidentified as a transfer having participatedin a competition with another team just once. In this process, 304 transferred swimmers wereidentified in our dataset.
Second, the records of all members who belonged to the teams that accepted the transfer swimmers
were extracted. If there were many imports, the effects of newcomers would overlap, and thus thepeer effect could not be determined clearly. Therefore, the teams that accepted one transfer only oncein our dataset or that had a time gap between each transfer longer than one year were used. The
assumption that a newcomer is no longer a newcomer when one year has passed was examined in
Section 3.3, by changing the data analysis, using two periods: half-year and two-year gaps.
So far, the records included all swimming styles and race types7. At this point, we kept only the
50-meter freestyle records in short-course pools, because this category is held year-round and has thelargest number of participants8. In addition, the data on adults and university students were excluded,
because they often belong to more than one team in Japan. It is also common practice for college
5

The Japanese school year begins in April.
Several inconsistencies in the spelling of team names were observed, because they were registeredmanually for each competition. For example, variations of the team name “Hayashi Swimming Club”
included “Hayashi SC” or “HSC.” However, the “Hayashi Swimming Center” could also be
abbreviated as “HSC.” In cases such as this, distinguishing between the two was a challenge. In
addition, the Japanese language uses four character types, namely, Kanji, Hiragana, Katakana, andRomaji, which further complicated the differentiation. Therefore, other information, such as physicaladdresses and member names of a team before and after competitions, was checked to correct theteam names.
7 There are four swimming styles, namely, freestyle, butterfly, breast stroke, and backstroke.
Swimming also has two types of races. The first is “finals,” in which only swimmers who survive
their heats swim in the final round. This system is used in the Olympic Games. The second type is“timed finals,” which has no final round. In timed finals, all players swim only once, and eachswimmer’s time in the heats determines their final rank.
8 There are two pool sizes in official swimming competition. The short-course means a 25-meterpool and long-course is a 50-meter pool. Swimming is a sports for all season in Japan, andshort-course races are the most common throughout the year.
6

5

students to nominally belong to a university swimming club in whose name they participate in themeets but to actually continue training with their original club. Thus, only those on high-school
students and below were used.
The data on meets, which can only be participated in under the school’s name as a member of theschool’s club, were excluded. However, this may have ignored the influences by peers of the school
club activities. We cannot identify one’s peer of school club activity completely because not all
swimmers are participated to the meets with school name. Although the presence of a ‘superstar’ as apeer of school club could have an especially strong influence on others, there are some swimmers
who are more influenced by peers in school club activities than peers from the extracurricularswimming team, whereas other swimmers are not. This is a limitation of the dataset, however, it
should not be considered as harming our results.
To summarize, our dataset consists of unbalanced panel data of 50-meter freestyle records inshort-course pools from 2007 to 2015. It contained data of first-year elementary school to highschool students. Note that the time indicator is each meet, which are held almost every weekend.
Thus, there were 77,798 records of 6,258 individuals (3,264 males and 2,994 females) with 304
transfers (153 males and 151 females). These records were associated with their personal best record
at that time, gender, school age, and the date when the transfer took place.

2.3 Estimation Model
The estimation model used is as follows:
𝑅𝑒𝑐𝑜𝑟𝑑𝑖𝑚𝑡 = 𝛼𝑖𝑚 + 𝛽1 𝐴𝑓𝑡𝑒𝑟𝑖𝑚𝑡 + 𝛽2 𝐵𝑒𝑠𝑡𝑖𝑚𝑡 + 𝛽3 𝐷𝑎𝑡𝑒𝑖𝑚𝑡 + 𝜀𝑖𝑚𝑡

(1)

where 𝑅𝑒𝑐𝑜𝑟𝑑𝑖𝑚𝑡 denotes the record of swimmer i of team m at competition t. Again, note that thetime indicator is each meet. After is a key variable, which takes a value of 1 after a newcomer joins
his/her swimming team, and is otherwise 0. Best refers to swimmer i’s personal best records at t. To
obtain the Best value, all previous records of each swimmer were collected. This model was
estimated with fixed effects for i using robust standard errors clustered by team. The individual
characteristics such as gender and school age are included in the fixed effect. If the transfer of a
newcomer did not affect a swimmer’s performance, the coefficient of After is not significant. Date isalso a control variable, because swimmers become faster as they develop. Date contains a serialvalue in which the date January 1, 1900 takes the value of 1, the date January 2, 1900 takes 2, and soon. The variable Date squared is divided by 1,000,000, because the digit number is too large. We
adopted the fixed effect model and individual characteristics such as age and gender are included in
the fixed effect.
To consider the characteristics of a newcomer, a cross-term was employed in the regression model:
𝑅𝑒𝑐𝑜𝑟𝑑𝑖𝑚𝑡 = 𝛼𝑖𝑚 + 𝛾1 𝐴𝑓𝑡𝑒𝑟𝑖𝑚𝑡 + 𝛾2 𝐴𝑓𝑡𝑒𝑟 ∗ 𝐹𝑎𝑠𝑡𝑖𝑚𝑡 + 𝛾3 𝐵𝑒𝑠𝑡𝑖𝑚𝑡 + 𝛾4 𝐷𝑎𝑡𝑒𝑖𝑚𝑡 + 𝜀𝑖𝑚𝑡(2)

where After*Fast denotes the cross-term of After and Fast newcomer dummy variables. The After
6

transfer dummy variable equals 1 after a newcomer joins his/her swimming team. The Fast
newcomer dummy variable equals 1 when the newcomer’s personal best is faster than one’s personal
best at the timing of transfer. Using this cross-term, the effect of a fast or a slow newcomer could be
distinguished.

3. Results
3.1 Descriptive statistics
We begin by presenting the descriptive statistics of key variables. The sample comprised 304
transferred swimmers (153 males and 151 females). As shown in Table 1, the mean personal bests ofa swimmer and a newcomer were 33.22 and 32.50 seconds, respectively; their mean school ages
were 5.6 and 6.03 years, respectively; note that the lower value of a personal record indicate a better
performance. School age refers to educational stage, such that 1 means first-grade elementary
school; 7, first-grade middle school; and 12, third-grade high school.

Table 1: Descriptive statistics of subjects
Variable

Mean

Std. Dev.

Min

Max

Own record

32.76

4.38

22.83

78.82

Own personal best record

33.22

4.93

22.92

85.03

Own school age

5.60

2.14

1.00

12.00

A newcomer’s personal best record

32.50

3.56

23.35

51.95

A newcomer’s school age

6.03

1.83

3.00

12.00

After transfer dummy

0.50

0.50

0.00

1.00

Fast newcomer dummy

0.53

0.50

0.00

1.00

3.2 Regression results
Table 2 shows the main results of the regression analysis. The findings showed that the coefficientof After dummy is negatively significant, indicating that a swimmer’s performance improves after a
newcomer joins the team in specification (1). In addition, the estimation result of specification (2)shows the cross-term of fast newcomer and after dummy variables are negatively significant,indicating that the faster the newcomer is, the greater the improvement in the performance of his/her
teammates. The null hypothesis, which stated that the sum of the After dummy and cross-term is zero,
was rejected (F(1, 278) = 29.97, p < .001).

7

Table 2: Results of the regression analysis on the effect of a newcomerSpecification (1)Specification (2)Variables

Coef.

Coef.

After transfer dummy-0.072 [0.024]**

Std. Err

After dummy * Fast newcomer dummy

Std. Err

0.085 [0.025]**-0.299 [0.048]**

Own personal best record

0.638 [0.015]**0.634 [0.015]**Date-0.001 [0.001]-0.001 [0.001]

Date squared

0.006 [0.025]-0.002 [0.025]

Constant

37.311 [9.320]**

36.231 [9.442]**

Observations

77,79877,731

Number of subjects

6,258

6,258

Note: ** 1%, * 5% significance.

3.3 Robustness checks
Four tests were performed to check the robustness of our analysis. First, in the placebo test, transfer
swimmers, who were different from actual transfers of each swimmer, were randomly combined. For
example, swimmer A in team B, which received transfer C on January 1, 2016 whose individual bestrecord was 30 seconds, was combined with a counterfactual importer D, whose transfer date was
October 30, 2015 and best record was 25 seconds. The dummy variable for after transfer was createdin this dataset, and examined using the regression specification (1) in Table 2. After conducting
1,000 placebo trials, the After dummy variable was found to be insignificant in 94% of trials.
The exogeneity of the transfer was examined next. Transferring to a swimming team could include
endogenous factors: other transferred swimmers might have changed their team because they havefriends in a new team; alternatively, their records did not improve or the teaching method of theprevious team did not suit them. To exclude the endogenous factors and treat the transfer as a clearly
exogenous factor, newcomers who transferred to another swimming team in a different prefecture
were used in the analysis. As transfers between prefectures were often attributed to relocation of theswimmer, the data were assumed to exclude those transfers that involved a swimmer’s problem withthe team, teammate, or coach. Of the 304 transfer swimmers, 67 came from another prefecture,
covering 19,342 records, which were used to investigate the influence of inter-prefecture newcomers.
Table 3 shows the results of the analysis on newcomers from another prefecture. The after-transfer
dummy variable was found not significant but marginally significant at a 5% level, and thecross-term significantly negative. As these results are consistent with those in Table 2, there is little
endogeneity in the transfer of swimmers.

8

Table 3: Results of the regression analysis on newcomers from another prefecture
Specification (1)Specification (2)Variables

Coef.

Coef.

After transfer dummy-0.102 [0.054]

Std. Err

Std. Err

0.046 [0.051]

After dummy * Fast newcomer dummy-0.279 [0.090]**

Own personal best record

0.605 [0.027]**0.601 [0.027]**Date-0.003 [0.002]-0.003 [0.002]

0.04 [0.049]

0.033 [0.050]

51.091 [17.926]**50.481 [18.190]**

Observations

19,342

19,328

Number of subjects

1,626

1,626

Date squared
Constant

H0: After dummy + After dummy * Fast newcomer dummy = 0

F(1, 66) = 9.08
Prob > F = 0.004

Note: ** 1%, * 5% significance.

Third, the period within which the influences of transfer disappeared were investigated. As noted in
Section 2, this study considered that a newcomer was no longer a newcomer once a year had passed,
and that therefore peer effects do not overlap if there was a gap longer than one year between two
transfers. To examine whether this assumption was correct, two datasets were used. In the two-year
gap dataset, the teams that accepted one transfer only once in our dataset, or the teams that had atime gap between each transfer longer than two years were used. Meanwhile, in the one-year gap
dataset, the teams that accepted one transfer only once in our dataset, or the teams that had a time
gap between each transfer longer than half a year, were used. There were 130 and 599 transfer
swimmers in the two-year and half-year gap datasets, respectively. The estimation results shown in
Table 4 are almost consistent with those of Table 2.

9Table 4: Results of the regression analysis using the two-year ad half-year gap datasets
Two-year gap dataset
Coef.
After transfer
dummyStd. Err-0.025 [0.048]

Std. Err

0.132 [0.057]*

After * Fast

Coef.

Std. Err-0.039 [0.012]**-0.311 [0.068]**

newcomer dummy
Own personal best

Coef.

Half-year gap dataset
Coef.

Std. Err

0.074 [0.012]**-0.206 [0.020]**0.628 [0.020]**0.624 [0.020]**0.741 [0.007]**0.737 [0.007]**Date-0.004 [0.002]**-0.004 [0.002]*-0.001 [0.000]*-0.001 [0.000]

Date squared

0.082 [0.042]

0.068 [0.044]

0.003 [0.012]-0.003 [0.013]

Constant

64.154 [14.856]**

60.973 [15.291]**

25.547 [4.532]**

24.729 [4.664]**

9,075

9,069

17,4285

17,4159

670

669

14,22814,227

recordObservations
Number of subjects

H0: After dummy + After dummy * Fast newcomer dummy = 0
F(1, 127) = 8.46

F(1, 459) = 54.83

Prob > F = 0.004

Prob > F = 0.000
Note: ** 1%, * 5% significance.

Finally, the effect of swimmers leaving a team was examined. As this could disrupt the process ofanalyzing the effect of a newcomer, teams that lost swimmers during the year were excluded fromthe analysis. The effect was to give a ‘super-clean’ body of data as there was only one possible
variable—the arrival of a single newcomer—to be taken into consideration. There were 75 transfer
swimmers (42 males and 33 females). The estimation results shown in Table 5 are similar to those in
Table 2.
As results had not changed following all four robustness checks, it can therefore be concluded thata newcomer to a swimming team improves other swimmers’ performances, and this effect is larger
when the newcomer is a better swimmer than the original team members. These findings indicate apositive peer effect among teammates.

10

Table 5: Results of the regression analysis excluding teams losing members
Specification (1)Specification (2)Variables

Coef.

Coef.

After transfer dummy-0.149 [0.067]*

Std. Err

Std. Err-0.018 [0.041]

After dummy * Fast newcomer dummy-0.217 [0.086]*

Own personal best record

0.608 [0.021]**0.607 [0.021]**Date-0.004 [0.002]-0.004 [0.002]

Date squared

0.076 [0.064]

0.072 [0.064]

Constant

62.813 [23.679]**

62.334 [23.414]**

Observations

11,757

11,740

Number of subjects

1,176

1,176

H0: After dummy + After dummy * Fast newcomer dummy = 0

F(1, 71) = 6.12
Prob > F = 0.02
Note: ** 1%, * 5% significance.

3.4 Most effective peer effect
In this section, the focus was on the detailed characteristics of the newcomers and whether theirinfluence on peers is limited to those immediately around them or expand to all members of the team.
Most studies on social comparison have considered the resemblance of attributes as important. For
example, as noted in the Introduction, the “boutique model” states that students are enhanced when
they are surrounded by others like themselves. Similarly, it is often considered that people compare
themselves with “people like me” in the context of happiness and social comparisons (Clark, Frijters,
and Shields, 2008). To investigate whether such a similarity is a determinant of the impact of peer
effect on teammates, similarities between personal best records and demographic factors, such as
gender and school age, were considered. First, similar-record newcomer was defined as a newcomer
whose personal best was within 5% of a team-mate’s9. For example, a newcomer whose personal
best is 30 seconds is considered a similar-record newcomer for swimmers whose personal bests are
between 28.5 and 31.5 seconds. A dummy variable, which takes a value of 1 after a similar-record
newcomer joins his/her swimming team and is otherwise 0, was created. Then its cross-term wasused with the After dummy in regressions. Based on the findings of Table 6, the cross-term is not
significant, whereas the After dummy variable is negatively significant, 10 suggesting that the9

Five percent was adopted as threshold because a one-second difference between 24 and 25 seconds
is not the same as a one-second difference between 49 and 50 seconds. Thus, relative instead ofabsolute difference was used. However, for newcomers whose personal best was within one secondof their team-mates’, the significance of the estimated coefficients did not change.
10 The null hypothesis on cross-term, which states that the sum of the After and similar-record
11similarity of records does not affect the impact of peer effect.
In addition, similarities of gender and school age were examined. A dummy variable, which takes
the value of 1 if a swimmer and a transfer swimmer have the same gender and is otherwise 0, wascreated. Then its cross-term among after-transfer dummy variables was used in the estimation.
Similarly, near-school-age newcomer takes the value of 1 if the difference in school age between aswimmer and a newcomer is within a year, and is otherwise 0. For example, a newcomer at sixth
grade is a near-school-age newcomer for a swimmer from the fifth to seventh grade. The findings
showed that similarity of gender does not change the impact of peer effect, whereas the closeness ofschool age significantly increased the effect. As Table 6 shows, only the cross-term between
near-school-age newcomer and the after transfer dummy variables is negatively significant.

Table 6: Impact of a newcomer who has similar attributions as existing swimmers
Variables
After-transfer dummy

Coef.

Std. Err-0.062 [0.026]*

Coef.

Std. Err-0.071 [0.031]*

Coef.

Std. Err-0.061 [0.031]

After dummy *Similar-record newcomer-0.040 [0.020]-0.039 [0.020]

dummy
After dummy *Near-school-age dummy
After dummy *Same-gender dummy-0.168 [0.066]*-0.168 [0.067]*

0.014 [0.034]

0.014 [0.034]

Own personal best record

0.638 [0.015]**

0.638 [0.015]**

0.638 [0.015]**Date-0.001 [0.001]-0.001 [0.001]-0.002 [0.001]

Date squared

0.007 [0.025]

0.006 [0.025]

0.007 [0.025]

Constant

37.483 [9.341]**

37.442 [9.335]**

37.614 [9.356]**

Observations

77,79877,79877,798

Number of subjects

6,258

6,258

6,258

Note: ** 1%, * 5% significance.

Next, the impact of peer effect in terms of a newcomer’s relative position in the new team,specifically, the difference in the impact of the peer effect between a relatively superior and an
inferior newcomer, was investigated. This also examined the superstar effects and spillover models,such as the shining light and bad apple models. The average of personal best records for each teamwas calculated, and then the above average dummy, which takes 1 if the newcomer’s best at the time
newcomer dummy is zero, was rejected (F(1, 278) = 15.81, p < .001).
12

of transfer is faster than the team average, was created. A regression model with the cross-term
between above average and After transfer dummies was adopted. For example, in the shining lightmodel, this cross-term was expected to be significantly positive. As shown in Table 7, the coefficientof the cross-term is not significant. Then, the sample was divided according to whether the
newcomer was above or below the team average to test specification (2). The findings showed thatthe coefficient of the after-transfer dummy is negatively significant only when the newcomer isfaster than the team average in specification (1). However, the cross-term between After and fast
newcomer, which takes 1 if the newcomer is faster than the swimmer, is significantly negative inboth samples. The findings indicate that the important determinants for the effectiveness of peers iswhether the newcomer is faster than them and not faster than the team average. However, there is apossibility that a swimmer could be aware of a newcomer whose age or gender is the same as his/herbut does not mind the one whose characteristics are different from him/her. Therefore, an analysis
was also performed on a newcomer’s relative performance against the team average, as is shown in
Table 7. Table 8 shows that similar results were obtained using the newcomer’s relative performanceagainst those of similar school age and gender as the newcomer was used. A newcomer’s relative
performance in the team is not influenced by a swimmer’s performance, but a swimmer compareshim/herself with the newcomer and is enhanced by a superior peer.

13

Table 7: Results of the regression analysis with peer’s relative positions
Effect of newcomers
who are faster than

Newcomers who are slower than their teamaveragetheir team average
Coef.
After transfer dummy

Std. Err-0.028 [0.048]

Coef.

Std. Err-0.022 [0.030]

Coef.

Std. Err

0.022 [0.029]

Newcomers who are faster than their team average

Coef.

Std. Err-0.093 [0.031]**

Coef.

Std. Err

0.146 [0.035]**

After dummy *Above-average-newcomer-0.061 [0.069]

dummy
After dummy *-0.288 [0.101]**

Fast-newcomer dummy-0.359 [0.052]**

Own personal best

0.638 [0.015]**

0.696 [0.029]**0.696 [0.028]**0.619 [0.016]**0.613 [0.016]**Date-0.001 [0.001]-0.003 [0.002]-0.002 [0.002]-0.001 [0.001]-0.001 [0.001]

Date squared

0.005 [0.025]

0.045 [0.039]

0.036 [0.040]-0.01 [0.030]-0.016 [0.030]

Constant

37.058 [9.323]**43.358 [16.070]**

40.748 [16.475]*

34.201 [10.846]**

34.698 [10.839]**

Observations

77,79821,373

21,35356,42556,378

Number of subjects

6,258

1,637

1,637

4,687

4,687

H0: After dummy + After dummy * Fast-newcomer dummy = 0

F(1, 82) = 6.75

F(1, 202) = 27.24

Prob > F = 0.011

Prob >F = 0.000
Note: ** 1%, * 5% significance.

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Table 8: Results of the regression analysis with peer’s relative positions
Effect of newcomers
who are faster thantheir team average
Coef.
After-transfer dummy

Std. Err-0.001 [0.037]

Newcomers who are slower than

Newcomers who are faster than

the team average of their age and gender groupthe team average of their age and gender group

Coef.

Coef.

Std. Err-0.015 [0.031]

Coef.

Std. Err

0.044 [0.028]

Std. Err-0.117 [0.036]**

Coef.

Std. Err

0.144 [0.042]**After dummy *Above-average-newcomer-0.128 [0.065]

dummy
After dummy *-0.165 [0.062]**

Fast-newcomer dummy-0.396 [0.058]**

Own personal best record

0.638 [0.015]**0.656 [0.027]**0.654 [0.027]**0.621 [0.018]**0.614 [0.017]**Date-0.001 [0.001]-0.002 [0.001]-0.001 [0.001]-0.001 [0.001]-0.001 [0.001]

Date squared

0.005 [0.025]

0.012 [0.033]

0.006 [0.034]

0.003 [0.035]-0.001 [0.036]

Constant

37.047 [9.392]**

36.69 [12.865]**

35.102 [13.163]**

39.038 [13.261]**

40.42 [13.083]**

Observations

77,79834,71434,681

43,08443,050

Number of subjects

6,258

2,713

2,713

3,634

3,634

H0: After dummy + After dummy * Fast newcomer dummy = 0

F(1, 82) = 6.75

F(1, 202) = 27.24

Prob > F = 0.011

Prob > F = 0.000
Note: ** 1%, * 5% significance.

15

4. Conclusions
Using the transfer of newcomers to another team, the peer effect of teammates was examined.
Specifically, this study investigated how a newcomer to a team influences the performance of theexisting members of such team, and how the environment affects their performance. We found thatthe performance of an existing member is improved when a newcomer joins the swimming team.
This effect is larger when the newcomer is superior to the other existing members of the team.
This study also revealed the relations between the characteristics of peers and the impact of peer
effects. Peer effect from similar peers is assumed to be greater. However, based on our findings, thecloseness of peer characteristics, such as gender, school age, and personal best record, between
newcomers and existing members does not change the impact of peer effect. We found that theswimmers evaluated their peers based on their own performance and not on the team average. In
other words, they are aware of their superior peers even though the latter are different fromthemselves. Establishing who exactly people compare themselves to is an important finding in
relation to the study of peer effects.
This study also contributes to the discussion on the effectiveness of ability grouping, and provides
evidence on how to optimally organize teams to achieve maximum productivity. Based on ourfindings, to maximize the performance of an organization, superior people must be distributed acrossall teams to improve everyone’s performance.
The findings in this study differ from those found elsewhere that suggest an absence of peer effect.
Zimmerman (2003) and Hayashi (2016) showed a lack of effect among roommates in terms ofacademic achievements. Although they did not examine the transfer of newcomers and their methods
were quite different from ours, it is noteworthy that their examination of a similar situation produced
opposite results. We consider that it would come from the characteristics of outputs. It is possiblethat academic achievements are also influenced by other social effect, for example the effect fromtheir parents. In contrast, swimming data are free from other social effects because swimming canonly be practiced at a swimming school, and students who come to a swimming school are sure totrain. Similar results are suggested in other fields. For example, Zajonc (1965) suggested that thecharacteristics of tasks influence the direction of peer effects, indicating that there are positive peer
effects in well-learned tasks, and negative peer effects in complicated tasks. This suggests further
directions the study of peer effects could take in future research.
The limitations of this study include the lack of information in the database on coaches and theirprinciples of guidance. The impact of teaching methods on peer effect could also be studied further.

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