Tsukuba Economics Working Papers
No. 2017-002

Is Racial Salary Discrimination Disappearing in the NBA? Evidence
from Data during 1985--2015
byHisahiro Naito
and
Yu Takagi
Current Version: February 2017
(Forthcoming from International Review of Applied Economics)

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANIs Racial Salary Discrimination Disappearing in the
NBA? Evidence from Data during 1985–2015

Hisahiro Naito∗†
Department of Economics
Graduate School of Humanities and Social Sciences
University of Tsukuba

Yu Takagi ‡College of International Studies
University of Tsukuba
Revised Version
November 5, 2016∗

Address: Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573; e-mail: naito@dpipe.tsukuba.ac.jp
This work was supported by a financial grant from the Ministry of Education, Science, Sports
and Culture(JSPS Kakenhi Grant Number 15K0351) and the faculty research fund‡
Address: Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573; e-mail: yu.baske.sumeo@gmail.com†

Abstract
This study re-examines the racial salary gap of National Basketball Association
players by constructing a long unbalanced panel covering the 1985–1986 to 2015–2016
seasons. Contrary to the results of previous studies, we find that non-white players arepaid equally to white players with similar characteristics in the 1980s and 1990s, butthat white players started to be paid about 20 percent more than non-white players in
the last 10 years. Our results are robust to all specification checks, such as quantile regressions, controlling sample selection, different contract types, and player nationality.
We find that neither employer preference nor income gap of white and black residents
explains this increasing salary gap.
JEL Classification: C25
Keywords: racial discrimination, NBA, labor markets, salary discrimination

1

Introduction

The issue of whether discrimination in the labor market is disappearing has important
policy implications. Because various external factors, such as globalization, taxes, andcompetition with other product markets, affect the degree of wage discrimination, it isimportant to know how wage discrimination changes over time.
In the literature of discrimination in labor markets pioneered by Becker (1971),
researchers have used the data of professional sports athletes to analyze the issue of
discrimination because of the availability of information on each player’s productivity
in a large number of dimensions (Kahn, 2000). Among studies that used informationof professional sports athletes, studies that use information of players of the National
Basketball Association (NBA) provide an interesting case for several reasons. First,
in the previous studies that utilized the data in 1980s and 1990s, it is reported thatin the 1980s, there was a white premium of the NBA’s salary—the salary of a white
player is higher than that of a non-white player with the same productivity (Kahnand Sherer, 1988; Koch and Vander Hill, 1988; Wallace, 1988; Brown et al., 1991).1
However, several studies report that such a premium decreased or disappeared in the
1990s (Dey, 1997; Hamilton, 1997; Bodvarsson and Brastow, 1998; Gius and Johnson,
1998; Erick Eschker and Siegler, 2004; Hill, 2004; Groothuis and Hill, 2013).2 Somestudies report that in the 2000s, there was a negative white premium against white
players (Yang and Lin, 2012; Groothuis and Hill, 2013). Thus, it is natural to examine1

For example, Kahn and Sherer (1988) finds a strong white (20 percent) premium controlling
productivity and other covariates using the salary data in the 1985–1986 season.
2
Hamilton (1997) finds evidence of racial pay differences only at the upper end of the 1994–1995
season of salary distribution. Hill (2004) shows that the importance of controlling the height or
position. He argues that the white premium was the return of the height, not the racial gap. A study
most closely related to the current study is Groothuis and Hill (2013). They use unbalanced panel
data from 1990 to 2008 and control for the sample selection problem. They find that there was nowhite premium in their dataset.

1whether this trend continues in more recent years and whether the white premium
eventually disappeared.
Second, there is a discrepancy between anecdotal and empirical evidence regarding
discrimination in the NBA. Although the previous literature suggests that the racialsalary gap was disappearing in the 1990s and might even have been moving in the
opposite direction in the 2000s (negative white premium against white players), there
is anecdotal and empirical evidence of racial discrimination against non-white players
in the NBA. For example, in April 2014, the owner of Los Angeles Clippers was banned
from the NBA permanently and fined $2.5 million for his racial comments. Kanazawa
and Funk (2001) finds that TV viewing is affected strongly by the ratio of white players
in the team by examining the viewing data in the 1996–1997 season. Price and Wolfers(2010) shows that a referee prefers a player whose race is the same as that of thereferee when he or she makes a decision on fouls. Such anecdotal or empirical evidence
warrants further investigation of racial salary discrimination by using a longer dataset.
Third, the NBA experienced substantial globalization in the 2000s. In 2002, 2005,
and 2006, the first pick in the draft was an international player.3 The season’s MVP
during the 2004–2005, 2005–2006, and 2006–2007 seasons were international players.4
In addition, broadcasting the NBA’s games to countries outside the United States
became common in those years. In the literature of labor economics, there is increasing
interest whether globalization would lead to a decrease or increase of discrimination in
the labor market (Berik et al., 2004; Black and Brainerd, 2004; Busse and Spielmann,2006). The salary data of the NBA provide an interesting case for the study.
This study revisits the issue of the white premium of the salary of NBA players. We
3

The first picks in the draft of 2002, 2005, and 2006 were Yao Ming (China), Andrew Bogut(Australia), and Andrea Bargnani (Germany), respectively.
4
The MVP of the 2004–2005 season, the 2006–2006 season, and the 2006–2007 season were SteveNash (South Africa), Steve Nash (South Africa), and Dirk Nowitzki (Germany), respectively.

2

contribute to the literature in two ways. First, we analyze a long period of unbalanced
panel data of salaries and other indices of performance, which covers the 1985–1986
season to the 2015–2016 season for annual salaries and the 1984–1985 season to the
2014–2015 season for the indices of performance. Previous studies obtain different
results on the white premium of salaries of NBA players as a result of using samples
in different periods. By using a long period of unbalanced data, we can check how
choosing particular sample periods affects the estimation results of the white premium.
Second, we use a very extensive dimension of indices of performances to control player
characteristics. Using such a large dimension of performance characteristics reduces
the chance that estimates are biased due to unobserved characteristics. Third, we use
several estimation methods, such as ordinary least square (OLS) estimation, quantile
regression estimation, and sample selection estimation. In the NBA, there are a few
superstar players whose salaries are substantially higher than those of other players.
We control such an effect by using quantile regression estimation. Second, we apply
sample selection estimation because, as noted by Groothuis and Hill (2013), signing acontract at period t can be an outcome of racial discrimination. Even if a non-white
player and a white player were to have the same performance at season t-1, only a white
player might be signed in season t. Due to such a potential sample selection problem,
we estimate the white premium by using a Heckman two-step estimation. Furthermore,
we conduct extensive robustness checks, such as regressions on the performance indicesat period t-2 instead of t-1, restricting the sample to US-born players, and controlling
the team fixed effect interacted with the time dummy. These additional checks show
the robustness of our results.
We find that in the 1980s, non-white players were paid equally to white players.
Second, consistent with the previous literature, which shows that the racial salary gap

3was disappearing in the 1990s, we find that during the 1980s and 1990s, there wasno white premium. However, in the 2000s, we find that the white premium becomes
about 9 percent (p < 0.05) and in the 2010s, it reached about 20 percent (p < 0.01)
in the various estimating methods. Our results show that the result of the previous
literature that the racial salary gap is disappearing is quite temporary, and in fact, it
increased in the late 2000s and, especially, in the 2010s.
Our results show an interesting contrast to the results of the previous literature,
which shows a white premium in the mid-1980s but not in the 1990s and 2000s. We
explain the difference between our results and those of the previous studies as follows. First, in relation to studies that use a dataset for the 1985–1986 season, we findthat when we drop several control variables from our regression equation, which are
not included in the previous studies, the magnitude of the coefficient and statisticalsignificance becomes similar to the results of the previous studies. This suggests theimportance of including those control variables in the estimation. Second, we show
the pattern of the white coefficient by estimating the white premium for each year
using the dataset between 1985 and 2015. We show that the white premium during
1990–2006 is very close to 0, which explains the difference between our results and thatof Groothuis and Hill (2013).
Although we do not find the exact cause of the rise of salary gap, we examine
two possible causes. First, we examine whether the race of the owner or the generalmanager (GM) of the team affects the white premium. We find that even if we control
the race of owner and GM, the pattern of the white premium does not change at all.
In addition, we examine the effect of income distribution of white and black residents.
Again, we find that the pattern of the white premium does not change, even if wecontrol the income distribution of residents in the team’s state.

4

The organization of the rest of our paper is as follows. In Subsection 2.1, we discuss
the data and how we construct the variables. In Subsection 2.2, we discuss empirical
specifications. In the Subsection 2.3, we present the data description. In Section 3, we
present the main results. In Section 4, we provide a brief conclusion.

2

Dataset and Empirical Specification

2.1

DatasetThis study obtains information on salaries and player productivity from several sources.
For information on the annual salaries of players, we obtain information of the annual
salaries from the ESPN salary-ranking website5 , NBA’s reference website6 , and fans’
website7 . We check the consistency of the salary information among three sources andfind that information on those three sources is consistent.8 We collect demographic
variables, such as nationality, height, weight, and birth year, from the reference website
of NBA players9 . In addition, we check the consistency of indices from these two sources
and find that the information is consistent between these two sources. The salary data
are available from the 1985–1986 to the 2015–2016 seasons, except for the 1986–1987
and 1989–1990 seasons.10 The information on performance is available from the 1984–
1985 to 2014–2015 seasons. The median income of black and white residents in eachstate and year is calculated from the CPS data available from the IPUMS CPS.11
5

http://espn.go.com/nba/salaries
http://www.basketball-reference.com/players/
7
https://www.eskimo.com/~pbender/
8
From 1999-2000 to 2015-2016 season, we use ESPN salary-ranking website for salary information.
The ESPN website does not provide salary information before 1999-2000 season. From 1985-1986 to
1998-1999 season, we use https://www.eskimo.com/~pbender/.
9
http://www.basketball-reference.com/players/
10
For the 1986–1987 and 1989–1990 seasons, the NBA, player’s union and individual teams refused
to release salary information.
11
https://cps.ipums.org/cps/
65Some players are posted on the waiver list during the season after the contract issigned. When another team makes a claim on this waived player within 48 hours after
he was posted on the waiver list, the contract is transferred to this new team and the
new team needs to take full responsibility for the contract, including payment of theremaining salary. In most cases, however, no team lodges a claim within 48 hours tothis posted player. In this case, the player obtains free agency. In this case, a newteam can make an offer to this player with the minimum salary that is determined by
the NBA. In our dataset, about 5 percent of observations experience these kinds oftransfers during the season. It is natural to assume that the characteristics of thosewaived players are different from non-waived players. Thus, we exclude those waived
players from our dataset. To check the sensitivity of our regression result due to the
exclusion of those waived players, we conduct robustness checks by including thoseplayers and re-estimating the equation. Our robustness checks show that the result
does not change, even if we include those players.
The following outlines our construction of variables. For the dummy variable indicating whether a player is selected in an All-Star game, we set the All-Star dummy
variable to 1 if a player is selected in the All-Star game at least once in the past
three seasons. For the number of draft picks, we develop 9 dummy variables indicating
whether a player is picked in the 1st–5th pick, 6th–10th pick, 11th–15th pick, 16th–
20th pick, etc. Players whose numbers of draft picks are more than or equal to the
41st or who were not drafted at all are defined as one group.12
12

Initially, we control the effect of the draft pick number by linear and quadratic function of the draft
pick number. However, we find that the non-linear effect of the draft pick number is not well-capturedby the quadratic function. Thus, we devise nine dummies for the draft numbers.

6

2.2

Empirical Specification

The main regression equation that we utilize in this study is as follows:

ln Sijt = β0 + γW hitei + β1 X1,ij,t−1 + αj + αt + εijt

(1)where i is an index of individual, t is the index of the season, and j is the index of thecurrent team. We define the year t to year t+1 season as season t. Sijt is the annual
salary of the player i in the season t who belongs to the team j. W hitei is a dummy
variable that takes 1 if a player is white and takes 0 otherwise. αj is the team’s fixed
effect. αt is the time fixed effect. εijt is the error term. Xij,t−1 is a vector of player i’s
performance in season t-1. Because of the potential endogenous effect from the amountof salary at season t to performance at season t, we regress the performance at season
t-1 instead of the performance at season t. Since it is possible that a player’s salary isdetermined by performance a few years before season t, we conduct a robustness checkby replacing X1,ij,t−1 with X1,ij,t−k where k = 2 or 3.
The coefficient of our main interest is γ, which measures by what percentage theannual salary increases when the race of a player is white, controlling the productivityof this player and the characteristics of the team he belongs to.
Since we use panel data, εijt can be serially correlated for the same i. We apply
various estimation methods, such as OLS, quantile regression estimation, and the Heckman two-step estimation method, with the assumption that the error term is clusteredat the player’s level. We do not use the random effect model of the panel data. The
random effect model is more efficient than the OLS with the clustering robust standard
error if it is correctly specified. However, once misspecified, the random effect model

7

generates an inconsistent estimate of the standard error.13
In the NBA, the salaries of several players are substantially higher than thoseof others. It is known that estimates by OLS are affected substantially by outliers.
To check whether our results are affected by outliers, we regress (1) by the quantile
regression method at several quantiles.
Another issue of estimating equation (1) is the potential non-randomness of thedataset. In the presence of racial discrimination, the observation of salary data at
season t might not be equally selected between non-white and white players, even if
the performance at season t-1 is the same for non-white and white players. In sucha case, running OLS can introduce bias for estimating γ. To solve such non-random
sampling, we apply Heckman’s two-step estimation. To estimate Heckman’s two-stepestimation, it is important to have some excluded variables that enter the selection
equation but do not enter the salary equation. For such excluded variables, we use
dummy variables that indicate the team to which a player belonged at season t-1.

2.3

Data Description

Table 1 and Table 2 provide the summary statistics of the variables used in our regression. We have 9822 observations and 1856 players. Among the observations, 24
percent are white players and 13 percent are foreign players. We classify the positions of players into three categories—center, guard, and forward. About 40 percentof the observations is guard, another 40 percent is forward, and 20 percent is center.
We control for age, age squared, experience, and experience squared.14 The analysis
13

Since the random effect model is a particular type of the generalized least square (GLS) estimation,
the standard criticism of the GLS also applies to the random effect model. For the standard criticism
applying the GLS model, see subsection 1.6 of Hayashi (2000).
14
Our robustness check shows that including experience squared is important. See our discussionin Table 7.

8

of the sample selection model suggests the importance of the inclusion of age.15 The
average age is 27 years and the average experience is 5.5 years. The previous study(Hill, 2004) shows the importance of controlling the height or position.16 Table 2 lists
the 16 indices of performance variables used in our regressions. Table 2 shows thatmany indices are different between non-white and white players. Table 2 suggests that
if those indices affect the level of salary of a player, we need to include those variables
as control variables to estimate the effect of the race of a player.
Figure 1 shows that ratio of white players during 1985–2015. The ratio is initiallyaround 25 percent. After 1990, it starts to decline to around 20 percent. After the
mid-2000s it starts to rise to between 25 and 30 percent. Figure 1 suggests that theratios of white and non-white players are not constant across periods and that thewhite premium might not be time invariant.

3

Empirical Results

Table 3 shows our main regression results.17 The row White displays the estimatedcoefficient of the white dummy in the regression equation. In the regression equation,
the dependent variable is the logarithm of the annual salary at season t. The explanatory variables are the white dummy, foreign player dummy, performance indices listed
in Table 2 at season t-1, age, age squared, experience, experience squared, two position
dummies, height, weight, team dummy at season t, nine dummy variables to control
draft pick numbers, and a dummy indicating the selection in an All-Star game in the15

The analysis of the sample selection model shows that age is a significant variable in the firststage selection equation but experience is not. On the other hand, in the second-stage salary equation,
experience is significant but age is not. This suggests that age is an important variable to control
sample selection when the OLS or quantile regression is applied.
16
See the discussion in footnote 2.
17
The estimated coefficients and their standard errors of all control variables of the OLS estimation
are provided on Table A1 in appendix A.

9past three seasons.1819 For calculating the standard error, we assume that the error
term is clustered at the player’s level and apply the clustering robust standard error.
The first block of Table 3 shows the results of OLS. The first, second, third, fourth,and fifth columns show the estimated coefficients of the white dummy, numbers of observations, and R squared when all observations, observations in the 1980s, observationin the 1990s, observations in the 2000s, and observations in the 2010s are used for theestimation, respectively.
The first block of Table 3 shows that in the 1980s and 1990s, non-white players are as
equally paid as white players with similar characteristics. The estimated coefficient of
the white player dummy using the 1980s and 1990s samples is economically very smalland statistically insignificant. In the 2000s and 2010s, however, the white premium
becomes significant economically and statistically. In the 2000s, the white premium
becomes about 10 percent (p < 0.05). In the 2010s, the white premium becomes morethan 20 percent (p < 0.01).
The second, third and fourth blocks show the results of 50 percent, 25 percent, and75 percent quantile regression estimation.20 The dependent variable and explanatory
variables are the same as in the OLS case. The result of the 50 percentile quantile
regression result shows that even with the quantile regression, the pattern of the whitepremium does not change—in the 1980s and 1990s, there was no white premium, but
in the 2000s, the white premium began to emerge and, in the 2010s, it reached nearly
20 percent. The third fourth blocks show that the results of 25 percent and 75 percent
quantile regression, respectively, are very similar to those of the 50 percent quantile
18

In the data section, it is explained how the nine dummy variables for draft number pick areconstructed,
19
Our robustness check in the next section shows the importance of including experience squared.
20
The estimated coefficients and their standard errors of all control variables of the quantile regression model are provided in Tables B1, B2, and B3 of the supplemental appendix B.

10regression. The results of the quantile regression suggest that the results of the OLS
are not driven by outliers.
In Table 4, we conduct several robustness checks. In the first block of Table 4,
we control the sample selection problem, as discussed in the empirical specification
section. We use the Heckman two-step estimation to control the sample selection
problem. The first-stage selection equation estimates the selection equation by using
the sample of players who played at season t-1. The second-stage salary equation
provides an estimation by using a sample with contracts at season t. The dependent
variable of the selection equation is a dummy variable indicating that a player has
a contract at season t. The dependent variable of the second-stage salary equationis the logarithm of annual salary. The explanatory variable of the salary equationis the same as that in the equation used in OLS and quantile regression estimationin Table 3. The explanatory variables in the selection equation are the same as the
explanatory variables used in the salary equation except the team dummy. In thesalary equation, we use the team dummy at season t, since the salary at season t is adependent variable. By contrast, in the selection equation, we use the team dummy fora player having played at season t-1.21 The result of the sample selection estimationshows that the pattern of the white premium is the same as that of the OLS estimation.
This shows that in the 2010s, the white premium is 20 percent.
In Table A2, we display the estimated coefficient of all control variables in thesample selection model. Table A2 shows that in the selection equation, the whitedummy is not significant when we assume that the coefficients are different in each
decade.22 . In addition, Table A2 shows that the effect of age is different in the selection
21

Implementing the Heckman two-step estimation, it is important to find an excluded variable thatenters the selection equation but is not included in the second-stage equation. The team dummy at
t-1 is the excluded variable.
22This is consistent with Groothuis and Hill (2013)11

equation and the salary equation. Although age is not significant in the salary equation,
it is statistically significant in the selection equation. This suggests that when we do
not apply a sample equation estimation method, such as OLS or quantile regression,
it is important to include age to control the effect of selection indirectly.
As shown by Kahn and Shah (2005), the white premium can be quite sensitive
to the type of contract, as discussed in the introduction. Players with experience ofmore than 3–5 years (depending on the initial contract) can become free agent players.
On the other hand, the salaries of the drafted rookie players with less experience aredetermined by NBA’s rules. Thus, there is no room for racial discrimination for eachdrafted rookie player if his years of experience number less than 3 years. This implies
that the inclusion of rookie players with less experience could affect the white premium
substantially, as demonstrated by Kahn and Shah (2005). In the second block of Table
4, we restrict the sample to players who have 5 years or more of experience. The
dependent variable and explanatory variable are the same as in Table 3. The resultof the second block shows that the result of OLS with all players continues to holdeven in the restricted sample. This suggests that the type of contract does not affectthe pattern of the white premium over time.23 . The third block confirm this finding.
More specifically, we restrict the sample to players who are drafted with early picks
and whose experience is less than or equal to 3 years. The salaries of players whose
draft pick number is low and whose experience is lower than or equal to 3 years aretightly controlled by NBA’s rules. Thus, for those players, the white premium is not
likely to exist. If we find a white premium, it indicates that we are picking up othereffects. The third block of Table 4 estimates the white premium for those players. As
the theory indicates, there is no white premium for those players.
23

The estimated coefficients and their standard errors of all control variables of this model areprovided in Table B4 in appendix B

12In the fourth block, we restrict the sample to US-born players.24 Since the NBA
experienced a dramatic globalization in the 2000s, fully controlling for the birthplace of
players seems to be important. The result of the fourth block of the Table 4 shows that
controlling for foreign players fully does not affect the pattern of the white premium
at all.25
Recently, Erick Eschker and Siegler (2004) and Motomura (2014) show that players
who graduated from US colleges have some premium for their salaries. Thus, the whitepremium in the 2010s might have been due to the US college premium. To check such a
possibility, in the fifth block of Table 4, we restrict the sample to players who graduatedfrom US colleges. The result shows that even with this sample, the pattern of the whitepremium does not change at all.
In Table 5, we conduct additional robustness checks. In Table 3, we use the indices of the performances in the previous season to control the productivity of players.
However, it is quite possible that the salary of the player at season t is affected by
the performance at t-2 or t-3, not t-1. The first and second blocks of Table 5 show
the coefficients of the white dummy when the indices of performance at season t-2 ort-3 are used as the control variables and the dependent variable is the log of salaryat season t.26 The first and second blocks are estimated by OLS. The third block isestimated by quantile regression. The first, second, and third blocks of Table 5 show
24

In all the above regressions, we include the foreign player dummy as an explanatory variable.
Thus, we control the birthplace of players to some degree. However, we do not make other variables
interact fully with the foreign player dummy. Thus, we implicitly assume that the coefficient of otherexplanatory variables is the same between US-born and foreign players. If the effect of those control
variables differ between foreign and US-born players, our estimate of the coefficient of the whitedummy can be biased. To solve this problem, we restrict our sample to US-born players and apply
the same regression equation, except for the foreign player dummy.
25
The estimated coefficients and their standard errors of all control variables of this model areprovided on Table B5 in appendix B.
26
The estimated coefficients and their standard errors of all control variables are provided in Tables
B6 and B7 in appendix B.

13

that the pattern of the white premium does not change from Table 3.
In the fourth block of Table 5, we control another sample selection issue. As
discussed in the data section, we exclude players posted during the season. This isbecause often they are hired by the second team at the minimum salary. The fourth
block of Table 5 shows the results of the regression when we include those players inthe sample. The results of the fourth block Table 5 show that the results of Table 3
do not change even if those waived players are included.27
In all the abovementioned regressions, we include the team dummy to control theteam’s fixed effect. In the fifth block of Table 5, we include the interaction term of theteam dummy and year dummy. The fifth block of Table 5 shows that the pattern of
the white premium is not affected at all, even if we include the interaction of the teamdummy and year dummy.
In Table 6, we drop several variables to conduct additional robustness checks. In
the regressions employed in Tables 3, 4 and 5, we include some variables as control
variables that can be affected by the presence of discrimination. For example, in theabove regressions, we include the number of games played, the average minutes played
per game, selection to the All-Star game and the number of draft picks. Those variables
could be affected by the presence of discrimination itself. For example, the GM canhave a preference for white players, and non-white players might play a smaller numberof games, even if black and white players have potentially the same performance level.
Fans can have preference for non-white or white players, which can affect selection to
All-Star games. In such cases, those variables can be the outcome of discrimination,and controlling by the outcome variable can be very misleading for estimating the white27

The estimated coefficients and their standard errors of all control variables are provided on Table
B8 in appendix B.

14

premium.28
In the first block of Table 6, we estimate equation (1), which was estimated in Table
3, by dropping the number of games played. The pattern of the estimated coefficientof the white dummy is the same as in Table 3. In the second block, we conduct theestimation by dropping the number of games played and the average minutes played.
In the third block, we drop the number of games played, average minutes played,
attendance to All-Star games, and nine dummy variables related to draft number picks.
In the fourth block, we apply the sample selection estimation method to the sample
used in the fourth block. All four blocks shows that the pattern of the white premium
is very similar to the pattern of the white premium in Table 3.

3.1

Reconciling with Previous Results

One might ask the source of the difference between our results and those of previous
studies. Several studies report the presence of a white premium in the 1980s. In
addition, previous studies that use data in the 1990s and 2000s show that the whitepremium does not exist (Groothuis and Hill, 2013). To check for consistency, we runa regression separately for each year in Table 3 and plot the coefficients of the whitedummy. Figure 2 shows the estimated coefficient of the white dummy for each year.
Figure 2 shows that in the 1985–1986 season, the coefficient of the white dummy ispositive but keeps declining and becomes almost 0 or sometime negative. In the late
2000s, it starts to increase. Our estimated coefficient that used the data of the 1985–
1986 season alone is positive but statistically insignificant. To observe the differences ofour estimated results and those of previous results that use the sample of the 1985–1986
season, we conduct several robustness checks in Table 7.
28

For more on the dangers of controlling the outcome variable, see section 3.2.3 of Angrist Joshua
and Pischke (2009).

15

Column (0) in Table 7 is taken from Kahn and Sherer (1988), who report that thecoefficient of the white dummy is 20 percent and is statistically significant by using the
1985–1986 season sample. In column (1) of Table 7, we estimate the white dummy byusing salary information in the 1985–1986 season as well as performance informationin the 1984–1985 season in our dataset and by applying the same specification as Kahn
and Sherer (1988). We find that the white premium is 15 percent and is statisticallysignificant at the 10 percent level. In column (2), we add two variables to the specification of column (1): height and square of experience. When we add these two variables,
the white premium becomes 10.5 percent and becomes statistically insignificant, evenat the 10 percent level. For column (3), we estimate the white premium by replacing
points per game with the number of field goals per game, the number of three-pointshoots per game, and the number of free-throw shoots per game. When we include
these three variables, we drop the number of points per game because of obvious multicollinearity. Column (3) shows that the estimated coefficient of the white premium is11.7 percent and is statistically insignificant. In column (4), we add age, age squared,
weight, the foreign dummy, and the All-Star dummy. In column (5), we add the ninedraft rank dummies. In column (6), we use the same specification as that of Table 3.
The results of columns (2)–(6) show that with all specifications, the estimated coefficient of the white dummy is not statistically significant. This suggests that the resultof Kahn and Sherer (1988) is likely due to the lack of important control variables.29
Another previous study that shows a contrasting result is Groothuis and Hill (2013),
who applies the sample selection model using the dataset covering 1990–2006 and shows
there is no white premium and, in fact, there is a reverse white premium in somespecifications. Figure 3 explains the source of the difference between our results and29

Hill (2004) makes a similar observation using 1985–1986 data.

16

those of Groothuis and Hill (2013). Figure 3 estimates the white premium by applying
the sample selection model each year separately from 1990 to 2015. Figure 3 showsthat until the mid-2000s, the white premium is quite low or sometimes negative. Afterthe mid-2000s, the white premium started to emerge. Thus, it is not surprising that
Groothuis and Hill (2013) did not find a statistically significant white premium. The
white premium simply started to emerge after the mid-2000s. In Table 8, we reconcile
this with the result of Groothuis and Hill (2013). Column (0) of Table 8 is taken from
Groothuis and Hill (2013). It shows the estimated coefficient of the white dummy inthe salary equation and exit equation.30 Column (1) displays the estimated coefficientof the white dummy in the salary equation and the selection equation when the same
control variables as those in Groothuis and Hill (2013) are applied and the sample in
1990–2006 in our data set is used. Column (2) displays the estimated coefficient of thewhite dummy in both the salary equation and the selection equation when the same
specification as that in the first block of Table 4 is applied. Table 8 shows that we
obtain a similar result using our own dataset.

3.2

Effects of Race of Owner, GM, and Income Gap on White Premium

One natural question is why the racial salary gap increased in the 2000s and 2010s.
Although we find that the exact cause of this rising salary gap is beyond the scope of
this study, we could exclude some possibilities. First, we examine whether the race ofthe owner or GM of the team affects the white premium. We find that even if we control
the race of the owner and GM, the pattern of the white premium does not change atall. More specifically, we estimate equation (1) with the interaction term of the whitedummy and the race dummy of the owner of GM. The race dummy of the owner or
30

Groothuis and Hill (2013) use the exit equation instead of the selection equation for controllingthe sample selection.

17

GM is equal to 0 if the race of the owner or GM is white and otherwise it is equal to 1.
The idea of this regression is that the coefficient of the white dummy becomes bigger
in the team in which the race of the owner or GM is white if the primary reason of therise of the white premium derives from the racial preference of owners or GM. The first
block of Table 9 shows the estimated coefficient of the white dummy of players when
the race dummy of the owner and its interaction with the white dummy of players areincluded. The reported coefficient measures the white premium in the team in which
the race of the owner of the team is white. The second block of Table 9 shows theestimated coefficients of the white dummy of players when the race dummy of GM and
its interaction with the white dummy of players are included. The estimated coefficientof the white dummy measures the white premium in a team in which the race of the
GM is white. The first and second blocks of Table 9 show that the pattern of theestimated coefficient of the white dummy does not change at all, even if we control therace of the owner or GM.
In the third block of Table 9, we control the relative median income of white
residents over that of black residents in the state in which the team is located. If
the white premium is a reflection of a consumer’s willingness to pay for own race,then in an area in which the relative income of white is high, the white premium will
become higher. After the mid-2000s, the median income gap between white and blackincreased according to the CPS data. In the third block, we estimated the following
equation:ln Sijt = β0 + γ0 W hitei + γ1 W hitei × (Gapjt − Gap)
+ γ2 × (Gapjt − Gap) + β1 X1,ij,t−1 + γj + γt + εijt

18

(2)

where Gapjt is the ratio of median income of white residents to the median income ofblack residents at time t in the state in which team j is located. Gap is the sample
average of Gapjt . The idea of equation (2) is that white premium becomes higher ina state in which the median income of white residents is higher than that of black
residents. The ratio of the median income of black and white residents is calculated
from the CPS data. The third block of Table 9 shows that the pattern of the estimatedcoefficient of white dummy of players does not change even when we include the medianincome ratio of white residents and black residents and its interaction with the whitedummy.

4

Conclusion

This study revisits the issue of a racial salary gap in the NBA using an unbalanced
panel dataset that includes information on annual salary from the 1985–1986 season
to the 2015–2016 season and performance information from the 1984–1985 season to
the 2014–2015 season. In contrast to the result of previous studies, in which the racialsalary gap disappears in the 1990s and early 2000s, we find that the racial salary gap
starts to emerge in the 2000s and reaches about 20 percent in the 2010s. The results
are quite robust to many specifications, including OLS, quantile regression, sample
selection estimation, and restricting the sample to several sub-samples.
Although we demonstrated that there is a racial salary gap between non-white andwhite players in the 2000s, especially in the 2010s, we need to be careful about theinterpretation of the results. The presence of a salary gap might be caused by factors
other than discrimination. During the 2000s and 2010s, there were several externalchanges in the NBA, such as the globalization of teams, the introduction of luxury
taxes, and minor leagues. Thus, the rise of the salary gap from the mid-2000s could
19

be the effect of other factors than discrimination.
To find the cause of the rising racial salary gap, we examined whether the race ofowners and GMs or the income gap of white and black fans affects the white premium.
We found that those factors did not affect the white premium at all. Thus, the causeof the rising racial salary gap is unknown and remains as a topic for future research.

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22

Table 1: Summary Statistics of White Players and Non-white Players
All
White
Non-white
Difference of the

Variablesmean
s.d.
mean s.d.
mean s.d.
Annual Salary ($1000)
3,514 4,078 3,362 3,767 3,563 4,173
Foreign Dummy
0.128 0.334 0.327 0.469 0.0639 0.245
Age26.98 4.007 27.07 3.775 26.95 4.079
Experience
5.505 3.754 5.150 3.509 5.620 3.822
Weight (lb)218.0 27.96 227.1 27.86 215.1 27.36
Height (inch)
79.13 3.727 80.64 3.731 78.64 3.594
Guard Dummy
0.382 0.486 0.285 0.452 0.413 0.492
Forward Dummy
0.413 0.492 0.352 0.478 0.433 0.496
Center Dummy
0.205 0.404 0.363 0.481 0.154 0.361
Number of Draft Pick ≤40
0.651 0.477 0.650 0.477 0.652 0.476
Selected in All-Star Game at least oncein the past 3 seasons
0.104 0.305 0.0827 0.275 0.110 0.313
n=9822N
n=2394n=7428
Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

mean of whiteand non-white
players-201.2**(95.83)0.263***(0.00739)
0.120(0.0942)-0.471***(0.0881)11.97***(0.646)
1.996***(0.0853)-0.127***(0.0113)-0.0811***(0.0115)0.209***(0.00925)-0.00121(0.0112)-0.0277***(0.00716)

Table 2: Summary Statistics of White Players and Non-white Players (2)
All
White
Non-white

Performance Indicesmean s.d.
Number of games the athlete played in the season.
59.74 21.99
Average minutes the athlete played in one game
22.66 9.959
Average number of successful field goals in one game
3.520 2.263
Probability of successful field goals
0.447 0.0757
Average number of three-point shoots in one game
0.458 0.612
Probability of successful three-point shoots
0.222 0.178
Average number of free-throw shoots in one game
1.805 1.483
Probability of successful free throw
0.719 0.150
Average number of defensive rebounds taken in one game 1.175 0.903
Average number of defensive rebounds taken in one game 2.821 1.832
Average number of assists in one game
2.077 1.966
Average number of turn-overs in one game
1.381 0.814
0.742 0.485
Average number of steals in one game
0.481 0.551
Average number of blocks in one game
Averag scores in one game9.303 6.081
2.097 0.803
Average number of fouls
Contribution to the team
10.42 6.328
Assist percentage
13.47 9.524
Turn-over percentage
12.77 4.744
True shooting percentage
36.31 24.01
Usage percentage
19.10 5.093
Offensive rebound percentage
6.197 4.219
Defensive rebound percentage
14.17 5.974
N
n=9822
Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

mean
s.d.
57.36
22.32
20.04
9.717
2.924
1.997
0.448 0.0796
0.425
0.598
0.218
0.192
1.454
1.240
0.719
0.162
1.108
0.850
2.673
1.772
1.788
1.966
1.174
0.744
0.587
0.422
0.462
0.525
7.727
5.345
2.009
0.821
9.273
6.060
12.35
9.464
13.14
5.209
36.85
24.21
17.91
4.921
6.649
4.146
15.18
5.946
n=2394

mean
s.d.
60.51
21.83
23.50
9.890
3.712
2.311
0.447 0.0744
0.469
0.616
0.223
0.174
1.919
1.537
0.720
0.146
1.197
0.918
2.868
1.848
2.170
1.957
1.448
0.825
0.792
0.494
0.487
0.559
9.811
6.216
2.125
0.795
10.78
6.368
13.83
9.516
12.65
4.577
36.13
23.94
19.49
5.088
6.051
4.232
13.84
5.947
n=7428Difference of the mean
between white andnon-white players-3.153***-3.465***-0.788***0.000714-0.0435***-0.00489-0.464***-0.000318-0.0897***-0.196***-0.382***-0.274***-0.205***-0.0249*-0.115***-2.084***-1.511***-1.483***
0.486***
0.714-1.580***0.598***
1.339***(0.516)(0.231)(0.0526)(0.00178)(0.0144)(0.00419)(0.0345)(0.00352)(0.0212)(0.0430)(0.0460)(0.0189)(0.0112)(0.0129)(0.0188)(0.141)(0.148)(0.223)(0.111)(0.564)(0.119)(0.0990)(0.140)Table 3: Estimated Coefficients of White Dummy in OLS and Quantile Regressions
Dependent variableln (annual salary)
(1)

(2)

(3)

(4)

(5)

all

1980s

1990s

2000s

2010s

0.0817***0.0636-0.0411

0.113**

0.244***(0.0294)(0.0539)(0.0452)(0.0452)(0.0521)

N

9,822

745

3,203

3,650

2,224

R-squared

0.681
0.04360.751
0.05980.617
0.603-0.103** 0.130***0.570
0.186***(0.0297)(0.0789)(0.0480)(0.0477)(0.0616)

N

9822

745

3203

3650

2224

R-squared

0.676

0.737

0.606

0.594

0.556

0.0900***0.0251-0.03100.149***

0.192***(0.0287)(0.0743)(0.0562)(0.0500)(0.0686)

9822
0.674
0.0282(0.0322)
9822
0.665

745
3203
0.729
0.604
-0.00582 -0.0751(0.0563) (0.0508)
745
3203
0.721
0.588

3650
0.587
0.0848*(0.0444)
3650
0.582

2224
0.549
0.173***(0.0621)
2224
0.534

Estimation Method
OLS

Quantile Regression (50%)

Quantile Regression (25%)

Quantile Regression (75%)

White

White

White
N
R-squaredWhite
N
R-squared

Notes: The dependent variable of the regression equation is the logarithm of the annual salary at
season t. The explanary variables are white dummy, foreign player dummy, position dummy to control
positions (guard, center, forward), age, age squared, experience, experience squared, year dummy,height, weight, team dummy at season t, attendance of All-Star game in the past 3 seasons dummy,
draft-ranking dummy and all perfomance indices at season t-1 listed in Table 2. The regression
equation is estimated by using the unbalanced panel data covering salary information from the 1985–
86 season to the 2015–16 season. The row "white" shows the estimated coefficient of the whitedummy in different specificatons. Clustering robust standard errors are presented in parentheses andthe error term is clustered at the player's level in all specifications. Columns (1), (2), (3), (4), and (5)show the esimated coefficients of the white dummy, its standard error, the number of observations,and R² when we use all observations, observations in the 1980s, observations in the 1990s,
observations in the 2000s, and observations in the 2010s, respectively. The first, second, third, andfourth blocks display the results of OLS, the 50 percentile quantile regression, the 25 percentile
quantile regresion, and the 75 percentile qunatile regression, respectively.
*** p<0.01, ** p<0.05, * p<0.1

Table 4: Estimated Coefficient of White Dummy in the Sample Selection Model and in the
Restricted Samples
Dependent variable
Specifications
Heckman two-stepestimation

OLS: Restricted sample(Expereince≥5_)

OLS: Restricted sample(draft pick≤20& Experience ≤3)
OLS:Restricted sample(US-born only)

OLS: Restricted sample(Graduated from US
college)ln (annual salary)
(1)
(2)
(3)
(4)
(5)
all
1980s
1990s
2000s
2010s
White
0.0654** 0.0507 -0.0635
0.112** 0.207***(0.0297) (0.0566) (0.0433) (0.0451) (0.0494)
Mill's ratio -0.748*** -0.238* -0.775*** -0.419** -0.899***(0.112)(0.128) (0.148)(0.163) (0.166)
N
12302
1422
3867
4307
2706
White
0.103*** 0.0527 -0.0183 0.168** 0.280***(0.0383) (0.0757) (0.0561) (0.0666) (0.0717)
N
5,171
360
1,670
1,940
1,201
R-squared
0.669
0.748
0.584
0.516
0.514
White
-0.00211 0.0873 -0.0411 0.0599
0.0187(0.0317) (0.132) (0.0679) (0.0543) (0.0605)
N
1,396
140
465
455
336
R-squared
0.830
0.878
0.754
0.669
0.692
White
0.0552
0.0669 -0.0449 0.129** 0.204***(0.0342) (0.0568) (0.0483) (0.0538) (0.0630)
N
8,565
715
2,988
3,089
1,773
R-squared
0.685
0.748
0.611
0.600
0.583
White
0.05040.0605 -0.0631 0.114** 0.197***(0.0322) (0.0544) (0.0456) (0.0502) (0.0583)
N
8,742
740
3,125
3,089
1,788
R-squared

0.679

0.749

0.620

0.588

0.583

Notes: Clustering robust standard errors in parentheses and the error term is clustered at theplayer's level in all specifications. In the first block, the Heckman two-step estimation isapplied to control the endogeneity of having a contract in season t given that a player played
at season t-1. Players who played in season t-1 but who did not have a contract in season t areincluded in the first-stage selection equation. The row "Mill's ratio" displays the estimatedcoefficients of the inverse Mill's ratio. The standard error is calculated by using the bootstrap.
In the second-stage salary equation, the explanatory variables are the the same as ones usedin Table 3. In the first-stage selection equation, the explanatory variable is the same as that in
the second-stage salary equation except the team dummy. The team dummy at season t is
replaced by the team dummy at season t-1. In the second, third, fourth, and fifth blocks, thesample is restricted to players who have 5 or more years of experience at the NBA, players
whose salaries are tightly determined by the NBA rule (players whose experince is less thanor equal to 3 years and whose draft pick number is less than or equal to 20), US-bornplayers, and players who graduated from US colleges, respectively. The explanatory variablein the second, third, fourth and fifth blocks are the same as those used in Table 3.
*** p<0.01, ** p<0.05, * p<0.1

Table 5: Estimated Coefficient of White Dummy with Other Controls or in the Restricting Sample
ln (annual salary)
Dependent variable
Specifications
OLS: controlling theperformance at t-2

OLS: controlling theperformance at t-3

(1)
all
White
N
R-squaredWhite

N
R-squared
Quantile Regression:White
controlling the performance at t-2
NR-squared
OLS: including players
who experienced multiple
teams at season t
OLS:Adding year dummy
×team dummyWhiteN
R-squaredWhite
N
R-squared

(2)
1980s

(3)
1990s

(4)
2000s

(5)
2010s

0.0841**0.0177‐0.03160.120**

0.258***(0.0330)(0.0762)(0.0494)(0.0490)(0.0618)

8,248

429

2,754

3,172

1,893

0.637

0.715

0.583

0.553

0.514

0.0808**0.0162‐0.0579

0.110**

0.300***(0.0361)(0.0820)(0.0555)(0.0542)(0.0747)

7,135

360

2,397

2,750

1,628

0.614

0.737

0.549

0.510

0.461

0.02740.0263‐0.06010.0892*0.202**(0.0333)(0.116)(0.0604)(0.0475)(0.0803)

8,248

429

2,754

3,172

1,893

0.629

0.693

0.572

0.544

0.497

0.0739**0.0636‐0.0353

0.0937** 0.232***(0.0296)(0.0539)(0.0449)(0.0452)(0.0539)

10,091

745

3,265

3,773

2,308

0.674

0.751

0.613

0.597

0.567

0.0803***0.0605‐0.0468

0.106**

0.235***(0.0293)(0.0541)(0.0454)(0.0453)(0.0526)

9,822

745

3,203

3,650

2,224

0.707

0.766

0.646

0.628

0.601

Notes: Clustering robust standard errors in parentheses and the error term is clustered at the player's
level in all specifications. The dependent variable and control variables are the same as those in Table
3, except the performance variables at t-1. In the first and second blocks, the performance variables at
t-1 are replaced by the performance variables at t-2 and t-3. The row "white" dispalys the estimatedcoefficients of the white dummy. In the fourth block, players who experienced multiple teams areadded to the sample. For such players, the total salary in season t is used as the dependent variable. The
team that gave the highest salary during season t is classified as the team for such players. In the fourth
block, the interaction term of year dummy and team dummy is added as an additional control variable.
*** p<0.01, ** p<0.05, * p<0.1

Table 6: Other Robustness Checks (Dropping some variables)
Dependent variableln (annual salary)
(1)
(2)
(3)
(4)
all
1980s
1990s
2000s
Specifications
OLS: Drop games
White
0.0808*** 0.0625‐0.0457
0.113**
N
R-squared

(5)
2010s
0.244***(0.0295)(0.0539)(0.0454)(0.0451)(0.0523)

9,822

745

3,203

3,650

2,224

0.681

0.751

0.615

0.602

0.570

0.0691**0.0551‐0.06270.104**

0.234***(0.0298)(0.0539)(0.0468)(0.0455)(0.0530)

9,822

745

3,203

3,650

2,224

0.677

0.749

0.609

0.600

0.564

0.0732**

0.0562‐0.06290.101**

0.255***(0.0322)(0.0547)(0.0497)(0.0466)(0.0623)

9,822

745

3,203

3,650

2,224

0.655

0.715

0.586

0.571

0.494

0.0586*0.0398‐0.0778

0.0995**0.216***(0.0338)(0.0565)(0.0477)(0.0465)(0.0619)

12302

1422

3867

4307

2706

OLS: Drop games
played and average
minutes playedWhite
N
R-squared
OLS: Drop games
played, Minutes Played,
All star dummy anddraft number dummyWhiteN
R-squared
Sample slection model:
the same variables as
above
White
N

Notes: Clustering robust standard errors are in parentheses and the error term is clustered at theplayer's level in all specifications. The explanatory variables in the first block are the explanatory
variables used in Table 3 except the number of games played. In the second block, the average
minutes played is dropped in addition to the number of games played as the explanatory variable.
In the third block, in addition to those variables, the All-Star attendance dummy is dropped.
From the fourth block, the draft rank dummy variables are dropped additionally. In the first to
fourth blocks, OLS is applied. In the fifth block, the Heckman selection model is applied when
the number of games played, the average minutes played, All-Star attendance dummy, and draft
rank dummies are dropped from the explanatory variables.
*** p<0.01, ** p<0.05, * p<0.1

28

Table 7 : Reconciliation with the results of Kahn and Sherer (1988)
ln (annual salary)
Dependent variable
(0)
(1)
(2)
(3)
(4)
(5)
White
0.207*** 0.141*
0.105
0.117
0.07760.0865(0.068) (0.0796)(0.0788)(0.0799) (0.0686)(0.0713)
Experience
0.03*** 0.0632*** 0.108*** 0.107*** 0.118**
0.109*(0.011) (0.0119)(0.0343)(0.0334) (0.0457)(0.0479)
Experience squared-0.00354 -0.00338 0.00262
0.00229(0.00276) (0.00263) (0.00390)(0.00341)
Height0.03320.03010.01070.0126(0.0238)(0.0245) (0.0193)(0.0184)
Points per game
0.0558***0.0411*** 0.0427***(0.011) (0.00847) (0.00887)
Field goal
0.121*** 0.0826*** 0.0850***(0.0305) (0.0315)(0.0300)
Three-point shoots-0.214-0.07360.0647(0.224)(0.202)(0.222)
Free throw-0.00245 -0.0363-0.0399(0.0375) (0.0338)(0.0345)Age0.384*
0.404*(0.220)(0.195)A squaredAged-0.00858**
0 00858** -0.00888**
0 00888**(0.00423)(0.00363)
Weight0.00679*** 0.00678***(0.00256)(0.00253)
Selected in All-Star
0.241**
0.235**
games(0.118)(0.113)

29

(6)
0.108
(0.0815)0.132**(0.0520)0.000722(0.00362)-0.000758(0.0204)-0.0841(0.114)0.0362(0.265)-0.101(0.0885)0.353(0.200)-0.00794*
0 00794*(0.00375)
0.00629**(0.00274)
0.231*(0.124)

Foreign dummy
Three-point successprobability
Constant

10.378** 11.37***(0.463) (0.316)
Draft rank dummy No
No
No
Current team dummyNo

8.747***(1.622)NoNo

8.987***(1.636)
No
No

4.786
(3.014)Yes
No

0.0000293(0.124)0.0310(0.140)-0.295-0.310(0.196)
4.240(3.094)
Yes
Yes(0.203)5.999*(3.350)Yes
Yes

Other performancevariables
No
No
No
No
No
No
Yes
Observations
226
241
241
241
241
241
241
R-squared
0.723
0.664
0.670
0.674
0.766
0.809
0.815
Notes: Robust standard errors are parentheses. Column (0) is copied from the OLS result of Kahn and
Sherer (the first column of Table 1). Columns (1)–(6) are estimated using salary information in the 1985–
1986 season and performance information in the 1984–1985 season in our dataset. The following
variables are used in columns (1)–(6) in addition to the above listed variables: the number of games
played, the average minutes played, the position dummy (center and forward), free throw successprobability, field goal success probability, the number of offensive rebounds, the number of defensive
rebounds, assits, steals, blocks, and personal fouls. These are used as explanatory variables in allp), contribution to the team,, assist ppercentage,g , turn-over ppercentage,g , true
specifications.
In column ((6),
shooting percentage, usage percentage, offensive rebound percentage, defensive rebound percentage, andturn-over are used as additional explanatory variables.
* p<0.1 ** p<0.05 *** p<0.0130

Table 8: Reconciliation with the results of Groothuis and Hill (2013)
(0)
(1)
(2)
Wage equationln (annual salary)
Dependent variable
White dummy
Selection equation
Dependent variable
White dummy
Inverse Mill's Ratio
N-0.087(0.04)-0.0295(0.0354)

exit dummy-0.023(0.060)-0.43(0.018)6530

0.0197(0.0355)

contract dummy
0.07960.0893-0.286***(0.0276)7312-0.390***(0.0185)7312

Standard errors in parentheses. For column (0), the number is taken from Hilland Gooldhill. In column (0), the exit dummy is 1 if the player played at season
t-1 but did not have a contract at the season t and otherwise it is equal to 0. For
columns (1) and (2), a contract dummy is equal to 1 if a player played at season
t-1 and had a contract at t. For columns (1) and (2), we use the sample of the
1990–2006 season in our data for estimation. For the second-stage salaryequation, foreign player dummy, height, points per season, rebounds per
season, blocks per season, assists per season, draft number, draft number
squared, experience, experience squared, and inverse Mill's ratio are used as
explanatory variables in addition to the white dummy. In the selection equationof column (0), fourth order polynomial of experience, body mass index, points
per season, rebounds per season, draft number, and draft number squared areused as additional explanatory variables. In column (1), the same specificationas column (1) is applied. In column (2), the same specification of the first blockof Table 4 is applied to the sample covering 1990–2006.

*** p<0.01, ** p<0.05, * p<0.1

31

Table 9: Estimated Coefficients of White Dummy in Other Specifications
Dependent variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
all
1980s
1990s
2000s
2010s
Specifications
OLS: Adding owner's
race dummy and its
interaction with thewhite dummyWhite
0.0802*** 0.0596‐0.04510.111**
0.250***
N
R-squared(0.0297)(0.0548)(0.0460)(0.0458)(0.0532)

9,822

745

3,203

3,650

2,224

0.682

0.751

0.617

0.603

0.570

0.0658**0.0550‐0.0544

0.0917*0.220***(0.0303)(0.0583)(0.0475)(0.0487)(0.0551)

9,822

745

3,203

3,650

2,224

0.682

0.752

0.617

0.603

0.571

OLS: Adding GM's race
dummy and its
interaction with thewhite dummyWhiteN
R-squared
OLS: Adding relative
income and its
interaction with thewhite dummyWhiteN
R-squared

0.0905***0.0655‐0.0258

0.113**

0.238***(0.0309)(0.0559)(0.0498)(0.0458)(0.0531)

9,470

736

3,080

3,505

2,149

0.684

0.756

0.622

0.605

0.573

Notes: Clustering robust standard errors in parentheses and the error term is clustered at theplayer's level in all specifications. The explanatory variables in the first block are the explanatory
variables used in Table 3, the owner's race dummy, and its interaction with the white dummy ofthe player. The owner's race dummy is equal to 1 if the race of the owner is non-white, and 0
otherwise. In the second block, the explanatory variables are those used in Table 3: the GM's
race dummy and its interaction with the white dummy of the player. The GM's race dummy isequal to 1 if the race of the GM is non-white, and 0 otherwise. Thus, in the first and second
blocks, the coefficient of the white dummy shows the white premium in a team in which theowner or the GM's race is white. In the third block, the explanatory variables are the explanatory
variables used in Table 3: the relative median income of white residents over black residents inthe state where the team is located and its interaction with the white dummy of the player.
*** p<0.01, ** p<0.05, * p<0.1

32

Ratio of White Players.25 .3.2

Ratio of White Players
1980

1990

2000
year

2010

Figure 1: The ratio of white players during 1985–2016.

33

2020

.4
.2
0
−.2
−.4

coefficient of white dummy
90%CI(lower bound)
1980

1990

2000
year

90%CI(upper bound)

2010

2020

Figure 2: Coefficients of white dummy and its 90% confidence intervals. The estimatedcoefficient of the white dummy is plotted when the equation used in Table 3 is applied foreach year separately.

34

.4
.2
0
−.2
−.4

coefficient of white dummy
90%CI(lower bound)
1990

1995

2000

90%CI(upper bound)

2005

2010

2015

yearhat1

Figure 3: Coefficients of white dummy and its 90% confidence intervals in the sample selection model. The estimated coefficient of the white dummy is plotted when the equation usedin the first block of Table 4 is applied separately for each year.

35Appendix A
dependnet variable
VARIABLESWhite dummy

Table A1: Estimated Coefficients in the OLS Modelln (annual salary)
(1)
(2)
(3)
(4)
all
1980s
1990s
2000s

(5)
2010s

0.0817***0.0636-0.0411
0.113**
0.244***(0.0294)(0.0539)(0.0452)(0.0452)(0.0521)
Foreign dummy
0.165***
0.164*
0.134*
0.123**
0.269***(0.0372)(0.0990)(0.0749)(0.0521)(0.0547)Age
0.04030.0285-0.0613-0.02850.0544(0.0485)(0.149)(0.0882)(0.0603)(0.0844)
Age squared-0.00147-0.00166
0.000314
6.29e-05-0.00234(0.000925) (0.00279) (0.00165) (0.00114) (0.00154)
Experience
0.255*** 0.0996*** 0.171***
0.335***
0.339***(0.0157)(0.0333)(0.0268)(0.0234)(0.0292)
Experience squared-0.0106*** 0.00138 -0.00713*** -0.0161*** -0.0133***(0.00118) (0.00266) (0.00191) (0.00169) (0.00197)
Weight0.000751
0.00154-0.00189
0.000641 0.00368**(0.000741) (0.00153) (0.00122) (0.00106) (0.00152)
Height0.01050.001030.0500*** 0.000543-0.0175(0.00701)(0.0136)(0.0103)(0.0105)(0.0135)
Forward Dummy-0.0353
0.0430-0.05530.00967-0.0784(0.0378)(0.0827)(0.0580)(0.0618)(0.0741)
Center Dummy-0.01450.188
0.03460.0863-0.178*(0.0580)(0.118)(0.0879)(0.0940)(0.107)
Number of the games played
0.00217*** 0.00104 0.00304*** 0.00147*
0.00155(0.000529) (0.00113) (0.000889) (0.000810) (0.00118)
Average minutes
0.0345*** 0.0241** 0.0382*** 0.0276*** 0.0427***(0.00521)(0.0117)(0.00779) (0.00767)(0.0102)
Field goals per game
-0.0274-0.00132
0.0289-0.0677-0.0481(0.0308)(0.0665)(0.0450)(0.0468)(0.0683)
Field goal success probability
0.130
0.879*
0.632-0.2540.252(0.263)(0.465)(0.400)(0.569)(0.970)Three point shoots per game
0.03240.03210.03300.0406-0.0415(0.0268)(0.0840)(0.0427)(0.0430)(0.0602)
Three point success probability -0.150**-0.175-0.187*-0.0419-0.171(0.0602)(0.133)(0.102)(0.0971)(0.140)Free throw per game
0.00673-0.0261
0.0678**-0.0132-0.0405(0.0203)(0.0462)(0.0325)(0.0284)(0.0403)
Free throw success probability
0.02150.298
0.407**-0.212*0.110(0.0880)(0.225)(0.167)(0.117)(0.166)
Offensive rebound per game
0.0567*0.00157
0.0758*0.0696-0.0676(0.0319)(0.0881)(0.0452)(0.0491)(0.0730)
Defensive rebound per game
0.01120.0197
0.0948*-0.0236-0.0482(0.0284)(0.0628)(0.0509)(0.0417)(0.0530)
Assists per game
0.03330.04310.0846**0.02930.000580

Turn-over per game
Steals per game
Blocks per game
Personal fouls per gameContribution to the teamAssist percentage
Turn-over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games atleast once in the last threeseasons
1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant
Year dummy
Team dummy(0.0241)0.0675(0.0439)-0.0131(0.0378)0.145***(0.0314)0.00608(0.0216)-0.0169(0.0176)-0.00429(0.00312)-0.00205(0.00333)
0.00238(0.00279)
0.0193***(0.00517)
0.00311(0.00450)-0.00296(0.00452)(0.0627)0.135(0.105)-0.0917(0.0710)0.0776(0.0572)-0.0354(0.0369)
0.00344(0.0420)-0.00812(0.00812)-0.0118(0.00769)-0.656(0.398)0.0219*(0.0117)0.00596(0.0211)0.000431(0.00931)(0.0348)-0.0632(0.0794)
0.00286(0.0597)0.170***(0.0536)
0.00724(0.0336)-0.0630**(0.0299)
0.00670(0.00413)
0.000364(0.00627)
0.00819(0.00566)
0.0144**(0.00656)
0.00369(0.00693)-0.00437(0.00921)(0.0404)
0.0361(0.0648)0.00725(0.0604)
0.0936**(0.0458)
0.0463(0.0316)0.0218(0.0254)-0.0106**(0.00528)-0.00270(0.00498)
0.00135(0.00610)
0.0261***(0.00697)-9.58e-05(0.00761)-0.00630(0.00654)(0.0553)0.0502(0.106)0.0754(0.0733)
0.0595(0.0707)-0.0339(0.0426)
0.0155(0.0388)-0.0126*(0.00646)
0.00282(0.00695)-0.00238(0.0102)0.0189*(0.0105)
0.0120(0.00747)
0.000854(0.00665)

0.471***(0.0472)
0.466***(0.0384)
0.332***(0.0363)0.265***(0.0350)0.148***(0.0381)0.132***(0.0381)0.104**(0.0442)-0.0127(0.0700)-0.0609(0.0599)9.051***(0.872)
Yes
Yes

0.119*(0.0686)0.485***(0.0671)
0.296***(0.0643)0.235***(0.0726)0.160**(0.0627)
0.108(0.0917)0.0348(0.0808)0.0540(0.107)0.0264(0.0909)10.43***(2.268)Yes
Yes

0.178***(0.0475)0.470***(0.0609)0.330***(0.0531)0.260***(0.0521)0.223***(0.0574)
0.267***(0.0584)
0.183***(0.0583)-0.141(0.110)-0.123(0.0909)8.434***(1.433)
Yes
Yes

0.486***(0.0588)0.457***(0.0603)0.340***(0.0611)0.324***(0.0560)
0.152***(0.0576)
0.0580(0.0575)
0.0705(0.0589)0.0642(0.109)0.0111(0.0914)12.78***(1.178)Yes
Yes

0.932***(0.0898)
0.466***(0.0799)
0.346***(0.0710)0.232***(0.0695)
0.128*(0.0728)0.0759(0.0708)
0.0979(0.0914)0.154(0.123)-0.0864(0.101)
12.89***(1.520)Yes
Yes

N
R-squared

9,822
0.681

745
0.751

3,203
0.617

3,650
0.603

Notes: Clustering robust standard errors in parenthes. The error term is clustered as player's level.

*** p<0.01, ** p<0.05, * p<0.1

2,224
0.570

Table A2: Estimated Coefficients in the Heckman Two Step Estimation
Second stage equation
dependent variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
VARIABLES
all
1980s
1990s
2000s
2010s
White dummy
0.0654** 0.0507-0.0635 0.112** 0.207***(0.0297) (0.0566) (0.0433) (0.0451) (0.0494)
Foreign dummy
0.161*** 0.129
0.157** 0.122** 0.244***(0.0388) (0.111)(0.0754) (0.0562) (0.0558)
Age0.03700.0417-0.0886 -0.0299 0.0825(0.0484) (0.152)(0.105)(0.0579) (0.0866)
Age squared-0.00108 -0.00196 0.00119 0.000275 -0.00243(0.000948)(0.00290) (0.00198) (0.00108) (0.00159)
Experience
0.262*** 0.103*** 0.194*** 0.334*** 0.343***(0.0136) (0.0337) (0.0255) (0.0202) (0.0333)
Experience squared-0.0112***0.00144 -0.00908**-0.0160***-0.0135***(0.00113) (0.00289) (0.00198) (0.00148) (0.00210)
Weight0.000504 0.00188 -0.00184 0.000581 0.00174(0.000691)(0.00167) (0.00122) (0.00112) (0.00168)
Height0.01010.00199 0.0466*** -0.000258 -0.00867(0.00705) (0.0154) (0.00984) (0.00944) (0.0135)
Forward Dummy-0.0254 0.0348-0.0576 0.0121-0.0355(0.0408) (0.0877) (0.0710) (0.0635) (0.0835)
Center Dummy-0.0319 0.151-0.0295 0.0808-0.106(0.0598) (0.136)(0.103)(0.0917) (0.118)
Number of the games played-0.00133**0.000267 -0.00108 -0.000206 -0.00298**(0.000663)(0.00140) (0.000924)(0.00102) (0.00126)
Average minutes
0.0233*** 0.0254* 0.0270*** 0.0205** 0.0276**(0.00543) (0.0134) (0.00880) (0.00804) (0.0111)
Field goals per game
0.00709 0.00214 0.0579-0.0589 0.0146(0.0335) (0.0697) (0.0438) (0.0465) (0.0766)
Field goal success probability-0.0158 1.138
0.355-0.1380.234(0.294)(1.012)(0.421)(0.645)(0.960)Three point shoots per game
0.03970.07250.02250.0558-0.0328(0.0276) (0.103)(0.0438) (0.0426) (0.0643)
Three point success probability-0.125* -0.189-0.143-0.0177 -0.141(0.0645) (0.136)(0.104)(0.113)(0.130)Free throw per game
0.0283-0.0271 0.0840*** 0.000989 -0.00459(0.0179) (0.0488) (0.0297) (0.0310) (0.0451)
Free throw success probability-0.0196 0.282
0.391** -0.204* 0.0266(0.0920) (0.276)(0.182)(0.114)(0.169)
Offensive rebound per game
0.0668** 0.0320
0.110*
0.0815* -0.0510(0.0338) (0.0915) (0.0618) (0.0448) (0.0830)
Defensive rebound per game
0.03790.0152
0.0971* -0.0133 -0.0278(0.0280) (0.0696) (0.0570) (0.0415) (0.0571)
Assists per game
0.04000.03690.0935** 0.0253
0.0302(0.0255) (0.0698) (0.0364) (0.0399) (0.0580)
Turn-over per game
0.0771* 0.132-0.0669 0.0640
0.0561(0.0416) (0.101)(0.0794) (0.0674) (0.105)
Steals per game
-0.00844 -0.0635 0.000490 0.000102 0.0356(0.0368) (0.0676) (0.0545) (0.0618) (0.0697)
Blocks per game
0.152*** 0.0868
0.187*** 0.0806* 0.106(0.0297) (0.0596) (0.0513) (0.0436) (0.0766)
Personal fouls per game
-0.0137 -0.0370 -0.0123 0.0345-0.0679(0.0248) (0.0403) (0.0362) (0.0319) (0.0425)

Contribution to the teamAssist percentage
Turn-over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games at least
once in the last three seasons
1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Inverse Mill's Ratio
Constant
Year dummy
Team dummy at t
dependent variable
VARIABLESWhite dummy
Foreign dummyAge
Age squared
Experience
Experience squared-0.0326* -0.0112 -0.0735** 0.0190-0.00667(0.0187) (0.0441) (0.0298) (0.0260) (0.0434)-0.00333 -0.00515 0.00570 -0.0103** -0.0126**(0.00341) (0.00904) (0.00443) (0.00519) (0.00622)-0.00186 -0.0127 0.00365 -0.00359 -0.000317(0.00351) (0.00849) (0.00658) (0.00535) (0.00664)
0.00385 -0.624
0.0102-0.000556 -0.00201(0.00337) (0.859)(0.00628) (0.00689) (0.0106)
0.0138** 0.0193* 0.00961 0.0223*** 0.0133(0.00568) (0.0115) (0.00687) (0.00763) (0.0103)
0.00190 0.00188 -0.000219 -0.00241 0.00633(0.00550) (0.0185) (0.0129) (0.00668) (0.0102)-0.00491 0.00338 -0.00477 -0.00703 0.00201(0.00406) (0.0106) (0.0112) (0.00676) (0.00792)
0.386*** 0.220*** 0.170*** 0.440*** 0.722***(0.0418) (0.0755) (0.0485) (0.0696) (0.0974)
0.448*** 0.475*** 0.471*** 0.444*** 0.439***(0.0410) (0.0774) (0.0520) (0.0649) (0.0764)
0.300*** 0.291*** 0.315*** 0.326*** 0.276***(0.0369) (0.0685) (0.0589) (0.0622) (0.0788)
0.207*** 0.220*** 0.173*** 0.304*** 0.146**(0.0344) (0.0703) (0.0559) (0.0567) (0.0734)
0.113*** 0.139** 0.167*** 0.141** 0.0977(0.0373) (0.0619) (0.0574) (0.0608) (0.0846)
0.103*** 0.106
0.210*** 0.0473
0.0713(0.0355) (0.0936) (0.0570) (0.0534) (0.0891)
0.0842* 0.0145
0.169** 0.0498
0.108(0.0457) (0.102)(0.0721) (0.0706) (0.0977)
0.00159 0.0297-0.0882 0.0550
0.210*(0.0690) (0.103)(0.122)(0.107)(0.117)-0.0434 0.0301-0.0924 0.00959 -0.0612(0.0615) (0.117)(0.0985) (0.0868) (0.108)-0.748*** -0.238* -0.775*** -0.419** -0.899***(0.112)(0.128)(0.148)(0.163)(0.166)9.587*** 10.16*** 10.11*** 13.43*** 12.95***(0.929)(2.371)(1.669)(1.083)(1.370)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Selection equation
dummy variable indicating selection
(1)
(2)
(3)
(4)
(5)
all
1980s
1990s
2000s
2010s
0.0909* 0.488
0.112
0.04600.103(0.0549) (0.409)(0.0801) (0.0915) (0.121)-0.0237 0.246-0.146-0.0193 0.0528(0.0585) (3.137)(0.156)(0.0930) (0.102)-0.224*** -1.250-0.0958 -0.237** -0.289*(0.0642) (1.300)(0.185)(0.120)(0.164)0.00251** 0.0252
0.000197 0.00255 0.00367(0.00113) (0.0244) (0.00324) (0.00219) (0.00292)-0.00258 0.146-0.0804 0.0305
0.0164(0.0195) (0.294)(0.0524) (0.0361) (0.0488)-0.000517 -0.0324 0.00527 -0.00265 -0.00217(0.00133) (0.0242) (0.00332) (0.00251) (0.00308)

Weight
HeightForward DummyCenter Dummy
Number of the games played
Average minutes
Field goals per game
Field goal success probability
Three point shoots per gameThree point success probability
Free throw per game
Free throw success probability
Offensive rebound per game
Defensive rebound per game
Assists per gameTurn-over per game
Steals per game
Blocks per game
Personal fouls per gameContribution to the teamAssist percentage
Turn-over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games at least
once in the last three seasons

0.00238* 0.00404 0.000664 0.000629 0.00673**(0.00127) (0.0105) (0.00301) (0.00270) (0.00322)-0.00438 -0.0640 0.0150
0.00273 -0.0301(0.0134) (0.107)(0.0262) (0.0208) (0.0266)-0.0620 0.110-0.0121 0.0165-0.162(0.0849) (0.612)(0.127)(0.124)(0.154)0.07330.778
0.225
0.0827-0.149(0.116)(1.025)(0.198)(0.175)(0.260)0.0132*** 0.0185** 0.0136*** 0.0118*** 0.0147***(0.000956)(0.00765) (0.00150) (0.00150) (0.00225)
0.0343*** -0.0165 0.0320* 0.0347** 0.0394*(0.0113) (0.134)(0.0167) (0.0173) (0.0224)-0.126* -0.219-0.0933 0.0328-0.282*(0.0683) (0.877)(0.0936) (0.110)(0.149)-0.351-6.086
0.0529-1.282-0.0406(0.288)(6.138)(0.525)(1.104)(1.267)-0.0290 -0.544
0.0150-0.0605 0.00259(0.0659) (0.981)(0.121)(0.128)(0.138)-0.195* -0.0477 -0.330** -0.191-0.0197(0.110)(1.273)(0.157)(0.193)(0.284)-0.122*** 0.214-0.109-0.127-0.151(0.0454) (0.542)(0.0766) (0.0823) (0.0967)-0.0527 -0.586-0.0230 -0.177
0.0809(0.106)(1.361)(0.221)(0.203)(0.232)0.00861 -0.923-0.0812 0.0733
0.150(0.0769) (1.181)(0.135)(0.171)(0.173)-0.136** 0.437-0.0404 -0.126-0.153(0.0586) (1.045)(0.103)(0.0978) (0.131)-0.0425 -0.0441 -0.0190 0.0413-0.112(0.0533) (0.801)(0.0985) (0.0741) (0.118)-0.0560 -0.838
0.0146-0.242* 0.0461(0.101)(1.139)(0.178)(0.136)(0.219)0.0461-0.4380.06130.07980.218(0.0804) (0.706)(0.134)(0.156)(0.178)0.04780.315
0.00285 0.257*-0.267(0.0863) (0.975)(0.126)(0.150)(0.190)0.0811** 0.356
0.05310.08000.103(0.0403) (0.277)(0.0761) (0.0657) (0.0919)
0.107*** 0.341
0.09270.04450.140*(0.0359) (0.539)(0.0609) (0.0584) (0.0828)-0.00402 -0.0203 -0.000966 -0.000995 -0.00836(0.00369) (0.0690) (0.00635) (0.00637) (0.00761)
0.00128 0.0422-0.00835 0.00880 0.00682(0.00405) (0.0497) (0.00864) (0.00611) (0.0110)-0.000444 -1.103-0.00148 0.0131-0.00717(0.00335) (7.563)(0.00672) (0.0127) (0.0143)
0.0158*** 0.0838
0.0137* 0.0165* 0.0187(0.00577) (0.0948) (0.00767) (0.0100) (0.0165)-0.00423 0.0857-0.00510 -0.000421 -0.00876(0.00689) (0.127)(0.0104) (0.0156) (0.0171)
0.00357 -0.125
0.00383 0.00438 -0.00469(0.00516) (0.0988) (0.0101) (0.00758) (0.00984)
0.603*** 0.723

0.08640.471*

1.280(0.129)(0.162)(0.272)(0.785)(2.409)1 ≤number of draft pick ≤5

0.121
0.01540.04050.07460.203(0.0792) (0.624)(0.151)(0.133)(0.163)6 ≤number of draft pick ≤10
0.195** 0.0319
0.101
0.203
0.389**(0.0833) (0.440)(0.130)(0.128)(0.151)11 ≤number of draft pick ≤15
0.314*** 0.454
0.559*** 0.140
0.293**(0.0660) (0.628)(0.137)(0.118)(0.145)16 ≤number of draft pick ≤20
0.168*** 0.589
0.224*
0.107
0.135(0.0620) (0.557)(0.114)(0.131)(0.181)21 ≤number of draft pick ≤25
0.09540.09500.215
0.06400.0214(0.0890) (0.429)(0.131)(0.169)(0.124)26 ≤number of draft pick ≤30
0.06660.696
0.04840.183-0.113(0.0620) (2.022)(0.121)(0.133)(0.178)31 ≤number of draft pick ≤35-0.0496 0.729-0.1580.0290-0.168(0.0793) (1.594)(0.141)(0.167)(0.188)36 ≤number of draft pick ≤40-0.0848 0.0724-0.158-0.0255 -0.112(0.0986) (1.822)(0.144)(0.155)(0.182)
Team dummy at t-1
YES
YES
YES
YES
YES
Year dummy
YES
YES
YES
YES
YES
N
12302
1422
3867
4307
2706
Notes: Clustering robust standard errors are presetned in parenthes. The error term is clusteredas player's level. The standard error is calculated based on clustered bootstrap.
*** p<0.01, ** p<0.05, * p<0.1

Appendix B
Appendix B is for the purpose of refereeing, not for publication. It will be put on the journal's
website and author's website.

Table B1: Estimated Coefficients of Important Variables in the 50 % Quantile Regression Modelln (annual salary)dependent variable
VARIABLESWhite dummy
Foreign dummyAge
Age squared
Experience
Experience squared
Weight

(1)

(2)

(3)

(4)

(5)

all

1980s

1990s

2000s

2010s

0.0436

0.0598‐0.103**0.130***

0.186***(0.0297)(0.0789)(0.0480)(0.0477)(0.0616)0.123***

0.167

0.03340.04590.277***(0.0394)(0.113)(0.0910)(0.0530)(0.0698)

0.09340.129

0.0427‐0.0299

0.113(0.0591)(0.264)(0.111)(0.0660)(0.103)‐0.00248**‐0.00313‐0.00153

0.0000896‐0.00337*(0.00113)(0.00516)(0.00218)(0.00129)(0.00196)

0.266***

0.06060.160***

0.361***

0.333***(0.0189)(0.0663)(0.0351)(0.0284)(0.0289)‐0.0110***0.00353‐0.00596**‐0.0178***‐0.0128***(0.00153)(0.00612)(0.00270)(0.00242)(0.00235)

0.00133

0.000176‐0.000639

0.000954

0.00474**(0.000821)(0.00270)(0.00140)(0.00131)(0.00187)

0.0123*0.006190.0478***0.0114‐0.0357**(0.00696)(0.0250)(0.0113)(0.0114)(0.0150)

Forward Dummy‐0.00837

0.0262‐0.0987

0.002240.0657(0.0401)(0.110)(0.0671)(0.0736)(0.0912)Center Dummy

0.000807

0.193

0.03200.00219‐0.0158(0.0572)(0.161)(0.0964)(0.111)(0.147)

Number of the games played

0.00161***0.001170.00282***0.000727

0.000556(0.000513)(0.00148)(0.00108)(0.000817)(0.00124)

0.0423***0.01940.0400***0.0404***0.0537***(0.00561)(0.0165)(0.00862)(0.00860)(0.0117)‐0.0633**‐0.00881‐0.0302‐0.110**‐0.127(0.0261)(0.107)(0.0582)(0.0473)(0.0866)‐0.317

0.637

0.577

0.118

0.928

(0.253)(0.675)(0.549)(0.771)(1.397)0.0112‐0.0191

0.00250

0.00302‐0.0602(0.0274)(0.178)(0.0500)(0.0458)(0.0844)‐0.171**‐0.237‐0.0832‐0.120‐0.152(0.0712)(0.159)(0.114)(0.124)(0.149)‐0.00990‐0.0582

0.0521‐0.0337‐0.0687(0.0189)(0.0902)(0.0429)(0.0307)(0.0473)‐0.0800

0.160

0.278

‐0.1060.143(0.0834)(0.395)(0.179)(0.125)(0.154)0.0155‐0.0199

0.02300.0738‐0.178**(0.0255)(0.137)(0.0470)(0.0450)(0.0718)

HeightAverage minutes
Field goals per game
Field goal success probability
Three point shoots per gameThree point success probability
Free throw per game
Free throw success probability
Offensive rebound per gameDefensive rebound per game
Assists per gameTurn-over per game
Steals per game
Blocks per game
Personal fouls per gameContribution to the teamAssist percentage
Turn-over percentage
True shooting percentage‐0.0165

0.02330.0522‐0.0666‐0.0564(0.0278)(0.0983)(0.0532)(0.0462)(0.0537)0.01580.00779

0.0732*‐0.0155‐0.0591(0.0200)(0.0788)(0.0380)(0.0420)(0.0580)0.06040.174

0.04440.03180.0728(0.0391)(0.158)(0.0855)(0.0663)(0.123)‐0.0932**‐0.103‐0.0591‐0.00948‐0.0119(0.0378)(0.105)(0.0767)(0.0686)(0.0820)0.118***

0.0182

0.117*

0.07350.0169(0.0301)(0.0905)(0.0681)(0.0465)(0.101)0.00882‐0.0379

0.01380.0217‐0.0196(0.0209)(0.0469)(0.0339)(0.0372)(0.0534)

0.00379

0.0257‐0.0375

0.03380.0502(0.0146)(0.0738)(0.0396)(0.0268)(0.0411)‐0.00169‐0.00254

0.00230‐0.00212‐0.0101(0.00243)(0.0132)(0.00396)(0.00617)(0.00661)‐0.00237‐0.0168‐0.00638

0.000480‐0.00127(0.00264)(0.0116)(0.00624)(0.00511)(0.00768)

0.00523*‐1.092*0.0111**‐0.00331‐0.0111(0.00284)(0.620)(0.00556)(0.00788)(0.0142)0.0261***0.01340.0166*0.0357***0.0288**(0.00533)(0.0178)(0.00936)(0.00862)(0.0119)0.001390.00584

0.00524‐0.00930

0.0143**(0.00262)(0.0329)(0.00461)(0.00709)(0.00634)

0.00207‐0.000231‐0.00212

0.000982‐0.00237(0.00503)(0.0158)(0.00687)(0.00830)(0.00697)

0.396***

0.09890.136**

0.470***

0.798***(0.0489)(0.0981)(0.0645)(0.0638)(0.0971)

0.428***

0.522***

0.438***

0.433***

0.471***(0.0410)(0.118)(0.0654)(0.0614)(0.0909)

6 ≤number of draft pick ≤100.308***

0.368***

0.286***

0.348***

0.365***(0.0418)(0.103)(0.0592)(0.0713)(0.0875)

11 ≤number of draft pick ≤15

0.273***

0.295**

0.213***

0.313***

0.284***(0.0376)(0.119)(0.0606)(0.0574)(0.0927)

16 ≤number of draft pick ≤20

0.123***

0.184*

0.159**

0.181***

0.0414(0.0401)(0.0948)(0.0734)(0.0618)(0.104)0.136***

0.135

0.220***

0.02140.0182(0.0363)(0.0996)(0.0647)(0.0651)(0.0916)

0.0939*0.09740.160**

0.0896

0.0544(0.0504)(0.0916)(0.0739)(0.0799)(0.0902)‐0.0899

0.0482‐0.164*‐0.0269‐0.0307(0.0596)(0.252)(0.0992)(0.116)(0.174)

Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games at least
once in the last three seasons
1 ≤number of draft pick ≤5

21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant‐0.160***0.0771‐0.200‐0.119‐0.273***(0.0616)(0.146)(0.179)(0.120)(0.0956)8.350***

9.441**

7.296***

11.98***

13.33***(0.954)(4.211)(1.765)(1.258)(1.654)

Year dummy

Yes

Yes

Yes

Yes

Yes

Team dummy

Yes

Yes

Yes

Yes

Yes

Observations

9822

745

3203

3650

2224

R-squared

0.676

0.737

0.606

0.594

0.556

Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's level.
*** p<0.01, ** p<0.05, * p<0.1

Table B2: Estimated Coefficients of Important Variables in the 25 % Quantile Regression Modeldependnet variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
VARIABLES
all
1980s
1990s
2000s
2010s
White Dummy
Foreign DummyAge
Age squared
Experience
Experience squared
WeightHeightForward DummyCenter Dummy
Number of the games
Average minutes
Field goal
Field goal success probability
Three point shoots
Three point success probability
Free throw
Free throw success probability
Offensive reboundDefensive reboundAssists
Turn-over
Steals

0.0900***(0.0287)
0.116***(0.0416)0.0472(0.0550)-0.00191*(0.00109)0.258***(0.0170)-0.0103***(0.00139)-0.000283(0.000801)
0.0104(0.00727)-0.0361(0.0357)
0.0162(0.0551)
0.00286***(0.000642)
0.0322***(0.00644)-0.0138(0.0397)
0.157(0.321)0.0379(0.0375)-0.116(0.0788)0.0114(0.0294)
0.0442(0.130)0.0500(0.0356)
0.0380(0.0310)0.0431(0.0297)
0.0354(0.0626)-0.0333

0.0251(0.0743)
0.187*(0.112)0.227(0.170)-0.00516(0.00321)
0.0419(0.0435)
0.00539(0.00336)
0.00276(0.00218)
0.00388(0.0205)-0.0706(0.108)0.0474(0.147)0.00272*(0.00148)
0.0173(0.0159)
0.0265(0.0608)
0.328(0.414)0.0640(0.174)-0.294(0.236)-0.0230(0.0501)
0.0383(0.237)-0.00140(0.102)0.0243(0.0859)
0.0227(0.0731)0.144(0.126)-0.0739-0.0310(0.0562)0.167**(0.0798)-0.0139(0.0846)-0.000673(0.00161)
0.167***(0.0303)-0.00633***(0.00201)-0.000286(0.00133)
0.0330***(0.0128)-0.0560(0.0640)-0.00874(0.107)0.00515***(0.00126)
0.0365***(0.0113)0.0199(0.0649)
0.642(0.551)0.0806(0.0533)-0.0474(0.102)0.0896**(0.0411)0.264(0.205)
0.0618(0.0453)0.0809(0.0595)
0.0839*(0.0451)-0.0646(0.107)-0.0249

0.149***(0.0500)
0.00552(0.0576)-0.0420(0.0818)0.0000781(0.00160)
0.329***(0.0337)-0.0160***(0.00258)-0.00159(0.00121)
0.00993(0.0127)-0.0105(0.0717)0.0982(0.104)0.00151(0.00106)0.0228**(0.00901)-0.0330(0.0620)0.248(1.447)0.101*(0.0603)-0.0666(0.0932)
0.00862(0.0392)-0.0742(0.251)0.108*(0.0615)0.0244(0.0562)
0.0689(0.0502)-0.0753(0.0910)0.04500.192***(0.0686)0.196**(0.0773)-0.0571(0.111)-0.000582(0.00211)
0.356***(0.0400)-0.0149***(0.00282)
0.00264(0.00182)-0.0189(0.0145)0.0300(0.0903)-0.00577(0.130)0.00276*(0.00145)
0.0465***(0.0138)-0.112(0.0766)-0.391(1.130)-0.137**(0.0697)-0.0950(0.207)-0.0938*(0.0534)
0.139(0.279)-0.0913(0.0828)-0.0874(0.0650)-0.0266(0.0597)
0.159(0.110)0.0167

Blocks
Personal fouls
Contribution to the teamAssist percentage
Turn-over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games
1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant
Year dummy
Team dummy
Observations
R-squared(0.0487)
0.143***(0.0381)-0.00473(0.0264)-0.0241(0.0228)-0.00384(0.00318)-0.00354(0.00445)
0.00323(0.00343)
0.0184***(0.00619)
0.00713***(0.00276)-0.00400(0.00342)
0.445***(0.0603)0.620***(0.0513)0.408***(0.0478)
0.374***(0.0462)0.205***(0.0475)0.207***(0.0498)
0.175***(0.0540)-0.0556(0.0553)-0.0562(0.0568)
9.014***(0.945)
Yes
Yes(0.0852)-0.00801(0.0724)-0.0358(0.0558)
0.00993(0.0436)-0.0114(0.00961)-0.00889(0.00855)-0.439(0.570)0.00517(0.0117)-0.000687(0.0178)0.0000564(0.0132)
0.0273(0.110)0.631***(0.0908)
0.396***(0.0973)
0.242**(0.0982)
0.286***(0.0854)0.129(0.131)0.0208(0.144)-0.0168(0.124)0.0341(0.116)7.823***(2.866)
Yes
Yes(0.0697)
0.166**(0.0657)-0.00219(0.0371)-0.0547(0.0363)0.00480(0.00545)-0.00261(0.00587)
0.000781(0.00557)
0.00819(0.0146)0.0107**(0.00517)-0.00192(0.00751)
0.144**(0.0714)0.580***(0.0924)
0.395***(0.0800)
0.354***(0.0761)0.270***(0.0803)0.333***(0.0836)
0.222**(0.0940)-0.160(0.115)-0.133(0.0992)
8.648***(1.672)
Yes
Yes(0.0645)
0.108*(0.0576)
0.0590*(0.0350)-0.00718(0.0329)-0.00972(0.00677)-0.0000715(0.0106)
0.000129(0.0137)
0.0290***(0.00991)
0.00467(0.00993)-0.0101(0.0106)
0.573***(0.0800)
0.626***(0.0751)0.445***(0.0799)
0.468***(0.0892)
0.238***(0.0773)
0.165**(0.0783)
0.122(0.103)0.0415(0.125)-0.00968(0.0788)
12.46***(1.495)
Yes
Yes(0.0810)-0.0638(0.0948)-0.0283(0.0565)0.0376(0.0405)-0.0137*(0.00828)-0.00484(0.0120)0.00363(0.0113)0.0285**(0.0125)
0.0120(0.0122)0.000878(0.0116)
1.061***(0.116)
0.606***(0.127)0.352**(0.145)0.305***(0.114)0.162(0.118)0.143(0.115)0.121(0.129)0.108(0.110)-0.180*(0.103)14.34***(1.723)
Yes
Yes

9822
0.674

745
0.729

3203
0.604

3650
0.587

2224
0.549

Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's level.
*** p<0.01, ** p<0.05, * p<0.1

Table B3: Estimated Coefficients of the 75 % Quantile Regression Modelln (annual salary)dependnet variable
(1)
(2)
(3)
(4)
VARIABLES
all
1980s
1990s
2000s
White dummy
0.0282-0.00582-0.07510.0848*Foreign dummyAge
Age squared
Experience
Experience squared
WeightHeightForward DummyCenter Dummy
Number of the games played
Average minutes
Field goals per game
Field goal success probability
Three point shoots per gameThree point success probability
Free throw per game
Free throw success probability
Offensive rebound per game
Defensive rebound per game
Assists per gameTurn-over per game
Steals per game

(0.0322)0.113***(0.0406)
0.143**(0.0645)-0.00299**(0.00126)0.225***(0.0194)-0.00917***(0.00155)
0.00148*(0.000884)
0.0202**(0.00857)-0.0705(0.0444)-0.0773(0.0640)-0.0000955(0.000612)
0.0337***(0.00647)-0.0420(0.0406)-0.343(0.418)-0.00262(0.0337)-0.183***(0.0693)-0.0137(0.0260)-0.0131(0.118)-0.0221(0.0331)-0.0259(0.0395)-0.0104(0.0363)
0.122**(0.0603)-0.0187(0.0563)0.101(0.157)-0.132(0.156)0.00159(0.00277)
0.136***(0.0467)-0.00222(0.00266)
0.000619(0.00180)
0.00736(0.0159)
0.0859(0.102)0.206(0.149)-0.000451(0.00132)
0.0150(0.0118)0.0886(0.0932)
1.500**(0.634)
0.0975(0.109)-0.114(0.149)0.0274(0.0676)
0.536**(0.273)0.0896(0.0899)
0.0767(0.0908)
0.118(0.0770)0.0424(0.116)-0.0352(0.0508)0.0159(0.121)0.0504(0.103)-0.00150(0.00185)
0.134***(0.0316)-0.00485**(0.00201)-0.00136(0.00144)
0.0558***(0.0128)-0.110*(0.0627)-0.0374(0.0943)0.000184(0.00121)
0.0358***(0.00919)-0.0505(0.0503)-0.188(0.468)0.00467(0.0561)-0.264**(0.104)-0.00548(0.0340)0.319**(0.157)0.0558(0.0464)-0.0202(0.0505)
0.00876(0.0427)
0.0631(0.0890)-0.0418(0.0444)0.0931(0.0612)-0.00723(0.0554)-0.0000836(0.00104)
0.359***(0.0259)-0.0176***(0.00171)0.00137(0.00113)
0.00600(0.00966)-0.00270(0.0644)0.0428(0.0932)-0.000454(0.00106)
0.0351***(0.00850)-0.104*(0.0573)
0.351(0.368)-0.00391(0.0400)-0.158(0.120)-0.0290(0.0333)
0.0311(0.140)0.00598(0.0476)-0.0761*(0.0460)-0.00844(0.0390)0.0452(0.0797)-0.0161(5)2010s0.173***(0.0621)0.272***(0.0627)0.215**(0.102)-0.00468***(0.00181)0.306***(0.0404)-0.0120***(0.00254)
0.00428***(0.00155)-0.0173(0.0150)-0.0370(0.0783)-0.178(0.124)-0.00119(0.00125)0.0257**(0.0123)-0.0131(0.0843)
1.295(1.253)0.0649(0.0738)-0.0701(0.175)0.0620(0.0521)0.0466(0.128)-0.0455(0.0733)
0.0223(0.0679)-0.000881(0.0718)
0.0386(0.116)
0.0598

Blocks per game
Personal fouls per gameContribution to the teamAssist percentage
Turn-over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games at least
once in the last three seasons
1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant
Year dummy
Team dummy
Observations
R-squared(0.0432)
0.107***(0.0371)-0.00395(0.0229)
0.0124(0.0230)-0.000897(0.00536)-0.00442(0.00482)
0.00281(0.00375)
0.0168**(0.00687)
0.00869*(0.00504)
0.00210(0.00549)(0.101)0.146*(0.0816)-0.0784(0.0488)-0.0549(0.0635)-0.00752(0.00908)-0.00640(0.0104)-1.146***(0.435)0.0140(0.0121)-0.00272(0.0148)0.0103(0.0107)(0.0697)
0.117**(0.0562)0.0210(0.0354)
0.00714(0.0273)
0.00671(0.00626)-0.00585(0.00859)
0.0104*(0.00627)
0.0158*(0.00958)-0.00448(0.00419)
0.00301(0.00857)(0.0616)
0.0519(0.0521)0.0361(0.0309)
0.0449(0.0300)-0.00684(0.00475)-0.00266(0.00446)-0.00923*(0.00557)
0.0316***(0.0106)-0.00888(0.00743)
0.00533(0.00481)(0.0892)
0.144(0.0902)-0.0762*(0.0442)-0.00395(0.0468)-0.00636(0.00635)
0.000603(0.00718)-0.0154(0.0122)0.000708(0.00997)
0.00516(0.00524)-0.000732(0.00972)

0.327***

0.07160.168***

0.316***

0.594***(0.0443)
0.339***(0.0439)0.233***(0.0399)0.140***(0.0391)
0.0460(0.0439)0.00325(0.0428)-0.00766(0.0547)-0.0547(0.133)-0.0830(0.0811)7.414***(1.094)
Yes
Yes
9822
0.665

(0.0869)
0.454***(0.0856)
0.391***(0.0745)0.312***(0.0573)
0.209**(0.0822)0.146(0.101)-0.00625(0.106)0.0499(0.138)-0.0322(0.116)12.36***(2.329)
Yes
Yes
745
0.721

(0.0580)
0.334***(0.0615)0.232***(0.0661)
0.141**(0.0673)
0.0831(0.0580)0.102(0.0639)0.0241(0.0688)-0.202*(0.119)-0.253**(0.107)7.436***(1.650)
Yes
Yes
3203
0.588

(0.0595)
0.368***(0.0587)0.254***(0.0557)
0.199***(0.0613)0.0505(0.0555)-0.0582(0.0574)-0.0450(0.0601)
0.00350(0.144)-0.0589(0.105)
12.58***(1.058)
Yes
Yes
3650
0.582

(0.105)0.517***(0.0786)
0.345***(0.0872)0.130(0.0826)
0.0451(0.0973)-0.0526(0.0722)-0.0613(0.0988)0.133(0.0869)-0.248(0.156)
11.64***(1.947)
Yes
Yes
2224
0.534

Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's level.
*** p<0.01, ** p<0.05, * p<0.1

Table B4: Estimated Coefficients with Restricted Sample (Experience≥5)dependnet variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
VARIABLES
all
1980s
1990s
2000s
2010s
White Dummy
0.103***
0.0527-0.0183
0.168**
0.280***(0.0383)(0.0757)(0.0561)(0.0666)(0.0717)
Foreign Dummy
0.0985**-0.0549-0.08120.09840.203***(0.0493)(0.119)(0.0842)(0.0790)(0.0732)
Age
0.123
0.146
0.278
0.0899-0.0513(0.0987)(0.332)(0.208)(0.129)(0.150)Age squared-0.00312* -0.00380-0.00557-0.00205-0.00116(0.00169) (0.00568) (0.00338) (0.00221) (0.00254)
Experience
0.167***
0.06530.111
0.210*** 0.211***(0.0359)(0.107)(0.0734)(0.0541)(0.0626)
Experience squared-0.00530*** 0.00315-0.00240 -0.00871*** -0.00546*(0.00202) (0.00593) (0.00378) (0.00308) (0.00309)
Weight0.000236 0.00441* -0.00208 0.000375
0.00291(0.000946) (0.00236) (0.00174) (0.00142) (0.00205)
Height0.0162*0.01060.0456*** 0.00574-0.00933(0.00887) (0.0215)(0.0132)(0.0139)(0.0189)
Forward Dummy-0.0471-0.0377-0.0497-0.0402-0.0590(0.0503)(0.124)(0.0729)(0.0869)(0.104)Center Dummy-0.00966-0.0194
0.0826-0.00387-0.157(0.0763)(0.171)(0.110)(0.134)(0.144)
Number of the games-0.000583 0.000678 0.000247 -0.000932 -0.00134(0.000748) (0.00192) (0.00125) (0.00124) (0.00142)
Average minutes
0.0481***0.01790.0623*** 0.0464*** 0.0509***(0.00725) (0.0195)(0.0117)(0.0110)(0.0145)
Field goal
-0.0698
0.0982-0.0460-0.177***0.0169(0.0429)(0.114)(0.0542)(0.0635)(0.0960)
Field goal success probability
0.06701.596*
0.710
0.284-1.973(0.413)(0.860)(0.562)(1.141)(1.280)Three point shoots
0.01930.102
0.03500.0248-0.0900(0.0325)(0.107)(0.0568)(0.0607)(0.0787)
Three point success probability -0.232***-0.151-0.474***0.0379-0.299*(0.0840)(0.193)(0.142)(0.140)(0.178)
Free throw-0.00483
0.01490.0538-0.0226-0.0528(0.0244)(0.0684)(0.0379)(0.0363)(0.0510)
Free throw success probability
0.01740.185
0.196-0.3600.180(0.139)(0.456)(0.238)(0.220)(0.273)
Offensive rebound-0.01120.0131-0.0753
0.00693-0.0459(0.0481)(0.180)(0.0836)(0.0794)(0.0955)
Defensive rebound-0.0473
0.124
0.00188-0.101-0.0404(0.0349)(0.113)(0.0652)(0.0649)(0.0709)
Assists-0.0247
0.187-0.0523-0.0351-0.0244(0.0308)(0.115)(0.0454)(0.0502)(0.0806)
Turn-over
0.103*
0.04770.00923
0.0631
0.0544(0.0542)(0.167)(0.0894)(0.0810)(0.133)
Steals-0.0451-0.118-0.0500-0.04290.0653(0.0440)(0.125)(0.0745)(0.0677)(0.0967)
Blocks
0.113***
0.118
0.163**
0.07240.101

(0.0383)(0.0994)(0.0637)(0.0596)(0.0958)
0.0143-0.0659
0.0302
0.0611-0.0361(0.0258)(0.0532)(0.0361)(0.0413)(0.0564)
Contribution to the team
0.0180-0.0606-0.00953 0.0712**
0.00667(0.0227)(0.0686)(0.0335)(0.0335)(0.0525)
Assist percentage
0.00124-0.0192 0.0233*** -0.00763-0.00678(0.00475) (0.0185) (0.00868) (0.00725) (0.0107)
Turn-over percentage-0.0100** -0.00677-0.00999-0.0133*
0.00424(0.00503) (0.0178) (0.00782) (0.00737) (0.00991)
True shooting percentage-0.00242-0.234-0.00712-0.00717
0.0167(0.00436)(0.743)(0.00700) (0.0120)(0.0137)
Usage percentage
0.0163*0.01420.00991 0.0426*** 0.00323(0.00872) (0.0235) (0.00814) (0.0121)(0.0158)
Offensive rebound percentage
0.00756
0.03630.02220.00552
0.00942(0.00992) (0.0499)(0.0193)(0.0175)(0.0150)
Defensive rebound percentage
0.00737-0.0133
0.006710.004200.00180(0.00694) (0.0179)(0.0152)(0.0123)(0.0122)
Selected in All Star Games
0.483***
0.206**
0.182*** 0.462*** 0.952***(0.0509)(0.0845)(0.0516)(0.0636)(0.0993)
1 ≤number of draft pick ≤50.259*** 0.361*** 0.308*** 0.296***0.178*(0.0448)(0.0790)(0.0699)(0.0752)(0.0948)
6 ≤number of draft pick ≤100.176***
0.131
0.151**
0.216***
0.208**(0.0486)(0.0891)(0.0705)(0.0801)(0.0966)
11 ≤number of draft pick ≤15
0.129***
0.257**
0.105
0.193**-0.00162(0.0457)(0.111)(0.0773)(0.0780)(0.0980)16 ≤number of draft pick ≤20
0.02780.213*
0.0681
0.108-0.0427(0.0524)(0.115)(0.0824)(0.0807)(0.0989)21 ≤number of draft pick ≤25
0.114**
0.130
0.236**
0.119
0.0348(0.0539)(0.138)(0.0925)(0.0867)(0.0941)
26 ≤number of draft pick ≤30
0.08890.01100.08600.164**
0.0380(0.0551)(0.108)(0.0800)(0.0800)(0.126)31 ≤number of draft pick ≤35
0.01730.0507-0.1610.185
0.244(0.103)(0.172)(0.146)(0.146)(0.208)36 ≤number of draft pick ≤40-0.1270.0882-0.248*-0.0176-0.201(0.0829)(0.104)(0.145)(0.134)(0.133)
Constant
8.059***
7.398
4.058
11.09*** 15.05***(1.574)(5.135)(2.978)(2.130)(2.535)
Year dummy
Yes
Yes
Yes
Yes
Yes
Team dummy
Yes
Yes
Yes
Yes
Yes
Observations
5,171
360
1,670
1,940
1,201
R-squared
0.669
0.748
0.584
0.516
0.514
Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's
level.
*** p<0.01, ** p<0.05, * p<0.1
Personal fouls

Table B5: Estimated Coefficients with Restricted Sample (US-born Players)
dependnet variableln (annual salary)
VARIABLESWhite DummyAge
Age squared
Experience
Experience squared
WeightHeightForward DummyCenter Dummy
Number of the games
Average minutes
Field goal
Field goal success probability
Three point shoots
Three point success probability
Free throw
Free throw success probability
Offensive reboundDefensive reboundAssists
Turn‐over
StealsBlocks
Personal fouls
Contribution to the teamAssist percentage(1)
all0.0552(0.0342)‐0.0162(0.0580)‐0.000717(0.00111)
0.276***(0.0179)‐0.0113***(0.00135)
0.000416(0.000816)
0.0104(0.00766)‐0.0458(0.0395)
0.0128(0.0613)0.00222***(0.000557)
0.0385***(0.00528)‐0.0571*(0.0305)
0.0798(0.293)0.0179(0.0282)‐0.151**(0.0659)0.000869(0.0209)‐0.0344(0.0947)0.0411(0.0337)0.00457(0.0302)0.0256(0.0251)0.0471(0.0475)‐0.0344(0.0403)0.136***(0.0339)0.000528(0.0225)‐0.00414(0.0180)‐0.00548(2)
1980s
0.0669(0.0568)‐0.0171(0.156)‐0.000822(0.00292)
0.111***(0.0350)0.000466(0.00278)
0.00133(0.00166)
0.00276(0.0143)0.0289(0.0852)
0.165(0.121)0.00112(0.00118)
0.0276**(0.0121)‐0.00386(0.0681)0.897*(0.472)0.0288(0.0862)‐0.185(0.141)‐0.0186(0.0468)
0.311(0.230)0.0174(0.0907)0.0278(0.0647)
0.0561(0.0633)
0.0843(0.103)‐0.101(0.0727)
0.0717(0.0579)‐0.0397(0.0384)‐0.00224(0.0423)‐0.00916

(3)
(4)
1990s
2000s
‐0.04490.129**(0.0483)(0.0538)‐0.0956‐0.0843(0.0945)(0.0694)0.000623
0.000891(0.00177)(0.00131)
0.203***
0.350***(0.0283)(0.0262)‐0.00812*** ‐0.0167***(0.00202)(0.00186)‐0.00195
1.63e‐05(0.00132)(0.00119)0.0492***0.00660(0.0111)(0.0117)‐0.0450‐0.0155(0.0589)(0.0668)
0.0667
0.0984(0.0905)(0.101)0.00298*** 0.00154*(0.000919) (0.000884)
0.0416*** 0.0316***(0.00823)(0.00791)‐0.0185‐0.109**(0.0470)(0.0533)
0.594
0.107
(0.434)(0.742)0.0171
0.0672(0.0438)(0.0494)‐0.177‐0.0655(0.112)(0.105)0.04360.00181(0.0319)(0.0311)0.347*‐0.285**(0.178)(0.127)
0.06140.0888*(0.0478)(0.0529)
0.0933*‐0.0695(0.0518)(0.0432)
0.0758**0.0180(0.0352)(0.0432)‐0.0678
0.0347(0.0817)(0.0720)‐0.0252‐0.0317(0.0615)(0.0650)0.176***
0.0789(0.0588)(0.0493)0.00569
0.0441(0.0347)(0.0343)‐0.04480.0388(0.0307)(0.0281)0.00649‐0.0115*(5)2010s
0.204***(0.0630)‐0.00462(0.101)‐0.00176(0.00185)
0.379***(0.0341)‐0.0142***(0.00223)
0.00301*(0.00170)‐0.0199(0.0149)‐0.0842(0.0797)‐0.175(0.117)0.00111(0.00133)
0.0456***(0.0111)‐0.0346(0.0767)‐0.0741(1.082)‐0.102(0.0662)‐0.0929(0.178)‐0.0526(0.0466)
0.0727(0.182)‐0.0805(0.0832)‐0.0332(0.0571)‐0.00891(0.0613)0.00493(0.118)0.0894(0.0821)0.0352(0.0792)‐0.0513(0.0457)
0.0168(0.0419)‐0.0127*

Turn‐over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games

1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant

Observations
R‐squared(0.00342)‐0.00181(0.00371)
0.00286(0.00305)
0.0256***(0.00517)
0.00115(0.00479)‐0.00239(0.00503)
0.466***(0.0486)0.477***(0.0414)0.353***(0.0381)0.270***(0.0372)
0.173***(0.0403)0.175***(0.0391)
0.112**(0.0478)‐0.0349(0.0723)‐0.0976(0.0667)
9.985***(0.990)(0.00832)‐0.0101(0.00790)‐0.634(0.403)0.0244**(0.0121)0.00635(0.0218)‐0.000512(0.00981)
0.141*(0.0718)
0.505***(0.0663)0.314***(0.0662)0.243***(0.0740)0.158**(0.0652)
0.111(0.0927)
0.0487(0.0856)
0.0575(0.112)0.0325(0.0897)
10.85***(2.386)(0.00435)‐0.00119(0.00641)
0.00696(0.00608)
0.0245***(0.00760)
0.00371(0.00754)‐0.00796(0.00974)
0.179***(0.0503)0.492***(0.0648)0.363***(0.0557)
0.255***(0.0532)
0.233***(0.0597)
0.283***(0.0593)0.174***(0.0593)‐0.112(0.116)‐0.131(0.0948)9.000***(1.540)(0.00601)‐0.00464(0.00609)‐0.00256(0.00800)
0.0306***(0.00864)‐0.00766(0.00819)
0.000278(0.00702)
0.493***(0.0598)
0.449***(0.0641)
0.336***(0.0664)
0.326***(0.0628)0.161**(0.0635)
0.112*(0.0619)0.0689(0.0659)
0.00991(0.119)0.0213(0.115)13.28***(1.319)(0.00718)
0.00636(0.00801)
0.00162(0.0110)0.0187(0.0125)
0.00953(0.00804)‐0.00107(0.00750)
0.954***(0.0965)0.496***(0.0921)0.373***(0.0775)
0.277***(0.0719)0.241***(0.0728)0.149*(0.0764)0.157(0.103)0.154(0.129)‐0.158(0.100)14.22***(1.759)

8,565
0.685

715
0.748

2,988
0.611

3,089
0.600

1,773
0.583

Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's
level.
*** p<0.01, ** p<0.05, * p<0.1

Table B6: Estimated Coefficients When Controlling the Performance at Season t-2
dependnet variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
VARIABLES
all
1980s
1990s
2000s
2010s
White Dummy
0.0841**0.0177‐0.03160.120**
0.258***(0.0330)(0.0762)(0.0494)(0.0490)(0.0618)
Foreign Dummy
0.144***‐0.02450.145*
0.08950.237***(0.0415)(0.137)(0.0784)(0.0592)(0.0621)Age
0.08000.0931‐0.0671‐0.00292
0.172(0.0546)(0.250)(0.101)(0.0664)(0.106)Age squared‐0.00243** ‐0.00244
0.000113‐0.000504 ‐0.00505***(0.00104)(0.00476)(0.00189)(0.00125)(0.00195)
Experience
0.197***
0.01600.129***
0.252***
0.284***(0.0175)(0.0522)(0.0297)(0.0262)(0.0345)
Experience squared‐0.00831*** 0.00412‐0.00540** ‐0.0131*** ‐0.0101***(0.00132)(0.00463)(0.00211)(0.00196)(0.00241)
Weight0.00130
0.00394*‐0.00188
0.000376 0.00548***(0.000800) (0.00231)(0.00126)(0.00117)(0.00157)
Height0.00847‐0.0223
0.0393***0.00635‐0.0274*(0.00772)(0.0179)(0.0110)(0.0109)(0.0166)
Forward Dummy‐0.0292
0.0893‐0.0449
0.0283‐0.103(0.0419)(0.110)(0.0596)(0.0667)(0.0909)
Center Dummy
0.00998
0.263*
0.0664
0.0933‐0.181(0.0643)(0.156)(0.0896)(0.102)(0.134)
Number of the games
0.00248*** 0.000759
0.00281** 0.00232**
0.00147(0.000645) (0.00155)(0.00113)(0.00105)(0.00127)
Average minutes
0.0432***0.0270*0.0455*** 0.0367*** 0.0437***(0.00564)(0.0155)(0.00790)(0.00904)(0.0101)
Field goal
‐0.0865***0.122‐0.0740‐0.109**‐0.0486(0.0311)(0.0939)(0.0480)(0.0546)(0.0708)‐0.502‐3.741‐0.0640‐0.478‐1.579Field goal success probability(0.343)(2.786)(0.369)(0.989)(1.300)Three point shoots‐0.0334‐0.221‐0.03840.00908‐0.187**(0.0303)(0.170)(0.0449)(0.0574)(0.0750)
Three point success probability‐0.0532
0.0852‐0.110‐0.00378‐0.0564(0.0643)(0.171)(0.103)(0.110)(0.158)
Free throw‐0.00873‐0.0107
0.0318‐0.0227‐0.0509(0.0213)(0.0663)(0.0344)(0.0386)(0.0441)
Free throw success probability‐0.151‐0.3440.0668‐0.197‐0.0770(0.110)(0.475)(0.252)(0.165)(0.168)
Offensive rebound‐0.0204‐0.0343‐0.05530.0172‐0.112(0.0410)(0.129)(0.0752)(0.0715)(0.0816)
Defensive rebound‐0.03270.0879‐0.00924‐0.0750‐0.0468(0.0299)(0.0893)(0.0519)(0.0548)(0.0588)
Assists‐0.000777
0.123
0.02200.00516‐0.0252(0.0264)(0.0949)(0.0413)(0.0478)(0.0575)
Turn‐over0.04430.0327‐0.0267
0.0258‐0.0468(0.0451)(0.160)(0.0726)(0.0917)(0.105)
Steals‐0.0533‐0.0120‐0.0850‐0.03110.0551(0.0385)(0.0885)(0.0573)(0.0647)(0.0803)
Blocks0.116***
0.195**
0.07990.106**
0.0828(0.0319)(0.0810)(0.0509)(0.0521)(0.0831)
Personal fouls
0.0210‐0.05930.04300.0360‐0.0164(0.0218)(0.0497)(0.0310)(0.0363)(0.0493)
Contribution to the team
0.0231‐0.0723
0.0227
0.05340.0403

(0.0191)(0.0578)(0.0299)(0.0364)(0.0425)
5.96e‐05‐0.01070.00539‐0.00324‐0.00588(0.00324)(0.0134)(0.00598)(0.00457)(0.00591)
Turn‐over percentage‐0.00366‐0.00523‐0.00590‐0.00365
0.00662(0.00380)(0.0151)(0.00602)(0.00708)(0.00743)
True shooting percentage
0.00606*
5.287*
0.01060.00152
0.0127(0.00338)(2.789)(0.00829)(0.00972)(0.0130)
Usage percentage
0.0264***0.01980.0217**0.0350***0.0165*(0.00502)(0.0190)(0.00862)(0.00768)(0.00976)
Offensive rebound percentage
0.0155**0.01680.02160.01390.0149*(0.00691)(0.0328)(0.0174)(0.0131)(0.00766)
Defensive rebound percentage‐0.00133
0.00998
0.002020.00141‐0.00730(0.00536)(0.0145)(0.0102)(0.00879)(0.00824)
Selected in All Star Games
0.478***
0.256***
0.160***
0.422***
0.955***(0.0503)(0.0832)(0.0467)(0.0633)(0.0891)
1 ≤number of draft pick ≤50.354***
0.362***
0.389***
0.293***
0.349***(0.0395)(0.0923)(0.0625)(0.0603)(0.0810)6 ≤number of draft pick ≤100.247***
0.248***
0.253***
0.232***
0.254***(0.0388)(0.0896)(0.0536)(0.0637)(0.0834)
11 ≤number of draft pick ≤15
0.189***
0.200**
0.193***
0.230***
0.0958(0.0385)(0.0948)(0.0541)(0.0614)(0.0792)
16 ≤number of draft pick ≤20
0.0805*0.102
0.148**
0.08270.0481(0.0419)(0.0784)(0.0583)(0.0641)(0.0849)
21 ≤number of draft pick ≤25
0.0790*0.08070.173**
0.00487‐0.0120(0.0446)(0.126)(0.0727)(0.0709)(0.0833)
0.06900.02590.167**
0.0460‐0.0178
26 ≤number of draft pick ≤30(0.0507)(0.128)(0.0651)(0.0657)(0.114)31 ≤number of draft pick ≤35
0.002930.169‐0.188*0.111
0.188(0.0864)(0.153)(0.113)(0.142)(0.162)36 ≤number of draft pick ≤40‐0.0989‐0.0854‐0.1570.0455‐0.217(0.0745)(0.150)(0.108)(0.0950)(0.153)
Constant
9.258***
10.53***
9.982***
12.14***
12.59***(0.973)(3.698)(1.618)(1.269)(1.871)
Year dummy
Yes
Yes
Yes
Yes
Yes
Team dummy
Yes
Yes
Yes
Yes
Yes
Observations
8,248
429
2,754
3,172
1,893
R‐squared
0.637
0.715
0.583
0.553
0.514
Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's level.
*** p<0.01, ** p<0.05, * p<0.1
Assist percentage

Table B7: Estimated Coefficients When Controlling the Performance at Season t-3
dependnet variableln (annual salary)
(1)
(2)
(3)
(4)
(5)
VARIABLES
all
1980s
1990s
2000s
2010s
White Dummy
0.0808**0.0162-0.0579
0.110**
0.300***(0.0361)(0.0820)(0.0555)(0.0542)(0.0747)
Foreign Dummy
0.168***-0.1410.179**
0.108
0.252***(0.0452)(0.136)(0.0850)(0.0655)(0.0728)Age
0.003260.304-0.110-0.0905
0.0860(0.0589)(0.347)(0.128)(0.0712)(0.122)Age squared-0.00129-0.00660 0.000666 0.000893 -0.00390*(0.00112) (0.00672) (0.00238) (0.00133) (0.00225)
Experience
0.137***-0.04290.0717** 0.189*** 0.194***(0.0191)(0.0649)(0.0348)(0.0293)(0.0385)
Experience squared-0.00604*** 0.00832-0.00302 -0.0113*** -0.00542**(0.00141) (0.00658) (0.00246) (0.00218) (0.00269)
Weight-4.68e-05
0.00111 -0.00358** -7.40e-05
0.00299(0.000918) (0.00237) (0.00157) (0.00131) (0.00183)
Height0.00907-0.00508 0.0373*** 0.01000-0.0299*(0.00862) (0.0183)(0.0120)(0.0126)(0.0180)
Forward Dummy
0.003410.03470.06870.0107-0.0166(0.0480)(0.120)(0.0701)(0.0757)(0.104)Center Dummy
0.03890.199
0.176
0.0748-0.0967(0.0735)(0.167)(0.109)(0.117)(0.150)
Number of the games
0.00257*** -0.00156 0.00542*** 0.00152
0.000142(0.000713) (0.00190) (0.00131) (0.000925) (0.00160)
Average minutes
0.0325*** 0.0341* 0.0368*** 0.0279*** 0.0266*(0.00647) (0.0184)(0.0101) (0.00910) (0.0137)
Field goal
-0.04610.0852-0.02410.00698-0.0635(0.0360)(0.120)(0.0580)(0.0588)(0.0952)
Field goal success probability
0.151-8.499*** 1.374*** -2.045**-1.107(0.360)(3.161)(0.481)(0.975)(1.452)Three point shoots
0.00772-0.407**0.0170-0.0258-0.139*(0.0320)(0.198)(0.0519)(0.0570)(0.0827)
Three point success probability-0.105-0.164-0.124-0.0632-0.163(0.0727)(0.159)(0.129)(0.119)(0.185)
Free throw
0.00917-0.168**0.0696-0.0349-0.0508(0.0237)(0.0809)(0.0423)(0.0341)(0.0518)
Free throw success probability-0.139-0.657-0.149-0.210-0.181(0.143)(0.522)(0.350)(0.211)(0.199)
Offensive rebound-0.00328
0.124
0.0218-0.00441-0.0964(0.0517)(0.144)(0.0735)(0.0651)(0.109)
Defensive rebound0.00689-0.0688
0.0631-0.0248-0.0425(0.0355)(0.0937)(0.0607)(0.0488)(0.0771)
Assists
0.02480.06800.0827*
0.0360-0.000956(0.0288)(0.111)(0.0485)(0.0444)(0.0761)
Turn-over0.03220.0597-0.07560.00102-0.0358(0.0505)(0.155)(0.0910)(0.0915)(0.120)
Steals-0.0341-0.0107-0.07540.04520.103(0.0409)(0.111)(0.0662)(0.0672)(0.0855)
Blocks
0.122***
0.116
0.128**
0.109**
0.0627(0.0350)(0.0982)(0.0555)(0.0529)(0.0953)0.0202-0.0895
0.01500.02370.0370(0.0242)(0.0582)(0.0402)(0.0361)(0.0549)
Contribution to the team
0.0124-9.52e-05-0.02210.02340.0700(0.0202)(0.0693)(0.0365)(0.0331)(0.0506)
Assist percentage-0.00150-0.0130
0.00254-0.00148-0.0136(0.00403) (0.0161) (0.00677) (0.00524) (0.00982)
Turn-over percentage-0.00181-0.00289-0.00149
0.001130.00218(0.00518) (0.0140) (0.00804) (0.00924) (0.0102)
True shooting percentage
0.00495
11.12*** 0.0260** 0.0244**
0.00839(0.00383)(3.373)(0.0127)(0.0105)(0.0156)
Usage percentage
0.0180***0.02200.0204** 0.0218**
0.00991(0.00625) (0.0225)(0.0103) (0.00924) (0.0135)
Offensive rebound percentage
0.0172-0.0292
0.00353 0.0332***
0.0212(0.0108)(0.0317)(0.0167)(0.0124)(0.0187)
Defensive rebound percentage-0.00864
0.0350*-0.00251-0.00248-0.0224*(0.00792) (0.0191)(0.0123)(0.0103)(0.0118)
Selected in All Star Games
0.494*** 0.260*** 0.230*** 0.407*** 0.982***(0.0505)(0.0763)(0.0545)(0.0623)(0.0899)
1 ≤number of draft pick ≤50.288*** 0.277*** 0.349*** 0.219*** 0.264***(0.0434)(0.0996)(0.0680)(0.0651)(0.0895)
6 ≤number of draft pick ≤10
0.211***
0.197**
0.206***
0.181**
0.227**(0.0438)(0.0918)(0.0625)(0.0713)(0.0902)
11 ≤number of draft pick ≤15
0.186***
0.184*
0.217*** 0.227***0.0449(0.0443)(0.104)(0.0640)(0.0716)(0.0922)16 ≤number of draft pick ≤20
0.0766*
0.07330.123*
0.0896
0.0315(0.0464)(0.0947)(0.0680)(0.0741)(0.0932)
21 ≤number of draft pick ≤25
0.125**
0.104
0.210**
0.03820.0260(0.0528)(0.123)(0.0922)(0.0823)(0.0982)
26 ≤number of draft pick ≤30
0.0990*0.04770.147*
0.09260.0391(0.0566)(0.100)(0.0779)(0.0736)(0.136)31 ≤number of draft pick ≤35
0.05600.0830-0.1430.116
0.328*(0.0907)(0.135)(0.121)(0.146)(0.185)36 ≤number of draft pick ≤40-0.101-0.0656-0.176
0.0663-0.244(0.0783)(0.219)(0.117)(0.102)(0.169)constant
10.79***
6.747
11.07*** 13.11*** 15.68***(1.056)(4.665)(1.889)(1.373)(2.069)Year dummy
Yes
Yes
Yes
Yes
Yes
Team dummy
Yes
Yes
Yes
Yes
Yes
Observations
7,135
360
2,397
2,750
1,628
R-squared
0.614
0.737
0.549
0.510
0.461
Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's
level.
*** p<0.01, ** p<0.05, * p<0.1
Personal fouls

Table B8: Estimated Coefficients with Restricted Sample (including players who experience
multiple team in one season)
dependnet variableln (annual salary)
VARIABLESWhite Dummy
Foreign DummyAge
Age squared
Experience
Experience squared
WeightHeightForward DummyCenter Dummy
Number of the games
Average minutes
Field goal
Field goal success probability
Three point shoots
Three point success probability
Free throw
Free throw success probability
Offensive reboundDefensive reboundAssists
Turn‐over
StealsBlocks
Personal fouls(1)
all
0.0739**(0.0296)0.173***(0.0380)
0.0410(0.0480)‐0.00148(0.000911)
0.256***(0.0155)‐0.0105***(0.00114)
0.00110(0.000749)
0.0104(0.00703)‐0.0411(0.0386)‐0.0237(0.0595)
0.00213***(0.000532)
0.0361***(0.00523)‐0.0301(0.0307)
0.128(0.262)0.0348(0.0267)‐0.175***(0.0602)0.00170(0.0203)
0.0182(0.0891)0.0493(0.0315)0.00485(0.0281)0.0272(0.0238)
0.0645(0.0442)‐0.00879(0.0379)
0.146***(0.0315)0.00475(0.0216)

(2)
(3)
(4)
(5)
1980s
1990s
2000s
2010s
0.0636‐0.0353
0.0937**0.232***(0.0539)(0.0449)(0.0452)(0.0539)0.164*
0.136*
0.131**
0.288***(0.0990)(0.0753)(0.0522)(0.0559)
0.0285‐0.0637‐0.02800.0455(0.149)(0.0870)(0.0601)(0.0840)‐0.00166
0.000360
5.15e‐05‐0.00215(0.00279)(0.00162)(0.00113)(0.00153)
0.0996***0.173***
0.335***
0.338***(0.0333)(0.0264)(0.0232)(0.0287)
0.00138 ‐0.00722*** ‐0.0158*** ‐0.0132***(0.00266)(0.00184)(0.00165)(0.00190)
0.00154‐0.00184
0.000858 0.00465***(0.00153)(0.00122)(0.00108)(0.00153)
0.00103
0.0512***0.00328‐0.0242*(0.0136)(0.0104)(0.0104)(0.0137)
0.0430‐0.0700‐0.00829‐0.0555(0.0827)(0.0581)(0.0614)(0.0767)0.188
0.001900.0749‐0.156(0.118)(0.0889)(0.0928)(0.111)0.00104
0.00300*** 0.00158*
0.00127(0.00113) (0.000876) (0.000808) (0.00118)
0.0241**0.0396*** 0.0265*** 0.0489***(0.0117)(0.00777)(0.00763)(0.0102)‐0.00132
0.0270‐0.0705‐0.0712(0.0665)(0.0444)(0.0461)(0.0679)
0.879*
0.655*‐0.1890.537(0.465)(0.396)(0.578)(1.000)0.03210.02580.0520‐0.0318(0.0840)(0.0420)(0.0438)(0.0598)‐0.175‐0.196*‐0.0767‐0.218(0.133)(0.101)(0.0975)(0.140)‐0.0261
0.0689**‐0.0141‐0.0575(0.0462)(0.0323)(0.0285)(0.0397)
0.298
0.339**‐0.1830.158(0.225)(0.166)(0.118)(0.177)0.00157
0.07160.0750‐0.0959(0.0881)(0.0449)(0.0502)(0.0668)
0.01970.0884*‐0.0277‐0.0658(0.0628)(0.0499)(0.0410)(0.0526)
0.04310.0767**0.0222‐0.000362(0.0627)(0.0341)(0.0400)(0.0552)
0.135‐0.0578
0.05620.0125(0.105)(0.0791)(0.0652)(0.107)‐0.09170.00955‐0.000472
0.0677(0.0710)(0.0595)(0.0603)(0.0738)
0.07760.172***
0.0908**0.0464(0.0572)(0.0535)(0.0459)(0.0703)‐0.03540.00279
0.0375‐0.0174(0.0369)(0.0339)(0.0316)(0.0424)

Contribution to the teamAssist percentage
Turn‐over percentage
True shooting percentage
Usage percentage
Offensive rebound percentage
Defensive rebound percentage
Selected in All Star Games

1 ≤number of draft pick ≤5
6 ≤number of draft pick ≤10
11 ≤number of draft pick ≤15
16 ≤number of draft pick ≤20
21 ≤number of draft pick ≤25
26 ≤number of draft pick ≤30
31 ≤number of draft pick ≤35
36 ≤number of draft pick ≤40
Constant

Observations
R‐squared‐0.0141(0.0174)‐0.00343(0.00307)‐0.00182(0.00332)
0.00241(0.00276)
0.0195***(0.00514)
0.00325(0.00452)‐0.00213(0.00445)
0.461***(0.0478)0.474***(0.0386)
0.348***(0.0368)
0.272***(0.0351)0.158***(0.0391)0.139***(0.0395)
0.110**(0.0438)‐0.00986(0.0720)‐0.0595(0.0604)
8.932***(0.869)0.00344(0.0420)‐0.00812(0.00812)‐0.0118(0.00769)‐0.656(0.398)0.0219*(0.0117)0.00596(0.0211)0.000431(0.00931)
0.119*(0.0686)0.485***(0.0671)
0.296***(0.0643)0.235***(0.0726)0.160**(0.0627)
0.108(0.0917)0.0348(0.0808)0.0540(0.107)0.0264(0.0909)10.43***(2.268)‐0.0616**(0.0294)0.00770*(0.00409)
0.000149(0.00629)
0.00779(0.00543)
0.0138**(0.00641)
0.00408(0.00679)‐0.00336(0.00905)
0.172***(0.0476)
0.475***(0.0604)
0.334***(0.0528)
0.261***(0.0525)0.231***(0.0575)
0.281***(0.0584)
0.191***(0.0589)‐0.140(0.109)‐0.116(0.0917)8.384***(1.417)0.0256(0.0253)‐0.0101*(0.00516)‐0.00323(0.00492)‐9.09e‐05(0.00614)
0.0233***(0.00675)‐0.00408(0.00794)‐0.00619(0.00628)
0.471***(0.0605)
0.466***(0.0597)
0.363***(0.0620)0.320***(0.0556)0.171***(0.0585)
0.0717(0.0613)0.0723(0.0588)
0.0768(0.113)0.0144(0.0902)
12.58***(1.158)0.0267(0.0383)‐0.0132**(0.00635)
0.00587(0.00705)‐0.00510(0.0104)
0.0265**(0.0105)
0.0139*(0.00735)
0.00269(0.00671)
0.924***(0.0900)
0.473***(0.0814)0.370***(0.0723)0.255***(0.0691)
0.115(0.0794)
0.0663(0.0750)0.105(0.0880)
0.128(0.118)‐0.0827(0.107)13.00***(1.520)10,091
0.674

745
0.751

3,265
0.613

3,773
0.597

2,308
0.567

Notes: Clustering robust standard errors in parentheses. The error term is clustered as player's
level.
*** p<0.01, ** p<0.05, * p<0.1