Tsukuba Economics Working Papers
No. 2017-001

Is there A Positive Association between Increasing Salary Discrimination in the NBA
and Unshrinking Racial Income Gap of White and Black Citizens ?
byHisahiro Naito
and
Yu Takagi
February 2017

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANIs there A Positive Association between Increasing
Salary Discrimination in the NBA and Unshrinking
Racial Income Gap of White and Black Citizens ?

Hisahiro Naito∗†‡
Department of Economics
University of Tsukuba

Yu Takagi §College of International Studies
University of Tsukuba
February 1, 2017∗

Address: Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573; e-mail: naito@dpipe.tsukuba.ac.jp
Corresponding author‡
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: s1210369@u.tsukuba.ac.jp†

Abstract
From the late 2000s, racial salary discrimination against black players emerged in
the National Basketball Association (NBA) league. At the same time in the UnitedStates, the income gap between white and black citizens, which had been decreasing in
the previous 20 years, stalled in the mid-2000s and started to increase again from thelate 2000s. In this study, we examine whether increasing racial salary discrimination
against black players in the NBA is positively associated with the non-shrinking income
gap between white and black citizens. Using census data, we calculate the medianincome ratio of white and black males in each metropolitan area where at least one
NBA team is located. Then, we examine whether the white premium of the salary of
an NBA player is correlated with the median income ratio between white and black
citizens of the metropolitan area where the player’s team is located. We find that thewhite premium becomes higher in a metropolitan area where the median income gapof the citizen becomes smaller. Thus, increasing salary gap against black players in
the NBA in the late 2000s and 2010s is not positively associated with non-shrinking
income gap between white and black citizens
JEL Classification: J71
Keywords: racial discrimination, NBA, labor markets, salary discrimination, black–white wage gap

1

Introduction

Racial discrimination in the National Basketball Association (NBA) league has attracted considerable attention in both the media and economics literature. For example, in April 2014, the owner of Los Angeles Clippers was banned from the NBA
permanently and fined $2.5 million for his racism comments. Kahn and Sherer (1988)
finds a strong white (20 percent) salary premium controlling productivity and othercovariates using the salary data in the 1985–1986 season. On the other hand, several
studies report that such a white salary 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). Some studies report that in the 2000s, there was even a negative white premium against white players(Yang and Lin, 2012; Groothuis and Hill, 2013).
However, recently, making a long unbalanced panel data of salary and productivity covering 1985-2015, we find that while in the 1980s and 1990s there was nodiscrimination against black players, from the late 2000s, there was significant salary
discrimination against non-white players (Naito and Takagi, 2017). More specifically,
we find that white players are paid 20–25 percent more than non-white player withsimilar characteristics in the late 2000s and 2010s, and that this result is robust to
many specification checks, such as quantile regression and controlling sample selection,
type of contracts and foreign players.
A natural question arising from this finding is why racial discrimination started toarise in the NBA suddenly from the late 2000s.
Figure 1 shows the white premium of NBA players and income gap of white-black
citizens in the last three decades.1 The blue line shows the coefficient of white dummy
1

In our previous paper(Naito and Takagi, 2017), we compare the salary of white and non-white1when the natural log of the salary of each player in each year is regressed on whitedummy after controlling a large dimension of productivities. Red line shows the medianincome ratio between male white citizens and male black citizens in each year. The
red line shows that the white premium of NBA players started to rise in the middle
2000s. Interestingly, until the late 2000s, the median income ratio between white andblack citizens was falling. However, since the late 2000s, it started to rise again.
One interesting hypothesis is that the increasing salary discrimination against black
players in the NBA is related with the non-shrinking income gap between white andblack citizens in the United States. If each citizen prefers to see NBA players of theirown race and if a team is willing to pay a higher salary to a player whom wealthier fan
want to see, the increasing salary discrimination against black players might be just areflection of non-shrinking income gap between white and black citizens.
In this paper, we examine whether an increase of the racial salary gap between
white and black players with similar characteristics in the NBA is positively associatedwith the income gap between white and black citizens by using the census data andsalary data of NBA players. More specifically, we use census data from 1990, 2000,
and 2010, which contain information on the income of residents of each metropolitan
area where at least one NBA team is located. For each metropolitan area where atleast one NBA team is located, we calculate the ratio of median income of black andwhite residents in the 1990, 2000, and 2010 census. Then, we check whether the whitepremium in the NBA is positively associated with the increasing income gap between
white and black residents of the metropolitan area of the team to which the players
belong.
players. In this figure, however, we plot the white premium over black players with similar characteristics since the majority of non-white players are black and income gap of white and black citizen
displays a similar pattern.

22

Datasets

We obtain information on the annual salaries of players from the ESPN salary-ranking
website, the NBA’s reference website, and a fan website. For indexes of performance,birthplace, nationality, height, weight, and birth year, we collect information from the
ESPN website and the reference website of the NBA players.23
We pick up salary information of the season where the starting year of the season isequal to the census year (1990, 2000, 2010 ) or is 1 year before or 1 year after the census
year.4 The median incomes of white and black residents in each metropolitan area arecalculated from the census data, which are available from IPUMS USA5 . Because we
map the ratio of the median income of each metropolitan area, which is calculated fromthe US census, to the salary data, we drop the observations of players who belonged
to Canadian teams. We also restrict to white and black players.6

3

Empirical modelFirst, we estimate the following equation separately for three different periods (1990–
1991, 1999–2001, and 2009–2011):

ln Sijt = β0 + β1 W hitei + γ1 X1ijt + γ2 X2i + γj + γt + εijt2

(1)

The detailed explanation of the construction of the dataset is available from Naito and Takagi(2016).
3
For indexes of the performance of players, we use the 16 indexes. Those indexes are discussed indetail in Naito and Takagi (2016).
4This implies that we pick up the 1990–1991, 1991–1992, 1999–2000, 2000–2001, 2001–2002, 2009–2010, 2010–2011, and 2011–2012 seasons.
5
https://usa.ipums.org/usa/
6
Since the majority of non-white player are black, our results does not change even if we include
non-white and non-black players.

3where i is the index of the individual; j is the index of the team; and t is the index of
the season. Whitei is a dummy variable and it is equal to one if a player i is white andotherwise zero. To simplify the notation, we define the year t to year t+1 season as
season t. As discussed in the preceding dataset section, we select the observation whose
season is equal to the census year or 1 year before or 1 year after the census year7 .
X1,ijt are the indexes of performance in season t-1. To avoid an endogeneity problem,
we control the performance in season t-1 instead of the performance in season t. X2irepresents the time invariant player characteristics, such as height. γj is the team’stime invariant fixed effect. γt is the time fixed effect. β1 is the white premium andshows the percentage premium of the salary of a white player over a black player withsimilar characteristics.
In the next step, we examine whether an increase of the white premium in the late
2000s and 2010s is associated with a change of income gap of white and black citizen.
To denote three separate periods (1990–1991, 1999–2001, and 2009–2011), we write
1990–1991, 1999–2001, and 2009–2011 as the ar1990, ar2000, and ar2010, respectively.
Let T = {ar1990, ar2000, ar2010}. Then, we estimate the following equation from thepooled dataset:ln Sijt = β0 +X

β1,k W hitei × Dtk +

k∈T

+

X

X

β2,k W hitei × Std gapjt × Dtk

k∈T

γ1,k Std gapjt × Dtk + γ2 X1ijt + γ3 X2i + γj + γt + εijt

(2)

k∈T

Dar1990is the dummy variable indicating whether t belongs to the first period (1990–t1991). It is equal to 1 if t=1990 or 1991, and 0 otherwise. Dtar2000 and Dtar2010 are
7

The salary data of the 1989-1990 season are not available due to a dispute between players andthe owners of the NBA.

4

defined in a similar way. Std gapjt is the standardized income gap between black andwhite citizens and is defined as

Std gapjt = (gapjt − gapjt )/sd gap.

(3)

gapjt is the median income ratio of male white citizens to male black citizens in year t
in the metropolitan area where team j is located. gapjt is the sample mean of gapjt .
sd gap is the sample standard deviation of gapjt .
β1,k measures the white premium in period k∈ T of players who belong to a teamof which the income ratio of the white-black citizens of the metropolitan area is thenational average. γ1,k measures the how much percentage the salary increases in periodk∈ T when the income ratio of white and black citizen increases by one standard
deviation. Our main interest is β2,ar2010 , which shows how much the percentage of thewhite premium increases in 2009-2011 when the income ratio of white and black citizens
increases by one standard deviation. We expect β2,ar2010 is positive and statisticallyPsignificant. The term k∈T β1,k W hitei × Dtk in equation (2) allows the coefficients of
the white dummy to be different in three separate periods (1990–1991, 2009–2001, andP
2009–2011). The term k∈T β2,k W hitei × Std gapjt × Dtk allows the coefficients of thewhite dummy to be dependent on the income gap between white and black citizens
in three separate periods. To calculate the median income in each metropolitan area,
we restrict citizens to those aged from 23 to 60 years. For the season when the census
is conducted, we use the census data to calculate the median income. For the season
when the census is not conducted, we use the closest census data.

5

4

Results

Table 1 shows the estimation results. In columns (1)–(3), we estimate the white premium in three separate periods (1990–1991, 1999–2001, and 2009–2011). As shown in
our previous study (Naito and Takagi, 2016), the columns (1)–(3) indicate that thewhite premium is statistically and economically significant only in the 2009–2011 period. Column (1) shows that in 2009–2011 period, white players were paid 31 percent
more on average than non-white players with similar characteristics. In column (4), we
estimate the white premium by pooling the three samples and allowing the white premium to vary in each decade. Column (4) shows that only the interaction term of whitedummy and 2010s dummy is statistically significant. It shows that white players in
2009-2011 is paid 29 percent more, which is consistent with columns (1)-(3). Columns(5) and (6) examine the hypothesis that racial salary gap of the NBA is positively associated with income gap between white and black citizen. Columns (5) and (6) showthat in an area where the ratio of the median income of white and black males is one
standard deviation larger, the white premium is 9 percent lower in 2009-2011 period,
contrary to out expectation. In 1990-1991 and 1999-2001 periods, the racial salary gap
in the NBA is not associated with income gap between white and black citizen. In column (7), we assume that the error term is clustered at the metropolitan area× period.8
We do so because Std gapjt is the same for the same metropolitan area and the same
period. Column (7) shows that the statistical significance does not change even when
we change the assumption on clustering. The results from columns (4)–(7) show thatthe salary gap of white and black players in 2009-2011 is not positively associated with
the income gap between white and black male citizens, contrary to our expectation.
8

In columns (1)–(6), we have assumed that the error term is clustered for the same player.

65Conclusion

In the late 2000s and 2010s, salary discrimination against black players became significant in the NBA. White players were paid about 31 percent more than black playerswith similar characteristics in the 2010s. Concurrently, around the mid-2000s, the declining trend in the white–black income gap stalled and from the late 2000s, started toincrease.
This study examined whether salary discrimination against black players is positively associated with income gap between white citizens and black citizens. Contrary
to our prediction, we find that the salary gap between white and black players is negatively associated with income gap between white and black citizens. This suggests that
we need to look for different reasons that explain why salary discrimination against
black players increased from late 2000s.

7Variables
white
white× dummy(2009-2011)
white×dummy(1999-2001)
white×dummy(1990-1991)
white×dummy(2009-2011)×Standardized
income gap
white×dummy(1999-2001)×Standardized
income gap
white× dummy(1990-1991)× Standardized
income gap

Table 1: Estimated Coefficients of Main Variables of Interest
(1)
(2)
(3)
(4)
(5)
0.310***
0.09330.0239(0.0969)(0.0933)(0.0775)
0.271***
0.293***(0.0887)(0.0916)0.03460.0475(0.0847)(0.0955)
0.118
0.107
(0.0732)(0.0739)(6)(7)

0.356***(0.0988)
0.0683(0.103)0.00246(0.0750)

0.356***(0.106)
0.0683(0.105)0.00246(0.0750)‐0.102*‐0.0980*‐0.0980**(0.0537)0.0442(0 103)(0.103)(0.0527)
0.0520(0.102)(0 102)(0.0450)
0.0520(0.0914)(0 0914)0.03000.01750.0175(0.0678)(0.0595)(0.0491)
season dummy × player characteristics
No
No
No
No
No
Yes
Yes
clustering level of the error term
playerplayerplayerplayerplayerplayerarea×period
1990–1991,
1990–1991,
1990–1991,
1990–1991,
sample seasons
2009–2011 1999–2001 1990–1991 1999–2001,
1999–2001,
1999–2001,
1999–2001,
2009–20112009–20112009–20112009–2011
N
873
885
481
2,239
2,239
2,239
2,239
R-squared
0.520
0.608
0.605
0.616
0.616
0.645
0.645
Notes: The sample is restricted to white and black players. The year t to year t+1 season is defined as season t. The dummy(2009-2011) is equal to 1 if theseason is 2009, 2010, or 2011. The dummy(1999-2001) is equal to 1 if the season is 1999, 2000, or 2001. The dummy(1990-1991) is defined similarly. The
data for the 1989 season are not available owing to a dispute between players and owners. Standardized income gap is the standardized ratio of the medianincome of white and black males aged 23–60 years. Clustering robust standard errors are in parentheses. All specifications are clustered at the player's level
except (7). In (7), it is assumed that the error term is clustered within the metropolitan area × period, where period is defined as 1990–1991, 2009–2011, or
2009–2011. All regression specifications include the indexes of performance, weight, height, team fixed effect, and season fixed effect as additional
explanatory variables. In addition, specifications (4)–(7) include standardized income gap period dummy as explanatory variables. *** p<0.01, ** p<0.05,* p<0.1

2
1.4
1.6
1.8
Median Income Ratio

.3
.2
white
.1
0
−.1

1.2

−.2

1980

1990

2000
year

white

2010

2020

Median Income Ratio

Figure 1: White at year t plots the coefficient of the white dummy when the log of salary isregressed on the white dummy and other covariates using the sample of year t in the NBA
salary data. The sample is restricted to white and black players. The median income ratio
plots the ratio of the median income of male white and black citizens aged 23 to 60 years,
calculated from the CPS data.

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