Tsukuba Economics Working Papers
No. 2016-002

Does Increasing Salary Discrimination in the NBA Reflect Disparity ofFans' Purchasing Power?
byHisahiro Naito
andYu Takagi
June 2016

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANDoes Increasing Salary Discrimination in the NBA
Reflect Disparity of Fans’ Purchasing Power?

Hisahiro Naito∗†‡
Department of Economics
University of Tsukuba

Yu Takagi §College of International Studies
University of Tsukuba
June 1, 2016

∗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 inthe 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 a reflection of the non-shrinking disparity of purchasing power of 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 gap
is smaller. This suggests that the non-shrinking income gap between white and black
citizens is not the cause of increasing salary discrimination against black players in theNBA in the late 2000s and 2010s.
JEL Classification: J71
Keywords: racial discrimination, NBA, labor markets, salary discrimination, black–white wage gap

1Introduction

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. Kanazawa and Funk(2001) find that TV viewing is strongly affected by the ratio of white players in theteam by examining viewing data in the 1996–1997 season. Price and Wolfers (2010)
show that a referee prefers players whose race is the same as the referee when he or
she makes a decision about a foul.
Regarding racial salary discrimination in the NBA, the past economic literaturesuggests that although there was racial salary discrimination against black players in
the 1980s, from the 1990s, there was no racial salary discrimination against black players (for a comprehensive survey of the literature, see Kahn (2012)). The literatureeven suggests there is reverse racial salary discrimination (i.e., white players are discriminated against compared to black players (Yang and Lin, 2012; Ajilore, 2014)).
However, using unbalanced panel data of salaries and productivity over a long period,we find that while in the 1980s and 1990s there was no discrimination against nonwhite players, from the late 2000s, there was significant salary discrimination against
non-white players (Naito and Takagi, 2016). More specifically, we find that white players are paid 20–25 percent more than non-white player with similar characteristics inthe 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, foreign
players, and a team fixed effect interacted with a year dummy.1
1Since the majority of non-white players are black players, we assume that black players represent

non-white players. Our results do not change even if we restrict to the sample including only whiteand black players.

1

A natural question arising from this finding is why racial discrimination started toarise in the NBA suddenly from the late 2000s. One hypothesis is that the increasingsalary gap between white and non-white players in the NBA is a reflection of theincreasing disparity of purchasing power between white and black fans. Interestingly,
until the late 2000s, the median income ratio between white and black citizens wasfalling. However, since the late 2000s, it started to rise again (Figure 1). If each citizen
prefers to see NBA players of their own race, the increasing salary discrimination
against non-white players could be a reflection of the disparity of fans’ purchasing
power.
This study examines whether an increase of the racial salary gap between whiteand non-white players with similar characteristics in the NBA is a reflection of theincome gap between white and non-white fans. To examine this hypothesis, we use
census data from 1990, 2000, and 2010, which contain information on the income ofresidents of each metropolitan area where at least one NBA team is located. For eachmetropolitan area where at least one NBA team is located, we calculate the ratio of
median income of black and white residents in the 1990, 2000, and 2010 census. Then,
we check whether the white premium in the NBA is correlated with the increasing
income gap between white and black residents of the metropolitan area of the team towhich the players belong.

2Datasets

This study obtains information on salaries and players’ productivity from several
sources. We obtain information on the annual salaries of players from the ESPN

2

salary-ranking website2 , the NBA’s reference website3 , and a fan website4 . For indexesof performance, birthplace, nationality, height, weight, and birth year, we collect information from the ESPN website and the reference website of the NBA players.567
The salary data are available from the 1985–1986 to 2015–2016 seasons, except the1986–1987 and 1989–1990 seasons.8 We pick up observations where the starting yearof the season is equal to the census year or is 1 year before or 1 year after the census
year.9 The median incomes of white and black residents in each metropolitan area arecalculated from the census data, which are available from IPUMS USA10 . Because wemap the ratio of the median income of each metropolitan area calculated from the US
census to the salary data, we drop the observations of Canadian teams.

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 + εijt

(1)where i is the index of the individual; j is the index of the team; and t is the index
of the season. To simplify the notation, we define the year k to year k + 1 season2

http://espn.go.com/nba/salaries
http://www.basketball-reference.com/players/
4
https://www.eskimo.com/~pbender/
5
http://espn.go.com/nba/statistics
6
http://www.basketball-reference.com/players/7
For indexes of the performance of players, we use the 12 indexes. Those indexes are discussed in3

detail in Naito and Takagi (2016).
8
For the 1986–1987 and 1989–1990 seasons, the player’s Union and teams refused to release salary
information due to a dispute.
9This implies that we pick up the 1990–1991, 1991–1992, 1999–2000, 2000–2001, 2001–2002, 2009–2010, 2010–2011, and 2011–2012 seasons.
10
https://usa.ipums.org/usa/3

as season k. 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
year11 . 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 t.
X2i represents 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 fans.
To denote three separate periods (1990–1991, 1999–2001, and 2009–2011), we write
1990–1991, 1999–2001, and 2009–2011 as the 1990s, 2000s, and 2010s, respectively.
Let T = {1990s, 2000s, 2010s}. Then, we estimate the following equation:ln Sijt = β0 +X

β1,k W hitei Dtk +X

β2,k W hitei Std gapjt Dtk

k∈Tk∈T+γ1 X1ijt + γ2 X2i + γj + γt + εijt

(2)

D1990sis 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. Dt2000s is a dummy variableindicating whether t belongs to the second period (1999–2001) and is equal to 1 ift=1999, 2000, or 2001, and 0 otherwise. Dt2010s is a dummy variable that is equal to 1if t=2009, 2010, or 2011, and 0 otherwise. Std gapjt is the standardized income gap
between black and white citizens and is defined as
Std gapjt = (gapjt − gapjt )/sd gap.
11(3)The salary data of the 1989-1990 season are not available due to a dispute between players andthe owners of the NBA.

4

gapjt is the median income ratio of male white citizens to male black citizens in year t inP
the metropolitan area where team j is located. 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 pePriods (1990–1991, 2009–2001, and 2009–2011). The term k∈T β2,k W hitei Std gapjt Dtk
allows the coefficients of the white 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, we restrict citizens to those aged from 23 to 60 years. For theseason when the census is conducted, we use the census data to calculate the medianincome. For the season when the census is not conducted, we use the closest census
data. For example, to calculate gapjt (t = 2009, 2011), we use the median income ratio
calculated from the census in 2010. For gapjt (t = 1999, 2001), we use the ratio of themedian income between white and black citizens calculated from the census in 2000.
Similarly, for gapjt (t= 1991), we use the census in 1990. gapjt is the sample mean ofgapjt . sd gap is the sample standard deviation of gapjt . In equation (2), we do notinclude the term W hitei and W hitei Std gapjt themselves owing to multi-collinearity.
Our main interest is β2,2010s , which shows how much the percentage of the white premium increases when the white–black income ratio increases by one standard deviation.
If the increasing white premium is a reflection of the disparity of purchasing power ofwhite and black citizens, then β2,2010s should be positive.
4Result

Table 1 shows the estimation results. In columns (1)–(3), we estimate the white premium in three separate samples (1989–1991, 1999–2001, and 2009–2011). As shownin 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 sample. Column (1) shows that in 2009–2011, white players were paid 28 percent more5

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. Columns (5) and (6) examine the hypothesis that increasingsalary discrimination is a reflection of the disparity of fans’ purchasing power. Columns(5) and (6) show that in an area where the ratio of the median income of white andblack males is one standard deviation larger, the white premium is 9 percent lower.
In columns (1)–(6), we assume that the error term is clustered for the same player.
In column (7), we assume that the error term is clustered at the metropolitan area×period. We do so because Std gapjt is the same for the same metropolitan area andthe same period. Column (7) shows that the statistical significance does not changeeven when we change the assumption on clustering. The results from columns (4)–(7)suggest that the non-shrinking income gap between white and black males is not thecause of increasing salary discrimination against black players in the NBA.

5Conclusion

In the late 2000s and 2010s, salary discrimination against black players became significant in the NBA. White players were paid about 27 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 increasing salary discrimination against black players
is a reflection of a change of the purchasing power of white and black citizens. Contrary
to our prediction, we find that the income gap between white and black residents is not
the reason for the increasing salary gap between white and black NBA players. Thus,
the cause of increasing salary discrimination against black players remains unclear.

6Variables
white
white×2010s dummy
white×2000s dummy
white×1990s dummy

Table 1: Estimated Coefficients of Main Variables of Interest
(1)
(2)
(3)
(4)
(5)
0.276***
0.09770.0143(0.0897)(0.0897)(0.0727)
0.259***
0.263***(0.0875)(0.0875)0.04440.0406(0.0916)(0.0918)
0.106
0.107
(0.0703)(0.0705)(6)(7)

0.319***(0.0916)0.0636(0.0975)-0.0144(0.0719)0.319***(0.0966)
0.0636(0.102)-0.0144(0.0799)-0.0989**-0.0916*-0.0916**(0.0473)
0.00631(0.0967)(0.0476)0.0112(0.0967)(0.0400)
0.0112(0.0844)0.0204(0.0530)
No
1990–1991,1999–2001,
2009–2011
2,489
0.618

0.0203(0.0460)
Yes
1990–1991,
1999–2001,
2009–2011
2,489
0.650

0.0203(0.0393)
Yes
1990–1991,
1999–2001,
2009–2011
2,489
0.650

white×2010s dummy×Standardized income
gap
white×2000s dummy×Standardized income
gap
white× 1990s dummy× Standardized income
gap
season dummy × player characteristics
sample seasonsNR-squared

NoNoNo2009–2011

1999–2001

1990–1991

983
0.521

959
0.607

547
0.597

No
1990–1991,
1999–2001,
2009–20112,489
0.618

Notes: The year t to year t+1 season is defined as season t. The 2010s dummy is equal to 1 if the season is 2009, 2010, or 2011. The 2000s dummy is equal
to 1 if the season is 1999, 2000, or 2001. The 1990s dummy 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 median income 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 ofperformance, weight, height, team fixed effect, and season fixed effect as additional explanatory variables. In addition, specifications (4)–(7) include
standardized income gap as an explanatory variable.
*** p<0.01, ** p<0.05, * p<0.1

1.4
1.6
1.8
Median Income Ratio

2.4
white premium
0.2

1.2

−.219801990

2000yearwhite

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. The medianincome ratio plots the ratio of the median income of male white and black citizens aged 23
to 60 years, calculated from the CPS data.

References
Ajilore, Olugbenga, “Do white NBA players suffer from reverse discrimination?,”Economics Bulletin, 2014,1, 558–566.
Kahn, Lawrence M, “The Economics of Discrimination: Evidence from Basketball,” in Stephen Shmanske and Leo Kahane, eds.,The Oxford Handbook of Sports
Economics Volume 2: Economics through Sports, Vol. 2, Oxford University Press,2012, pp. 21–38.
Kanazawa, Mark T and Jonas P Funk, “Racial discrimination in professional
basketball: Evidence from Nielsen ratings,”Economic Inquiry, 2001,39 (4), 599–608.
Naito, Hisahiro and Yu Takagi, “Is Racial Salary Discrimination Disappearing
in the NBA? Evidence from Data during 1985–2015,”Tsukuba Economics Working
Papers, April 2016,2016-001, 1–28.
8

Price, Joseph and Justin Wolfers, “Racial Discrimination among NBA Referees,”The Quarterly Journal of Economics, 2010,125 (4), 1859–1887.
Yang, Chih-Hai and Hsuan-Yu Lin, “Is There Salary Discrimination by Nationality in the NBA? Foreign Talent or Foreign Market,”Journal of Sports Economics,
2012,13 (1), 53–75.

9