Benjamin Ayesu-Attah

Professor Jon Miller

Econ 490

May 5th, 2016

Is there Salary Discrimination in the National Basketball Association against foreign-born players?

Introduction:

This paper explores the labor market in the National Basketball Association (NBA).

Before the merger of the American Basketball Association (ABA) and the NBA, players were

recruited domestically and rarely were foreign players drafted or recruited to play on NBA

teams. In the late 1980s, foreign players particularly pioneers, such as Vlade Divac from Serbia

and Drazen Petrovic from Croatia, joined the NBA. The inclusion of these foreign players slowly

spurred on the growth and migration of such players into the NBA. In the labour market,

employers who are teams, in this case, compete to get the best players. This paper will evaluate

if there is a difference in NBA salaries based on the nationality of players. The NBA is a global

sport unlike football or hockey where the rise of viewership around the world has expanded the

scope of the NBA. According to the New York Times, in 2012, around 300 million people play

the sport of basketball, which is almost equivalent to the United States population. In a survey

done by Henry Abbott of ESPN, results showed that basketball was now the most popular

sports amongst young people worldwide. The popularity of the sport has been reached globally

and whether a player’s nationality influences his salary in the NBA, other things held constant,

will determine if there is bias domestically in the U.S.

Economic Literature

Eschker, Perez, and Siegler (2004) shows how international basketball players have done

relative to other basketball players who were trained in the United States , in terms of their

performances. They used yearly salary regressions and their results suggested that there was a

premium paid to international players for the 1996-97 and 1997-98 seasons. They found that

this was due to an inability of NBA scouts and general managers to properly evaluate the worth

of foreign born players who did not play college basketball in the U.S. In different study done by

Ying and Lin (2015), they used an unbalanced panel dataset from 1999-2008 and a two-stage

double fixed effect model to determine that there was evidence of salary discrimination against

international players. There is another paper that checks for the robustness of these techniques

by James Richard Hill and Peter A. Groothuis (2015). They used the same econometric

techniques and were unable to verify if there was existence of pay discrimination. There has

also been empirical research that has discussed the racial differences in players’ salaries in the

NBA. These studies look critically at African-Americans, compared to their counter-parts, and

explains sport has been economically positive to African-Americans offering, them a chance to

escape poverty and leave the ghetto (Mogull 1974). Another paper has examined if there is a

nationality bias in two specific leagues the National Basketball Association and Spanish

profession league (Liga ACB). In prior studies, bias has produced mixed results. These studies

provides consistent evidence that players born in the USA receive preferential treatment in

both the USA and Spain in terms of receiving more playing time on the court. The study also

shows that national origin plays a role in decisions made by coaches determining allocation of

time. Players of the same race or nationality tend to receive more playing time than those who

are not the same race but the reasoning behind this is not explained.

Economic Theory

International Immigration: In economic theory, the impact immigration has on wages and

employment should depend on whether the migrants’ skills supplement or replace the skills of

existent workers. Immigration should increase the supply of labor in the market place thus

driving down the average wages in the market.

Labor Markets: In basic neoclassical microeconomics labor markets are viewed the same as

other markets in the sense that they have the same market forces of supply and demand

combined, this determines the wage rate and the number of people employed. In the NBA, the

salary caps limits the total amount of money that an NBA team is allowed to pay their players.

The cap is in place to control costs like many other professional sports leagues. Teams are also

limited in the amount of players that can be employed on a team which further complicates the

labour market.

Wages: There are many determinants of wages in the labor market. Laws and negotiations

between two parties is one determinant of wage levels. An organization will pay wages to

employees based on their production especially in the NBA. Other variables that are considered

is years of experience, roles, and unique value. Wage segregation is a phenomenon that should

be explored in the NBA and whether certain ethnicities are paid more.

Supply and Demand: Supply and demand one of the simplest concepts in economics can also

be exercised in the NBA. If there is an abundance of international players in the NBA, we expect

the wages of the players to go down. If there is demand for international players in the NBA,

the expectation is that wages would go up. These are simple frameworks of the supply and

demand model that is still relevant today as a guideline but should also be explored further

considering the NBA. The Los Angeles Lakers who just lost Kobe Bryant, A future hall of famer,

to retirement is a good example of this. The Lakers will be in need of a shooting guard to

replace Kobe. They will be in search of a guard who can play the role of a starter and produce at

the same level as Kobe. In order to do this, the franchise will either pick a guard from the draft

or look into the free agency. Whichever route the Lakers take, there will be a limited supply of

guards and high demand from the Lakers. Guards will have higher bargaining power than the

Lakers which can drive up the salary of the player. The Lakers, alternatively, can recruit

overseas and get the same production as a more cost effective option.

Data and Methods

I will be gathering data from the 2014-2015 season. First I will pool in data from a

database on the NBA website, ESPN, and basketball-reference.com for the season of 2014-

2015. I will collect data on player performance based on their defensive and offensive statistics.

The purpose of this is to control for the productivity of a player. The single variable, I will be

using to encompass their productivity is the Player Efficiency Rating (PER). This variable is a

better measure of boiling down a player’s contribution into one number as opposed to having

several variable such as points per game, assists, rebounds, etc. The PER formula takes positive

stats and subtracts negative stats through a statistical point value. The rating a player gets is

then adjusted to a per minute basis so that substitutes and starters can be compared. I will also

include experience as this is usually an indicator of how much you are getting paid. A rookie

drafted in the first round is set to a salary depending which pick he is. In the 2015-2016 season

a rookie drafted as the number one overall pick was paid $4,753,000 compared to the 15th

overall pick who was paid $1,600,200. After the two year contract is up, there is a team option,

which can be accepted or declined. A player is then free to negotiate the terms of the contract.

I will only include players with two years of experience or more because, they are generally

paid more than players with less than two years of experience. I included height as a variable

because on average international players are taller than American players . This is important to

incorporate because if a team is looking for some size it may be more effective to recruit

overseas. The last variables will be an international dummy variable, where a one would

indicate they are an international player and a zero mean they are not. The other variables will

be a continental variable, a one would indicate which continent they are from and zeros will be

placed in continents they are not from. This will be done to see what continents are getting

premiums and discounts compared to American players. I will also gather data from a website

that has compiled team payroll as well as each individual contract of every professional

basketball player in the 2014-2015 season. I will then run an OLS linear regression model that

will have salary as the dependent variable and the PER, experience, height, international, and

continental as my independent variables to see the impacts on salaries. The performance

variables will have to be held constant in order to assess what the salary impact of nationality is

in each region. Past performance has a significant impact on a player’s salary in the future and

in order to account for those differences their performances will have to be held constant.

Y 0 1x1 2 x2 3 x3 4 x4 +

The dependent variable above which is salary indicates how much a players makes

depending on the independent variables. X1 is the NBA experience variable which shows the

length of their experience in the league. Every player with two or more years of experience in

the 2014-2015 season will be included in the model. I expect the more experience a player has

the more money a player will receive, everything held constant. X2 is the height of every NBA

player. My expectations for this variable is that the taller a player the more money they will

receive, everything held constant. X3 is the PER which will show how productive a player was

during the 2014-2015 season. I believe that the higher the PER, the more money a player will

get, everything else held constant. The last variable for the first model is X4, the international

dummy variable, 1 means they are an international player and 0 means they are not. I expect

that international players are paid less than American players.

Y 0 1x1 2 x2 3 x3 4 x4 + 5x5 + 6x6 + 7x7 + 8x8 + 9x9 + 10x10 +

The second regression model expands the model so that I can control for what

continent an international player is from. The first four variables remain the same as the

variables used in the first model. The international dummy variable is now omitted for

continental dummy variables. The dummy variables will be X5 (North America) excluding U.S

born players, X6 (South America), X7 (Europe), X8 (Africa), X9 (Australia), and X10 (Asia). A one

will indicate that they were born in a certain continent and a 0 means they were not. My

prediction is that any one born in those continents will get paid less than an American born

player.

Results

Table 1 presents the summary statistics for salary, as well as other variables included in

the regression analysis. The summary statistics is for all NBA players in the 2014-2015 season.

This will show the amount of variation there is in the NBA. The mean for salary is just about

$4.2million, but there is great deal of variation in the sample. The minimum value is about

$30,000 while the maximum value is $23.5million. In table 2, the observations are reduced

from 453 to 343 and 285 to reduce the amount of outliers. Those two observation counts come

from the exclusion of 1st and 2nd year players. Another thing to note from the summary

statistics is that NBA experience is relatively low compared to the range. The average NBA

experience is about 5 years compared to athletes who have 19 years of experience. This shows

that the league is relatively young and that NBA careers do not last very long.

Table 2 presents the first results of the regression. The variables show that with an extra

year of NBA experience, an NBA player gets paid $363,000. The international variable shows

that if you are an international born player, you actually get paid $653,000 more than an

American born player. This result was more consistent with past economic literature. It was

also against my prediction that international players got paid a discount compared to American

born players. Although my adjusted R-square is at about 45% and my international dummy

variable is not significant at the 5% level. It is intriguing to see that the results coincide with

previous literature. I was fairly certain that International players got paid a discount and it

seems to not be the case.

The next couple tables are the results of the model with the international dummy

variable omitted, and replaced with the continental variables. In Table 3, the regression was ran

with players who had more than two years of experience. The results show that for the

countries in North America, South America, Europe, Australia, and Asia, they are all paid

premiums compared to their U.S born counter parts. Anyone who was born in Africa was

actually paid $1.7 million dollars less than U.S born players. The reasoning for this is uncertain

but it could be attributed to the fact that the NBA has not reached that market like that have in

many other continents. In Table 4, the model was ran with players who had more than 3 years

of experience. This was done to see if there were any significant changes between having two

years of experience and having three. There were some significant impacts on each continent.

One of the most significant impacts was the North American dummy variable, in which if you

were born in North America you were paid 1.1 million dollars less than an U.S born player. One

thing to note is that in Table 4, the model only included players with three or more years of

experience. Doing this decreases the sample size from 343 to 285 and decreases the amount of

international players in proportion to U.S born players.

Conclusion and Future Research

The NBA is a good example of how international players move in the labor market. As

shown in Figure 1, there has been a steady rise in international players in the NBA. This study

was intended to show that international players take salary discounts in order to play in the

NBA. In a traditional market it is believed that international labor is cheaper to acquire

therefore taking away from domestic labor. This does not seem to be the case in the NBA,

where international players are getting paid a premium to come play in the NBA. This is more in

line with past literature and against my initial expectations. Productivity from players who are

international are relatively the same when looking at their PER, which means most

international players play at the same level as U.S born players. This may be explained by the

NBA wanting to explain their global reach and in order to do so they must penetrate markets by

paying a premium for international players. In figure 2, you can see the distribution of NBA

players. About half of the international players are European while the rest of the distribution is

made up of the other continents. This helps the NBA amass more fans globally and gain more

profits. An example of this was when Yao Ming was drafted into the NBA, there had not been a

superstar player quite like him from China. His drafting alone brought 300 million fans to view

his Games on the Houston Rockets. Ever since then, China has been one of the largest markets

for the NBA, even though Yao Ming retired in 2011. The NBA reaps the benefits of having such

players and international players are now getting paid for their abilities and services.

I had not seen studies that have shown how each continent faired against American

players. The results showed that essentially every continent was paid a premium except for

Africa. I think this is because the NBA has not had a polarizing African player in the last decade

or so and popularity in Africa has yet to rise as fast as other continents. It is evident in Table 3

and Table 4 that they were paid substantially lower than American born players.

The findings from the primary models indicate that international players are paid more

than U.S born players today. I also show that certain continents are paid more while a few

continents are paid less than U.S born players. A model on different years prior to the immense

growth in the NBA could show different results because there was less scouting and the cost of

going overseas was more. A model as such could show if there has been progression up until

today or if it has been stagnant in terms of wage discrimination. Alternative methods could also

be used such as incorporating an interaction variable or using a double fixed effects model but

due to constraints, I was unable to do. In future studies, I hope to further explore the impacts

that international players have had in the NBA and delve deeper into building a more accurate

model for salary discrimination in NBA.

Table 1

Descriptive Statistics

Mean Standard Deviation Minimum Maximum

Salary 4,175,005 4649452.711 29843 23500000

NBA Experience 5 4.191048435 0 19

Height 2.01 0.944736037 1.75 2.18

PER 9.9 5.774741602 -3 30.25

Observation 453

Years 2014-2015

Table 2

Regression Results (Experience >2)

Coefficients t Stat

Intercept -3337** [661] -

5.044248822

NBA Experience 363** [51] 6.993630569

Height 29 [182] 0.163901121

PER 531** [34] 15.48818174

International 653 [502] 1.301824144

Adjusted R Square 0.452857164

Standard Error 3665603.555

Observations 343

Coefficients read at thousands of dollars. Standards errors, displayed in brackets

** Indicates Significance at 5% level Table 3

Regression Results (Experience >2)

Coefficients t Stat

Intercept -3337 [666]** -5.010746686

NBA Experience 362 [525]** 6.905469421

Height 29 [183] 0.155374751

PER 532 [34]** 15.3743895

North American 617 [1412] 0.436864855

South American 1354 [1185] 1.14286508

European 607 [631] 0.960933769

African -1673 [2140] -0.781883488

Australia 662 [1524] 0.434303107

Asia 2683 [3692] 0.726522556

Adjusted R Square 0.447743876

Standard Error 3682692.053

Observations 343

Coefficients read at thousands of dollars. Standard errors, displayed in brackets

** Indicates significance at 5% level

Table 4

Regression Results (Experience >3)

Coefficients t Stat

Intercept -3222 [777]** -4.145477424

NBA Experience 270 [62]** 4.31752285

Height 95 {187] 0.050979852

PER 610 [38]** 15.74520113

North American -1135 [1704] -0.666485526

South American 898 [1348] 0.665534845

European 362 [711] 0.5089314

African -388 [2664] -0.145865062

Australia 2293 [2187] 1.048699912

Asia 1867 [3764] 0.49601787

Adjusted R Square 0.473778373

Standard Error 3750023.306

Observations 285

Coefficients read at thousands of dollars. Standard errors, displayed in brackets

** Indicates Significance at 5% Level 0

20

40

60

80

100

120

1980-1981 1985-1986 1990-1991 2000-2001 2013-2014 2014-2015 2015-2016

International Players

International Players

Figure 1 - Influx of International Players

Figure 2 - International player distribution

12%

16%

2%5%

56%

9%

Make up of NBA by Continent (Excluding U.S)

North America South America Asian African European Australia

References/ Sources

"These Are the Salaries of All NBA Teams This Season." HoopsHype. Web. 18 Feb. 2016. http://hoopshype.com/salaries/

"Player Index." NBA.com/Stats. Web. 18 Feb. 2016. http://stats.nba.com/players/

Mogull R. (1974). Racial discrimination in professional basketball. The American Economist, 1, 11–15.

Game Theory "Continental Divide." The Economist. The Economist Newspaper, 2013. Web. 18 Feb. 2016.

http://www.economist.com/blogs/gametheory/2013/12/competitive-balance-basketball?zid=319&ah=17af09b0281b01505c226b1e574f5cc1 Gaines, Cork. "SPORTS CHART OF THE DAY: International Players Are Still A Big Part Of The NBA Draft." Business Insider. Business Insider, Inc, 2012. Web. 18 Feb. 2016.

http://www.businessinsider.com/nba-chart- international-players-draft-2012-6

"NBA.com: International NBA Players Map." NBA.com: International NBA Players Map. Web. 18 Feb. 2016.

http://www.nba.com/global/playersinternational_071109.html "International Influence." NBA.com. Web. 18 Feb. 2016.

http://www.nba.com/news/international-influence-index/

(n.d.). Retrieved May 04, 2016, from http://espn.go.com/nba/salaries/_/year/2015

2014-2015 NBA Stats: Per Game | Basketball-Reference.com (n.d.). Retrieved May 04, 2016 from

http://www.basketball-reference.com/leagues/NBA_2015_per_game.html Eschker, E., Perez, S., & Siegler, M. (2004). The NBA and the influx of international basketball players. Applied Economics, 3, 1009-1020 Yang, C. & Lin, H. (2015). Is There Salary Discrimination by Nationality in the NBA?: Foreign Talent or Foreign Market. Journal of Sports Economics, 13, (1), 53-75. Hill, James Richard and Groothuis, Peter, (2015), Are Findings of Salary Discrimination Against Foreign-Born Players in the NBA Robust?, No 15-13, Working Papers, Department of Economics, Appalachian State University. http://econ.appstate.edu/RePEc/pdf/wp1513.pdf