the University of iowa

Hiring Discrimination at the NBA General Manager Position

An Empirical Analysis

Ryan Volk

5/9/2012

Recent racial discrimination studies of the NBA have resulted in mixed outcomes. This paper will look at discrimination in the hiring of general managers using data from the 2001-2002 to 2010-2011 seasons. Convincing evidence for hiring discrimination could not be found. Rather, this paper questions the relationship of black general managers with players’ predominately white agents and concludes that further study should be performed in that area.

Introduction

Early research on racial discrimination in sports has focused primarily on players since

they provide a generally reliable, measurable output in the form of performance. More recently,

research has also expanded to coaches as the sample of minority coaches has grown to a level

that makes statistical testing relevant. However, one of the most important positions in sports is

the general manager position, which has been widely ignored. Commonly, the general manager

of a professional sports team is responsible for signing, trading, and drafting players, as well as

hiring coaching staff. Forbes says “The general manager is the most influential and scrutinized

position in sports because he decides how the owner's money is spent on players.”1

The National Basketball Association (NBA) has earned a reputation as having a racial

and gender diverse office and executive staff. The UCF College of Business Administration “The

Institute for Diversity and Ethics in Sport” (TIDES) publishes an annual Racial and Gender

Report Card, giving grades to the various U.S. sports leagues. TIDES gave the NBA an A+ for

racial diversity for the 2010-11 season.2 Richard Lapchick, director of TIDES and primary author

of the report, said, “Throughout the history of the Racial and Gender Report Card, the NBA has

consistently been the leader on diversity issues in sport... Thirty-six percent of the professional

positions in the League Office are held by people of color while women held 42 percent of the

professional positions. Thirty-three percent of the coaches and 26 percent of the GMs are people

of color. All these categories are higher than in any other men’s professional league.”

Specifically at the general manager position, the NBA received an A grade for racial diversity.

Comparatively, in TIDES’ most recent reports, the NFL and MLB received a B/B+ and C,

1 http://www.forbes.com/2007/03/02/sports-greatest-gms-biz-cz_jg_0302gms.html2 http://www.tidesport.org/RGRC/2011/2011_NBA_RGRC_FINAL%20FINAL.pdf

respectively. The NBA is able to offer more observations of black general managers than these

other leagues, resulting in stronger statistical integrity within this paper.

TIDES determination of grades is based on percentages and not any statistical analysis.

This paper will attempt to draw a conclusion as to whether there is evidence for discrimination at

the general manager position in the NBA based on regression analysis. This paper will also

outline how black general managers in the NBA structure their team differently than white

general managers, and bring forth a question regarding the relationship between players’ agents

and team general managers.

Literature Review

There have been several papers monitoring racial discrimination of NBA players

in the form of wages. Kahn and Sherer (1988) found a salary deficit of approximately 20% for

black players when compared with similarly skilled white players during the 1985-86 season.

Brown et al. (1991) and Koch and Vander Hill (1988) examined the 1984-85 seasons and found

a wage gap of approximately 16% and 9%, respectively. However, Bodvarsson and Brastow

(1999) claimed that the wage gap disappeared between the time period of the previous studies

and the 1990-91 season due to institutional changes in the NBA. These changes resulted in

increased mobility and reduced monopsony power that teams had over young players.

Additionally, four new franchises entered the league between those time periods, resulting in

increased competition among teams. Bodvarsson and Brastow concluded that at least part of the

wage gap had been a result of racial discrimination by white team owners and managers, which

contrasts with earlier studies that claimed the primary source of discrimination was from

customers rather than management.

Furthermore, Price and Wolfers (2010) found taste-based discrimination in the officiating

of NBA referees. They found that more fouls are called against players when they are officiated

by an opposite-race officiating crew. They also found that the difference in foul calls is large

enough that it affects players’ statistical averages as well as team winning probabilities. Previous

research regarding wage differences used observable player statistics as an indicator for wage

discrimination. Considering that a majority of NBA players are black and around two-thirds of

referees are white, previous research may be skewed by biased refereeing in a way that

underestimated wage discrimination against blacks.

Finally, there have been recent studies on discrimination in the NBA at the head coach

position. Kahn (2006) found no racial differences in entry, pay, or retention probability after

controlling for payroll and team quality. Fort, Lee and Berri (2008) further this conclusion by

creating an enhanced model that controlled for the talents of individual players. They determined

that coaches are fired or retained based on their efficiency and there was no detectable race-

based difference in this efficiency.

Conclusions regarding discrimination at different levels of the NBA have been mixed.

This paper will attempt to test for discrimination in the hiring practices of NBA franchises when

hiring a team general manager. This paper will use a method similar to Szymanski (2000), which

tested for player discrimination in English professional soccer. After controlling for a team’s

wage bill, Szymanski compared the proportion of black players on each team to that team’s

league performance. If there was no discrimination, we would expect that the proportion of black

players would not significantly correlate with performance, as teams would spend equally for

black and white players of equal talent. However, Szymanski found the proportion of a team that

is black is positively correlated with team performance. This correlation implies that black

players are receiving a lower wage for their talents. This method has the advantage of ignoring

factors relating to player productivity and simply assuming that there is an efficient market for

talent. Rather than looking at racial composition of teams, this paper will examine whether the

general manager being black affects a team’s performance, measured by the number of regular

season games won by a team.

Description of Method and Data

This paper will attempt to detect discrimination in the hiring of black general managers in

the NBA by measuring the performance of NBA general managers and testing the significance of

race on performance. Discrimination implies that if candidates are otherwise equal in ability,

black candidates would be passed over for white candidates. This barrier to entry for black

general managers would result in the average performance of black general managers being

superior to white general managers after controlling for independent variables.

To measure performance we will use a regression in which team wins in a season is the

dependent variable. Independent variables for each team in each season include team salary

(Salary), team market size (Market), number of games that players missed for any reason

(Injury), how many years the general manager has held his position (Years), whether the general

manager is formerly an NBA player (Player), and whether the general manager is black

(Minority).

Total salary is based on the total amount of salary owed to players on each team for each

season. Due to the complications of the NBA salary cap and luxury tax, the total salaries paid to

players may underestimate the amount paid by teams above the luxury tax threshold. However,

total salaries paid are still relatively representative of team spending as all teams face the same

penalties for spending over the luxury tax. Total salary is normalized as a ratio, computed by

averaging team salaries for a given season, and dividing each individual team salary for that

season by the league-wide average team salary for that season. For example, if in Season X, team

Y spends $75 million when league average for Season X was $50 million, team Y will have a

total salary ratio of 1.5 for season X. This was done to normalize salaries across the observed

period since, as a general trend, team salaries increased year by year.

‘Injury’ is used as a blanket term for games missed due to injury, illness, suspension,

leave of absence, etc. The final value of the ‘injury’ component is found by taking the square

root of the sum of the number of games missed by each player on given a team. A concave

function is used since, presumably, the more games missed by particular players, the more a

team adapts to playing without that player. The square root function provided the strongest

correlation with performance among simple concave functions.

Market size is measured as the surrounding metro population in millions. Whether a

general manager has NBA playing experience may affect performance, and it is included it as a

binary input. ‘1’ denotes NBA playing experience and ‘0’ denotes none. Whether or not the

general manager is black was also input in a binary format. For a black general manager, the

‘minority’ value is ‘1’. For a white general manager, the ‘minority’ value is ‘0’.

Wins = α0 + α1 * Salary + α2 * Market + α3 * Years+ α4 * Player + α5 * Injury + α6 * Minority

We would anticipate that α1 is positive since teams that spend more money for players are

likely to have better players. We would anticipate that α1 is positively correlated with α2, since a

larger market is more likely to bring in more revenue for a good team, making them more willing

to spend the money necessary for a good team. We would anticipate that α2 is either insignificant

or positive assuming that given equal salary offers, a player may be more likely to sign with a

larger market team for the increased endorsement opportunities. The general assumption is that

α3 would be positive, as more experienced general managers may be able to utilize their

experience during any decisions they make. Whether α4 should have a positive or negative

coefficient is uncertain. Having played in the NBA may mean a general manager is better at

evaluating talent, or determining “team chemistry” when putting together a squad. However, a

general manager without NBA playing experience may mean he has more business savvy and be

able to sign players at a discount. We would anticipate that α5 is negative. When a player is

injured, he is typically replaced by a lower quality player, resulting in lower team performance.

α6 is the variable that would provide evidence of discrimination for or against black

applicants. If α6 is positive and statistically significant then we would conclude that black general

managers outperform white general managers after controlling for other independent variables.

Thus, there may be barriers to entry for blacks at the general manager position that results in

black candidates being passed over for white candidates of equal or lesser ability. If α6 is

negative and statistically significant after controlling for other variables, we would conclude that

black general managers perform worse than white general managers. Thus, there could be

incentives for owners to hire blacks over similarly able or more able white applicants. If α6 is not

statistically significant, we cannot reject the null hypothesis that there is no racial discrimination

in the hiring of NBA general managers.

Data on NBA team salaries was obtained from USA Today3, which lists team salaries

every year starting with the 2001-2002 season. The number of wins and playoff results for each

team in each season was obtained using basketball-reference.com4. Basketball-reference.com and

3 http://content.usatoday.com/sportsdata/basketball/nba/salaries/team4 http://www.basketball-reference.com/

Google Images were used to collect race and playing experience data on general managers.

Injury data was obtained from BrewHoop.5 For market size, Forbes NBA team valuation’s

definition of “metro population”6 was used. The unit of observation is the general manager of

NBA teams by year. There are 297 observations from the 2001-2002 to 2010-2011 NBA

seasons. Of the 297 observations, 54 are of black general managers. Data from the 2001-2002 to

2010-2011 seasons is used because that is the timeframe in which USA Today has reported team

salaries. An attempt could have been made to obtain data from previous seasons, using other

sources for team salaries. However, this paper uses the same source for all salaries in order to

avoid any inconsistencies in salary calculations that may result from midseason trades, released

players, or players that were brought up from or sent down to the NBDL, the equivalent of the

minor leagues for the NBA.

Descriptive statistics of variables:

Numerical Variables N Mean Std Dev Min Q1 Median Q3 MaxWins 297 41 12.22 12 33 42 50 67Sqrt Injury 297 8.499 2.648 2.236 6.671 8.185 10.173 16.340Market Size(Millions)

297 5.391 4.770 1.1 2.1 4.4 5.9 19.1

Year at Position 297 5.135 4.341 1 2 4 7 23Salary Ratios

Total Salary Ratio 297 1 .211 .3163 .8858 .9881 1.0917 1.7766Average Salary Ratio 297 1 .2276 .2623 .8575 .9809 1.1272 2.0842Median Salary Ratio 297 1 .3937 .2634 .7191 .9642 1.2182 2.3757Standard Deviation Ratio 297 1 .2970 .2363 .8076 .9675 1.1899 1.9540

Binary Variables Yes NoFormer NBA Player 297 149 148Minority 297 54 243Former NBA Player and Minority

297 42 255

Results

5 http://www.brewhoop.com/2011/7/21/2180038/comparing-the-bucks-injuries-to-a-decades-worth-of-data6 http://www.forbes.com/nba-valuations/

Regression 1: Wins vs. independent variables

Predictor Coef SE Coef T PConstant 37.505 (α0) 3.600 10.42 0.000Total Salary Ratio 20.018 (α1) 2.915 6.87 0.000 **Market Size -0.3227 (α2) 0.1296 -2.49 0.013 **Year at position 0.0832 (α3) 0.1419 0.59 0.558NBA Player 1.646 (α4) 1.249 1.32 0.188Sqrt Injury -1.8484 (α5) 0.2307 -8.01 0.000 **Minority -2.290 (α6) 1.638 -1.40 0.163R-Sq = 30.0%

Salary Ratio vs. Market Size

Predictor Coef SE Coef T PConstant 0.95843 0.01972 48.59 0.000Market Size 0.007710 0.002742 2.81 0.005 **R-Sq = 2.6%

As predicted, α1 is positive and is strongly correlated with wins and α5 shows a strong

negative correlation with wins. Unexpectedly, α2 is negative and significant. Market Size and

Salary Ratio are also positively correlated, though not as much as may be expected. A population

difference of 1 million predicts a spending difference of 0.7% of the league average for that

season.

In regression 1, the p-value for ‘minority’ is not statistically significant. However, it is

relatively close to being so. However, when average wins by each team are predicted based

strictly on average total salary ratio over the 2001-2002 to 2010-2011 seasons, there are two

clear outliers. The San Antonio Spurs (SAS) grossly outperformed expectation based simply on

their team salary, and the New York Knicks (NYK) severely underperformed expectations based

on their team salary.

Regression 2: Wins vs. independent variables (SAS and NYK removed)

Predictor Coef SECoef T PConstant 27.118 3.884 6.98 0.000Total Salary Ratio 26.826 3.112 8.62 0.000 **Market Size 0.0845 0.1461 0.58 0.563Year at Position -0.0421 0.1392 -0.30 0.762NBA Player 1.870 1.232 1.52 0.130Sqrt Injury -1.6316 0.2287 -7.13 0.000 **Minority -0.560 1.628 -0.34 0.731R-Sq = 34.1%

Removing SAS and NYK, reduces sample size from 297, to 277, and reduces

black general manager observations from 54 to 50. However, the samples of SAS and NYK

skewed Regression 1 results. This is evident in the change of the ‘Market Size’ coefficient,

which becomes statistically insignificant. Regression 2 displays no evidence of discrimination in

the hiring of black general managers in the NBA.

While there may not be evidence for racial discrimination in the hiring of general

managers, further investigation of player salaries shows there are differences in the way black

general managers structure their team. Regressions 3 and 4 show the effects of independent

variables on the standard deviation of player salaries and the median salary for a team. Similar to

Total Salary Ratio, Standard Deviation Ratio and a Median Salary Ratio are both ratios of the

average for each variable in each season, with 1 being the average for each variable in each

season.

Regression 3: Standard Deviation ratio vs. independent variables

Predictor Coef SECoef T PConstant 0.04341 0.06466 0.67 0.503Minority -0.10358 0.03429 -3.02 0.003 **Total Salary Ratio 0.91270 0.06101 14.96 0.000 **Market Size 0.004529 0.002710 1.67 0.096 *NBA Player 0.00928 0.02614 0.36 0.723Year at Position 0.005768 0.002929 1.97 0.050 **R-Sq=47.9%

Regression 4: Median Salary ratio vs. independent variables

Predictor Coef SECoef T PConstant -0.11487 0.09552 -1.20 0.230Minority 0.12579 0.05065 2.48 0.014 **Total Salary Ratio 1.08122 0.09012 12.00 0.000 **Market Size -0.000634 0.004004 -0.16 0.874NBA Player 0.08300 0.03861 2.15 0.032 **Year at Position -0.006276 0.004327 -1.45 0.148R-Sq=35.3%

It appears that black general managers spent their money differently than white general

managers. Black general managers have a smaller standard deviation among player salaries

relative to white general managers and the median salary on a black general manager’s team is

generally higher. A lower standard deviation of player salaries with higher median player

salaries, when controlling for total team salary, likely means that black general managers sign

less big contracts with superstars and spend more on mid-level “role players.” Black general

managers not signing superstars could be a result of different team structure preferences by black

and white general managers. Given that 78% of black general managers in this data set were

former NBA players, black general managers likely have similar basketball experiences and

preferences as those in the ‘former NBA players’ group as a whole, and should thus have similar

ideas with regards to how to structure a team. However, the ‘minority’ coefficient is still

statistically significant in Regressions 5 and 6, when looking only at former players.

Regression 5: Standard Deviation ratio vs. independent variables (former players only)

Predictor Coef SE Coef T PConstant 0.08702 0.09297 0.94 0.351Minority -0.13626 0.04236 -3.22 0.002 **Total Salary Ratio 0.88833 0.08662 10.26 0.000 **Market Size 0.008392 0.003902 2.15 0.033 **Year at Position 0.001650 0.003955 0.42 0.677R-Sq = 47.8%

Regression 6: Median Salary ratio vs. independent variables (former players only)

Predictor Coef SE Coef T PConstant -0.2126 0.1272 -1.67 0.097Minority 0.16220 0.05797 2.80 0.006 **Total Salary Ratio 1.2539 0.1185 10.58 0.000 **Market Size -0.006277 0.005339 -1.18 0.242Year at Position -0.000887 0.005412 -0.16 0.870R-Sq = 44.6%

Regression 7 shows that standard deviation among player salaries is the most important

salary measurement for predicting team success.

Regression 7: Wins vs. Salary Ratios

Predictor Coef SE Coef T PConstant 20.618 3.186 6.47 0.000Total Salary Ratio 3.944 6.855 0.58 0.566Average Salary ratio 5.832 8.355 0.70 0.486Median Salary ratio -0.902 3.070 -0.29 0.769Standard Deviation ratio 11.490 4.644 2.47 0.014 **R-Sq = 17.1%

Regression 8 shows that when controlling for the ‘standard deviation ratio’, the

‘minority’ coefficient is insignificant, eliminating doubt of different performance levels of black

and white general managers when forming a team with a similar makeup of player salaries.

Regression 8: Wins versus independent variables

Predictor Coef SE Coef T PConstant 40.834 3.077 13.27 0.000Std Dev ratio 15.615 2.101 7.43 0.000 **Sqrt Injury -1.6420 0.2302 -7.13 0.000 **Market Size -0.3499 0.1285 -2.72 0.007Year at Position -0.0299 0.1410 -0.21 0.832NBA Player 1.487 1.234 1.20 0.229Minority -1.099 1.644 -0.67 0.504R-Sq = 31.6%

An alternative to preferential differences between black and white managers with regards

to signing superstars could be reluctance by superstars or their agents to sign with teams that

have black general managers. The players in the NBA are around 80% black, which would

seemingly rule out player animus due to race. But players’ agents are still largely white. Agents

represent players and handle negotiations between teams and their player, and advise their client

much like a lawyer. Andre Farr, Chairman and Chief Executive Officer of the Black Sports

Agents Association (BSAA), says "for years, not only in sports, but every area of professional

management and business, [black athletes] would not do business with African-American

[agents]. They felt like they might be locked out of the process, or that they wouldn't be able to

negotiate a fair and equitable deal. That attitude hasn't vanished.”7 There may be discrimination

among some agents in signing their superstars with black general managers that results in

relatively lower performance by black general managers due to the limited supply of players they

can sign relative to their white counterparts. Further study needs to be conducted to draw any

empirical conclusions.

There are some limitations to this papers research and methods. NBA general managers’

salaries are not public information so we cannot test for wage discrimination. It is possible that

equally talented black and white general managers receive different pay even if they are hired at

the same rate. Also, while general managers have executive power over most aspects of a team,

a team owner still has veto power. Therefore, some of the decisions a general manager makes

may not reflect their primary preference. Additionally, newly hired general managers inherit

many of the contracts and hirings of previous general managers. It may take several years before

those contracts expire or a team can release a player without monetary penalty. Free agent

signings are also a major part of general manager responsibilities. When free agents are deciding

amongst contract offers from different franchises, there may be subjective factors that were not

captured in the data, such as local climate, proximity to hometown, etc.

Finally, since basketball rosters are relatively small, injuries to key players generally have

a larger effect than in other sports. While attempting to control for this with the independent

7 http://sports.espn.go.com/espn/blackhistory2008/news/story?id=3268714

variable ‘injury’, this variable is a cumulative of all the players on a team. Clearly, the absence of

different players will have varying effects.

Conclusions

Using a modification of Szymanski’s method for detecting taste-based

discrimination, this paper was unable to detect discrimination in the hiring of NBA general

managers. The TIDES report card for the NBA general manager position gave the NBA an A

grade and this paper found no evidence to dispute this grade. As with recent research that was

unable to find racial discrimination within NBA franchises at the player and coach level, this

paper is unable to find evidence of discrimination in hiring at the general manager level for the

given period.

This paper did find evidence that the composition of teams of black general managers is

different than teams of white general managers. Black general managers signed fewer large

salaried contracts and more contracts with middling salaries. Presumably, large contracts are

made with superstar players, leading to the question of why black general managers do not sign

as many superstars as white general managers. Preliminary expectations are that there might be a

discriminatory factor in the agent-general manager relationship. The next step would be to

determine whether there is in fact discrimination between agents and general managers, and if a

method can be utilized to determine who the discriminator is.

NBA franchises may not be able to afford to discriminate due to the heavy costs in a

competitive field. Consequently team owners and executives seemingly did not discriminate

during the given time period. However, the possibility of discrimination in the agent-general

manager relationship and Price and Wolfer’s findings of referee discrimination may be a result of

lack of competition in the agent and referee professions. Large agencies representing most

superstar players have abundant resources, established relationships, and connections that

smaller firms do not, resulting in limited competition. The National Basketball Referees

Association may decrease competition for refereeing jobs at the NBA level. The lack of

competition within these entities may result in a lingering presence of discrimination in the

NBA.

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