Submitted by: Philip Broyles & Bradley Keen


This research examines consumer discrimination in the NBA trading-card market. Using a sample of 298 NBA trading cards for the 1991-92 season, we find that race does not affect whether a trading cards sell above the common-player price. This is consistent with previous research on NBA trading cards. However, it was found that among players with common-player priced cards (average players), blacks out perform whites in points-per-game and assists-per-game. Further research is needed to see if black-white performance differences are related to discrimination in entry or retention in the NBA.


Of the major professional sports, basketball may seem the least likely place for racial discrimination. Since its integration in the 1940s, professional basketball has achieved the highest level of African American representation of the major professional sports in the United States. Today over 80 percent of NBA players are African American. Moreover, many of the most celebrated athletes today, such as Michael Jordan and Kobe Bryant, are African American basketball players. It is therefore surprising that numerous studies provide evidence of discrimination in the NBA labor market.

Labor market discrimination is defined as unequal treatment of equally qualified workers. A great deal of research on labor market discrimination has focused on the racial pay differences of NBA athletes (see Kahn, 2000, 1991). The results vary greatly. Rockwood and Asher (1976) and Mogul (1977, 1981) found no significant difference in white and black player’s salaries in the 1970s. Using a sample from the early 1980s, Scott et al. (1985) also found no relationship between player’s race and earnings. These studies, however, were based on small samples of athletes (N<30). Studies with larger samples from the 1980s consistently show a significant relationship between race and earnings. Kahn and Sherer (1988) found that white players earned 21-25 percent more than their black counterparts. Similarly, Wallace (1988) found that white players earned 18 percent more than black players and Koch and Vander (1988) found the difference to be 12 percent. Recent research by Hamilton (1997) suggests that differences in pay between black and white players may be disappearing. Using a sample of players from the 1994-95 season, Hamilton found that black players out-earned whites through the 75th percentile but at the top (90th percentile) whites were paid slightly more than blacks.

Kahn (2000) suggests that consumer discrimination may explain some of the racial pay gap observed in basketball. If fans are prejudiced against African Americans, teams may hire more white players or pay white players more. There is evidence from the 1980s that is consistent with this hypothesis. Numerous studies show that black players lower revenues or attendance (Kahn, 1991). For example, Kahn and Sherer (1988) found that during the 1980-86 period, white players generated as many as 13,000 additional fans per year. And other researchers have found that the racial makeup of NBA teams was similar to the racial composition of the area in which they were located (Burdekin and Idson, 1991; Hoang and Rascher, 1999). More recent research suggests that consumer discrimination may be on the decline. Dey (1997) found that in the 1987-93 period, white players only brought in an average of 60 additional fans per season.

Few studies have examined consumer discrimination more directly. One exception is a study by Stone and Warren (1999), which examines the price of NBA players’ trading cards. Using a sample of 258 NBA players from the 1976-77 season, they found that the price of NBA trading cards did not vary by player’s race. Their methodology, however, was based on the assumption that consumer discrimination is pervasive throughout the ability distribution. It is possible that card collectors only discriminate against the star athletes. Consumers may have discriminatory preferences for white stars but no real preference among black and white athletes of average ability. If this is the case, then the trading cards of white stars will be valued more than the cards of black stars. This hypothesis is consistent with Hamilton’s (1997) research on earnings discrimination, which showed that white players only had an advantage over black players at the superstar level-at the top of the earnings distribution. To further understand this issue, we examine a sample of 298 NBA trading cards produced in 1992.

Data and Methodology

Our sample consists of a complete set of Fleer NBA trading cards issued in 1992. We picked Fleer over other brands because they have been producing basketball cards the longest time and have had NBA production rights since 1986. Specialty cards (coaches, multiple players and so forth) were eliminated from the set, leaving a final sample of 298 cards representing NBA players active during the 1991-92 season-all have since retired. Eighty percent of the trading cards are of black NBA players.

The value of a player’s card is determined largely by the performance of the player. Unlike studies of other labor markets, job performance of professional athletes can be precisely measured. Comprehensive basketball statistics are kept for all facets of the game. In this study, we use multiple measure of performance, including field goal percentage, three-point field goal percentage, free throw percentage, rebounds per game, assists per game, points per game and games played. The performance data was collected from The Official NBA Encyclopedia (2000), which is a comprehensive source on basketball statistics. Additional information was collected from The Sporting News Official NBA Register (2000, 2001).

The value of a player’s card is also determined by the scarcity of the card, which is related to the age of the card and the number of cards. The influence of scarcity on card prices is minimal when a single set of cards is considered because cards from the same set are produced in the same number and are the same age-though there may be some small difference in the actual number of cards in circulation because cards are lost over time. Because we have selected trading cards from a single set, scarcity will not be a major determinant in the price of the cards we are examining.

A couple other factors also affect the value of cards. First, the condition of the card affects the value of player’s cards. Cards in better condition are worth more that others. To control for this effect, we examine cards in mint condition. Second, rookie cards are often worth more than other cards. Cards that mark the debut of a player are highly prized by collectors, especially those of star players. To control for this bias, we use a dummy variable to indicate rookie status. There are 42 (14%) rookie cards in the sample.

Several price guides exist for basketball trading cards. The most comprehensive and respected source for trading card prices is Beckett’s price guide. We use the Beckett 2003 Official Price Guide for Basketball Cards to determine the price for trading cards in mint condition. Most cards sell at the “common player” price, which is the minimum value of a card and is not related to performance of a player. Of the cards in our sample, 213 (71%) have the common-player price, only 85 (29%) are priced higher. The common-player price is $.05 and the maximum mint value for the sample is $2.00. Because a large number of cards in the sample are valued at the common-player price and because there is small variation among those cards priced above the common-player price, we can essentially identify two groups for comparison: average players (those with common-player priced cards) and star players (those with higher priced cards). Therefore, card price is coded as a dummy variable, contrasting common-player priced cards with higher priced cards.


Table 1 reports the descriptive statistics for the performance variables by race. Overall, there is little difference between the performance of black players and white players, with the exception of points-per-game. Black players score just under two points more per game than do white players. Black players appear to outperform white players slightly in rebounds-per-game and assists-per-game, but the differences are not statistically significant. Black players also play more games, on average, than do white players.

A linear regression was run to see if performance differences between black and white players affected the value of trading cards. Table 2 presents the results for the logistic regression. As the results in Table 2 indicate, three of the major performance variables are statistically significant: rebounds-per-game, points-per-game, assists-per-game. Each of these variables increases the odds that a trading card is worth more than the common-player price. For example, an increase of one point per game increases the odds of a trading card being worth more than the common-player price by 26 percent. Performance variables based on shooting percentages are not significant. Field goal and free throw percentages have no affect on whether cards are worth more than the common-player price. However, the number of games played also increases the odds that a card is worth more than the common-player price. And, as expected, rookie cards are more likely to be priced above common-player price, too.

Consistent with Stone and Warren (1999), race of the player has no significant effect on the value of NBA trading cards in our sample. Although the beta coefficient for race suggests that being white increases the odds that the player’s trading card is worth more than the common-player price, the coefficient is not statistically significant. Trading card enthusiasts do not value white NBA stars more than black stars. These findings lend to the mounting evidence that consumer discrimination may be declining in professional basketball.

Although it appears that consumers do not discriminate against black stars, it is still possible that consumers discriminate against average black players. To explore this issue, we calculated black and white means of performance variables for trading cards that were priced at the common-player price. Table 3 presents the means for the 213 cards that were priced at the common-player price. The results show that average black players outperform white players in points-per game and assists-per-game. The differences are rather small. Black players average almost two more points per game and less than one assist more per game than white players. Nonetheless, it appears that blacks must perform better than whites to retain a place on the bench. This may reflect consumer discrimination. Coaches may keep white players who don’t perform as well to appease white fans. It may also reflect employer discrimination by coaches. Coaches may have higher expectations for black players than for white players. Further research is needed to disentangle these processes.


This research adds to the mounting evidence that consumer discrimination in the professional basketball is on the wane. Similar to Stone and Warren (1999), little evidence was found of discrimination in the trading card market. Trading-card collectors show little preference for white stars over black stars. This may largely be due to the rise of popular superstars like Magic Johnson and Michael Jordan, whose celebrity appeal crosses racial lines. Fans today can identify with, and desire to emulate, black NBA stars.

Future research should examine the role of these performance differences in entry and retention discrimination.


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TABLE 1. Means for Performance Variables by Race (N=298)
Means Standard Deviation 
Number of Games Played








Field Goal Percentage Black






Three Point Percentage Black






Free Throw Percentage Black






Rebounds Per Game Black






Assists Per Game Black






Points Per Game* Black






*Statistically significant difference between means at the .05 level


TABLE 2. Logistic Regression Coefficients
Variable Beta Standard Error 
Constant -6.961 4.188
Race .616 .541
Rookie Card 3.436* .649
Center -1.349 .765
Number of Games Played .003* .001
Field Goal Percentage -7.629 7.147
Three-Point Field Goal Percentage 2.164 2.380
Free Throw Percentage .947 3.621
Rebounds Per Game .432* .129
Assists Per Game .278* .111
Points Per Game .228* .064
-2 Log Likelihood 194.781
*p < .01


TABLE 3. Means for Performance Variables by Race for Average Players (N=213) 
Means Standard Deviation 
Number of Games Played








Field Goal Percentage Black






Three Point Percentage Black






Free Throw Percentage Black






Rebounds Per Game Black






Assists Per Game* Black






Points Per Game* Black






*Statistically significant difference between means at the .05 level
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