{"id":187,"date":"2005-01-07T17:05:25","date_gmt":"2005-01-07T23:05:25","guid":{"rendered":""},"modified":"2015-03-20T11:13:22","modified_gmt":"2015-03-20T16:13:22","slug":"consumer-discrimination-in-the-nba-trading-card-market","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/consumer-discrimination-in-the-nba-trading-card-market\/","title":{"rendered":"Consumer Discrimination in the NBA Trading-Card Market"},"content":{"rendered":"

Submitted by: Philip Broyles & Bradley Keen<\/p>\n

Abstract<\/strong><\/p>\n

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.<\/p>\n

Introduction<\/strong><\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Data and Methodology<\/strong><\/p>\n

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.<\/p>\n

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).<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Findings<\/strong><\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Conclusion<\/strong><\/p>\n

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.<\/p>\n

Future research should examine the role of these performance differences in entry and retention discrimination.<\/p>\n

References<\/strong><\/p>\n

    \n
  1. Beckett, James (2002), Beckett 2003 Official Price Guide for Basketball Cards, New York: The Crown Publishing Group.<\/li>\n
  2. Burdekin, Richard C. K. and Todd L. Idson, (1991) “Customer Preferences, Attendance and the Racial Structure of Professional Basketball Teams,” Applied Economics, 23:179-186.<\/li>\n
  3. Dey, Matthew S., (1997), “Racial Differences in National Basketball Association Players’ Salaries: Another Look,” The American Economist, 19 (3):293-318.<\/li>\n
  4. Hamilton, Barton Hughes, (1997),”Racial Discrimination and Professional Basketball Salaries in the 1990s,” Applied Economic, 29:287-296.<\/li>\n
  5. Hoang, Ha and Dan Rasher, (1997), “The NBA, Exit Discrimination, and Career Earnings,” Industrial Relations, 38 (1):69-91.<\/li>\n
  6. Hubbard, Jan., (ed.), (2000), The Official NBA Encyclopedia, New York: Doubleday.<\/li>\n
  7. Kahn, Lawrence M., (2000), “A Level Playing Field? Sports and Discrimination,” Pp. 115-130 in William S. Kern (ed.), The Economics of Sport Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.<\/li>\n
  8. Kahn, Lawrence M. (1991), “Discrimination in Professional Sports: A Survey of the Literature,” Industrial and Labor Relations Review, 44 (April):395-418.<\/li>\n
  9. Kahn, Lawrence M., and Peter D. Sherer, (1988), “Racial Differences in Professional Basketball Players Compensation,” Journal of Labor Economics, 6 (1):40-61.<\/li>\n
  10. Koch, James V., and C. Warren Vander Hill, (1988), “Is there Discrimination in the ‘Black Man’s Game’?” Social Science Quarterly, 69 (1) 83-94.<\/li>\n
  11. Mogul, Robert G., (1981), “Salary Discrimination in Professional Sports,” Atlantic Economic Journal, 21(3):106-110.
    \n12. Robert G., (1976), “A Note on Racial Discrimination in Professional Basketball: A Reevaluation of the Evidence,” American Economist, 21 (2):71-71.<\/li>\n
  12. Paur, Jeff, David Walton, John Gardella and John Hareas (eds.), (2000), The Sporting News 2001-2002 Official NBA Register, St. Louis: The Sporting News.<\/li>\n
  13. Rockwood, Charles E. and Ephraim Asher, (1976), “Racial Discrimination in Professional Basketball Revisited,” American Economist, 20 (1):59-64.<\/li>\n
  14. Scott, Frank A., Jr., James E. Long and Ken Somppi, (1985), “Salary vs. Marginal Revenue Product under Monopsony and Competition: The Case of Professional Basketball,” Atlantic Economic Revue, 13 (3):50-59.<\/li>\n
  15. Stone, Eric W. and Ronald S. Warren, (1997), “Customer Discrimination in Professional Basketball: Evidence from the Trading-Card Market,” Applied Economics, 31 (6): 679-686.<\/li>\n
  16. Wallace, Michael, (1988), “Labor Market Structure and Salary Determination among Professional Basketball Players,” Work and Occupations, 15 (3):294-312.<\/li>\n
  17. Walton, David and John Gardella (eds.), (2000), The Sporting News Official NBA Register 2002-2003 Edition, St. Louis: The Sporting News.<\/li>\n<\/ol>\n\n\n\n\n\n\n\n\n\n\n\n\n
    TABLE 1. Means for Performance Variables by Race (N=298)<\/strong><\/td>\n<\/tr>\n
    <\/td>\n<\/td>\nMeans<\/strong><\/td>\nStandard Deviation\u00a0<\/strong><\/td>\n<\/tr>\n
    Number of Games Played<\/strong><\/p>\n

     <\/td>\n

    		Black<\/p>\n

    White<\/td>\n

    		712.67<\/p>\n

    658.24<\/td>\n

    		326.13<\/p>\n

    328.30<\/td>\n<\/tr>\n

    Field Goal Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.466<\/p>\n

    .469<\/td>\n

    		.040<\/p>\n

    .037<\/td>\n<\/tr>\n

    Three Point Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.238<\/p>\n

    .268<\/td>\n

    		.116<\/p>\n

    .143<\/td>\n<\/tr>\n

    Free Throw Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.743<\/p>\n

    .757<\/td>\n

    		.077<\/p>\n

    .093<\/td>\n<\/tr>\n

    Rebounds Per Game<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		4.36<\/p>\n

    4.02<\/td>\n

    		2.48<\/p>\n

    2.32<\/td>\n<\/tr>\n

    Assists Per Game<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		2.59<\/p>\n

    2.12<\/td>\n

    		2.00<\/p>\n

    1.90<\/td>\n<\/tr>\n

    Points Per Game*<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		10.75<\/p>\n

    8.99<\/td>\n

    		4.92<\/p>\n

    5.00<\/td>\n<\/tr>\n

    *Statistically significant difference between means at the .05 level<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    TABLE 2. Logistic Regression Coefficients<\/strong><\/td>\n<\/tr>\n
    Variable<\/strong><\/td>\nBeta<\/strong><\/td>\nStandard Error\u00a0<\/strong><\/td>\n<\/tr>\n
    Constant<\/strong><\/td>\n-6.961<\/td>\n4.188<\/td>\n<\/tr>\n
    Race<\/strong><\/td>\n.616<\/td>\n.541<\/td>\n<\/tr>\n
    Rookie Card<\/strong><\/td>\n3.436*<\/td>\n.649<\/td>\n<\/tr>\n
    Center<\/strong><\/td>\n-1.349<\/td>\n.765<\/td>\n<\/tr>\n
    Number of Games Played<\/strong><\/td>\n.003*<\/td>\n.001<\/td>\n<\/tr>\n
    Field Goal Percentage<\/strong><\/td>\n-7.629<\/td>\n7.147<\/td>\n<\/tr>\n
    Three-Point Field Goal Percentage<\/strong><\/td>\n2.164<\/td>\n2.380<\/td>\n<\/tr>\n
    Free Throw Percentage<\/strong><\/td>\n.947<\/td>\n3.621<\/td>\n<\/tr>\n
    Rebounds Per Game<\/strong><\/td>\n.432*<\/td>\n.129<\/td>\n<\/tr>\n
    Assists Per Game<\/strong><\/td>\n.278*<\/td>\n.111<\/td>\n<\/tr>\n
    Points Per Game<\/strong><\/td>\n.228*<\/td>\n.064<\/td>\n<\/tr>\n
    -2 Log Likelihood<\/strong><\/td>\n194.781<\/td>\n<\/tr>\n
    *p < .01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
    TABLE 3. Means for Performance Variables by Race for Average Players (N=213)\u00a0<\/strong><\/td>\n<\/tr>\n
    <\/td>\n<\/td>\nMeans<\/strong><\/td>\nStandard Deviation\u00a0<\/strong><\/td>\n<\/tr>\n
    Number of Games Played<\/strong><\/p>\n

     <\/td>\n

    		Black<\/p>\n

    White<\/td>\n

    		629.59<\/p>\n

    570.82<\/td>\n

    		316.15<\/p>\n

    306.85<\/td>\n<\/tr>\n

    Field Goal Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.462<\/p>\n

    .463<\/td>\n

    		.042<\/p>\n

    .036<\/td>\n<\/tr>\n

    Three Point Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.224<\/p>\n

    .246<\/td>\n

    		.115<\/p>\n

    .146<\/td>\n<\/tr>\n

    Free Throw Percentage<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		.737<\/p>\n

    .738<\/td>\n

    		.079<\/p>\n

    .096<\/td>\n<\/tr>\n

    Rebounds Per Game<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		3.76<\/p>\n

    3.68<\/td>\n

    		2.00<\/p>\n

    2.09<\/td>\n<\/tr>\n

    Assists Per Game*<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		2.19<\/p>\n

    1.67<\/td>\n

    		1.67<\/p>\n

    1.50<\/td>\n<\/tr>\n

    Points Per Game*<\/strong><\/td>\nBlack<\/p>\n

    White<\/td>\n

    		9.10<\/p>\n

    7.37<\/td>\n

    		3.89<\/p>\n

    4.16<\/td>\n<\/tr>\n

    *Statistically significant difference between means at the .05 level<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"
    Submitted by: Philip Broyles & Bradley Keen<\/div>\n

    Abstract<\/h2>\n

    \nThis 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. <\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[290,296],"tags":[27,64,8,63],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-31","jetpack-related-posts":[{"id":2803,"url":"https:\/\/thesportjournal.org\/article\/determinants-of-nba-player-salaries\/","url_meta":{"origin":187,"position":0},"title":"Determinants of NBA Player Salaries","date":"May 29, 2015","format":false,"excerpt":"Submitted by Dr. Robert Lyons Jr.1*, Dr. E. Newton Jackson Jr.2*, Dr. Aaron Livingston3* 1*\u00a0Associate Professor,\u00a0Sport Management,\u00a0Queens University of Charlotte,\u00a0Charlotte, NC 2*\u00a0Professor,\u00a0Sport Management,\u00a0University of North Florida,\u00a0Jacksonville, FL 3*\u00a0Assistant Professor,\u00a0Sport Management,\u00a0Hampton University ,\u00a0Hampton, VA Abstract Determining the factors that influence National Basketball Association (NBA) owners to pay players is of great\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"table1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2015\/05\/table1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":44,"url":"https:\/\/thesportjournal.org\/article\/alabama-high-school-soccer-undergoes-eligibility-rule-changes\/","url_meta":{"origin":187,"position":1},"title":"Alabama High School Soccer Undergoes Eligibility Rule Changes","date":"February 11, 2008","format":false,"excerpt":"Submitted by: Dr. Joe Manjone, Dean of Student Services, Continuing Education & Distance Learning, USSA Changes in the penalties for Alabama High School soccer players receiving misconducts have had very little effect on the numbers and types of cards issued during the 1997 and 1998 Alabama high school soccer seasons.(note:\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":194,"url":"https:\/\/thesportjournal.org\/article\/nba-salaries-role-players-and-superstars\/","url_meta":{"origin":187,"position":2},"title":"NBA Salaries: Role Players and Superstars","date":"March 4, 2005","format":false,"excerpt":"Submitted by: Jacqueline Agesa, Richard U. Agesa & Maria Toshkova Abstract This study uses quantile regressions to evaluate salaries of NBA players in the 2001\u20132002 season separately for big men and guards. Quantile regression allows the measurement of the return to player attributes for guards and big men at different\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5862,"url":"https:\/\/thesportjournal.org\/article\/nba-players-pay-and-performance-what-counts\/","url_meta":{"origin":187,"position":3},"title":"NBA Players’ Pay and Performance: What Counts?","date":"August 2, 2018","format":false,"excerpt":"Authors: Kevin Sigler and William Compton Corresponding Author: Kevin Sigler, PhD 601 College Road Department of Economics and Finance Cameron School of Business UNC Wilmington Wilmingtomn, NC 28403 siglerk@uncw.edu 910-200-2076 Kevin Sigler is Professor of Finance in the Cameron School of Business, UNC Wilmington William Compton is Professor of Finance\u2026","rel":"","context":"In "Commentary"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2018\/08\/Table-1.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":112,"url":"https:\/\/thesportjournal.org\/article\/minority-hiring-practices-in-professional-sports\/","url_meta":{"origin":187,"position":4},"title":"Minority Hiring Practices in Professional Sports","date":"February 14, 2008","format":false,"excerpt":"Submitted by: Brad Muster CHAPTER I INTRODUCTION Professional sports provide a source of entertainment for millions of people. Players and games are seen as diversions to everyday life. Yet to athletes, and to those who work behind the scenes in the front-offices, professional sports is a job. Running and managing\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":441,"url":"https:\/\/thesportjournal.org\/article\/implications-of-state-income-tax-policy-on-nba-franchise-success-tax-policy-professional-sports-and-collective-bargaining\/","url_meta":{"origin":187,"position":5},"title":"Implications of State Income Tax Policy on NBA Franchise Success: Tax Policy, Professional Sports, and Collective Bargaining","date":"January 2, 2012","format":false,"excerpt":"Timothy E. Zimmer ### Abstract The paper examines the relationship between state income tax rates and the success of National Basketball Association (NBA) franchises. The model indicates that state income tax policy has an influence on team performance. 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