teaching classes involving discrimination is to get students to understand how ... Adobe Photoshop CS3 Extended are reported in a range between 0 and 255.
An Exercise in Perceptions of Skin Tone: Are Subjective Measures of Skin Tone Systematically Related to the Outcomes? John Robst Department of Mental Health Law and Policy and Department of Economics University of South Florida And Jennifer VanGilder Department of Business and Economics Ursinus College
Abstract While the vast majority of research focuses on differences in economic outcomes across races, recent research has also considered disparities within racial groups. Intra-racial discrimination or colorism is defined as a bias between members of the same racial group. One challenge in teaching classes involving discrimination is to get students to understand how discrimination can be very subtle and that the students themselves may discriminate based on skin color. This paper discusses the design and application of an experiment designed to assess whether students assess skin color based on outcomes.
Electronic copy available at: http://ssrn.com/abstract=1490684
1 Introduction Discrimination is a frequent topic in many undergraduate economics classes, including labor economics, urban economics, poverty and discrimination, and the economics of gender. Racial disparities have been found in virtually every aspect of economic life. From wages, to health, to housing; blacks earn less, have lower health, and face greater discrimination in housing. While the vast majority of research focuses on differences across races, recent research has also considered disparities within racial groups. For example, among blacks, skin tone is related to a variety of outcomes including the likelihood of committing crimes (GyimahBrempong and Price, 2006), educational attainment (Hersch, 2006), and wages (Goldsmith, Hamilton, and Darity, 2006). One challenge in teaching about discrimination is getting students to understand how pervasive discrimination can be in society. While it is easy to for students to understand blatant segregation as discriminatory, students often struggle with the idea that they may also discriminate based on skin color or skin tone without being aware of it. Classroom experiments are often a useful tool for helpful students understand concepts. This paper discusses the application of a classroom experiment designed to assess the accuracy of student perceptions of skin tone. Students in a poverty and discrimination class at a liberal arts college are asked to rate the skin tone of highly paid African American men, namely professional basketball players. The subjective ratings are compared to an objective measure of skin tone. The experiment can be linked to research on the effects of skin tone on economic outcomes. Studies have typically relied on the interviewer’s subjective reports of skin tone (e.g., dark, medium, light). Such reports may be measured with error, with the error potentially related to the outcome of interest (Hersch, 2006). For example, the 1995 Detroit Area Study: Stress,
Electronic copy available at: http://ssrn.com/abstract=1490684
2 Racism, and Health Protective (DAS) asks the respondent and the interviewer to report the respondent’s skin tone. The respondent and interviewer reports only match 65% of the time. One concern would be that interviewers perceive individuals as darker if the respondent has poorer economic and social outcomes. Such systematic bias could inappropriately lead to the conclusion that darker skinned blacks fair worse. To determine whether errors are related to economic outcomes, we examined whether measurement errors are related to the player’s salary. The experiment can be a useful exercise for teaching students about subtle biases and discrimination in our society.
Data The experiment requires pictures for each individual and some outcome measure. The data used for this study are for players who signed contracts as free agents in the NBA in the seasons 2001-2002 to 2006-2007. Data are included for several years given the limited number of free agents in any one year. From this larger sample, 22 African American players were randomly selected. The focus is on free agents because their salaries and on-court performance and likely to be highly correlated. This may be less true for players already under contract to a specific team. To become a free agent in the NBA, a player must generally have at least three years of experience in the league. Under the current NBA Collective Bargaining Agreement, teams face some restrictions in signing players based on a team salary cap and limits of contract duration that may be offered to players changing teams. Players are divided into two categories (low and high salary) based on the median salary in the sample. Salary is measured as the average salary over the length of the contract in
3 constant dollars. The simple high/low distinction is used because of the small sample size, and due to the lag between when these contracts were signed and when the experiment took place (fall 2008). Contracts signed several years ago may be less correlated with current on-court performance. The main variable is the tone of each player’s skin. The RGB color score is derived from a model in which red, green, and blue are combined in various ways to reproduce other colors. This model is referred to as an additive model in which the combination of these three primary colors in differing amounts produce the full range of colors. Adobe Photoshop is commonly used by photographers to measure and adjust skin tones in pictures. The color in the RGB color model can be described numerically by indicating how much of each of the red, green, and blue color is included. Each of the primary colors can vary from the minimum amount (no color) to the maximum amount (full intensity). The color values in Adobe Photoshop CS3 Extended are reported in a range between 0 and 255. Full intensity red would be reported as 255, 0, 0. A white image would be reported with high values (closer to 255) for red, green, and blue. A black image would be reported with low values (closer to 0) for red, green, and blue (Wright 2006). All images are from the NBA website. Using an image leveling tool of Photoshop, the players’ photos are normalized to eliminate bias due to camera or photograph differences. The RGB values are observed from three facial areas, the forehead, right cheek, and left cheek of each sample point. The R’s, G’s, and B’s for each area are averaged to eliminate significant variation of color over different sections of the face. Summing the average R, G, and B for the three areas yields a value between 0 and 765 (255 + 255 + 255). A smaller RGB score is indicative of a darker colored player, and a higher score suggests a lighter skinned player.
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The experiment The experiment is straightforward. Forty-two students from a poverty and discrimination class at a liberal arts college are shown a picture of each player and asked to rate their skin tone from 1 to 4 with 1 denoting “fair” and 4 denoting “dark”. Students are given no information about the players. We considered providing player salaries, but decided this might be providing information that could directly affect responses. Instead, students are also asked to rate their knowledge of the NBA from 1 to 10 with 1 being little or no knowledge. It is likely that students with more knowledge of the NBA have greater familiarity with the player’s success in the league. The subjective measures are compared to the objective RGB score. In order to be comparable, players are sorted into quartiles based on their RGB score. Because higher RGB scores denote lighter skin, to be consistent with the student ratings, higher RGB scores are placed into the lower quartiles. Differences in ratings are computed, and these differences compared using frequencies and logistic regressions for players in the top half and bottom half of the salary distribution. Differences in ratings for students reporting more versus less knowledge of the NBA are also compared. A bias in skin tone ratings based on player success requires some knowledge of the player’s success. Thus measurement error for students reporting less knowledge of the NBA is expected to be less correlated with player success. Some relationship might still exist since students with little knowledge of the NBA may hear about players through advertising, the news, etc. Clearly, there are likely to be differences among students in what constitutes dark skin or fair skin. However, these differences are likely to be found among interviewers for survey data
5 as well. In addition, the quartiles of RGB scores may not necessarily align with the four categories used by students. But the overall question is not whether there are differences among students or between students and the RGB score, the question is whether those differences correlate with a player’s success. Future experiments could use students from multiple classes, providing different levels of information to the classes. For example, player salaries might be provided to one class but not the other. Other measures of success, such as player statistics might also be used. A larger sample size might also allow the analysis to utilize the continuous nature of the salary variable instead of the ‘top half’ versus ‘bottom half’ distinction used here, and to examine how student race or gender affects any bias. As a teaching tool, the exercise may or may not lead to the conclusion that the errors in student ratings are linked to economic outcomes. If there is such a relationship, then the point of subtle discrimination can be made quite clearly. However, if there is no such relationship among a group of students, the exercise can still be a useful tool for stimulating discussion and illustrating how subtle discrimination may exist in society.
Results Table 1 contains a summary of the data with the players sorted by their average salary. The average salary, RGB score, RGB quartile, and modal student rating are reported. Comparing the modal student rating to the RGB quartile, seven players are rated lighter by the students than by the RGB method. Three players are rated as darker by the students. An interesting pattern is evident even at this summary level. The three players rated darker by the students are among the bottom seven players in salaries.
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Table 2 contains the distribution of the differences between the subjective and objective measures of skin tone. Overall, there are 924 observations for the 22 players and 42 students. Students were in agreement with the RGB categorization 48% of the time. Students rated the players lighter than the RGB category 35% of the time and darker for 17% of the observations.
Table 3 compares these differences for low and high salary players. Among the low salary players, nearly 30% are rated lighter by the students than the RGB score, and 26% are rated as darker. Among the high salary players, nearly 40% are rated as lighter by the students and only 9% are rated as darker. Thus, there is a clear difference between low and high salary players. The subjective ratings of high salary players tend to be biased towards rating a player as lighter than the RGB score. Thus, there is some evidence of a systematic bias in responses. Students reporting more knowledge of the NBA exhibit a clear systematic bias. High salary players are rated lighter by the students 39% of the time and darker only 8% of the time. Twenty seven percent of low salary players are misclassified as lighter and 26% as darker. Somewhat surprisingly, students reporting less knowledge of the NBA also exhibit a fairly strong systematic bias. Forty percent of high salary players are rated lighter by the students and 12% are rated darker. This compares to 32% of low salary players being rated lighter and 26% being rated darker.
Table 4 presents multinomial logit results. The dependent variable denotes whether students rated the player lighter than the RGB method or darker than the RGB method with the comparison group being when students rated the player the same as the RGB method.
7 Specifications were estimated with player salary being the sole independent variable, and with the RGB score added as a control variable. Results are reported from the first specification. The results from the second specification were qualitatively the same. Once again, high salary players are much less likely to be misclassified as darker by the students than by the RGB score. The results for players misclassified as lighter have the expected positive sign, but are not statistically significant. We also distinguish between students reporting more and less knowledge of the NBA. The negative relationship between player salary and being misclassified as darker is statistically significant for students reporting more knowledge of the NBA, but not for students reporting less knowledge. However, it is worth noting that the coefficients are not significantly different from each other.
Conclusion Many economics classes deal with issues of discrimination. Labor economics, poverty and discrimination, and the economics of gender are a few examples of classes where discrimination can be a frequent topic of discussion. While students can usually understand clear examples of discriminatory behavior, they often struggle with more subtle forms of discrimination and will deny being discriminatory themselves. This paper presented an exercise for students to participate in that can be used to illustrate how subtle and/or subconscious discrimination can occur among individuals. The exercise compared subjective and objective measures of skin tone among a group of highly paid African American men. Subjective measurement error was found to be associated with the player’s economic outcomes. Higher salaried players are less likely to be misclassified
8 as darker by the subjective ratings. The results are statically significant for students reporting more knowledge of the NBA. Such students are expected to have more knowledge of the player’s success and thus may be more susceptible to systematic bias whether at a conscious or subconscious level. The results of the exercise also have potential implications for research on the effects of skin tone. Errors in subjective ratings of skin tone may be related to outcomes, and thus lead to an estimated relationship between skin tone and economic outcomes. Researchers should be aware of this potential when designing studies that involve measures of skin tone.
9 References Binder, D. A. (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review, 51, 279 - 292. Goldsmith, A.H., D. Hamilton, and W. Darity, Jr. (2006). Shades of discrimination: Skin tone and wages. AEA Papers and Proceedings, 96(2), 242-245. Gyimah-Brempong, K. and G.N. Price. (2006). Crime and punishment: And skin hue too?. AEA Papers and Proceedings, 96(2), 246-250. Hersch, J. (2006). Skin-tone effects among African-Americans: Perceptions and reality. AEA Papers and Proceedings, 96(2), 251-255. Wright, S. (2006). Digital Compositing for Film and Video. Focal Press.
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Table 1 Summary of the data Modal rating David Wesley Monty Williams Jacque Vaughn Shammond Williams Devean George Juan Dixon Rafer Alston Reggie Miller Tony Battie Alvin Williams Mike James Earl Watson Clarence Weatherspoon Jerome Williams Kurt Thomas Tayshaun Prince Atoine Walker Kenyon Martin Shawn Merion Ben Wallace Allen Iverson Chris Webber
