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The effects of information and competition on racial discrimination: Evidence from a field experiment
Journal of Economic Behavior & Organization 80 (2011) 670–679
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Journal of Economic Behavior & Organization journal homepage: www.elsevier.com/locate/jebo
The effects of information and competition on racial discrimination: Evidence from a field experiment John M. Nunley a,1 , Mark F. Owens b,∗ , R. Stephen Howard c,2 a
Department of Economics, College of Business Administration, University of Wisconsin – La Crosse, La Crosse, WI 54601, United States Department of Economics and Finance, Jennings A. Jones College of Business, Middle Tennessee State University, Box 27, Murfreesboro, TN 37132, United States c Department of Biology, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, United States b
a r t i c l e
i n f o
Article history: Received 24 February 2011 Received in revised form 21 June 2011 Accepted 28 June 2011 Available online 7 July 2011 JEL classification: C93 J15 D82
a b s t r a c t We study racial discrimination by simultaneously selling identical products on eBay in pairs of auctions posted under different racially identifying names. We detect significant price differences, which are indicative of in-group biases. White names receive higher prices for distinctively white products, and black names receive higher prices for distinctively black products. But price differences only emerge for sellers who have low eBay feedback scores in less competitive markets. Because the price differences dissipate as sellers accumulate credible reputations, the patterns in the data are indicative of statistical discrimination. Overall, the results suggest that mechanisms designed to reduce informational asymmetries and increased competition are both effective at reducing discrimination in online auctions.
Keywords: Racial discrimination Statistical discrimination In-group bias Asymmetric information Field experiments Competition eBay
© 2011 Elsevier B.V. All rights reserved.
1. Introduction Racial discrimination has been studied extensively in economics and other social sciences, but parsing racial discrimination from other influences is difficult. Experiments have become increasingly popular ways to isolate racial discrimination, as these techniques circumvent many of the limitations associated with other methodological approaches by providing greater control over the variables of interest. In this study, we investigate racial discrimination on eBay, an online marketplace that uses second-price auctions to determine prices.3
∗ Corresponding author. Tel.: +1 615 898 5617; fax: +1 615 898 5596. E-mail addresses: [email protected] (J.M. Nunley), [email protected] (M.F. Owens), [email protected] (R.S. Howard). 1 Tel.: +1 608 785 5145. 2 Tel.: +1 615 898 2044. 3 Investigating discrimination in online product-market auctions has some advantages over field experiments that examine discrimination in labor and housing markets. In particular, we observe a continuous outcome variable (i.e. the price), which results from completed transactions. While the cost of discrimination is potentially much higher and more concerning in labor and housing markets, the frequency of transactions involving products is much greater. 0167-2681/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2011.06.028
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We identify racial discrimination in online product auctions by constructing a direct comparison of prices paid to otherwise identical white- and black-named sellers. Our study makes three extensions to the literature on racial discrimination. First, we select products for which the expected racial composition of buyers differs to investigate whether “in-group” biases found in laboratory experiments are supported by data from the field. Second, we use eBay’s feedback system as an observable metric for measuring gains in information regarding a seller’s credibility, which allows us to test whether the type of discrimination observed has an informational component. Finally, we examine products with different levels of market competition to investigate the role of competition as a determinant of racial discrimination in product markets. Much of the recent literature on racial discrimination focuses on whether discrimination stems from tastes or asymmetric information.4 eBay’s feedback system is specifically designed to reduce asymmetric information between buyers and sellers, and can be used to determine the type of discrimination observed. In our context, statistical discrimination arises when price differences arise but dissipate as sellers develop credible reputations. By contrast, finding price differences that persist despite the development of a credible reputation is indicative of taste-based discrimination. Our field experiment has some advantages over previous studies. First, we observe actual bidding decisions made by real buyers in a naturally occurring environment. Bidders are unaware that they are part of an economic experiment focusing on racial discrimination and are, to a large extent, anonymous participants in the market. Therefore, bidders will not adjust their behavior to appease either the experimenter or other bidders in the market. Second, we enter the product markets as passive sellers without the ability to bargain with or discriminate against buyers. We do not initiate any communication with bidders until the auctions are completed. The double-blind situation created by our experimental design eliminates the influence of the bargaining ability of sellers and their knowledge of the underlying distributions of the willingness-to-pay for different demographic groups (see List, 2004). Lastly, the auction framework provides a way to observe the willingness-to-pay of buyers with more precision than posted-price markets. Our data indicate that statistically significant price differences can emerge between white- and black-named sellers. The direction of the effect is consistent with in-group biases by race: white-named sellers receive higher prices than black-named sellers for products that are targeted toward white buyers, and black-named sellers receive higher prices than white-named sellers for products that are targeted toward black buyers. Prior field experiments have found statistical discrimination by whites against blacks, but this is the first study, to our knowledge, that identifies a positive effect associated with having a distinctively black name. The bias in favor of same-race sellers is identified only when certain market conditions are present. In particular, the price differences appear to be driven primarily by the lack of seller credibility, as the price differences dissipate as sellers accumulate credible reputations. In addition, price differences only emerge in markets characterized by low levels of competition. While finding that racial differences are only present at low levels of seller feedback is indicative of statistical discrimination, we are unable to determine whether the discrimination observed is derived from preferences, past experiences, or some combination of both. In either case, our results suggest, rather strongly, that market mechanisms designed to reduce asymmetric information and increased competition can aid in promoting non-discriminatory outcomes in online product auctions. 2. Theoretical framework There are two primary economic models of conscious discrimination: one based on tastes (Becker, 1971) and the other based on statistical discrimination arising from incomplete or asymmetric information (Arrow, 1973; Phelps, 1972).5 In the taste-based model, different economic outcomes emerge for majority and minority groups because of animosity. Economic agents pay a premium, either in terms of lost revenue or higher prices, to avoid trading with individuals from a particular class of people. By contrast, models based on asymmetric information assume economic agents have no animosity. Instead, agents facing incomplete information rely on making inference about an individual’s unobservable characteristics using observable characteristics, such as race or gender. Nevertheless, statistical discrimination results in differential treatment of persons who differ based on observable characteristics. Under any model of discrimination, a discriminating bidder must have a lower willingness-to-pay for the same product sold by a particular seller. Statistical discrimination could be related to perceived race, seller credibility, or other factors. In our context, a prospective buyer shopping on eBay may use the racial distinctiveness of sellers’ names as a proxy for the expected probability that the product will arrive in the stated condition. If discrimination is based on asymmetric information, there is an opportunity to minimize such discrimination through market mechanisms designed to reduce informational asymmetries. Initially, seller quality is unobserved and racially distinct names may serve as a signal that draws attention to prior beliefs that buyers have
4
For example, see Antonovics et al. (2005), Antonovics and Knight (2009), Fershtman and Gneezy (2001), Levitt (2004), and List (2004). An alternative theory of discrimination is based on implicit or unintentional biases (see Bertrand et al., 2005). Implicit discrimination is unlikely to arise in online auctions because buyers have opportunities to examine the products, the characteristics of sellers, and whether identical products or close substitutes are being sold by other sellers. Our winning bidders posted approximately five bids in each auction, which is highly suggestive of “conscious” decision-making. An implicit discriminator could observe a racially distinct name and exit an auction without making a conscious decision to discriminate. But such a decision is an intermediate step toward the determination of the final price in an auction, which is determined by the interaction of several bidders. 5
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about the trustworthiness of different racial groups. However, as sellers’ feedback scores increase, more information about seller quality is revealed, and any priors that buyers may have about quality differences by race become less informative. Under eBay auction rules, the maximum willingness-to-pay of the second highest bidder determines the selling price. Because non-discriminating bidders have a higher willingness-to-pay, increasing the number of bidders competing in a market raises the probability that the two bidders with the highest willingness-to-pay for a given product are non-discriminating and thus raises the price. In the context of Becker (1971), competitive pressure stemming from increases in the number of bidders reduces the impact of the marginal discriminator in the market, leading to smaller disparities in economic outcomes between majority and minority groups.6 Thus, we expect to observe larger price differences in markets with less competition on the demand side of the market. 3. Experimental design We perform a series of auctions using eBay to create a direct comparison of prices paid for identical products sold by whiteand black-named sellers. We use standard eBay auction format with ascending English auction rules and a computerized proxy-bid system (see Lucking-Reiley, 1999; Lucking-Reiley et al., 2007). The price for an item sold in an eBay auction reveals information about the willingness-to-pay of the bidder with the second highest value for the item.7 By contrast, posted-price markets only convey whether buyers have a willingness-to-pay that equals or exceeds the posted price. Therefore, postedprice markets would capture racial price differences only if the willingness-to-pay to one seller is greater than or equal to the posted price and the willingness-to-pay to another seller is less than the posted price. The auction framework provides a unique opportunity to study racial discrimination, as it identifies a more complete picture of buyers’ true willingness-to-pay (Ely and Hossain, 2009; Gray and Reiley, 2007). We sell products (discussed in Section 4) under different racially identifiable seller names creating a comparison by race similar to Bertrand and Mullainathan (2004). Names are selected from the list of distinctively black and distinctively white names provided by Levitt and Dubner (2005). The names chosen to represent our “white” sellers are Jake and Dustin, while the names chosen to represent our “black” sellers are DeShawn, Tyrone, and Jamal. For each group of products, we open new seller accounts using the chosen racially distinct names for each market. This means that each seller account begins with a feedback score of zero for each group of products. We schedule the auctions such that each seller within a given market posts only one auction per week. We pair white- and black-named sellers and sell identical products simultaneously. The paired nature of our auctions and the comparison of prices between names within weeks limits the nuisance variance, but it also has the potential to limit the variance in prices by race. Likewise, selling commonly purchased products may also limit variation in prices, which may work against finding evidence of racial discrimination. Bidders likely have a sensible estimate of the retail prices, making it unlikely that a buyer would have a substantially higher willingness-to-pay than other prospective buyers. Therefore, our experimental design may make it difficult to identify racial discrimination, implying that any price differences detected would likely represent lower bounds. We attempt to hold all variables constant, with the exception of the seller’s racially distinct name. Heckman and Siegelman (1993) emphasize the importance of choosing the proper variables with which to standardize the experiment, as the choice of these variables likely affects the results. Most features of our experimental design are standardized across auctions, but some features of the auctions cannot be made identical without alerting buyers that two seller accounts are from the same source. For features that cannot be held constant, we take great care to ensure that any differences do not influence the results. The details regarding these procedures can be found in an Online Appendix. 4. Data Over a period of 11 months, we conducted 288 auctions (144 pairs) selling various goods from three different sets of products: fishing lures, distinctively black toys, and distinctively white toys. We sell new, unopened products with retail prices ranging from $5.00 to $7.00. The auctions for the fishing lures were conducted over 24 weeks from February 2009 to July 2009. The distinctively black products were sold over the course of 32 weeks from May 2009 to December 2009. The distinctively white toys were sold over the course of 16 weeks from August 2009 to December 2009. A detailed list of the products sold is provided in Table 1.
6 The theoretical argument for competition to reduce the impact of the marginal discriminator in Becker (1971) assumes that discrimination is based on tastes. However, the theory does not require discrimination to result from tastes. In fact, Becker (1971, p. 18) notes that, “. . . many variables in addition to tastes take prominent roles in determining market discrimination, and, indeed, tastes sometimes play a minor part.” As such, the predictions made by Becker regarding the impact of competition on discrimination extend to models in which the disparity in outcomes arises from statistical discrimination. 7 We recognize a point related to whether the “true” willingness-to-pay of the second-highest bidder is revealed without noise in an eBay auction. Assume that the same high bidders are competing over multiple auctions for the same product. It is possible for the strategy of bidders to change so that the second-highest bid is not necessarily equal the second-highest value (Peters and Severinov, 1997, 2006). While we observe some bidders making bids across auctions, these bidders tend to enter only low bids, and they rarely end an auction as either the highest or second-highest bidder. In our view, the final transaction price is a close proxy for the value of the second-highest bidder in our data.
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Table 1 Products sold and the level of competition (measured by retail shelf space). Product
Category
N
Expected race of buyers
Level of competition
Culprit 7.5 Worms Berkley Powerbait 7 Worms Strike King Mini-King Spinners Strike King Bleeding Bait Chatterbait Chatterfrogs Stanley Spinners Mini-King Bleeding Bait Beach Party Barbie Loving Family Mom and Toddler Loving Family Dad and Sister Loving Family Grandma and Brother Barbie Beach Party Nikki Loving Family Brother and Sister Fisher Price Little People AA Husband–Wife Loving Family Mom and Baby Loving family Dad and Baby Peek-a-boo Barbie-Jemila of Johannesburg Beach Party Steven Ballerina Nikki Barbie Fairy Bratz Angelz Bratz Ballerinaz Sasha Sweet Secrets Morgan
Fishing Fishing Fishing Fishing Fishing Fishing Fishing Fishing Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll
32 16 10 8 8 8 8 6 32 12 12 8 34 32 14 8 8 8 8 6 4 2 2 2
White White White White White White White White White White White White Black Black Black Black Black Black Black Black Black Black Black Black
High High Low Low Low Low Low Low High High High High High Low Low Low Low Low Low High High High Low Low
Notes: The heading ‘Product’ lists the names of the products sold; ‘Category’ defines whether the good is a fishing product or a doll; ‘N’ is the number of auctions posted for each product; ‘Expected race of buyers’ lists our expectations regarding whether the buyers of the product are expected to be white or black; ‘Level of competition’ identifies whether the product has a large amount of display space in Wal-Mart, Toys R Us, or Bass Pro Shop. A larger shelf space devoted to a product is indicative of high buyer demand, while a small display is indicative of relatively less buyer demand. All products were purchased at retail chains except the African-American Loving Family Dollhouse figurines, which were purchased online.
The first group of products is fishing lures, which are commonly used for bass fishing. These products can be categorized as either “Plastic Worms” or “Spinner Baits”. We sold two fishing lures per auction and selected identical brands, sizes, and color combinations to ensure that the products sold are perfect substitutes within each pair. While fishing lures have no inherent race-specific characteristics, research by the U.S. Fish and Wildlife Service indicates that African-Americans represent only five percent of all anglers, and they account for less than four percent of the total amount spent on fishing merchandise (Pullis, 2000). As a result, we refer to fishing lures as distinctively white products from this point forward. The other products sold have obvious racially identifying features. The majority of the data are from auctions selling two racially distinct sets of children’s toys from the same product lines. We sell white Barbie dolls and black Nikki dolls from Mattel’s Beach Party Barbie series and white and black figurines from the Fisher Price Loving Family Dollhouse series. Fig. 1 displays examples of these products. The black versions are considered distinctively black products, while the white versions are considered distinctively white products. While selling these products we make use of eBay’s feedback system, which allows buyers and sellers to signal their credibility by publicizing their feedback scores and the comments provided by previous trading partners. Studies report that the development of a positive reputation via eBay’s feedback system enhances seller credibility (Resnick et al., 2006), and the feedback scores play an important role as determinants of long-run success on eBay (Cabral and Hortacsu, 2010). Upon completion of an eBay auction, the buyer and seller have the opportunity to leave feedback for each other. Buyers and sellers receive ‘−1 if the buyer is displeased with the transaction; a ‘+1 if the buyer is satisfied with the transaction; or a ‘0 if the buyer is neutral regarding the transaction. Buyers can also leave comments describing their experience with the seller. After receiving a positive feedback score of 10, eBay users receive a yellow-star award, which appears alongside their usernames. We carefully coordinate the auctions and ship the products in a timely manner so that our sellers never incur negative feedback. The feedback scores lag behind the number of actual auctions completed because buyers provide feedback after they receive the product. In addition, buyers are not required to rate a seller. As a result, slight differences in feedback scores emerge that are beyond our control. But we are able to hold constant differences in the feedback scores, as the these characteristics are observable. One of our remaining challenges is to quantify the level of competition present in each product market. Ideally, we would like to know the number of potential bidders for our products, but this is unobservable. We present results using the retail shelf space devoted to our products in Wal-Mart, Toys R Us, or Bass Pro Shop as a proxy for the level of competition, as the shelf space devoted to a product is an observable indicator of buyer demand. Products given more space are characterized as highly competitive markets, while products given less space are considered low competition markets. Our proxy for
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Fig. 1. Examples of distinctively white and black toys.
market competitiveness has limitations in that it is somewhat removed from buyer demand on eBay.8 However, given the characteristics of bidding behavior found in our data, we are confident that this classification captures differences in buyer competition between products. As the number of bidders increases, we expect the difference between the highest losing bids to converge. This tendency coupled with the proxy-bid feature on eBay, which allows bidders to enter reservation prices at any time, implies that bids will increase at different rates in auctions characterized by different numbers of potential bidders. Within this context, an increase in the number of bidders making proxy bids implies that it is more likely for two higher value bidders to meet earlier in an auction’s duration. Therefore, we expect the current high bid in a highly competitive market to be closer, on average, to the final price than it would be if the market were less competitive. We calculate the ratio of the high bid at the half-way point to the final price for each of our auctions to measure the rate of convergence to the final price.9 Bids in our high competition markets escalate to their final prices at a faster rate, as the mean ratio at the midpoint of the high competition markets is 0.672 versus 0.581 in the low competition markets. A Wilcoxon–Mann–Whitney test indicates that the difference in the price ratios in high and low competition markets is highly statistically significant (Z = 2.843, p = 0.0045). We recognize the limitations of using measures that are somewhat subjective. As a result, we check the robustness of our findings to a variety of alternative classifications of high- and low-competition markets. We find similar regression results, and similar significant differences in the ratios of halfway prices to final prices, regardless of how the level of competition in a given market is classified.10 We emphasize that each measure of market competitiveness assigns the products to roughly
8 Prior to choosing our products, we also examined the statistics provided by “eBay Pulse,” which shows the most popular searches entered by prospective ¨ ¨ ¨ ¨ consistently rated in the top 10 in the “Dolls and Bears” category. While the statistics and Barbie Dollsare buyers on eBay. For example, the terms Barbie from eBay Pulse accurately reflect searches for the products with the highest demand on eBay, information on low-competition products is limited. 9 We would like to examine the highest two losing bids, but we do not have access to this information. We did record the high bid at the halfway point in all of our auctions, and we make use of this information. We thank the editor for suggesting that we examine bidding behavior as a measure of demand-side competition. 10 There are two related studies that examine the effect of competition on racial discrimination. Each of these studies reach conclusions that are similar to ours, with each using a different measure of competition. List and Livingston (2010) compare between levels of competition for two particular baseball cards with different and constant quantities. We are unable to use this approach, as the quantities supplied of our products are unknown. Doleac and Stein (2010) sell iPods and use the number of competing sellers on online classified advertisement websites to proxy market competitiveness. We use the number of competing sellers on eBay as a proxy for the level of competition in one of our robustness checks, finding similar results to those reported in the text. These results are available upon request.
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Table 2 Summary statistics: higher prices within a pair and mean prices (same race versus different race by level of competition).
Low competition Higher price – same race Higher price – different race Test statistic Mean price – same race
Mean price – different race
Test statistic High competition Higher price – same race Higher price – different race Test statistic Mean price – same race
Mean price – different race
Test statistic
All
No yellow star
Feedback 0–6
35 28 0.116 6.22 (0.34) [65] 5.74 (0.30) [65] 0.476
25 17 0.013** 6.32 (0.51) [40] 5.61 (0.44) [38] 0.376
20 12 0.006*** 6.58 (0.60) [33] 5.61 (0.51) [31] 0.276
34 38 0.633 6.04 (0.24) [79] 6.43 (0.27) [79] 0.589
20 25 0.376 6.20 (0.32) [58] 6.47 (0.30) [52] 0.636
16 20 0.542 5.85 (0.28) [41] 6.15 (0.25) [44] 0.765
Notes: Standard errors are in parentheses and the numbers of observations are in brackets. Test statistic is the p-value from a Wilcoxon–Mann–Whitney test of “same race” versus “different race”. ‘Same’ and ‘different’ race rows by seller feedback do not have equal numbers due to the natural escalation of feedback scores. The number of auctions receiving a higher price for same race and different race do not sum to the number of observations reported in mean prices due to ties. ** Statistical significance at the five percent level. *** Statistical significance at the one percent level.
the same categories, and these classifications are consistent with the observed bidding behavior in our auctions. This is reassuring due to the absence of a clear indicator for market competitiveness. 5. Results In Table 2, we examine the mean prices received and number of times a same-race seller (i.e. the races of sellers match the products) receives a higher price than a different-race seller (i.e. the races of sellers do not match the products) within an auction pair in markets characterized by different levels of competition and seller feedback. In low-competition markets, same-race sellers receive a higher price within a pair more often than different-race sellers, regardless of seller feedback. Wilcoxon–Mann–Whitney tests for the difference in receiving higher prices for those without a yellow star (Z = 2.482, p = 0.013) and for feedback scores of six or less (Z = 2.730, p = 0.006) are both statistically significant. While the mean selling prices are higher for same-race sellers, the differences in the mean prices are not significantly different from zero, even for sellers with low feedback scores. There are no significant differences in the number of times a higher price is received or in the mean prices for the high-competition subsample. The differences-in-means tests do not indicate statistically significant differences because the variation in prices between different products and the variation in the price of the same product in different weeks are both greater than the variation in prices received between different names within a given week. However, the variation in the prices received by whiteand black-named sellers within a given pair is of primary interest to us. To examine within-pair variation in prices received, we turn to regression analysis, which allows us to hold constant auction-pair fixed effects. In our baseline model specification, we estimate the following ordinary least squares (OLS) regression model: Price = ˇ0 + ˇ1 Same Race + ˇ2 Ends First + ˇ3 Yellow Star + ˇ4 Auction Pair + ε.
(1)
The variable Price is the price received; Same Race is an indicator variable which equals one if the racial distinctiveness of the seller’s name matches the racial classification of the product and zero otherwise; Ends First is an indicator variable that equals one when an auction within a given pair ends first and zero when it ends last; Yellow Star is an indicator variable that equals one when sellers accumulate positive feedback scores of 10 or more and zero otherwise; Auction Pair represents auction-pair fixed effects which capture the influence of other determinants of the price common to both sellers in a given week, including seasonal variation and the influence of other competitors in the market; ε is the error term; and ˇi are parameters to be estimated. Once the auction-pair fixed effects are held constant, the parameters from Eq. (1) represent the effect of the explanatory variables within a seller-pair for a given week. We are primarily interested in the parameter ˇ1 , which captures the difference in prices received between sellers whose racially distinct names match the racial classification of the product and sellers whose names do not match the racial classification of the product.
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Table 3 The effects of same race names on prices received (all products). Explanatory variable
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
Full sample
Low competition
High competition
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.015 (0.18) – – – – – – – – – – 0.815 0.624 288
– – 0.018 (0.22) 0.009 (0.31) – – – – – – 0.815 0.621 288
– – – – – – 0.217 (0.25) −0.384 (0.31) 0.154 (0.44) 0.818 0.625 288
0.487** (0.23) – – – – – – – – – – 0.879 0.748 130
– – 0.761** (0.30) 0.075 (0.37) – – – – – – 0.883 0.752 130
– – – – – – 1.006*** (0.31) −0.320 (0.41) 0.309 (0.49) 0.891 0.766 130
−0.391 (0.26) – – – – – – – – – – 0.768 0.520 158
– – −0.525* (0.31) −0.067 (0.49) – – – – – – 0.769 0.517 158
– – – – – – −0.463 (0.36) −0.409 (0.44) −0.067 (0.71) 0.768 0.508 158
Notes: Standard errors are in parentheses. Each specification includes auction pair fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level. *** Statistical significance at one percent level.
In supplemental models, we estimate Eq. (1) for subsamples partitioned by the levels of competition present in a given market. We also augment Eq. (1) to estimate the effects of Same Race by the feedback scores received by sellers. In particular, we make use of the “yellow star” received by sellers who accumulate a positive feedback score of 10. To obtain a better idea of how the feedback scores interact with the racial distinctiveness of the sellers’ names, we also estimate the effects of Same Race by low, medium, and high levels of feedback.11 Because each of our sellers accumulates similar feedback over the course of the experiment, we are able to compare sellers with comparable reputations who only differ by the racial distinctiveness of their usernames. We analyze the impact of racially distinct names on prices received by pooling the data from all product markets. Table 3 displays the results for each specification for the full sample (columns 1, 2 and 3), the low-competition subsample (columns 4, 5, and 6), and the high-competition subsample (columns 7, 8 and 9). The estimated overall effect of Same Race in the full sample is positive but it is not statistically different from zero (columns 1, 2 and 3). We check the robustness of these results to the exclusion of three pairs of observations for which we observed odd bidding behavior. The details of these observations are discussed in Appendix A, and regressions using the data without these pairs are displayed in Table A1. Overall, the results from the pooled sample indicate that sellers receive similar prices, regardless of whether the race of their name matches the racial classification of the product. However, the results from the full sample mask some important patterns in the data. In particular, price differences between white- and black-named sellers emerge in less competitive markets but do not in highly competitive markets. The results for markets characterized by low levels of competition each indicate statistically significant price differences in favor of same-race sellers (columns 4, 5, and 6). Overall, Same Race sellers in low-competition markets receive a premium of $0.49 (8.2 percent of mean) per auction, and this effect is statistically significant at the five-percent level (column 4). However, the statistically significant effect of Same Race is driven by the price differences at low levels of seller feedback, and these differences dissipate as sellers accumulate credible reputations via eBay’s feedback system (columns 5 and 6). Same-race sellers with low feedback scores earn between $0.76 (12.7 percent of mean) and $1.01 (16.5 percent of mean) more per auction than different-race sellers with low feedback scores. The estimated effect of Same Race is statistically significant at the five-percent level for sellers with feedback scores less than ten and at the one-percent level for feedback scores less than seven. The effect of Same Race is not statistically significant at higher levels of feedback. The results are different for the subsample of highly competitive markets, in which the effect of Same Race is negative and statistically significant at the 10 percent level in column 8, but not statistically different from zero at conventional levels in the specifications in columns 7 and 9.12 We repeat the analysis separately for subsamples of the distinctively white and black products. The partitioned samples allow us to examine whether same-race biases are present in both white- and black-product markets or if the differences
11 Feedback scores of 0–6 are coded as low; feedback scores of 7–12 are coded as medium; and feedback scores of 13 or greater are coded as high. These cutoffs are empirically motivated, as they provide three groups with similar numbers of observations. Regressions with other categorizations of feedback yield qualitatively similar results. 12 The negative and statistically significant coefficient in column 8 is driven by two pairs of auctions, which are described in more detail in Appendix A, that occur after the sellers accumulate their seventh positive feedback. Notice that the effect of same-race with low feedback (0–6) in column 9 is not statistically significant, implying that auctions taking place between the seventh and tenth feedback score are driving the price difference. Table A1 shows that when these auction pairs are excluded, the equivalent estimate is not statistically significant.
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Table 4 The effects of racially distinct names on prices received in low competition markets. Explanatory variable
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
White products
Black products
(1)
(2)
(3)
(4)
(5)
(6)
0.562* (0.32) – – – – – – – – – – 0.818 0.592 48
– – 0.847* (0.45) 0.249 (0.47) – – – – – – 0.825 0.589 48
– – – – – – 1.283** (0.51) 0.018 (0.55) 0.232 (0.59) 0.845 0.616 48
0.580* (0.11) – – – – – – – – – – 0.899 0.784 82
– – 0.816** (0.39) 0.146 (0.53) – – – – – – 0.902 0.784 82
– – – – – – 0.930** (0.39) −0.233 (0.67) 0.328 (0.74) 0.905 0.787 82
Notes: Standard errors are in parentheses. Each specification includes auction-pair fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level.
found in the pooled sample are driven by discrimination against a particular racial group. Table 4 presents the findings for the white- and black-product subsamples in low competition markets.13 In Table 4, the symmetry between the results for the white- and black-product subsamples is striking. In low competition markets, white-named sellers receive a premium of $0.56 (10.2 percent of mean) in white-product markets (column 1), while black-named sellers receive a premium of $0.58 (9.3 percent of mean) in black-product markets (column 4). Both are statistically significant at the 10 percent level. However, the effect in each subsample is driven by price differences at low levels of seller feedback. In the white-product subsample, white-named sellers with low feedback scores earn between $0.85 (14.8 percent of mean) and $1.28 (20.4 percent of mean) more per auction than black-named sellers with low feedback scores (columns 2 and 3). These estimated effects are statistically significant at the ten and five percent levels, respectively. In the black-product subsample, the premium to black-named sellers with low feedback ranges between $0.82 (13.5 percent of mean) and $0.93 (15.4 percent of the mean) depending on the specification (columns 5 and 6). These estimated effects are both statistically significant at the five-percent level. In both the black- and white-product subsamples, the price differences dissipate when sellers receive a feedback score of seven or more. 6. Summary and discussion We conduct a field experiment to investigate racial discrimination in online product auctions by selling identical goods simultaneously on eBay under different seller names. One seller who is assigned a distinctively black name is paired with another seller who is assigned a distinctively white name. Using this design, we make the following contributions to the literature on racial discrimination. First, we choose products that are marketed toward and likely to be purchased by buyers from different racial groups. This enables us to investigate whether discrimination occurs in favor of sellers whose racially identifying names match the racial distinctiveness of the products, rather than universal discrimination against certain groups. Second, we use eBay’s seller feedback scores as an observable measure for the information available to bidders, which provides a way to test whether the observed discrimination has an informational component. Lastly, we choose products that vary by the level of competition present in the market. We find evidence that in-group biases, which have been identified in a number of studies in the economics literature (see Anderson et al., 2006; Antonovics and Knight, 2009; Ball et al., 2001; Donahue and Levitt, 2001; Fershtman et al., 2005; Glaeser et al., 2000; Price and Wolfers, 2010), can emerge based on race in a natural product market setting. We find the direction of discrimination depends on the combination of the racial characteristics of the products and the racial distinctiveness of the sellers’ names. For the white products, we observe discrimination against blacks just as other studies of product markets have found (see List, 2004; List and Livingston, 2010; Doleac and Stein, 2010). However, we detect a benefit associated with having a black name in the distinctively black product markets. We are unaware of any study in the literature that finds benefits associated with having a distinctively black name. In our data, same-race biases are detected only under certain market conditions. In particular, we find evidence of samerace biases for sellers who have yet to develop credible reputations in markets characterized by low levels of competition. As sellers establish more credibility, price differences are no longer observed. The finding that price differences only arise
13 In the interest of brevity, we do not present the estimates from the full sample or the high-competition subsample, as the estimates are not statistically different from zero in either sample.
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in markets characterized by low levels of competition is consistent with theory (Becker, 1971) and other field experiments (List and Livingston, 2010; Doleac and Stein, 2010). The fact that price differences arise, but dissipate with seller feedback, is a clear indication that the type of discrimination observed has a strong informational component. At low levels of seller feedback, it appears that bidders are making decisions based on prior beliefs that same-race sellers are more reliable and trustworthy than different-race sellers, indicating that similar findings by Glaeser et al. (2000) in laboratory experiments are supported with data from the field. In our case, the importance of the buyers’ prior beliefs regarding the trustworthiness of seller with a racially distinct name diminishes as other pertinent information about seller credibility becomes available. Our choice to sell brand new, low priced products, under the protections in place on eBay minimizes the risk facing buyers. It is interesting that statistical discrimination emerges in a market setting for which better information can generate relatively small benefits. Our data indicate patterns that are different from those found in existing studies. For instance, the use of auctions and double-blind procedures eliminates the role of bilateral bargaining in our markets and removes the possibility that perceived lower reservation values among minorities are the source of price differences, as found in List (2004). Instead, it appears that buyers use perceived race as a signal for the likelihood that the product will be delivered as advertised. Thus, statistical discrimination in our study is based on different factors than those identified previously. An alternative interpretation for the effects of racially distinct names is that they could reflect perceived differences in socioeconomic status rather than perceived differences in race (Ball et al., 2001; Bertrand and Mullainathan, 2004). However, the symmetry of same-race biases in the white- and black-product subsamples suggests this is not the case in our data. If the socioeconomic statuses associated with the names are driving the results, the low socioeconomic status names would receive lower prices across all products, independent of whether the products are distinctively white or distinctively black. Because it is unlikely that buyers in different markets would form opposite views about the socioeconomic status signaled by the same racially distinct name, the racial information itself seems to be central to the discrimination observed in our data. While bidders appear to have more favorable prior beliefs about sellers whose race matches the racially distinct product, it is unclear whether these priors stem from past experiences, animosity, or other factors. Whatever the root cause, our results suggest, rather strongly, that reductions in informational asymmetries and increased competition can aid in minimizing or eliminating discriminatory outcomes in online product auctions. Appendix A. Regressions on data without potential outliers We encountered two auctions with extremely high prices, and one with an extremely low price relative to other auctions for the same products. The auctions are from markets classified as highly competitive. These auctions do not affect the main conclusions of the paper, as the low-competition subsample is unaffected by these data points. Since our analysis is based on within-auction-pair variation, these prices lead to very large price differences for the three pairs of auctions that are affected. In the first pair, a white Barbie doll was sold by a black-named seller with a feedback score of eight for $13.50 and a white-named seller with a feedback score of seven for $5.51 in the same week. The difference in prices is greater than the mean price of $5.98 for a white Barbie doll in our sample. In the second pair, a Nikki doll sold for $13.50 to a white-named seller with a feedback score of seven and for $6.55 to a black-named seller with a feedback score of Table A1 The effects of same race names on prices received (all products) no outliers. Explanatory variable
Full sample (1)
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
0.191 (0.15) – – – – – – – – – – 0.860 0.714 282
Low competition (2) – – 0.297 (0.19) −0.007 (0.26) – – – – – – 0.860 0.714 282
(3) – – – – – – 0.342* (0.21) −0.054 (0.27) 0.175 (0.37) 0.861 0.713 282
(4) **
0.487 (0.23) – – – – – – – – – – 0.879 0.748 130
High competition
(5)
(6)
(7)
(8)
(9)
– – 0.761** (0.30) 0.075 (0.37) – – – – – – 0.883 0.752 130
– – – – – – 1.006*** (0.31) −0.320 (0.41) 0.309 (0.49) 0.891 0.766 130
−0.076 (0.20) – – – – – – – – – – 0.843 0.675 152
– – −0.065 (0.24) −0.101 (0.37) – – – – – – 0.843 0.670 152
– – – – – – −0.242 (0.27) 0.163 (0.35) −0.010 (0.54) 0.845 0.669 152
Notes: Standard errors are in parentheses. Each specification includes product-week fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level. *** Statistical significance at one percent level.
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eight. In the third pair, a Nikki doll was sold by a black-named seller with a feedback score of five for $1.25 and a white-named seller with a feedback score of five for $9.85 in the same week. The price differences in pairs two and three exceed $5.80, which is the mean price of a Nikki doll in our sample. We suspect that both of the $13.50 prices may be mistakes in bidding because the next highest selling price for a Barbie is $10.50, but we cannot be certain. We have no explanation for why a Nikki doll sold for only $1.25, as the next lowest selling price is $3.75. We present regressions in Table A1 that use a sample with these three pairs of auctions excluded. The signs and statistical significance for the estimates are similar to those in Table 3, with two notable differences. First, the Same Race with Low Feedback coefficient becomes positive and statistically significant in the full sample (column 3). Second, the coefficient on Same Race with a Yellow Star is no longer statistically significant in the high competition sample (column 8). Appendix B. Supplementary data Supplementary information associated with the experimental design of this article can be found, in the online version, at doi:10.1016/j.jebo.2011.06.028. References Anderson, L.R., Fryer Jr., R.G., Holt, C.A., 2006. Discrimination: evidence from psychology and economics experiments. In: Rogers, W. (Ed.), Handbook on Economics of Racial Discrimination. Edward Elgar Publishing, Northampton, pp. 97–115. Antonovics, K., Knight, B.G., 2009. A new look at racial profiling: evidence from the Boston police department. Review of Economics and Statistics 91 (1), 163–177. Antonovics, K., Arcidiancono, P., Walsh, R., 2005. Games and discrimination: lessons from the weakest link. Journal of Human Resources 40 (4), 919–947. Arrow, K.J., 1973. The theory of discrimination. In: Ashenfelter, O., Rees, A. (Eds.), Discrimination in Labor Markets. Princeton University Press, Princeton, NJ, pp. 3–33. Ball, S.B., Eckel, C., Grossman, P.J., Zane, W., 2001. Status in markets. Quarterly Journal of Economics 116 (1), 161–188. Becker, G.S., 1971. The Economics of Discrimination, 2nd ed. University of Chicago Press, Chicago and London. Bertrand, M., Mullainathan, S., 2004. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review 94 (4), 991–1013. Bertrand, M., Chugh, D., Mullainathan, S., 2005. Implicit discrimination. American Economic Review: Papers and Proceedings 95 (2), 94–98. Cabral, L., Hortacsu, A., 2010. The dynamics of seller reputation: evidence from eBay. Journal of Industrial Economics 58 (1), 54–78. Doleac, J.L., Stein, L.C.D., 2010. The Visible Hand: Race and Online Market Outcomes. Stanford Institute for Economic Policy Research Discussion Paper 09-015. Donahue, J., Levitt, S.D., 2001. The impact of race on policing and arrests. Journal of Law and Economics 44, 367–394. Ely, J.C., Hossain, T., 2009. Sniping and squatting in auction markets. American Economic Journal: Microeconomics 1 (2), 68–94. Fershtman, C., Gneezy, U., 2001. Discrimination in a segmented society: an experimental approach. Quarterly Journal of Economics 161 (1), 351–377. Fershtman, C., Gneezy, U., Verboven, F., 2005. Discrimination and nepotism: the efficiency of the anonymity rule. Journal of Legal Studies 34 (2), 371–394. Glaeser, E., Laibson, D.I., Scheinkman, J.A., Soutter, C.L., 2000. Measuring trust. Quarterly Journal of Economics 115 (3), 811–846. Gray, S., Reiley, D.H., 2007. Measuring the Benefits to Sniping on eBay: Evidence from a Field Experiment. Unpublished Manuscript. Heckman, J.J., Siegelman, P., 1993. The urban institute audit studies: their methods and findings. In: Fix, M., Struyk, R. (Eds.), Clear and Convincing Evidence: Measurement of Discrimination in America. Urban Institute. Levitt, Steven D., 2004. Testing theories of discrimination: evidence from the weakest link. Journal of Law and Economics 47 (2), 431–452. Levitt, S.D., Dubner, S.J., 2005. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. Harper Collins, New York. List, J.A., 2004. The nature and extent of discrimination in the marketplace: evidence from the field. Quarterly Journal of Economics 119 (1), 49–89. List, J.A., Livingston, J.A., 2010. When the Weak Become Weaker: The Effect of Market Power on Third Degree Price Discrimination. Unpublished Manuscript. Available at http://works.bepress.com/jeffrey livingston/11. Lucking-Reiley, D., 1999. Using field experiments to test equivalence between auction formats: magic on the internet. American Economic Review 89 (5), 1063–1080. Lucking-Reiley, D., Bryan, D., Prasad, N., Reeves, D., 2007. Pennies from eBay: the determinants of price in online auctions. Journal of Industrial Economics 55 (2), 223–233. Peters, M., Severinov, S., 1997. Competition among sellers who offer auctions instead of prices. Journal of Economic Theory 75 (1), 141–179. Peters, M., Severinov, S., 2006. Internet auctions with many traders. Journal of Economic Theory 130 (1), 220–245. Phelps, E.S., 1972. The statistical theory of racism and sexism. American Economic Review 62 (4), 659–661. Price, J., Wolfers, J., 2010. Racial discrimination among NBA referees. Quarterly Journal of Economics 125 (4), 1859–1887. Pullis, G., 2000. Participation and Expenditure Patterns of African-American, Hispanic, and Women Hunters and Anglers: Addendum to the 1996 National Survey of Fishing, Hunting and Wildlife-Associated Recreation. United States Fish and Wildlife Services, Washington, DC, pp. 1–28. Resnick, P., Zeckhauser, R., Swanson, J., Lockwood, K., 2006. The value of reputation on eBay: a controlled experiment. Experimental Economics 9 (2), 79–101.
Contents lists available at ScienceDirect
Journal of Economic Behavior & Organization journal homepage: www.elsevier.com/locate/jebo
The effects of information and competition on racial discrimination: Evidence from a field experiment John M. Nunley a,1 , Mark F. Owens b,∗ , R. Stephen Howard c,2 a
Department of Economics, College of Business Administration, University of Wisconsin – La Crosse, La Crosse, WI 54601, United States Department of Economics and Finance, Jennings A. Jones College of Business, Middle Tennessee State University, Box 27, Murfreesboro, TN 37132, United States c Department of Biology, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, United States b
a r t i c l e
i n f o
Article history: Received 24 February 2011 Received in revised form 21 June 2011 Accepted 28 June 2011 Available online 7 July 2011 JEL classification: C93 J15 D82
a b s t r a c t We study racial discrimination by simultaneously selling identical products on eBay in pairs of auctions posted under different racially identifying names. We detect significant price differences, which are indicative of in-group biases. White names receive higher prices for distinctively white products, and black names receive higher prices for distinctively black products. But price differences only emerge for sellers who have low eBay feedback scores in less competitive markets. Because the price differences dissipate as sellers accumulate credible reputations, the patterns in the data are indicative of statistical discrimination. Overall, the results suggest that mechanisms designed to reduce informational asymmetries and increased competition are both effective at reducing discrimination in online auctions.
Keywords: Racial discrimination Statistical discrimination In-group bias Asymmetric information Field experiments Competition eBay
© 2011 Elsevier B.V. All rights reserved.
1. Introduction Racial discrimination has been studied extensively in economics and other social sciences, but parsing racial discrimination from other influences is difficult. Experiments have become increasingly popular ways to isolate racial discrimination, as these techniques circumvent many of the limitations associated with other methodological approaches by providing greater control over the variables of interest. In this study, we investigate racial discrimination on eBay, an online marketplace that uses second-price auctions to determine prices.3
∗ Corresponding author. Tel.: +1 615 898 5617; fax: +1 615 898 5596. E-mail addresses: [email protected] (J.M. Nunley), [email protected] (M.F. Owens), [email protected] (R.S. Howard). 1 Tel.: +1 608 785 5145. 2 Tel.: +1 615 898 2044. 3 Investigating discrimination in online product-market auctions has some advantages over field experiments that examine discrimination in labor and housing markets. In particular, we observe a continuous outcome variable (i.e. the price), which results from completed transactions. While the cost of discrimination is potentially much higher and more concerning in labor and housing markets, the frequency of transactions involving products is much greater. 0167-2681/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2011.06.028
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We identify racial discrimination in online product auctions by constructing a direct comparison of prices paid to otherwise identical white- and black-named sellers. Our study makes three extensions to the literature on racial discrimination. First, we select products for which the expected racial composition of buyers differs to investigate whether “in-group” biases found in laboratory experiments are supported by data from the field. Second, we use eBay’s feedback system as an observable metric for measuring gains in information regarding a seller’s credibility, which allows us to test whether the type of discrimination observed has an informational component. Finally, we examine products with different levels of market competition to investigate the role of competition as a determinant of racial discrimination in product markets. Much of the recent literature on racial discrimination focuses on whether discrimination stems from tastes or asymmetric information.4 eBay’s feedback system is specifically designed to reduce asymmetric information between buyers and sellers, and can be used to determine the type of discrimination observed. In our context, statistical discrimination arises when price differences arise but dissipate as sellers develop credible reputations. By contrast, finding price differences that persist despite the development of a credible reputation is indicative of taste-based discrimination. Our field experiment has some advantages over previous studies. First, we observe actual bidding decisions made by real buyers in a naturally occurring environment. Bidders are unaware that they are part of an economic experiment focusing on racial discrimination and are, to a large extent, anonymous participants in the market. Therefore, bidders will not adjust their behavior to appease either the experimenter or other bidders in the market. Second, we enter the product markets as passive sellers without the ability to bargain with or discriminate against buyers. We do not initiate any communication with bidders until the auctions are completed. The double-blind situation created by our experimental design eliminates the influence of the bargaining ability of sellers and their knowledge of the underlying distributions of the willingness-to-pay for different demographic groups (see List, 2004). Lastly, the auction framework provides a way to observe the willingness-to-pay of buyers with more precision than posted-price markets. Our data indicate that statistically significant price differences can emerge between white- and black-named sellers. The direction of the effect is consistent with in-group biases by race: white-named sellers receive higher prices than black-named sellers for products that are targeted toward white buyers, and black-named sellers receive higher prices than white-named sellers for products that are targeted toward black buyers. Prior field experiments have found statistical discrimination by whites against blacks, but this is the first study, to our knowledge, that identifies a positive effect associated with having a distinctively black name. The bias in favor of same-race sellers is identified only when certain market conditions are present. In particular, the price differences appear to be driven primarily by the lack of seller credibility, as the price differences dissipate as sellers accumulate credible reputations. In addition, price differences only emerge in markets characterized by low levels of competition. While finding that racial differences are only present at low levels of seller feedback is indicative of statistical discrimination, we are unable to determine whether the discrimination observed is derived from preferences, past experiences, or some combination of both. In either case, our results suggest, rather strongly, that market mechanisms designed to reduce asymmetric information and increased competition can aid in promoting non-discriminatory outcomes in online product auctions. 2. Theoretical framework There are two primary economic models of conscious discrimination: one based on tastes (Becker, 1971) and the other based on statistical discrimination arising from incomplete or asymmetric information (Arrow, 1973; Phelps, 1972).5 In the taste-based model, different economic outcomes emerge for majority and minority groups because of animosity. Economic agents pay a premium, either in terms of lost revenue or higher prices, to avoid trading with individuals from a particular class of people. By contrast, models based on asymmetric information assume economic agents have no animosity. Instead, agents facing incomplete information rely on making inference about an individual’s unobservable characteristics using observable characteristics, such as race or gender. Nevertheless, statistical discrimination results in differential treatment of persons who differ based on observable characteristics. Under any model of discrimination, a discriminating bidder must have a lower willingness-to-pay for the same product sold by a particular seller. Statistical discrimination could be related to perceived race, seller credibility, or other factors. In our context, a prospective buyer shopping on eBay may use the racial distinctiveness of sellers’ names as a proxy for the expected probability that the product will arrive in the stated condition. If discrimination is based on asymmetric information, there is an opportunity to minimize such discrimination through market mechanisms designed to reduce informational asymmetries. Initially, seller quality is unobserved and racially distinct names may serve as a signal that draws attention to prior beliefs that buyers have
4
For example, see Antonovics et al. (2005), Antonovics and Knight (2009), Fershtman and Gneezy (2001), Levitt (2004), and List (2004). An alternative theory of discrimination is based on implicit or unintentional biases (see Bertrand et al., 2005). Implicit discrimination is unlikely to arise in online auctions because buyers have opportunities to examine the products, the characteristics of sellers, and whether identical products or close substitutes are being sold by other sellers. Our winning bidders posted approximately five bids in each auction, which is highly suggestive of “conscious” decision-making. An implicit discriminator could observe a racially distinct name and exit an auction without making a conscious decision to discriminate. But such a decision is an intermediate step toward the determination of the final price in an auction, which is determined by the interaction of several bidders. 5
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about the trustworthiness of different racial groups. However, as sellers’ feedback scores increase, more information about seller quality is revealed, and any priors that buyers may have about quality differences by race become less informative. Under eBay auction rules, the maximum willingness-to-pay of the second highest bidder determines the selling price. Because non-discriminating bidders have a higher willingness-to-pay, increasing the number of bidders competing in a market raises the probability that the two bidders with the highest willingness-to-pay for a given product are non-discriminating and thus raises the price. In the context of Becker (1971), competitive pressure stemming from increases in the number of bidders reduces the impact of the marginal discriminator in the market, leading to smaller disparities in economic outcomes between majority and minority groups.6 Thus, we expect to observe larger price differences in markets with less competition on the demand side of the market. 3. Experimental design We perform a series of auctions using eBay to create a direct comparison of prices paid for identical products sold by whiteand black-named sellers. We use standard eBay auction format with ascending English auction rules and a computerized proxy-bid system (see Lucking-Reiley, 1999; Lucking-Reiley et al., 2007). The price for an item sold in an eBay auction reveals information about the willingness-to-pay of the bidder with the second highest value for the item.7 By contrast, posted-price markets only convey whether buyers have a willingness-to-pay that equals or exceeds the posted price. Therefore, postedprice markets would capture racial price differences only if the willingness-to-pay to one seller is greater than or equal to the posted price and the willingness-to-pay to another seller is less than the posted price. The auction framework provides a unique opportunity to study racial discrimination, as it identifies a more complete picture of buyers’ true willingness-to-pay (Ely and Hossain, 2009; Gray and Reiley, 2007). We sell products (discussed in Section 4) under different racially identifiable seller names creating a comparison by race similar to Bertrand and Mullainathan (2004). Names are selected from the list of distinctively black and distinctively white names provided by Levitt and Dubner (2005). The names chosen to represent our “white” sellers are Jake and Dustin, while the names chosen to represent our “black” sellers are DeShawn, Tyrone, and Jamal. For each group of products, we open new seller accounts using the chosen racially distinct names for each market. This means that each seller account begins with a feedback score of zero for each group of products. We schedule the auctions such that each seller within a given market posts only one auction per week. We pair white- and black-named sellers and sell identical products simultaneously. The paired nature of our auctions and the comparison of prices between names within weeks limits the nuisance variance, but it also has the potential to limit the variance in prices by race. Likewise, selling commonly purchased products may also limit variation in prices, which may work against finding evidence of racial discrimination. Bidders likely have a sensible estimate of the retail prices, making it unlikely that a buyer would have a substantially higher willingness-to-pay than other prospective buyers. Therefore, our experimental design may make it difficult to identify racial discrimination, implying that any price differences detected would likely represent lower bounds. We attempt to hold all variables constant, with the exception of the seller’s racially distinct name. Heckman and Siegelman (1993) emphasize the importance of choosing the proper variables with which to standardize the experiment, as the choice of these variables likely affects the results. Most features of our experimental design are standardized across auctions, but some features of the auctions cannot be made identical without alerting buyers that two seller accounts are from the same source. For features that cannot be held constant, we take great care to ensure that any differences do not influence the results. The details regarding these procedures can be found in an Online Appendix. 4. Data Over a period of 11 months, we conducted 288 auctions (144 pairs) selling various goods from three different sets of products: fishing lures, distinctively black toys, and distinctively white toys. We sell new, unopened products with retail prices ranging from $5.00 to $7.00. The auctions for the fishing lures were conducted over 24 weeks from February 2009 to July 2009. The distinctively black products were sold over the course of 32 weeks from May 2009 to December 2009. The distinctively white toys were sold over the course of 16 weeks from August 2009 to December 2009. A detailed list of the products sold is provided in Table 1.
6 The theoretical argument for competition to reduce the impact of the marginal discriminator in Becker (1971) assumes that discrimination is based on tastes. However, the theory does not require discrimination to result from tastes. In fact, Becker (1971, p. 18) notes that, “. . . many variables in addition to tastes take prominent roles in determining market discrimination, and, indeed, tastes sometimes play a minor part.” As such, the predictions made by Becker regarding the impact of competition on discrimination extend to models in which the disparity in outcomes arises from statistical discrimination. 7 We recognize a point related to whether the “true” willingness-to-pay of the second-highest bidder is revealed without noise in an eBay auction. Assume that the same high bidders are competing over multiple auctions for the same product. It is possible for the strategy of bidders to change so that the second-highest bid is not necessarily equal the second-highest value (Peters and Severinov, 1997, 2006). While we observe some bidders making bids across auctions, these bidders tend to enter only low bids, and they rarely end an auction as either the highest or second-highest bidder. In our view, the final transaction price is a close proxy for the value of the second-highest bidder in our data.
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Table 1 Products sold and the level of competition (measured by retail shelf space). Product
Category
N
Expected race of buyers
Level of competition
Culprit 7.5 Worms Berkley Powerbait 7 Worms Strike King Mini-King Spinners Strike King Bleeding Bait Chatterbait Chatterfrogs Stanley Spinners Mini-King Bleeding Bait Beach Party Barbie Loving Family Mom and Toddler Loving Family Dad and Sister Loving Family Grandma and Brother Barbie Beach Party Nikki Loving Family Brother and Sister Fisher Price Little People AA Husband–Wife Loving Family Mom and Baby Loving family Dad and Baby Peek-a-boo Barbie-Jemila of Johannesburg Beach Party Steven Ballerina Nikki Barbie Fairy Bratz Angelz Bratz Ballerinaz Sasha Sweet Secrets Morgan
Fishing Fishing Fishing Fishing Fishing Fishing Fishing Fishing Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll Doll
32 16 10 8 8 8 8 6 32 12 12 8 34 32 14 8 8 8 8 6 4 2 2 2
White White White White White White White White White White White White Black Black Black Black Black Black Black Black Black Black Black Black
High High Low Low Low Low Low Low High High High High High Low Low Low Low Low Low High High High Low Low
Notes: The heading ‘Product’ lists the names of the products sold; ‘Category’ defines whether the good is a fishing product or a doll; ‘N’ is the number of auctions posted for each product; ‘Expected race of buyers’ lists our expectations regarding whether the buyers of the product are expected to be white or black; ‘Level of competition’ identifies whether the product has a large amount of display space in Wal-Mart, Toys R Us, or Bass Pro Shop. A larger shelf space devoted to a product is indicative of high buyer demand, while a small display is indicative of relatively less buyer demand. All products were purchased at retail chains except the African-American Loving Family Dollhouse figurines, which were purchased online.
The first group of products is fishing lures, which are commonly used for bass fishing. These products can be categorized as either “Plastic Worms” or “Spinner Baits”. We sold two fishing lures per auction and selected identical brands, sizes, and color combinations to ensure that the products sold are perfect substitutes within each pair. While fishing lures have no inherent race-specific characteristics, research by the U.S. Fish and Wildlife Service indicates that African-Americans represent only five percent of all anglers, and they account for less than four percent of the total amount spent on fishing merchandise (Pullis, 2000). As a result, we refer to fishing lures as distinctively white products from this point forward. The other products sold have obvious racially identifying features. The majority of the data are from auctions selling two racially distinct sets of children’s toys from the same product lines. We sell white Barbie dolls and black Nikki dolls from Mattel’s Beach Party Barbie series and white and black figurines from the Fisher Price Loving Family Dollhouse series. Fig. 1 displays examples of these products. The black versions are considered distinctively black products, while the white versions are considered distinctively white products. While selling these products we make use of eBay’s feedback system, which allows buyers and sellers to signal their credibility by publicizing their feedback scores and the comments provided by previous trading partners. Studies report that the development of a positive reputation via eBay’s feedback system enhances seller credibility (Resnick et al., 2006), and the feedback scores play an important role as determinants of long-run success on eBay (Cabral and Hortacsu, 2010). Upon completion of an eBay auction, the buyer and seller have the opportunity to leave feedback for each other. Buyers and sellers receive ‘−1 if the buyer is displeased with the transaction; a ‘+1 if the buyer is satisfied with the transaction; or a ‘0 if the buyer is neutral regarding the transaction. Buyers can also leave comments describing their experience with the seller. After receiving a positive feedback score of 10, eBay users receive a yellow-star award, which appears alongside their usernames. We carefully coordinate the auctions and ship the products in a timely manner so that our sellers never incur negative feedback. The feedback scores lag behind the number of actual auctions completed because buyers provide feedback after they receive the product. In addition, buyers are not required to rate a seller. As a result, slight differences in feedback scores emerge that are beyond our control. But we are able to hold constant differences in the feedback scores, as the these characteristics are observable. One of our remaining challenges is to quantify the level of competition present in each product market. Ideally, we would like to know the number of potential bidders for our products, but this is unobservable. We present results using the retail shelf space devoted to our products in Wal-Mart, Toys R Us, or Bass Pro Shop as a proxy for the level of competition, as the shelf space devoted to a product is an observable indicator of buyer demand. Products given more space are characterized as highly competitive markets, while products given less space are considered low competition markets. Our proxy for
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Fig. 1. Examples of distinctively white and black toys.
market competitiveness has limitations in that it is somewhat removed from buyer demand on eBay.8 However, given the characteristics of bidding behavior found in our data, we are confident that this classification captures differences in buyer competition between products. As the number of bidders increases, we expect the difference between the highest losing bids to converge. This tendency coupled with the proxy-bid feature on eBay, which allows bidders to enter reservation prices at any time, implies that bids will increase at different rates in auctions characterized by different numbers of potential bidders. Within this context, an increase in the number of bidders making proxy bids implies that it is more likely for two higher value bidders to meet earlier in an auction’s duration. Therefore, we expect the current high bid in a highly competitive market to be closer, on average, to the final price than it would be if the market were less competitive. We calculate the ratio of the high bid at the half-way point to the final price for each of our auctions to measure the rate of convergence to the final price.9 Bids in our high competition markets escalate to their final prices at a faster rate, as the mean ratio at the midpoint of the high competition markets is 0.672 versus 0.581 in the low competition markets. A Wilcoxon–Mann–Whitney test indicates that the difference in the price ratios in high and low competition markets is highly statistically significant (Z = 2.843, p = 0.0045). We recognize the limitations of using measures that are somewhat subjective. As a result, we check the robustness of our findings to a variety of alternative classifications of high- and low-competition markets. We find similar regression results, and similar significant differences in the ratios of halfway prices to final prices, regardless of how the level of competition in a given market is classified.10 We emphasize that each measure of market competitiveness assigns the products to roughly
8 Prior to choosing our products, we also examined the statistics provided by “eBay Pulse,” which shows the most popular searches entered by prospective ¨ ¨ ¨ ¨ consistently rated in the top 10 in the “Dolls and Bears” category. While the statistics and Barbie Dollsare buyers on eBay. For example, the terms Barbie from eBay Pulse accurately reflect searches for the products with the highest demand on eBay, information on low-competition products is limited. 9 We would like to examine the highest two losing bids, but we do not have access to this information. We did record the high bid at the halfway point in all of our auctions, and we make use of this information. We thank the editor for suggesting that we examine bidding behavior as a measure of demand-side competition. 10 There are two related studies that examine the effect of competition on racial discrimination. Each of these studies reach conclusions that are similar to ours, with each using a different measure of competition. List and Livingston (2010) compare between levels of competition for two particular baseball cards with different and constant quantities. We are unable to use this approach, as the quantities supplied of our products are unknown. Doleac and Stein (2010) sell iPods and use the number of competing sellers on online classified advertisement websites to proxy market competitiveness. We use the number of competing sellers on eBay as a proxy for the level of competition in one of our robustness checks, finding similar results to those reported in the text. These results are available upon request.
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Table 2 Summary statistics: higher prices within a pair and mean prices (same race versus different race by level of competition).
Low competition Higher price – same race Higher price – different race Test statistic Mean price – same race
Mean price – different race
Test statistic High competition Higher price – same race Higher price – different race Test statistic Mean price – same race
Mean price – different race
Test statistic
All
No yellow star
Feedback 0–6
35 28 0.116 6.22 (0.34) [65] 5.74 (0.30) [65] 0.476
25 17 0.013** 6.32 (0.51) [40] 5.61 (0.44) [38] 0.376
20 12 0.006*** 6.58 (0.60) [33] 5.61 (0.51) [31] 0.276
34 38 0.633 6.04 (0.24) [79] 6.43 (0.27) [79] 0.589
20 25 0.376 6.20 (0.32) [58] 6.47 (0.30) [52] 0.636
16 20 0.542 5.85 (0.28) [41] 6.15 (0.25) [44] 0.765
Notes: Standard errors are in parentheses and the numbers of observations are in brackets. Test statistic is the p-value from a Wilcoxon–Mann–Whitney test of “same race” versus “different race”. ‘Same’ and ‘different’ race rows by seller feedback do not have equal numbers due to the natural escalation of feedback scores. The number of auctions receiving a higher price for same race and different race do not sum to the number of observations reported in mean prices due to ties. ** Statistical significance at the five percent level. *** Statistical significance at the one percent level.
the same categories, and these classifications are consistent with the observed bidding behavior in our auctions. This is reassuring due to the absence of a clear indicator for market competitiveness. 5. Results In Table 2, we examine the mean prices received and number of times a same-race seller (i.e. the races of sellers match the products) receives a higher price than a different-race seller (i.e. the races of sellers do not match the products) within an auction pair in markets characterized by different levels of competition and seller feedback. In low-competition markets, same-race sellers receive a higher price within a pair more often than different-race sellers, regardless of seller feedback. Wilcoxon–Mann–Whitney tests for the difference in receiving higher prices for those without a yellow star (Z = 2.482, p = 0.013) and for feedback scores of six or less (Z = 2.730, p = 0.006) are both statistically significant. While the mean selling prices are higher for same-race sellers, the differences in the mean prices are not significantly different from zero, even for sellers with low feedback scores. There are no significant differences in the number of times a higher price is received or in the mean prices for the high-competition subsample. The differences-in-means tests do not indicate statistically significant differences because the variation in prices between different products and the variation in the price of the same product in different weeks are both greater than the variation in prices received between different names within a given week. However, the variation in the prices received by whiteand black-named sellers within a given pair is of primary interest to us. To examine within-pair variation in prices received, we turn to regression analysis, which allows us to hold constant auction-pair fixed effects. In our baseline model specification, we estimate the following ordinary least squares (OLS) regression model: Price = ˇ0 + ˇ1 Same Race + ˇ2 Ends First + ˇ3 Yellow Star + ˇ4 Auction Pair + ε.
(1)
The variable Price is the price received; Same Race is an indicator variable which equals one if the racial distinctiveness of the seller’s name matches the racial classification of the product and zero otherwise; Ends First is an indicator variable that equals one when an auction within a given pair ends first and zero when it ends last; Yellow Star is an indicator variable that equals one when sellers accumulate positive feedback scores of 10 or more and zero otherwise; Auction Pair represents auction-pair fixed effects which capture the influence of other determinants of the price common to both sellers in a given week, including seasonal variation and the influence of other competitors in the market; ε is the error term; and ˇi are parameters to be estimated. Once the auction-pair fixed effects are held constant, the parameters from Eq. (1) represent the effect of the explanatory variables within a seller-pair for a given week. We are primarily interested in the parameter ˇ1 , which captures the difference in prices received between sellers whose racially distinct names match the racial classification of the product and sellers whose names do not match the racial classification of the product.
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Table 3 The effects of same race names on prices received (all products). Explanatory variable
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
Full sample
Low competition
High competition
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.015 (0.18) – – – – – – – – – – 0.815 0.624 288
– – 0.018 (0.22) 0.009 (0.31) – – – – – – 0.815 0.621 288
– – – – – – 0.217 (0.25) −0.384 (0.31) 0.154 (0.44) 0.818 0.625 288
0.487** (0.23) – – – – – – – – – – 0.879 0.748 130
– – 0.761** (0.30) 0.075 (0.37) – – – – – – 0.883 0.752 130
– – – – – – 1.006*** (0.31) −0.320 (0.41) 0.309 (0.49) 0.891 0.766 130
−0.391 (0.26) – – – – – – – – – – 0.768 0.520 158
– – −0.525* (0.31) −0.067 (0.49) – – – – – – 0.769 0.517 158
– – – – – – −0.463 (0.36) −0.409 (0.44) −0.067 (0.71) 0.768 0.508 158
Notes: Standard errors are in parentheses. Each specification includes auction pair fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level. *** Statistical significance at one percent level.
In supplemental models, we estimate Eq. (1) for subsamples partitioned by the levels of competition present in a given market. We also augment Eq. (1) to estimate the effects of Same Race by the feedback scores received by sellers. In particular, we make use of the “yellow star” received by sellers who accumulate a positive feedback score of 10. To obtain a better idea of how the feedback scores interact with the racial distinctiveness of the sellers’ names, we also estimate the effects of Same Race by low, medium, and high levels of feedback.11 Because each of our sellers accumulates similar feedback over the course of the experiment, we are able to compare sellers with comparable reputations who only differ by the racial distinctiveness of their usernames. We analyze the impact of racially distinct names on prices received by pooling the data from all product markets. Table 3 displays the results for each specification for the full sample (columns 1, 2 and 3), the low-competition subsample (columns 4, 5, and 6), and the high-competition subsample (columns 7, 8 and 9). The estimated overall effect of Same Race in the full sample is positive but it is not statistically different from zero (columns 1, 2 and 3). We check the robustness of these results to the exclusion of three pairs of observations for which we observed odd bidding behavior. The details of these observations are discussed in Appendix A, and regressions using the data without these pairs are displayed in Table A1. Overall, the results from the pooled sample indicate that sellers receive similar prices, regardless of whether the race of their name matches the racial classification of the product. However, the results from the full sample mask some important patterns in the data. In particular, price differences between white- and black-named sellers emerge in less competitive markets but do not in highly competitive markets. The results for markets characterized by low levels of competition each indicate statistically significant price differences in favor of same-race sellers (columns 4, 5, and 6). Overall, Same Race sellers in low-competition markets receive a premium of $0.49 (8.2 percent of mean) per auction, and this effect is statistically significant at the five-percent level (column 4). However, the statistically significant effect of Same Race is driven by the price differences at low levels of seller feedback, and these differences dissipate as sellers accumulate credible reputations via eBay’s feedback system (columns 5 and 6). Same-race sellers with low feedback scores earn between $0.76 (12.7 percent of mean) and $1.01 (16.5 percent of mean) more per auction than different-race sellers with low feedback scores. The estimated effect of Same Race is statistically significant at the five-percent level for sellers with feedback scores less than ten and at the one-percent level for feedback scores less than seven. The effect of Same Race is not statistically significant at higher levels of feedback. The results are different for the subsample of highly competitive markets, in which the effect of Same Race is negative and statistically significant at the 10 percent level in column 8, but not statistically different from zero at conventional levels in the specifications in columns 7 and 9.12 We repeat the analysis separately for subsamples of the distinctively white and black products. The partitioned samples allow us to examine whether same-race biases are present in both white- and black-product markets or if the differences
11 Feedback scores of 0–6 are coded as low; feedback scores of 7–12 are coded as medium; and feedback scores of 13 or greater are coded as high. These cutoffs are empirically motivated, as they provide three groups with similar numbers of observations. Regressions with other categorizations of feedback yield qualitatively similar results. 12 The negative and statistically significant coefficient in column 8 is driven by two pairs of auctions, which are described in more detail in Appendix A, that occur after the sellers accumulate their seventh positive feedback. Notice that the effect of same-race with low feedback (0–6) in column 9 is not statistically significant, implying that auctions taking place between the seventh and tenth feedback score are driving the price difference. Table A1 shows that when these auction pairs are excluded, the equivalent estimate is not statistically significant.
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Table 4 The effects of racially distinct names on prices received in low competition markets. Explanatory variable
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
White products
Black products
(1)
(2)
(3)
(4)
(5)
(6)
0.562* (0.32) – – – – – – – – – – 0.818 0.592 48
– – 0.847* (0.45) 0.249 (0.47) – – – – – – 0.825 0.589 48
– – – – – – 1.283** (0.51) 0.018 (0.55) 0.232 (0.59) 0.845 0.616 48
0.580* (0.11) – – – – – – – – – – 0.899 0.784 82
– – 0.816** (0.39) 0.146 (0.53) – – – – – – 0.902 0.784 82
– – – – – – 0.930** (0.39) −0.233 (0.67) 0.328 (0.74) 0.905 0.787 82
Notes: Standard errors are in parentheses. Each specification includes auction-pair fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level.
found in the pooled sample are driven by discrimination against a particular racial group. Table 4 presents the findings for the white- and black-product subsamples in low competition markets.13 In Table 4, the symmetry between the results for the white- and black-product subsamples is striking. In low competition markets, white-named sellers receive a premium of $0.56 (10.2 percent of mean) in white-product markets (column 1), while black-named sellers receive a premium of $0.58 (9.3 percent of mean) in black-product markets (column 4). Both are statistically significant at the 10 percent level. However, the effect in each subsample is driven by price differences at low levels of seller feedback. In the white-product subsample, white-named sellers with low feedback scores earn between $0.85 (14.8 percent of mean) and $1.28 (20.4 percent of mean) more per auction than black-named sellers with low feedback scores (columns 2 and 3). These estimated effects are statistically significant at the ten and five percent levels, respectively. In the black-product subsample, the premium to black-named sellers with low feedback ranges between $0.82 (13.5 percent of mean) and $0.93 (15.4 percent of the mean) depending on the specification (columns 5 and 6). These estimated effects are both statistically significant at the five-percent level. In both the black- and white-product subsamples, the price differences dissipate when sellers receive a feedback score of seven or more. 6. Summary and discussion We conduct a field experiment to investigate racial discrimination in online product auctions by selling identical goods simultaneously on eBay under different seller names. One seller who is assigned a distinctively black name is paired with another seller who is assigned a distinctively white name. Using this design, we make the following contributions to the literature on racial discrimination. First, we choose products that are marketed toward and likely to be purchased by buyers from different racial groups. This enables us to investigate whether discrimination occurs in favor of sellers whose racially identifying names match the racial distinctiveness of the products, rather than universal discrimination against certain groups. Second, we use eBay’s seller feedback scores as an observable measure for the information available to bidders, which provides a way to test whether the observed discrimination has an informational component. Lastly, we choose products that vary by the level of competition present in the market. We find evidence that in-group biases, which have been identified in a number of studies in the economics literature (see Anderson et al., 2006; Antonovics and Knight, 2009; Ball et al., 2001; Donahue and Levitt, 2001; Fershtman et al., 2005; Glaeser et al., 2000; Price and Wolfers, 2010), can emerge based on race in a natural product market setting. We find the direction of discrimination depends on the combination of the racial characteristics of the products and the racial distinctiveness of the sellers’ names. For the white products, we observe discrimination against blacks just as other studies of product markets have found (see List, 2004; List and Livingston, 2010; Doleac and Stein, 2010). However, we detect a benefit associated with having a black name in the distinctively black product markets. We are unaware of any study in the literature that finds benefits associated with having a distinctively black name. In our data, same-race biases are detected only under certain market conditions. In particular, we find evidence of samerace biases for sellers who have yet to develop credible reputations in markets characterized by low levels of competition. As sellers establish more credibility, price differences are no longer observed. The finding that price differences only arise
13 In the interest of brevity, we do not present the estimates from the full sample or the high-competition subsample, as the estimates are not statistically different from zero in either sample.
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in markets characterized by low levels of competition is consistent with theory (Becker, 1971) and other field experiments (List and Livingston, 2010; Doleac and Stein, 2010). The fact that price differences arise, but dissipate with seller feedback, is a clear indication that the type of discrimination observed has a strong informational component. At low levels of seller feedback, it appears that bidders are making decisions based on prior beliefs that same-race sellers are more reliable and trustworthy than different-race sellers, indicating that similar findings by Glaeser et al. (2000) in laboratory experiments are supported with data from the field. In our case, the importance of the buyers’ prior beliefs regarding the trustworthiness of seller with a racially distinct name diminishes as other pertinent information about seller credibility becomes available. Our choice to sell brand new, low priced products, under the protections in place on eBay minimizes the risk facing buyers. It is interesting that statistical discrimination emerges in a market setting for which better information can generate relatively small benefits. Our data indicate patterns that are different from those found in existing studies. For instance, the use of auctions and double-blind procedures eliminates the role of bilateral bargaining in our markets and removes the possibility that perceived lower reservation values among minorities are the source of price differences, as found in List (2004). Instead, it appears that buyers use perceived race as a signal for the likelihood that the product will be delivered as advertised. Thus, statistical discrimination in our study is based on different factors than those identified previously. An alternative interpretation for the effects of racially distinct names is that they could reflect perceived differences in socioeconomic status rather than perceived differences in race (Ball et al., 2001; Bertrand and Mullainathan, 2004). However, the symmetry of same-race biases in the white- and black-product subsamples suggests this is not the case in our data. If the socioeconomic statuses associated with the names are driving the results, the low socioeconomic status names would receive lower prices across all products, independent of whether the products are distinctively white or distinctively black. Because it is unlikely that buyers in different markets would form opposite views about the socioeconomic status signaled by the same racially distinct name, the racial information itself seems to be central to the discrimination observed in our data. While bidders appear to have more favorable prior beliefs about sellers whose race matches the racially distinct product, it is unclear whether these priors stem from past experiences, animosity, or other factors. Whatever the root cause, our results suggest, rather strongly, that reductions in informational asymmetries and increased competition can aid in minimizing or eliminating discriminatory outcomes in online product auctions. Appendix A. Regressions on data without potential outliers We encountered two auctions with extremely high prices, and one with an extremely low price relative to other auctions for the same products. The auctions are from markets classified as highly competitive. These auctions do not affect the main conclusions of the paper, as the low-competition subsample is unaffected by these data points. Since our analysis is based on within-auction-pair variation, these prices lead to very large price differences for the three pairs of auctions that are affected. In the first pair, a white Barbie doll was sold by a black-named seller with a feedback score of eight for $13.50 and a white-named seller with a feedback score of seven for $5.51 in the same week. The difference in prices is greater than the mean price of $5.98 for a white Barbie doll in our sample. In the second pair, a Nikki doll sold for $13.50 to a white-named seller with a feedback score of seven and for $6.55 to a black-named seller with a feedback score of Table A1 The effects of same race names on prices received (all products) no outliers. Explanatory variable
Full sample (1)
Same Race Same Race without Yellow Star Same Race with Yellow Star Same Race with Low Feedback Same Race with Medium Feedback Same Race with High Feedback R-squared Adj R-Squared Observations
0.191 (0.15) – – – – – – – – – – 0.860 0.714 282
Low competition (2) – – 0.297 (0.19) −0.007 (0.26) – – – – – – 0.860 0.714 282
(3) – – – – – – 0.342* (0.21) −0.054 (0.27) 0.175 (0.37) 0.861 0.713 282
(4) **
0.487 (0.23) – – – – – – – – – – 0.879 0.748 130
High competition
(5)
(6)
(7)
(8)
(9)
– – 0.761** (0.30) 0.075 (0.37) – – – – – – 0.883 0.752 130
– – – – – – 1.006*** (0.31) −0.320 (0.41) 0.309 (0.49) 0.891 0.766 130
−0.076 (0.20) – – – – – – – – – – 0.843 0.675 152
– – −0.065 (0.24) −0.101 (0.37) – – – – – – 0.843 0.670 152
– – – – – – −0.242 (0.27) 0.163 (0.35) −0.010 (0.54) 0.845 0.669 152
Notes: Standard errors are in parentheses. Each specification includes product-week fixed effects as controls. * Statistical significance at ten percent level. ** Statistical significance at five percent level. *** Statistical significance at one percent level.
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eight. In the third pair, a Nikki doll was sold by a black-named seller with a feedback score of five for $1.25 and a white-named seller with a feedback score of five for $9.85 in the same week. The price differences in pairs two and three exceed $5.80, which is the mean price of a Nikki doll in our sample. We suspect that both of the $13.50 prices may be mistakes in bidding because the next highest selling price for a Barbie is $10.50, but we cannot be certain. We have no explanation for why a Nikki doll sold for only $1.25, as the next lowest selling price is $3.75. We present regressions in Table A1 that use a sample with these three pairs of auctions excluded. The signs and statistical significance for the estimates are similar to those in Table 3, with two notable differences. First, the Same Race with Low Feedback coefficient becomes positive and statistically significant in the full sample (column 3). 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