What ’ s in a Picture ? Evidence of Discrimination from Prosper . com

54 The Journal of Human Resources discrimination is consistent with these theories is an important goal. However, it is often difficult to test for discrimination in markets and generally even harder to assess the different theories of discrimination. This paper examines discrimination in a new type of credit market known as peerto-peer lending. Specifically, we study data from the website Prosper.com, a leader in online peer-to-peer lending in the United States. Peer-to-peer lending is an alternative credit market that allows individual borrowers and lenders to engage in credit transactions without traditional banking intermediaries. While still small, these markets are growing quickly and may represent an important niche, especially in the area of consumer-debt consolidation. Websites like Prosper aggregate small amounts of money provided by a number of individual lenders to create moderately sized, uncollateralized loans to individual borrowers. In order to request funding, borrowers in these markets create a loan listing that resembles auction listings for goods on websites like eBay. Like most standard credit applications, this listing displays desired loan parameters and reports information from the prospective borrower’s credit profile. Unlike typical credit applications, however, borrowers may include optional and unverified personal information in their listings in the form of pictures and text descriptions. These pictures and descriptions often provide potential lenders with signals about characteristics such as race, age, and gender that antidiscrimination laws typically prevent traditional lending institutions from using. Our first research question focuses on the determinants of access to credit in the Prosper marketplace, and in particular on how signals from pictures about characteristics, such as race, age, and gender, affect the likelihood of receiving loan funding and the interest rates borrowers pay. In the language of the legal literature we test for “disparate treatment” of certain groups by estimating whether they are treated differently than their counterparts who are similar on other dimensions. Our empirical approach uses observational market data. The typical problem with this type of analysis is the potential for omitted variable bias. An advantage of our data set 2. See Altonji and Blank (1999) and Blank, Dabady, and Citro (2004) for reviews of empirical work on assessing discrimination in labor markets, and Ross and Yinger (2002) for a similar review in credit markets with a focus on mortgage lending. 3. A few notable papers that have used clever empirical methodologies to examine statistical discrimination vs. taste-based discrimination include Altonji and Pierret (2001), Knowles, Persico, and Todd (2001), Levitt (2004), Antonovics and Knight (2004), and Charles and Guryan (2007). 4. See Freedman and Jin (2008) for an analysis of the evolution of the Prosper market and the profitability of loans on Prosper. 5. The other important definition within the legal literature is “disparate impact,” which arises when decision-makers do not explicitly account for characteristics such as race and gender, but use variables that are highly correlated with these characteristics. See Ross and Yinger (2002) for a discussion of disparate treatment vs. disparate impact with a focus on discrimination in credit markets. 6. This observational-market-data approach is similar to that used in the influential studies of redlining and racial discrimination in mortgage lending by the Boston Federal Reserve (Munnell, Tootell, Browne; McEneaney 1996; and Tootell 1996). 7. Audit studies and field experiments are an important alternative technique for examining the existence of discrimination (specifically disparate treatment) in a range of markets. For instance, in a very influential paper Bertrand and Mullainathan (2004) study racial discrimination in the labor market by randomly assigning race to fictitious resumes and find that resumes with black-sounding names are less likely to receive a call-back for an interview. Examples of audit studies include Turner et al. (2002) on mortgage lending, Turner et al. (1991) on the labor market, and Ayers and Siegelman (1995) on automobile purchases. Pope and Sydnor 55 is that we are able to control for nearly all of what a lender knows about a borrower when making funding decisions. Prosper.com generously provides a data set that contains information from loan listings created on the site, including links to pictures included with the listings. In order to conduct the analysis, we systematically coded variables from pictures and text descriptions for over 110,000 loan listings that were created on Prosper.com between June 2006 and May 2007. The empirical analysis reveals significant racial discrimination in this market. Compared to the response to otherwise similar whites, we estimate that listings with blacks in the picture are 2.4–3.2 percentage points less likely to be funded. Compared to the average probability of funding, 9.3 percent, this represents an approximately 30 percent reduction in the likelihood of receiving funding. A range of specifications and numerous robustness checks and alternative cuts of the data reveal very stable effects of race on the likelihood of funding. This discrimination against blacks in the lending decision is also reflected in the interest rates these borrowers pay conditional on receiving a loan; their interest rates are 60–80 basis points higher than those of whites with similar credit profiles. While smaller and less robust than the results for race, we find a number of other interesting market responses to the information in pictures and text. For instance, the market discriminates somewhat against the elderly and significantly overweight, but in favor of women and those that signal military involvement. The market also favors listings where the borrower expresses a desire to pay down credit-card debt (the most popular stated loan purpose) over credit requests for other purposes, such as loans for business expansions or automotive repairs/purchases. It is perhaps somewhat surprising that we find evidence of discrimination in this market. Because the pictures and descriptions are optional and unverifiable, a natural prediction would be that the market would respond little to this type of “cheap talk.” Yet the fact that borrowers include a wide variety of pictures and the market responds to those signals, suggests that the information is not treated as cheap talk in the market. In fact, we find the Prosper market responds negatively to listings that do not include a picture. Another reason that the finding of racial discrimination might be somewhat surprising is that lenders are given a wide range of information about each borrower’s credit profile, including credit grade, debt-to-income ratio, and a measure of income. However, we find that lenders respond to signals about race above and beyond this wealth of credit information. Given that we find discrimination in this market, an obvious question is whether this discrimination is efficient for lenders—in other words, are these differences consistent with lenders engaging in accurate and economically efficient statistical discrimination? Because of the availability of data and the nature of the market, we By manipulating the race or gender of applicants for jobs or loans, these types of studies are able to identify clean causal links between group status and treatment without concerns of omitted variables or the correct empirical specification. On the other hand, because they usually lack any ex-post performance data, with audit or field-experiment approaches, it is generally hard to assess different theories of the sources of discrimination. Heckman (1998) argues that the audit approach over-states the importance of discrimination, arguing that while some employers, salesmen, or lenders may discriminate, that minorities will seek out those who do not, thereby lessening the impact of the discrimination. It is worth noting that the discrimination we find on Prosper.com is at the market level. 56 The Journal of Human Resources can address this question using loan-performance data. A unique feature of the Prosper market is that it operates as an auction that allows interest rates to be bid down below an initial rate set by the borrower, if enough lenders find a loan attractive. The basic intuition behind the analysis, then, is that if lenders care only about the net return of a loan (adjusted for expected default), funds will flow to loans that are attractive given the observable information to lenders. This process should adjust their interest rates and equalize expected returns. If the market correctly incorporates characteristics from pictures and text when assessing creditworthiness, accurate statistical discrimination will result in funded loans that have equal average net returns irrespective of the listing characteristics. On the other hand, if taste-based discrimination is the sole cause of disparate treatment in the market, loans made to the group subject to negative discrimination should have higher net returns ex post. The comparison of the net return on loans made to blacks and otherwise similar whites is striking. The estimated average net return on a dollar from investing in a loan from a black borrower is 7.3–8.6 percentage points lower over a three-year period. Although blacks are discriminated against in the lending process, the higher interest rates that they pay are not enough to account for their greater propensity to default. This runs counter to the predictions of both accurate statistical discrimination (equal net returns) and taste-based animus against blacks (higher net returns on loans to blacks). How can we reconcile the evidence of discrimination against blacks in the lending process with the fact that their loans result in lower net returns? The evidence