Gender bias in P2P lending

This project investigates mechanisms of the gender gap in pricing of loans on peer-to-peer (P2P) lending platforms. Research has widely established gender bias in various markets, including but not limited to finance. P2P lending is seen as an alternative to financial institutions, aimed at improving access to finance, especially for those who are either excluded from traditional finance, or unhappy with standardised products offered by traditional financial institutions like banks. Very little is known about behavioural biases, and particularly the gender bias, in alternative finance. On the one hand, if biases are due to misperceptions rooted in the traditional financial sector, we may expect that the emerging alternative finance is less vulnerable to them. On the other hand, if biases stem from tastes and preferences common to all people, then the lack of regulation and guidelines in the alternative finance may lead to an even stronger manifestation of biases in P2P lending. A relatively small recent strand of literature suggests some evidence of a gender bias in alternative finance, yet its mechanisms are yet poorly understood. Some alternative finance platforms introduce algorithms to manage pricing, which may potentially reduce the bias if the algorithm is gender-neutral. However, if machine learning and artificial intelligence that underly algorithms rely on data from past human decisions, there is no guarantee the algorithm is gender-neutral. Whether other solutions can be developed to prevent unfair treatment of market participants due to their gender and other sensitive characteristics, crucially depends on our understanding of mechanisms that underly such biases. This is exactly where this project contributes.

With respect to lending through formal institutions, although access to banking is positively associated with social improvements, health and well-being, inter alia (especially in developed countries, see, e.g., Kellard et al., 2024), an extensive body of literature documents that women face more constraints in receiving bank credit and are charged higher interest rates compared to men (Alesina et al., 2013; Bellucci et al., 2010; Calcagnini et al., 2014; Muravyev et al., 2009). In particular, Alesina et al. (2013) document that although male borrowers are on average riskier than females, Italian women entrepreneurs paid interest rates by 9 and 11 basis points higher than those paid by men. For larger companies, in contrast, having a female director helps ensure a lower interest on loans (Karavitis et al., 2021). Female-owned enterprises have been also less successful, relative to male-owned, in obtaining loans (Calcagnini et al., 2014). Muravyev et al. (2009) and Bellucci et al. (2010) find that generally women face more constraints in receiving bank credit. Within the financial industry itself, although women are seen as bringing more prudent and sustainable decision-making within financial institutions, they are significantly less likely to serve in leadership positions at banks, partly due to a strong masculine culture (Girardone et al., 2021)

These findings for financial industry are consonant with those from other well-established markets, such as real estate and arts. In the U.S. real estate market, Kim et al. (2019) find that female sellers accept lower prices from the sale of their houses, while Goldsmith‐Pinkham and Shue (2023) report that women have lower housing returns, which they attribute to choices of where and when to buy and sell, and negotiation skills. Similar results have been obtained by Bach et al. (2022) for the Swedish market. However, for the Danish market, Andersen et al. (2021) find that the gender difference vanishes when controlling for property heterogeneity. In the arts market, paintings of female artists are sold at substantial discounts in the secondary market for comparable characteristics of the painting and the age of the painter (Adams et al., 2021; LeBlanc & Sheppard, 2021), however this holds mainly in the top-end market (with price tags over $1m), while in the less expensive segment female artists achieve sales at higher prices (Bocart et al., 2021). Cameron et al. (2019) echo this latter result for the market of new artists, however they suggest barriers to access the art market for females, might filter female art in terms of quality relative to male ones. Results from non-financial markets so far stress that gender discrimination is not unique to traditional finance, and indicate such factors as long- established markets, traditions and rooted perceptions as potential drivers of the gender gap, while newer markets may appear to be less prone to it.

In modern digital economies ordinary people increasingly get access to financial markets via digital investment tools and fintech. P2P lending is a relatively new, alternative source of finance alongside conventional financial intermediaries. P2P platforms act as substitutes for banks when serving infra-marginal, low-quality borrowers (Di Maggio and Yao (2021); Balyuk (2023)), and borrowers with small collaterals (Beaumont et al. (2022)). This role of P2P lending is particularly pronounced in periods when banks face tight regulatory constraints (Tang, 2019; de Roure et al., 2022). However, implications of this improved access to finance via P2P, both for borrowers and for lenders, yet need to be understood. As a market for financial products that are substantially different from those operated in traditional financial markets, it may potentially be less prone to biases, as suggested by findings from the real estate and art markets. At the same time, unlike traditional financial institutions like banks, with established sets of rules and internal policies, and heavily supervised by regulators, alternative finance enables matching of ordinary people who wish to borrow with similar people who have an opportunity to lend, who are not bound by any strict guidelines and rules, and therefore may be much more likely to follow behavioural biases than credit officers at formal institutions.

Evidence on this issue is rather scarce. Using data from Prosper, the second largest P2P platform in the US, Pope and Sydnor (2011) show that black people, old-aged borrowers and those who looked unhappy face discrimination in accessing loans compared to white borrowers. The trustworthiness impressions of the profile pictures of the borrowers can affect the lending decision in the P2P market by increasing the loans’ applications approval probability and by reducing the cost of lending (Duarte et al., 2012). In a survey-experiment, Ravina (2019) provides evidence for taste-based discrimination against unattractive borrowers, who appear to be less likely to get funded although their default rates are lower than those of borrowers deemed beautiful. Gafni et al. (2021) find that female entrepreneurs are less likely to receive crowdfunding support for their projects from male investors on Kickstart platform. Bhutta et al. (2021) observe that Hispanic and black borrowers were charged higher mortgage interest relative to non-Hispanic white borrowers. Similar results were obtained by Bartlett et al. (2022) in both Fintech and non-Fintech lenders, where Latin and black borrowers are charged higher credit rates on their securitised loans. Interestingly, Mohammadi and Shafi (2018) found discrimination of female borrowers by female investors, who  preferred to invest in males’ projects on FundedByMe crowdfunding platform.

Chen et al. (2020) report that female borrowers have to demonstrate higher creditworthiness to the investors of Renwrendai P2P platform, in order to secure a comparable success rate of funding their loan applications to their male peers. Unlike them, Barasinska and Schäfer (2014) did not find any discrimination against women in terms of the probability of being funded on the Smava P2P platform in Germany.

Our preliminary investigation using loan level data from a leading EU P2P lending platform reveals a gap in pricing: women were charged a higher interest rate than men for loans of comparable quality/quantity; the maximum accepted interest rates of women were higher than those requested by men. Notably, after this lending platform introduced algorithmic pricing that assigns a fixed interest rate based on fundamental loan characteristics, the pricing differential flipped: female borrowers were assigned interest rates by 2.14% lower than males. As the algorithm makes gender-neutral assessments, the above flip in pricing indicates that the overcharging of female borrowers prior to its introduction was not related to the quality of loans. It follows that a likely explanation of the pre-algorithm gender gap in pricing is in gender-biased decisions of market participants. Our objective is to better understand the mechanisms of such a gender bias in loan pricing in P2P lending.

Traditional explanations of loan price differences resort to differences in risk (risk premium) or liquidity (liquidity premium). As highlighted above, objective loan characteristics, including risk and liquidity, cannot explain the gender gap in pricing. However, subjective perceptions of risk and liquidity may be gender-biased. In particular, Mohammadi and Shafi (2018) suggest discrimination of female projects is possibly due to the psychological stereotyping that females are not that competent in investments. More generally, gender gap in pricing can be driven either by the supply side or by the demand side, or both. On the supply side, private investors/lenders may act on their gender preferences and presumptions, overestimating or underestimating the likelihood of moral hazard, delinquency, restructuring,  for a particular gender. On the demand side, individual borrowers may offer high interest rates to reduce time to find lenders, to increase the likelihood of funding, based either on their own perceptions of investors views on their loan, and these perceptions may differ across genders. For example, if men are more confident than women, they would be more likely to request funding at lower interest rates. Evidence suggests women are more risk-averse: they choose to invest less riskily (Sundan and Surette, 1998; Bajtelsmit and VanDerhei, 1997; Hinz et al., 1997; Jianakoplos & Bernasek, 1998; Bernasek & Shwiff, 2001), place more weight on the possibility of loss and uncertainty (Finucane et al, 2000; Olsen & Cox, 2001; Fehr-Duda et al, 2006). As an implication, being more risk-averse than men, women would be likely to pay a premium for reducing the risk of not being funded. Which of these potential mechanisms are blocked by the algorithmic pricing in the above example, is unclear. Identifying the mechanism(s) responsible for the pricing gap is instrumental in designing better future marketplaces free of gender- and similar biases.

Equipped with our preliminary results, we will collect further data from the same platform, to distinguish between the supply (funds by lenders) and demand (loan requests by borrowers) sides. Exploring detailed borrower characteristics (such as ethnicity, age, education, retirement status, being a parent, being a hose owner or a tenant, income and overall debt) is instrumental in tracing the demand factors in the primary market. Analysing lenders’ behaviour is particularly challenging because such detailed characteristics of them are normally unavailable. To overcome the challenge, we will analyse the behaviour of lenders in the secondary market on the same platform to test whether they exhibit gender bias in selling and buying male- and female-originated loans. Having data for the secondary market from the same platform is crucial as it characterises the same group of lenders who form supply in the primary market. Although no individual data on lenders is available, studying the secondary market helps isolate lenders from borrowers.

To gain an additional insight on individual behaviour of potential lenders, we wil run a survey-experiment in countries where the above platform operates. Using surveys helps collect individual data otherwise unavailable for lenders. Within a survey experiment we will ask respondents to think of a lending decision regarding a specific borrower profile. By varying the gender on the borrower profile, we will obtain an out-of-sample characterisation of the gender bias in peer-to-peer lending.

Overall, the project adds to our understanding of the gender bias in P2P lending, its drivers and mechanisms. Using this newly generated knowledge, we will elaborate on ways to limit or eradicate the gender bias in alternative finance.

The primary market data characterises borrowers (as described above) and their loans (such as the interest rate charged, estimated probability of default, estimated loss given default, expected loss and expected return, whether the loan defaulted, the a priori credit rating of the applicant, the loan amount and duration). For the identification of the gender gap in pricing, the dependent variable is the interest charged, and the main independent of interest is the borrower’s gender. Credit rating and probability of default will be used to characterise the credit channel. Education will be used to characterise the confidence channel. Age and income (both related to risk aversion) will be used to characterise the risk-aversion channel. Platform-wide announcements about exogenous events on the secondary market will be used to characterise the perceived liquidity channel.

To characterise lenders’ behaviour, secondary market data from the same platform will be used. This comprises loan data (as above), the fraction of loan being sold, the discount/premium at which the loan is offered for sale, the status of the sale (successful vs. unsuccessful). Distinguishing between successful and unsuccessful loans is instructive for the isolation of ask prices (unsuccessful if no matching bid price) from equilibrium prices (successful). We will use exogenous events, such as changes to the selling mechanisms on the platform, to trace changes in the difference between secondary market prices of female- and male-originated loans. 

The survey experiment follows the Randomised Control Trial (RCT) methodology (as, e.g., in Coibion et al., 2022; Coibion et al., 2023; Hoffmann et al., 2022) and adopts the profiling approach as in Ravina (2019): after a common questionnaire, responents are randomly assigned to one of the borower profiles, and are asked about (a) the interest rate one would charge, (b) the amount one would be happy to lend, and (c) the price they would be happy to accept (WTA) if hypothetically selling this loan, in a case when the interest rate was predetermined by an algorithm. Having identical borrower profiles of two genders (randomly shown to respondents) pins any potential gender bias in pricing. Having below and above average (median) loan profiles helps externally validate the survey experiment with the actual secondary market data from the platform. 

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