Peer-to-peer lending and bias in crowd decision-making (original) (raw)
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Despite the lack of delegated monitor and of collateral guarantees P2P lending platforms exhibit relatively low loan and delinquency rates. The adverse selection is indeed mitigated by a new screening technology (information processing through machine learning) that provides costless public signals. Using data from Prosper and Lending Club we show that loans' spreads, proxing asymmetric information, decline with credit scores or hard information indicators and with indications from "group ties" (soft information from social networks). Also an increase in the risk of bank run in the traditional banking sector increases participation in the P2P markets and reduces their rates (substitution effect). We rationalize this evidence with a dynamic general equilibrium model where lenders and borrowers choose between traditional bank services (subject to the risk of bank runs and early liquidation) and P2P markets (which clear at a pooling price due to asymmetric information, bu...
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Analyzing the interpersonal lending network of a Hungarian village in a disadvantaged region, we find strong intermediary activity and a tiered core-periphery structure. We show that the main motive behind lending is not altruism or profit-seeking, but risk-sharing which is the most accentuated in poor-to-poor and Roma-to-Roma relations. Comparing this informal lending market to a formal interbank market, we find more similarities than differences. In both markets, intermediation is a key element in risk-sharing and an effective tool to cope with liquidity risk. Regulatory and development policies should respect the existing institutions of risk-sharing.
Journal of Business & Economic Policy
International business and economic development researchers theorize that cultural affinity impacts crossborder investment decisions, but these theories produce inconclusive findings. In this study, we propose a theoretical and empirical framework that explores cross-border capital flows in a context where the effect of the levels of cultural affinity is likely to be salient for private direct investments in micro and small enterprises in developing countries. We disaggregate crosscountry effects into three dimensionsculture, institutional context, and geographic distance and argue that these dimensions should have separate effects on investment. We test our hypotheses using crosscountry panel data from Kiva, a peer-to-peer micro-lending platform used by investors to lend in the broad global market, and especially to developing country entrepreneurs. Our results reveal that controlling for macroeconomic and business cycle financial effects, lenders invest more in entrepreneurs that are geographically distant, have fewer socioeconomic resources, but are culturally similar. The nature and type of institutional challenges facing the borrowing country also matter.
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SSRN Electronic Journal, 2018
We study how the introduction of microfinance changes the networks of interactions in two settings: (1) among 16476 households in 75 villages around Karnataka, India and (2) among XXX households in 102 neighborhoods in Hyderabad, India. We then develop a new dynamic model of network formation to explain the empirical findings. In the Karnataka dataset, none of the villages were exposed to microfinance by 2006, 43 villages were through 2010, and we have a two-wave panel of network data collected in 2006 and 2012. The Hyderabad dataset is from a randomized controlled trial (RCT) in which half the neighborhoods were randomized to have a microfinance entry. In both cases, we compare changes in networks in villages/neighborhoods exposed to microfinance relative to those not exposed. Networks exposed to microfinance experience a significantly greater loss of links-for credit relationships as well as advice and other types of relationshipscompared to those not exposed. Microfinance not only results in decreases in relationships among those likely to get loans, but also decreases in relationships between those unlikely to get loans. These patterns are inconsistent with models of network formation in which people have opportunities to connect with whomever they wish, but are consistent with a model that emphasizes chance meetings that depend on relative efforts to socialize coupled with conditional choices of whom to connect with, as well as externalities in payoffs across relationships between pairs and triples of people.
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Online peer-to-peer (P2P) lending, a new form of microfinance, has been touted as to its prominent potential for reducing world poverty. Although a growing body of IS research has been devoted to examining online P2P lending, how such platforms actually make a difference in curbing poverty has yet to be fully explored. The Ebola outbreak of 2014 provides us a unique empirical opportunity to explore such broader impacts of online P2P lending. Leveraging this event as a natural experiment, we investigate how the demand and supply sides of P2P lending platforms react to an unpredictable crisis. Employing a difference-indifference identification strategy with data from Kiva.org, we conduct country-and loan-level estimations. Preliminary results show upward trends on both demand and supply sides of P2P lending; borrowers request more financial capital and lenders are more active in their lending behaviors in the post-crisis period. We extend online P2P lending literature by investigating the influences of "off-platform" shocks on within-platform behaviors.
In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries.
Financial Crises and the Composition of Cross-Border Lending
IMF Working Papers, 2014
We examine the composition and drivers of cross-border bank lending between 1995 and 2012, distinguishing between syndicated and non-syndicated loans. We show that on-balance sheet syndicated loan exposures account for almost one third of total cross-border loan exposures during this period. Furthermore, syndicated loan exposures increased during the global financial crisis due to large drawdowns on credit lines extended before the crisis. Our empirical analysis of the drivers of cross-border loan exposures in a large bilateral dataset shows three main results. First, banks with lower levels of capital favor syndicated over other kinds of cross-border loans. Second, borrower country characteristics such as level of development, economic size, and capital account openness, are less important in driving syndicated than non-syndicated loan activity, suggesting a diversification motive for syndication. Third, information asymmetries between lender and borrower countries, which are important both in normal and crisis times, became more binding for both types of cross-border lending activity during the recent crisis.
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