Soumajyoti Sarkar, Hamidreza Alvari
arXiv 20 Jun 2020 · Econometrics · 1 citations (OpenAlex)
arXiv:2006.12995 · PDF · DOI · OpenAlex · Extracted main text
Over the last couple of decades in the lending industry, financial disintermediation has occurred on a global scale. Traditionally, even for small supply of funds, banks would act as the conduit between the funds and the borrowers. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Kiva for example, works with Micro Finance Institutions (MFIs) in developing countries to build Internet profiles of borrowers with a brief biography, loan requested, loan term, and purpose. Kiva, in particular, allows lenders to fund projects in different sectors through group or individual funding. Traditional research studies have investigated various factors behind lender preferences purely from the perspective of loan attributes and only until recently have some cross-country cultural preferences been investigated. In this paper, we investigate lender perceptions of economic factors of the borrower countries in relation to their preferences towards loans associated with different sectors. We find that the influence from economic factors and loan attributes can have substantially different roles to play for different sectors in achieving faster funding. We formally investigate and quantify the hidden biases prevalent in different loan sectors using recent tools from causal inference and regression models that rely on Bayesian variable selection methods. We then extend these models to incorporate fairness constraints based on our empirical analysis and find that such models can still achieve near comparable results with respect to baseline regression models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Thai T Pham and Yuanyuan Shen (2017) A deep causal inference approach to measuring the effects of forming group loans in online non-profit microfinance platform | 0.928 | 4 | 3 | 100% |
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| 3 | Pramesh Singh, Jayaram Uparna, Panagiotis Karampourniotis, Emoke-Agn… (2018) Peer-to-peer lending and bias in crowd decision-making | 0.737 | 3 | 2 | 100% |
| 4 | Pierre Ly and Geri Mason (2010) Individual preferences over ngo projects: Evidence from microlending on kiva | 0.644 | 2 | 2 | 100% |
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| 6 | Vineeth Rakesh, Wang-Chien Lee, and Chandan K Reddy (2016) Probabilistic group recommendation model for crowdfunding domains. In Proceedings of the Ninth ACM International Conference on W… | 0.511 | 2 | 1 | 100% |
| 7 | Susan Athey, Guido W Imbens, Stefan Wager, et al (2016) Efficient inference of average treatment effects in high dimensions via approximate residual balancing | 0.405 | 1 | 1 | 100% |
| 8 | Susan Athey, Guido Imbens, Thai Pham, and Stefan Wager (2017) Estimating average treatment effects: Supplementary analyses and remaining challenges | 0.405 | 1 | 1 | 100% |
| 9 | Abhijit Banerjee, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan (2015) The miracle of microfinance? Evidence from a randomized evaluation | 0.405 | 1 | 1 | 100% |
| 10 | Alexandre Belloni, Victor Chernozhukov, and Christian Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.405 | 1 | 1 | 100% |
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