Community influence on microfinance loan defaults under crisis conditions: Evidence from Indian demonetization
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Bibliographic record
Abstract
Abstract Research Summary The microfinance “group lending” approach has achieved widespread success in promoting high rates of repayment, and thus the viability of financial access, in very low‐income environments. Yet group lending, which relies on social connections between borrowers to reinforce repayment, may be vulnerable under crisis conditions in which defaults are commonplace. We explore this possibility in the context of the liquidity crisis that followed India's 2016 demonetization policy. Using proprietary data on the repayment behavior of about two million microfinance borrowers, we find evidence of disproportionate localization of defaults within lending communities. Further analysis reveals evidence consistent with borrower‐to‐borrower spread of defaults not only through formal joint‐liability connections but also through informal community‐level connections, the latter effect being especially pronounced for borrowers from the same religion. Managerial Summary Microfinance lenders have successfully employed a “group lending” approach that holds borrowers within a small group responsible for each other's loans, thus creating strong social pressures for repayment. The findings of this study underscore potential vulnerabilities in the group lending model during economic crises. We analyze the loan repayment behavior of two million microfinance borrowers during a liquidity crisis precipitated by India's 2016 demonetization policy, finding that the resulting defaults were clustered within particular lending communities. Further analysis suggests that social processes within communities played a role in spreading defaults, not only through formal ties between borrowers who were responsible for each other's repayments, but also through informal social ties. The estimated effect of informal social ties was particularly strong for borrowers who shared the same religion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it