Bibliographic record
Abstract
Personal relationships are a common feature of financial intermediation. However, existing research offers different expectations about whether personal ties prove detrimental or beneficial for lenders. Research on embeddedness from economic sociology highlights the advantages lenders accrue when they develop personal ties with borrowers, including enhanced trust, information-sharing and greater social control. Yet research from social psychology offers reason to suspect that personal relationships can be costly because lenders who feel personally tied to borrowers run the risk of escalating commitment to poor performers. Drawing on these lines of research, this study uses data from a Latin American microfinance bank to ask: When are personal relationships detrimental or beneficial for financial intermediaries? It shows that, when lenders and borrowers have personal relationships, lenders are less likely to cut ties with poor performers and borrowers miss fewer payments, consistent with expectations from both literatures. However, these trends vary with frequency of contact. When lenders and borrowers interact less frequently, lenders continue to show heightened commitment, but borrowers become less compliant, creating potential problems for lenders. Overall, this study integrates theories from economic sociology and social psychology to offer a more balanced, temporally-informed understanding of personal ties in finance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".