Essays in development economics: evaluating solutions to asymmetric information in credit markets
Bibliographic record
Abstract
This thesis examines the impacts of interventions in credit markets characterised by severe problems of asymmetric information. The first essay, “Group lending and borrower default: empirical evidence,” examines the relative effectiveness of competing lending methodologies. Group lending theory claims to mitigate problems of asymmetric information that lead to adverse selection, moral hazard, state verification and contract enforcement. This chapter, using data from a Toronto-based microcredit program, presents empirical evidence that group lending, while leading to assortative matching, does not lower default rates when compared to conventional individual lending. The second essay, “Never do business with your friends: group lending, joint liability and dynamic incentives,” develops two simple theoretical models to explore the dynamic nature of joint liability contracts. From these models, I derive testable implications and then evaluate the predictions utilizing a unique panel data set. I find that dynamic incentives are an important feature of group lending programs.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".