Three essays on microcredit and payday lending
Bibliographic record
Abstract
The three essays on microcredit and payday lending in this dissertation contribute to the existing literature on financial exclusion by addressing some burning questions. The first essay investigates and compares the observable characteristics of the payday loans and microcredit borrowers. We examine three nationwide surveys in Canada that contain information on payday lending and a household level field survey on the use of microcredit in Bangladesh. Using simple probit and instrumental variable probit models, we find household income, wealth, the region of residence, age and education of the household head as key determinants of both payday loan and microcredit borrowing. Financial knowledge is found as a significant determinant of payday loan borrowing. Our findings suggest that countries can benefit from each other by adapting, modifying regulatory policies and improving the level of financial knowledge. The second essay examines the linkages between the high frequency of installments of microcredit loans, and the very high repayment rate and the existence of informal moneylenders in the market. Our developed model shows that the poor borrower has strong incentives and opportunities to commit voluntary default at different time periods of the contract, which along with a low level of collateral, make the single installment repayment contract infeasible for microcredit lenders. The multiple-instalment loan leads the borrower to borrow from the third party because installments are due before project yields. This reduces the incentive to default and raises the microcredit repayment rate. In the third essay, we analyze the macroeconomic and distributional effects of microcredit programs across 21 countries using a general equilibrium approach. We developed a model of financial intermediation and occupational choice to find out the impact of microcredit on five macroeconomic aggregates - average capital per worker, wealth inequality, the lending interest rate, income per capita and entrepreneurship. Our findings show that the macroeconomic and distributional impact of microcredit, subsidized microcredit and high-frequency of installments of microcredit loan significantly varies both in magnitudes and in directions across countries. Findings suggest designing country-specific microcredit contracts rather than using a typical contract in all countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".