Consumer Credit Regulation and Lender Market Power
Bibliographic record
Abstract
We investigate the welfare consequences of consumer credit regulation in a dynamic, heterogeneous-agent model with endogenous lender market power. We incorporate a decentralized credit market with search and incomplete information frictions into an off-the-shelf Eaton–Gersovitz model of consumer credit and default. Lenders post credit offers and borrowers apply for credit. Some borrowers are informed and direct their application toward the lowest offers while others are uninformed and apply randomly. Equilibrium features price dispersion—controlling for a borrower’s default risk, both high- and low-cost lending exist. Importantly, the distribution of loan prices and the extent of lenders’ market power are disciplined by borrowers’ outside options. We calibrate the model to match characteristics of the unsecured consumer credit market, including high-cost options such as payday loans. We use the calibrated model to evaluate interest rate ceilings. In a model with a competitive financial market, ceilings can only harm borrower welfare. In contrast, with lender market power, interest rate ceilings can raise borrower welfare by reducing markups, but that requires households to have some degree of financial illiteracy (lack of information about interest rates).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".