Credit Risk, Regulatory Costs and Lending Discrimination in Efficient Residential Mortgage Markets
Bibliographic record
Abstract
Significant differences in loan terms between demographically distinct groups of borrowers in the United States are often interpreted as evidence of systematic ethnic, racial or gender discrimination by lenders. The appearance and interpretation of such discrimination has long been a controversial issue in public policy and has significant implications for both the economic efficiency and equity of credit markets. Arising from concern for borrowers disadvantaged by such discrimination, the design and implementation of regulations preventing the disparate treatment of demographically distinct groups by lenders are generally considered to have enhanced the equality of access to credit. Unfortunately, existing research has not examined whether this gain in social equity comes at a cost in efficiency borne by all market participants. The reliance on adverse selection or moral hazard in current models of limited lending and credit rationing poses difficulties in empirical testing for the presence and magnitude of such costs. This paper offers a novel theoretical framework in which lending discrimination can endogenously arise in the presence of value-maximizing lenders competing in an economy with complete markets, common knowledge and arbitrage-free pricing. By avoiding the reliance of current models on the exogenous presence of adverse selection or moral hazard, this framework allows potential efficiency costs to beexamined in a market environment without an ex ante assumption of informational market failure. Owing to the presence of common knowledge among participants, we first show how equilibrium loan terms to borrowers in different demographic classes can diverge in such an efficient environment. We then apply the properties exhibited in market equilibria to measure the potential costs of misallocating credit risk owing to the type of regulations observed in actual credit markets.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".