How bank regulation, supervision, and lender identity impact loan pricing: a cross-country comparison - summary
Bibliographic record
Abstract
This paper assesses the extent to which a country’ bank regulation and supervision practices impact the pricing of both domestic and foreign loans to borrowers in that country, after accounting for the effects of the countries’ legal and institutional characteristics. For the first time in the literature we show that banking-commerce integration and banking concentration are important determinants of loan pricing, through a cross-country study involving 49 countries. We find that lender identity also plays an important role in the setting of loan pricing. In countries with high degrees of integration of banking and commerce, domestic lenders charge lower rents due to stronger lender-borrower relationships and reduced agency costs while foreign lenders exercise greater monitoring and extract higher loan rents as a way of compensating their greater risk exposure. However, the benefit of lower loan costs received from domestic lenders (due to banking-commerce integration) vanishes in countries with high banking concentration. Additionally, in countries with higher banking concentration, foreign lenders provide favorable contract terms to attract borrowers. Our results suggest that failure to recognize the impact of a country’s bank regulation and supervision practices and the identity of the lender (whether domestic or foreign) in the examination of loan contract terms, may lead to incorrect conclusions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".