Supervisor Ratings and the Contraction of Bank Lending to Small Businesses
Bibliographic record
Abstract
Bank lending to small firms in the U.S. fell substantially during the recent financial crisis and the ensuing recession. Because small firms account for a disproportionate share of new job creation, lending to these firms could have important implications for the pace of economic recovery. A number of factors may have contributed to the decline in small business lending over this period. This paper examines the extent to which changes in banks' supervisory ratings are associated with changes in the rate of growth of their lending to small businesses. Limiting our sample to small banks (those with total assets of $5 billion or less), we estimate the relationship between changes in supervisory CAMELS ratings and changes in small commercial and industrial (C&I) or small commercial real estate (CRE) loans to businesses, between 2007 and 2010. Controlling for other relevant factors, including several balance sheet measures of bank health, we find that small banks that experienced ratings downgrades during 2007-2010 exhibited significantly lower rates of growth in small C&I loans and small CRE loans outstanding compared with banks that maintained their ratings at healthy levels during the same period. We also find evidence suggesting that the slower growth in small business lending at downgraded banks is attributable primarily to aspects of the banks' financial health that were not fully reflected in balance sheet data, rather than to the ratings downgrades themselves or the supervisory process surrounding the downgrades.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".