Small Business Lending and Economic Well-Being in U.S. Counties During the Great Recession.
Bibliographic record
Abstract
Previous research shows that small business lending declined significantly during the Great Recession. In this paper, we examine the effects of small business lending on measures of socioeconomic development in U.S. counties during this time period. Citing literature which shows that small business owners in nonmetropolitan counties depend on traditional bank loans more than their metropolitan counterparts, we propose that the effects of small business lending will be more important in nonmetropolitan counties. We utilize data from Community Reinvestment Act Federal Financial Institutions Examination Council and U.S. Census. We use two measures of small business lending: the average per loan small business lending from 2005-2010 and change in small business lending amount in the county between 2005 and 2010. We find that the per loan average amount of small business lending between 2005-2010 increased the 2010 median family income and 2010 county poverty rate in nonmetropolitan counties. The effects in metropolitan counties show no benefits of small business lending. Change in the amount of business loan had no consistent effects. Implications for existing and future research are discussed. Keywords: rural development; small business lending
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".