The Impact of the Small Business Lending Fund on Community Bank Lending to Small Businesses
Bibliographic record
Abstract
Following the financial crisis, total outstanding loans to businesses by commercial banks dropped off substantially. Large loans outstanding began to rebound by the third quarter of 2010 and essentially returned to their previous growth trajectory while small loans outstanding continued to decline. Furthermore, much of the drop in small business loans outstanding was evident at community banks. To address this perceived lack of supply of credit to small businesses, the Small Business Lending Fund (SBLF) was created as part of the 2010 Small Business Jobs Act. The fund was intended to provide community banks with low‐cost funding that they could then lend to their small business customers. As of 31 December, 2013, the US Department of the Treasury reports that SBLF participants had increased their small business lending by $12.5 billion over their baseline numbers. The current paper uses Call Report data from community banks and thrift institutions to look at the impact of receiving funds from SBLF on their small business lending. The analysis controls for economic and demographic conditions, market structure and competition. Simple regression estimates indicate that participants in the SBLF program increased their small business lending by about 10 percent more than their non‐participating counterparts, in line with numbers reported by Treasury. However, estimates that control for the ongoing growth path in small business lending indicate no statistically significant impact of SBLF participation on small business lending.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".