Paycheck Protection Program: County-Level Determinants and Effect on Unemployment
Bibliographic record
Abstract
This paper uses U.S. county-level data to study the determinants and effects of the Paycheck Protection Program (PPP). The paper first overviews the timeline and institutional aspects of the PPP, implemented in the second quarter of 2020 and worth about $669 billion in forgivable small business loans guaranteed by the Small Business Administration (SBA). It then studies the determinants of the county-level ratios of PPP loans per job lost during the original unemployment surge associated with the onset of the COVID-19 pandemic in late March 2020 and finds that it does not appear to be a major driver of the PPP loan concentration; instead, it was primarily driven by the local banking conditions and demographic factors. The second part of this paper uses the method of local projections to determine whether the participation in the PPP program improved economic conditions following its implementation. Impulse responses in the standard linear framework are positive and statistically significant, albeit economically negligible, suggesting that the PPP was entirely ineffective in stabilizing labor market conditions. Extending the framework to state-dependent local projections reverses this result: PPP lending had a significant effect on reducing unemployment on average and especially in counties with strong banking liquidity and an educated labor force.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".