Bank Entry Barriers and Firms’ Risk-Taking
Bibliographic record
Abstract
ABSTRACT We study how nonfinancial firms’ operating risks change after bank competition increases. By exploiting the 1990s staggered regulatory reforms across U.S. states that allowed interstate banking and branching, we show that out-of-state bank entry was associated with lower borrower risk-taking on average. Large, profitable, safe, and geographically diversified firms signed up as new clients of large entrant banks, which offered larger and cheaper loans that reflected their higher efficiency and risk reduction through geographical diversification. We argue that these large banks could substitute for local relationship lending with more data collection from branches in multiple states. Firms that began borrowing from entrant banks increased capital expenditures and project-specific financing and kept R&D expenses stable but reduced R&D risk. Firms that continued borrowing from incumbent banks paid higher interest rates and increased their risk, suggesting that their credit access fell. States that opened up more had bigger changes in these outcomes. Data availability: Data are available from the public sources cited in the text. JEL Classifications: G21; G28; G32.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".