The Effect of Banking Deregulation on Borrowing Firms' Risk‐Taking Incentives*†
Bibliographic record
Abstract
ABSTRACT We examine how regulatory restrictions on capital market activity affect the compensation contracting environment within firms. This study aims to expand our understanding of how financial market development affects firm risk‐taking via management compensation designs. Specifically, taking advantage of the staggered implementation of the Interstate Banking and Branching Efficiency Act (IBBEA), which increases bank competition and loan geographical diversification, this study examines how borrowing firms' compensation structures change when banks increase risk tolerance in their loan portfolios. Using hand‐collected compensation data of firms with market capitalization less than $75 million, we hypothesize and find that borrowing firms are likely to increase risk incentives after IBBEA and that this increase is more pronounced for firms located in states with less banking competition in the pre‐IBBEA period. We also show the findings to be more significant for borrowers whose lenders acquire more diversification benefits after IBBEA. These findings suggest that following deregulation, when banks face increased competition as well as an enhanced ability to diversify their credit risk geographically, these same banks tend to increase their tolerance for borrowers' risk‐taking. That is, their clients—nonfinancial firms borrowing from them—adjust their compensation contracts that are previously constrained by bank distaste for risk. We also document that firms that increase their risk incentives the most invest more in R&D, suggesting that management compensation is a complementary channel through which IBBEA affects firm innovation.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".