Large tariff cuts and corporate hedging: Evidence from interest rate swaps
Bibliographic record
Abstract
• Large U.S. tariff cuts increase domestic firms’ use of interest rate swaps, shifting debt from fixed to floating rates. • The effect operates through intensified product market competition and lower expected borrowing costs, increasing incentives to preserve financial flexibility. • Strongest responses are observed for firms near the industry’s technological core, in highly substitutable markets, and with low cash reserves. • Results establish a novel link between trade policy shocks, corporate risk management, and firms’ debt structure choices. We examine how trade policy shocks affect firms’ interest rate hedging and debt structure. Using plausibly exogenous large U.S. tariff cuts from 1992–2015 as industry-level shocks, we find that exposed firms increase their use of interest rate swaps, converting fixed-rate debt into floating obligations. Drawing on real options and corporate hedging frameworks, we argue that large tariff cuts intensify competition and lower expected borrowing costs through reduced import prices and input costs, raising underinvestment risk while increasing incentives to maintain financial flexibility. Firms dynamically reoptimize their debt composition to capture expected savings and secure funding for time-sensitive projects. Effects are strongest among firms close to their industry’s technological core, in highly substitutable markets, and with low cash buffers. Our findings establish a novel link between international trade policy, corporate risk management, and debt structure.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".