MUTUAL DEPENDENCIES IN FINANCIAL MARKETS: INTEREST RATES, PRICING, AND TRADE CREDIT RELATIONS
Bibliographic record
Abstract
This study investigates the impact of trade credit on the effectiveness of monetary policy transmission, addressing the empirical observation that trade credit tends to mitigate the effects of central bank actions. To elucidate this phenomenon, we develop a partial equilibrium model that incorporates third-degree price discrimination and menu costs. Our primary finding reveals that optimizing credit terms and product prices yields minimal net present value (NPV) gains compared to the minute menu costs associated with short-term interest rate adjustments during low-inflation periods. Consequently, credit terms and product prices remain relatively stable over time, in alignment with empirical evidence presented by Ng, Smith, Smith (1999), and Mateut (2005). Furthermore, our model provides insights into the Meltzer (1960) hypothesis, suggesting that trade credit experiences less volatility than bank credit in the context of monetary policy transmission. This implies that firms strategically set trade credit terms to maximize NPV while considering menu costs, thereby rationalizing certain credit channel-related empirical phenomena. The paper also explores parallels between trade credit dynamics and exchange rate pass-throughs, shedding light on the effectiveness of monetary easing during a pandemic. These findings contribute to a deeper understanding of the intricate relationship between trade credit and monetary policy, with potential implications for policy design and implementation.
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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 | 0.014 |
| 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 | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".