PRICING STRATEGIES AND CREDIT DYNAMICS: UNDERSTANDING THE INTERCONNECTED WORLD OF INTEREST RATES, PRODUCT PRICES, AND TRADE CREDIT TERMS
Bibliographic record
Abstract
The growing interest in the effectiveness of monetary policy has prompted an exploration of the role of trade credit in shaping the credit channel of policy transmission. Despite increased attention, a comprehensive theoretical model elucidating the observed empirical phenomenon—wherein trade credit mitigates the impact of central bank actions—has been lacking. This paper addresses this gap by introducing a partial equilibrium model integrating third-degree price discrimination with menu costs. The key finding suggests that the incremental increase in net present value (NPV) resulting from the optimization of credit terms and product prices is insufficient to offset even negligible menu costs associated with short-term interest rate adjustments during low-inflation periods. As a consequence, credit terms and product prices exhibit remarkable stability over time. This outcome aligns coherently with the empirical evidence presented by Ng, Smith, and Smith (1999) and Mateut (2005), offering a theoretical foundation for the Meltzer (1960) hypothesis on the credit channel transmission of monetary policy. Specifically, our model implies that trade credit experiences less volatility than bank credit, rationalizing certain empirical phenomena related to the credit channel. In essence, firms strategically set trade credit terms to maximize NPV, taking into account the influence of menu costs. Additionally, we discuss the implications of our findings on broader economic phenomena, drawing parallels to exchange rate pass-throughs and reflecting on the effectiveness of monetary easing in the context of a pandemic. By shedding light on the intricate interplay between trade credit, monetary policy, and menu costs, this research contributes to a deeper understanding of the mechanisms at play in the credit channel, thereby enriching the discourse on policy effectiveness in contemporary economic landscapes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".