The Determinants of Trade Credit: A Study of Portuguese Industrial Companies
Bibliographic record
Abstract
Despite the relevance of trade credit as a source of business financing, the topic is far from being considered exhausted, especially because there is no general and integrated theory explaining the causes and consequences of trade credit.Our research aims to contribute towards the literature that studies the determinants for granting and receiving trade credit. In this sequence, the present study seeks to empirically test some theories about the reasons why companies grant and receive commercial credit. For this purpose we apply a fixed effect model to a panel of 11 040 Portuguese industrial companies, of which 360 are large companies and the majority 10 680 are Small and Medium Enterprises (SME) for the period between 2003 and 2009. We conclude that large companies (with greater access to credit market) serve as financial intermediaries to their clients with less access to finance. In addition, it was observed that the supplier companies use trade credit as a legal means of price discrimination. Finally, financially constrained enterprises, especially in times of financial crisis, use commercial credit as an alternative source of funding, endorsing the hypothesis of substitution between trade credit and bank credit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".