Financial Reporting and Trade Credit: Evidence from Mandatory <scp>IFRS</scp> Adoption*
Bibliographic record
Abstract
ABSTRACT We investigate the effect of mandatory IFRS adoption on trade credit. We document that firms in countries that adopt IFRS receive more trade credit from their suppliers, consistent with improved financial reporting quality and comparability playing a role in facilitating informal financing. This increase is larger for countries with a low level of societal trust, a poor pre‐IFRS‐adoption information environment, and stronger legal enforcement. These cross‐sectional results suggest that the conditions under which higher‐quality information is made publicly available affect suppliers' decisions to provide trade credit. This increase is also larger for firms with greater exposure to foreign markets, a finding that highlights the importance of more comparable international financial reporting standards in facilitating cross‐country trade credit. We also find that IFRS adoption has a stronger positive effect on trade credit for firms with greater liquidity needs. Finally, we find that firms in countries that adopt IFRS also extend more trade credit to their customers. Overall, our results support the notion that financial reporting can have a causal effect on trade 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 | 0.000 |
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".