Do differences in national cultures affect cross-country financial statement comparability under IFRS?
Bibliographic record
Abstract
I examine whether cultural differences in trust towards others, materialism, and risk aversion lower financial statement comparability between countries that require International Financial Reporting Standards (“IFRS”). Evidence from various academic disciplines suggest that cultural beliefs and values affect individuals’ estimates and judgments and their consequent decisions, including economic and financial decisions. I posit that certain cultural beliefs and values also affect the estimates and judgments of corporate managers, resulting in inconsistent reporting decisions for given economic events and lower financial statement comparability. I find that two countries have lower comparability when there are greater cultural differences in trust towards others, materialism, and risk aversion. In cross-sectional tests, I find weak evidence that stronger enforcement of IFRS moderates the cultural effects on cross-country financial statement comparability. Stronger enforcement of regulations and law does not moderate the cultural effects. These findings suggest that having a strong IFRS, regulatory, or legal enforcement does not effectively moderate the impact of culture on cross-country financial statement comparability. A possible explanation is that cultural influence on financial reporting is also manifested through enforcement officials; in other words, those in charge of the enforcement are also subject to the same cultural beliefs and values as others involved in the reporting process, making moderation less likely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".