Economic Consequences of IFRS Adoption: The Role of Changes in Disclosure Quality*
Bibliographic record
Abstract
ABSTRACT This study adopts a two‐step approach to highlight the disclosure quality channel that drives economic consequences of IFRS adoption. This approach helps address the identification challenge noted by prior research and offers direct evidence on the role of disclosure quality. In the first step, we document the impact of the IFRS mandate on changes in disclosure quality proxied by the granularity of line item disclosure in financial statements. We find that IFRS‐adopting firms provide more disaggregated information upon IFRS adoption, such as more granular disclosure of intangible assets and long‐term investments on the balance sheet and greater disaggregation of depreciation, amortization, and nonoperating income items on the income statement. In the second step, we link the observed disclosure changes to the benefits and costs of IFRS adoption. We show that greater disaggregated information due to IFRS adoption enhances market liquidity and decreases information asymmetry, but does not affect audit fees differentially. Our evidence has implications for standard setters as they evaluate cost‐benefit trade‐offs when considering disclosure changes in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".