Explaining the Determinants of International Financial Reporting Standard (IFRS) Disclosure: Evidence from Latin American Countries
Bibliographic record
Abstract
This study investigates the firm- and country-level determinants that influence the extent of financial disclosure under International Financial Reporting Standards (IFRS) in selected Latin American Organisation for Economic Co-operation and Development (OECD) members or countries in the accession process in the period under analysis. Using a sample of 168 publicly listed companies from Argentina, Chile, Colombia, Mexico, and Peru, we construct a self-developed disclosure index based on compliance with International Accounting Standards IAS 16 (Property, Plant and Equipment) and IAS 2 (Inventories). These standards were selected due to their relevance across a broad range of sectors in emerging markets. Drawing on agency theory, stakeholder theory, institutional theory, signaling theory, and legitimacy theory, we examine how internal firm characteristics, macroeconomic performance, and institutional quality impact disclosure practices. Our empirical findings show that firm size, leverage, Gross Domestic Product (GDP) growth, and shareholder protection have a positive and statistically significant influence on the level of IFRS disclosure. However, not all institutional variables are equally effective, highlighting the complex interplay between regulatory environments and corporate reporting behavior in developing countries. The study contributes to the ongoing debate on the applicability and effectiveness of IFRS in emerging economies by offering evidence from underexplored Latin American markets and emphasizing the need for context-specific policy and regulatory interventions.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".