Corporate Governance from a Cross-Country Perspective and a Comparison with Romania
Bibliographic record
Abstract
This paper investigates corporate governance from a cross-country perspective and makes a comparison with Romania. There are studies that examine the corporate governance issues related to Romanian companies, but these studies provide only qualitative and descriptive accounts of the research topic, with limited cross-country analysis. The present paper complements the literature by producing a quantitative analysis of cross-country corporate governance and makes a comparison with Romania. For this purpose, a set of corporate governance indicators from a large sample of 39 advanced and developing countries was collected for the 2006–2020 period. In terms of corporate governance dimensions, it was found that Romania underperforms other developing countries in the dimensions of director liability and ownership and control, while it outperforms them in the dimensions of corporate transparency, disclosure, and shareholder rights. The results indicate that the stagnant corporate governance scores and the low development level of stock markets stand out as important business challenges for the country. The correlation and regression analyses show that stock market development is closely associated with corporate governance dimensions and, overall, corporate governance scores matter greatly for the economic growth of countries, such as Romania, which can benefit greatly from the improvement of corporate governance codes and practices in the private sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".