Why Do Countries Matter So Much for Corporate Governance?
Bibliographic record
Abstract
This paper develops and tests a model of how country characteristics, such as legal protections for minority investors, and the level of economic and financial development, influence firms' costs and benefits in implementing measures to improve their own governance and transparency. The model focuses on an entrepreneur who needs to raise funds to finance the firm's investment opportunities and who decides whether or not to invest in better firm-level governance mechanisms to reduce agency costs. We show that, for a given level of country investor protection, the incentives to adopt better governance mechanisms at the firm level increase with a country's financial and economic development. When economic and financial development is poor, the incentives to improve firmlevel governance are low because outside finance is expensive and the adoption of better governance mechanisms is expensive. Using firm-level data on international corporate governance and transparency ratings for a large sample of firms from around the world, we find evidence consistent with this prediction. Specifically, we show that (1) almost all of the variation in governance ratings across firms in less developed countries is attributable to country characteristics rather than firm characteristics typically used to explain governance choices, (2) firm characteristics explain more of the variation in governance ratings in more developed countries, and (3) access to global capital markets sharpens firm incentives for better governance, but decreases the importance of homecountry legal protections of minority investors.
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".