The Impact of Internationalization of the Boardroom on Capital Structure
Bibliographic record
Abstract
We develop a theoretical model based on several theories, mainly pecking order theory and theory of information economics, as well as on theoretical arguments provided by economic sociology and psychology to investigate for the first time the impact of the presence of a foreign board member on capital structure. The sample of study covers 3773 non-financial U.S. firms and includes 23,196 observations over the period from 2010 to 2018. We used pooled OLS, fixed effects, random effects, and the general method of moments (GMM) in order to analyze the impact of foreign directors on capital structure after controlling for a range of factors, including size, year, and industry effects. The results of this empirical analysis support the proposed hypothesis. Of particular note is the finding that the proportion of foreign directors on the board correlates negatively with debt structure. Furthermore, we demonstrate that our findings hold up in the face of all appropriate robustness checks. Our study contributes to the existing literature by including an international dimension of board diversity, specifically the influence of foreign directors on corporate capital structure. We argue that increasing international diversity in the boardroom improves both the quantity and quality of the information exchange between insiders and shareholders, thereby reducing adverse selection costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".