Does Board Diversity Attract Foreign Institutional Ownership? Insights from the Chinese Equity Market
Bibliographic record
Abstract
The study aimed to empirically investigate the impact of board diversity variables (age, gender, nationality, education, tenure, and expertise) on the investment preferences of foreign institutional investors in an emerging market, China. For this, sample data consisted of 1374 nonfinancial Chinese firms from 2009 to 2018. The study used OLS regression as a baseline regression, a fixed effect model to control omitted variable bias, and the two-step systems GMM model to control the endogeneity problem. The study revealed that board diversity variables (gender, nationality, education, and financial expertise) are positively associated with foreign institutional ownership in Chinese nonfinancial firms, implying that foreign institutional investors own a high percentage of Chinese nonfinancial firms with diversity of gender, nationality, education, and financial expertise. Age and tenure of board diversity, on the other hand, have little correlation with foreign institutional ownership. Further, the robustness regressions also confirmed the relationship between board diversity and foreign institutional ownership. This study made a unique attempt to provide empirical evidence that firms having diverse boards attract foreign institutional ownership by reducing asymmetric information.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".