Independent Director, Executives Compensation and Corporate Performance-Correcting Self-Selection Bias by Matching Methods
Bibliographic record
Abstract
Based on data of listed companies on Taiwan Stock Exchange (TWSE) through 2001~2011, this paper examines whether board independence has effects on executive compensation and corporate performance. Existing studies lacked of considering self-selection of board independence in evaluating the effects of board independence on economic consequence. This may incur estimation bias because systematic factors determining firm’s introducing independent director also have influences on economic consequence. While Heckman (1979)’s two-step estimation addressed selection duo to unobservables, this paper employs propensity score matching (PSM) from Rosenbaum and Rubin (1983, 1985a,b) to address sample selection duo to observables, and forms two groups of samples, namely, firms with independent director and firms without independent director but share similar characteristics with the former. Empirical evidence from regression estimation shows divergent outcomes under before-matching versus after-matching samples. Before matching, greater degree of board independence is associated with higher profitability and higher level of total and average executive compensation. After matching, outperformance as well as overpay on executive compensation of firm with greater board independence is vanished. After controlling selection bias duo to observables versus unobservables, our evidence concludes that greater board independence is uncorrelated with greater corporate performance and executive compensation overpay.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".