Governance-Performance Relationship: A Re-examination Using Technical Efficiency Measures
Bibliographic record
Abstract
The objective of this study is to analyze further the governance-performance relationship while improving on two methodological issues: control for endogeneity and firm performance measurement. To mitigate the endogeneity problem, we, first, focus on sub-samples of firms for which we, ex-ante, expect better corporate governance to cause better performance. Second, we use Generalized Least Square (GLS) regressions for panel data. To control for potential measurement bias, we measure firm performance using Data Envelopment Analysis (DEA). The research is conducted in Canada over a five-year period from 2001 to 2005. Corporate governance is measured based on the ROB corporate governance index published by the Globe and Mail. Overall, the results show that better governed firms are more efficient. This study is in line with a growing number of recent studies that propose alternative measures of firm performance. By using DEA, this study brings together the corporate finance and productivity literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".