Bibliographic record
Abstract
Demerjian et al. (2012) provide theoretically and empirically rigorous measurement of managerial ability based on data envelopment analysis. We discuss that the method can provide a consistent estimator and suggest best practices for empirical researchers. The three-stage approach of conducting inference with managerial ability begins with the first-stage estimation of firm-efficiency with inputs and outputs. The second stage removes the impacts of contextual variables on the firm-efficiency to construct managerial ability. The third stage uses the measure as a dependent or an independent variable. We discuss why data envelopment analysis that incorporates production theory and allows multiple inputs and outputs is more appropriate than other methods to measure managerial ability. We then discuss specific choices that researchers need to make in three stages: returns to scale, the number of inputs and outputs, industry-specific inputs and outputs, outlier detection, choice of estimation sample, adjustments to yield a valid measure of managerial ability, choice of contextual variables, the functional form of the second-stage regression, advantage of residual approach, and consideration for the inference with managerial ability as a dependent and an independent variable. Our suggestions allow researchers to apply the rigorous approach of Demerjian et al. (2012) in many contexts yet to be explored.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".