Three-Stage Approach to Analyze Managerial Ability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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 it