Ownership–efficiency relationship and the measurement selection bias
Bibliographic record
Abstract
Abstract This study analyses the bias in the selection of performance measures for ownership comparisons, which depends on the specific objectives of the firms being compared. Our sample includes 13 Canadian state‐owned enterprises (SOEs), commercialized and/or privatized between 1976 and 2001. To replace profitability measures and reduce biases, we propose the use of technical efficiency, which provides for SOEs’ specificities. Overall, the results clearly support the view that privatization has no impact on a firm's technical efficiency, the only positive impact being related to a change in the objectives of the firm while using profitability measures. The results of this study raise the question of the validity of comparisons between SOEs and private firms when using profitability indicators. The potential bias in favour of the private firms contributes to a misleading image of the public sector being presented as inferior and inefficient. The use of more sophisticated measures, such as data envelopment analysis, suggests conflicting conclusions. This study also casts doubt on the legitimacy of the privatization program initiated around the world and more specifically in Canada in which the main justification for such a reform has been to increase the performance of SOEs.
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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 | 0.115 | 0.409 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".