An Examination of Very Low Efficiency Scores in Data Envelopment Analysis in the Pension Funds Industry
Bibliographic record
Abstract
Data Envelopment Analysis (DEA) is a powerful analytical tool that is considered as one of the most useful techniques to measure the efficiency of Decision Making Units (DMUs) in certain industry segments. However, there is a scarcity of reported use to assess pension funds’ performance due to the complexities of such funds. The few papers that can be found in literature do not consider the main characteristics of pension funds such as uncontrollable variables for managers, regulations, and funds’ status (fully funded/underfunded pension plans). Regulations affect such investment vehicles in many ways from investment strategy, tax status, reporting requirements and others. Also, as the by-product of our past research in this field the authors ran into some unexpected outcomes where some funds had achieved an extremely low efficiency score. This is very highly unusual and invited additional research. There are very few papers in the literature on extremely low efficiency scores, and there is a paucity of cogent explanations on why this is the case. Therefore, while evaluating the pension funds’ performance through DEA we worked on this problem in some detail to uncover the reason(s) for such low minimum efficiency scores for pension funds. We found that the presence of very low efficiency scores phenomena is not uncommon in pension funds industry but is in other industry studies.
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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 | 0.026 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".