What is the Return Rate of a Corporate Pension Scheme? Generalized Annuity Factors Simplify Calculation
Bibliographic record
Abstract
Corporate pension schemes are widely spread especially in Northern Europe, North America, Japan. Often the major portion of defined contributions to the scheme is shouldered by the employer. A crucial question for an employee is, whether the return from his/her corporate pension plan - taking into account the corporate engagement and eventually governmental savings promotion - is favourable in comparison to other capital products for the time of retirement. This question is not answered by the absolute return in form of the future pension amount. Additionally, the employee must know the relative return, the Pension Rate of Return (PRR), in relation to what he/she has invested in form of employee contributions into the pension plan during his/her work life. Focussing on better pension information and also on counteraction to melting interest return, two current topics will be addressed. A very useful evaluation instrument for this task is the Generalized Annuity Factor (GAF). It is a generalization of the well-known Annuity Factor, which is restricted to constant payments only. With GAF any time dependent payments, e.g. linear or more complex nonlinear payments over time can be valued by a compressed closed-form formula in the same manner as constant payments by the classic Annuity Factor. Pension payments regarding mortality are such complex payments depending systematically on age. Because of its computational efficiency the new instrument simplifies calculations to be done also in smaller funds, firms or public services with common spreadsheet programs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".