Measuring and reporting obligations of social security retirement systems: Actuarial perspectives
Bibliographic record
Abstract
Abstract The article is based on the International Actuarial Association (IAA) Social Security Committee's principles‐based paper with commentary on measurement and reporting obligations of social security retirement systems (SSRSs) and proposals for appropriate disclosure requirements, for consideration by national and international organizations when developing reporting standards in respect to SSRSs. The article argues that the method of measuring and reporting obligations should be consistent with the financing basis of the SSRS. In particular, SSRSs financed on a pay‐as‐you‐go (PAYG) or partially funded basis should use an open‐group method for measuring and reporting actuarial obligations. Only SSRSs that purport to be fully funded should use a closed‐group basis, since SSRSs are not analogous to large private‐sector pension plans. For most PAYG and partially funded SSRSs, accounting for obligations on a closed‐group basis would indicate huge actuarial unfunded liabilities, which might not be understood by the general public and could inappropriately create pressure to move towards fully‐funded systems. The methodologies used for accounting and/or statistical reporting should enable the accurate assessment of the long‐term financial sustainability of any SSRS without a bias for or against a particular financing approach. The article prefers measures of sustainability of an SSRS to measures of its funding level. A system that is fully funded currently may not be sustainable while a pure PAYG SSRS may be sustainable. In the case where there is a requirement to disclose obligations on a closed‐group basis, such disclosures should be supplemented by an open‐group analysis, with appropriate reconciliations and explanations (i.e. a multiple disclosure approach).
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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 | 0.077 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".