Protecting underfunded pensions: the role of guarantee funds
Bibliographic record
Abstract
Employer-related pensions are a common and extremely important component of the compensation paid to workers in both the public and private sectors of developed economies. Many private pensions are insufficiently funded, exposing workers to the risk of a loss should their employer cease operations and not be available to meet pension obligations. In this paper we study the role of guarantee funds as providers of insurance to workers against the failure of firms with underfunded defined benefit pension plans. Employing a model that predicts pension underfunding, we consider first how private guarantee funds might operate and then explore some potential advantages of public funds. Overall, we do find that both public and private funds provide insurance benefits. However, private guarantee funds requiring ex ante premia payments may be infeasible in the presence of capital market imperfections, and funds which rely upon ex post contributions may suffer from strategic uncertainty. A public fund can overcome this coordination problem. However, a public fund, such as that administered by the US Pension Benefit Guaranty Corporation, may lead to: (i) greater underfunding of pensions, (ii) distortions in the market participation decisions of firms and (iii) the inclusion of excessively risky assets in the pension portfolio. In some cases, a guarantee fund is not welfare improving.
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.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".