RISK POOLING AND THE MARKET CRASH: LESSONS FROM CANADA'S PENSION PLAN
Bibliographic record
Abstract
Defined contribution plans are now the nation’s primary private retirement income program and repository of retirement savings. About two thirds of the assets held in such plans are invested in equities, as is the case in the defined benefit plans they largely replaced. Equities can dramatically reduce the cost of providing retirement incomes, given their high expected returns. But, as illustrated by the recent market crash, equities are also risky. The resulting losses (and gains) in retirement income are also distributed very unevenly in the nation’s 401(k)-IRA system. The crash hardly affected the retirement prospects of the young: the bulk of the retirement income they will draw from 401(k)s and IRAs will come from future contributions and future returns. Those at the cusp of retirement, by contrast, are heavily exposed: retirement savings are then at their peak and there is little time to adjust work, saving, and retirement plans in response to the market crash. This concentration of risk is highly troubling, as the 401(k)-IRA system has become the nation’s primary private retirement income program, and has led to calls to reform. The challenge is to capture the higher expected returns equities offer in a way that provides reasonably secure and reliable incomes in retirement. One approach would make individual retirement accounts more secure and reliable through the use of mandates, defaults, guarantees, or risk-sharing arrangements. This brief offers a different approach, examining the Canada Pension Plan (CPP) and how it manages the risk that comes with investing retirement savings in equities...
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".