The Canadian National Retirement Risk Index: Employing Statistics Canada’s LifePaths to Measure the Financial Security of Future Canadian Seniors
Bibliographic record
Abstract
This article measures a Canadian National Retirement Risk Index (NRRI). Originally developed by the Center for Retirement Research at Boston College, the NRRI is a forward-looking measure that evaluates the proportion of working-aged individuals who are at risk of not maintaining their standard of living in retirement. The Canadian retirement income system has been very effective in reducing elderly poverty, but our results suggest that it has been much less successful in maintaining the living standards of Canadians after retirement. Since the earlier years of the new millennium, we find that approximately one-third of retiring Canadians have been unable to maintain their working-age consumption after retirement—a trend that is projected to worsen significantly for future Canadian retirees. The release of the Canadian NRRI is timely given the widespread concern that the current Canadian retirement income system is inadequate. Many proposals have recently emerged to extend and/or enhance Canadian public pensions, and the NRRI is a tool to test their merit. The methodology underlying the Canadian NRRI is uniquely sophisticated and comprehensive on account of our employment of Statistics Canada’s LifePaths, a state-of-the-art stochastic microsimulation model of the Canadian population. For instance, the Canadian NRRI is novel in that it models all of the relevant sources of consumption before and after retirement, while accounting for important features that are typically neglected in retirement adequacy studies such as family size, the variation of consumption over a person’s lifetime, and the heterogeneity among the life courses of individuals.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".