Labor Force Participation Rate and Expected Length of Retirement 1989-2066: Comparison of Several OECD Countries
Bibliographic record
Abstract
The world population is aging, which along with recent shifts in the labor force participation (LFP), is having a significant longevity influence on state pension programs across the board, including Canada, Finland, Japan, and Germany. Except Japan, these countries have set their statutory re-tirement age at 65, but the impact of aging workforce and declined fertility rates create wonders on the estimate future trends in the LFP, as well as the length of retirement. In this study, we fit the LFP rates of these countries, representing continents from Asia, Europe and North America among the OECD countries, using the Lee-Carter and Cairns-Blake-Dowd (CBD) stochastic models. The es-timates are then used for the projection of future LFP rates (1989-2066), and by combining the mortality forecasts from the United Nations, we project the expected length of retirement (1989-2066). This study provided a novel comparison between the Lee-Carter and the CBD LFP models by fitting and forecasting the LFP rates of senior employees between 50- to 74-year-olds. The results revealed disparities between models that provided proof of the presence of model risk for longer retirement durations. The study findings emphasized the importance for decision-makers in pension industry to have awareness of potential risks and limits associated with the LFP models. The Lee-Carter model outperformed the CBD model even though the CBD model is known for its ac-curate prediction in higher ages. Population aging should be considered in any analysis of the long-term viability of pensions, together with the participation rate trends for a sustainable labor market future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".