Social Security and the Retirement Decision y
Bibliographic record
Abstract
Despite the increase in life expectancy over the last decades, retirement from worklife hasn’t changed. It starts around age 50 and is nearly completed by age 65. The paper adresses the question: What are the main determinants of the decision to retire? This is important in general for policy design purposes and in particular for thinking about the upcoming retirement of the baby boomers. The analysis uses a life-cycle general equilibrium model with heterogenous agents and endogenous health and retirement choice. The model is calibrated to match key features of the U.S. economy and social security system. Heterogeneity is founded on stochastic shocks to earnings and health. The key …ndings are that most social security policies mainly a¤ect the worklife continuation decision post age 65, while the pre 65 decision is nearly policy invariant. The biggest forces on retirement are shocks to the health status and the earnings situation. We thank Frances Donald and Sarah Howcroft for research assistance. The views expressed herein are those of the authors and not necessarily those of Bank of Canada or the Government of Canada. y Over the next years the post World War II baby boomers will reach the normal retirement
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".