A systematic review of causes of recent increases in ages of labor market exit in OECD countries
Bibliographic record
Abstract
Ages of labor market exit have increased steadily since the late 1990s in OECD countries, but with continuing population aging, there are calls for further stimulation of labor force participation at older ages. Social scientists have extensively studied causes of variation in retirement timing between individuals and across countries, but have paid less attention to causes of variation over time. This study systematically reviews evidence of causes of increases in ages of labor market exit over the past 30 years in OECD countries. Two goals are pursued: first, to provide an overview of the retirement domains that have been subject to investigation; second to compare studies with respect to the magnitude of change in retirement behavior that they attributed to different causes, in different contexts. Nineteen studies were reviewed. Available evidence articulates itself around four domains: inter-cohort changes in labor force participation of women (3 studies), educational attainment (3 studies) and lifetime wealth (1 study), and changes to social security systems (16 studies). Determinants in all domains explain a significant amount of past increases in ages of labor market exit, though figures attributable to similar determinants vary between studies and across countries. Evidence suggests that further postponement of labor market exit may depend on further increases to normal retirement ages and more limited access to early retirement programs, but also on further increases in educational attainment and the continued integration of women in the labor market. However, a large share of the past increases in ages of labor market exit remains unexplained; therefore, other factors such as those related to work and organizational characteristics deserve further research.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".