Understanding older adults’ labour market trajectories: a comparative gendered life course perspective
Bibliographic record
Abstract
The recent push to keep older adults in the labour force glosses over who is likely to follow what kind of employment trajectory and why. In this paper, we broaden understandings of later-life labour market involvement by applying a comparative gendered life course perspective. Our data come from the Survey of Health, Ageing and Retirement in Europe and the Health and Retirement Study (US), two representative panel studies of individuals aged 50-plus. Using a unique modeling strategy, we examine employment biographies for older women and men from four nations with diverse policy regimes (Germany, Italy, Sweden, and the US), along with their links to family experiences and earlier attachment to the labour force. We find that, in every nation, women prevail in groups representing a weak(er) attachment to the labour market and men in groups signifying a strong(er) attachment. However, this pattern is much stronger for Germany and Italy than for Sweden and the US. Similarly, both family experiences and prior employment matter more for later-life labour market involvement in Germany and Italy. Our findings demonstrate that older adults’ employment trajectories are gendered; moreover, there is evidence that they are influenced by policies related not only to paid work but also to caregiving, and by those affecting not only current decisions but also those made earlier in the life course.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".