Why Have the Labour Force Participation Rates of Older Men Increased Since the Mid 1990s
Bibliographic record
Abstract
This paper seeks to explain the substantial increases in older men’s labour force participation rates that have been observed since the mid-1990s. Using data from the U.S. March Current Population Survey, the Canadian Labour Force Survey, and the U.K. Labour Force Survey, I investigate the hypothesis that husbands treat the leisure time of their wives as complementary to their own leisure at older ages. I exploit the cohort effects driving recent increases in older women’s participation rates to identify the effect of a wife’s participation decision on her husband’s participation decision. Given this complementarity in leisure time, a large portion of the increase in older men’s participation rates may be explained as a response to the recent increases in older women’s participation in the labour force. The methodology of Dinardo, Fortin, and Lemieux (1996) is used to decompose the changes in older married men’s participation rates, demonstrating that increases in wives’ participation in the labour force can explain roughly one quarter of the recent increase in participation in the U.S., almost one half of the recent increase in participation in Canada, and roughly one third of the recent increase in the U.K. Older men’s educational attainment is also an important factor explaining recent increases in participation, yet cannot be expected to drive further increases in participation rates. In contrast, expected increases in older wives’ participation over the next decade are expected to drive further increases in older men’s participation rates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".