Unmasking the Conflicting Trends in Job Tenure by Gender in the United States, 1983–2008
Bibliographic record
Abstract
Americans are convinced that employment stability has declined in recent decades, but previous research on this question has led to mixed conclusions. A key challenge is that trends for men and women are in opposite directions and appear to cancel each other out. We clarify this situation by examining trends in employer tenure by sex, marital status, and parental status. We find that married mothers are behind the increase in women’s job tenure, but men and never-married women have seen declines in tenure. Furthermore, we show that the timing of tenure trends for women parallels periods of increased labor force attachment. Finally, we find that shifts in industry and occupation composition can account for the decline in tenure among men and never-married women before 1996 but not afterward. We situate these diverging trends in two broad shifts in expectations, norms, and behaviors in the labor market: the end-of-work discourse and the revolution in women’s identification with paid work. Our findings support the view that job tenure is declining for all groups, but women’s greater labor force attachment, especially their more continuous employment around childbirth, countered and masked this trend.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".