The Impact of Informal Caregiving Intensity on Women's Retirement in the United States
Bibliographic record
Abstract
With increasing pressure on retirement-aged individuals to provide informal care while remaining in the workforce, it is important to understand the impact of informal care demands on individuals' retirement decisions. This paper explores whether different intensities of informal caregiving can lead to retirement for women in the United States. Using the National Longitudinal Survey of Mature Women, we control for time-invariant heterogeneity and for time-varying sources of bias with a two-stage least squares model with fixed effects. We find that there is no significant effect on retirement for all informal caregivers, but there are important incremental effects of caregiving intensity. Women who provide at least 20 hours of informal care per week are 3 percentage points more likely to retire relative to other women. We also find that when unobserved heterogeneity is controlled for with fixed effects, we cannot reject exogeneity. These findings suggest that policies encouraging both informal care and later retirement may not be feasible without allowances for flexible scheduling or other supports for working caregivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".