The Impact of Informal Caregiving Intensity on Labour Market Outcomes
Bibliographic record
Abstract
Most OECD countries have introduced policies shifting health care into community settings. These policies rely on informal caregivers to provide care to disabled or ill family and friends. At the same time, there are policies in place promoting labour market retention. To better understand how caregiving and labour policies may interact to affect the available pool of caregivers and labour force participants, we need more evidence about how informal caregiving is related to labour market outcomes. We explore this issue through three empirical studies, with a focus on caregivers who provide significant amounts of weekly care (i.e. intensive caregivers).The first study uses the Canadian cross-sectional General Social Survey to determine whether providing informal care is associated with various labour market states. We find that intense caregiving is associated with being fully retired for men and women. High intensity caregivers are also more likely to be retired before age 65. In the second study, we use the American National Longitudinal Survey of Mature Women and control for time-invariant heterogeneity and time-varying sources of bias amongst retirement-aged women. We find that women who provide at least 20 hours of informal care per week are 3 percentage points more likely to retire relative to other women, which supports the idea that intensive caregiving may cause women to retire. Finally, given changes in the policy, demographic, and cultural contexts, we use the American National Longitudinal Surveys of Young and Mature Women to explore whether labour market penalties have changed over time. Following two cohorts of pre-retirement aged women, we find that intensive informal caregiving is negatively associated with labour force participation for both pre-Baby Boomers and Baby Boomers. The caregiving effects are not significantly different across cohorts, implying that, despite the introduction of offsetting policies, labour market penalties for caregivers have persisted.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".