Men who care too much do not work: a systematic review and meta-analysis of the effect of unpaid caregiving by men on their labour market participation
Bibliographic record
Abstract
Abstract Objective: We conducted a systematic review and meta-analysis to assess the effect of unpaid caregiving by men on their labour force participation (LFP).Methods: English-language journal articles, dissertations and working/conference papers from the MEDLINE, Embase, AgeLine, EconLit, EconPapers and the International Bibliography of the Social Science databases were searched from January 2007 to February 2021. We pooled the marginal effect of caregiving on men’s LFP in a random-effects model where LFP was defined as being gainfully employed while non-LFP comprised all other circumstances. Non-parametric methods were used to estimate the probability that caregiving was negatively associated with LFP. Meta-regressions were conducted with countries stratified by Gross National Income (GNI) per capita as a moderator variable. Secondary analyses were performed on employed men to assess the potential caregiving effect on temporary work exit and permanent retirement.Results: Thirty-one studies (904,694 men) were included in the review with 28 of these studies (892,805 men) used in the meta-analysis. The random-effects model found men who were weekly, daily or primary caregivers to experience a 4.4%, 8.0% and 10.0% reduction in their LFP, when compared to their respective counterparts. We estimated weekly, daily or primary caregivers to have an 87.3% (95% CI: 70%-96%), 87.5% (95% CI: 84%-96%) and 80.8% (95% CI: 67%-100%) chance of a lower LFP. An hourly increase in caregiving was unrelated to LFP (p-value=0.106). Among employed men (24,319 men), caregiving was found to be unrelated with retirement (p-value=0.156) and no meta-analysis was conducted on temporary work exit. No difference was found across countries stratified by GNI per capita.Conclusions: These results urge policy decisionmakers to provide more flexible work arrangements and other protective measures to support male employees who are weekly, daily or primary caregivers. Future meta-analyses should explore the effect of caregiving on other dimensions of labour supply (e.g., hours of work) by men. The scarcity of studies conducted in low-/middle-income countries might be addressed by creating population-based cohorts following the protocol of existing studies such as the American HRS (Health and Retirement Study). JEL classification: J16, J20, J22, J26
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".