Rebalancing the Economy of Care: Policy Pathways to Reduce Gendered Poverty
Bibliographic record
Abstract
Gendered poverty persists as a systemic and global inequity rooted in the disproportionate burden of unpaid care work shouldered by women. Worldwide, women perform over 76% of unpaid care work, contributing an estimated $10.8 trillion annually in invisible economic value (Oxfam, 2022). This invisible labour constrains women's access to formal employment, limits social and economic mobility, and reinforces poverty cycles, especially among single mothers and low-income households. This paper examines how comprehensive and accountable childcare and equitable parental leave systems and policies can redistribute care responsibilities, enhance women’s labour force participation, and reduce gendered poverty. Employing secondary research, the study draws on labour market data, policy frameworks, and literature review from three welfare economies—Sweden, Norway, and Canada—to explore the socioeconomic impacts of care-supportive policy ecosystems. Findings reveal that in countries with universal childcare access and non-transferable, paid parental leave for both parents, women’s labour force participation exceeds 75%, gender wage gaps fall below 12%, and child poverty rates are markedly lower. The paper advocates for the urgent integration of unpaid care work into national accounting and economic policymaking and agendas. It supports the global adoption of care-centred policies as a foundational strategy for achieving SDG-1 (No Poverty) and SDG-5 (Gender Equality). Ultimately, acknowledging and funding the care economy is not only imperative for gender equity, but it is an economic necessity.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".