Who Cares? Unpaid Labor, Human Capital and Inequality In India
Bibliographic record
Abstract
India’s hidden economy with unpaid care, childcare, eldercare and any household maintenance is what shapes human capital formation yet continues to remain invisible in national statistics. This paper describes the tension between the static neoclassical view where households outsource care to minimize private costs and the feminist critique which emphasizes care work’s foundational role and the inequalities it reinforces. Using India’s 2014 female-to-male ratio of unpaid care work and the 2019- 20 Time Use Survey, we are able to document that the poorest households shoulder on average 53.9 more minutes of unpaid care per day than the richest households. Afterwards, the paper develops a simple two-period model where period-1 care inputs determine period-2 human capital, that helps show that neglecting the feedback of care can have a consequence of socially mediocre outcomes. Finally, it highlights policy interventions that include paid stipends, expanded public early childhood services and conditional cash transfers that all serve to realign the private incentives of individuals with long term efficiency in mind. The framework of the paper highlights how integrating feminist concerns into a much more dynamic neoclassical model now can guide policies to value and redistribute care and ultimately narrow international inequality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".