India’s Missing Working Women: Tracing the Journey of Women’s Economic Contribution Over the Last Five Decades, and During COVID-19
Bibliographic record
Abstract
India today is an economic powerhouse on the global stage; however, it faces a queer conundrum—despite considerable gains in female education, decreases in fertility rates and increasing economic growth, only a quarter of its women are in the labour force, amongst the lowest in the world. Based on analysis of time series data over the last five decades (1970-2018), this paper finds that women’s labour force and workforce participation rates have secularly declined to their lowest levels since Independence. Women’s average wages have consistently remained below that of men, with sticky wage gaps across rural and urban areas. The fall in labour force participation has been led by women in rural areas, while female unemployment rates have remained higher than men in urban areas. The paper finds that occupational segregation and concentration of women in low growth sectors, income effect of rising household-incomes, increased mechanisation and lack of tertiary education and skill training are leading factors giving rise to these trends. Recent high frequency data demonstrates that COVID-19 induced lockdowns and economic disruptions have further dampened female labour force participation. Thus, in the absence of targeted policy interventions designed to support retention and promote women’s workforce participation, women are likely to continue being excluded from India’s spectacular growth story.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".