Women’s Financial Empowerment and Financial Inclusion Through PMJDY: A Study of Trends, Barriers and Opportunities
Bibliographic record
Abstract
Over the past decade, women’s financial empowerment in India has undergone significant change, driven by targeted policies aimed at removing traditional barriers to their economic participation. Before these initiatives, many rural and low-income women were excluded from formal financial systems and relied on informal saving methods, often marginalized in household decisions. The introduction of the Pradhan Mantri Jan Dhan Yojana (PMJDY)in 2014 revolutionized access, allowing women to open bank accounts effortlessly without paperwork or collateral, resulting in women owning 56% of PMJDY accounts by 2025. This scheme offers zero-balance accounts, free debit cards, overdraft facilities, and direct benefit transfers, which have proved essential during crises like the COVID-19 pandemic. Complementary programs such as MUDRA and Stand-Up India have further supported women’s entrepreneurship, with women making up over 68% of MUDRA loan borrowers. However, challenges remain, including social norms, limited financial literacy, and a tendency among some women to save outside formal channels or cede financial control to male relatives. Opportunities to deepen impact include enhancing financial and digital literacy and increasing women’s presence among banking agents. Ultimately, PMJDY has empowered women to become more active savers and decision-makers, transforming their economic roles and contributing meaningfully to their families and communities. This study offers rich, contextual insights beyond mere statistics, guiding policies toward an India where women’s financial empowerment is a living reality every day.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".