Socio-Economic Factors and Women’s Empowerment: Evidence from Punjab, Pakistan
Bibliographic record
Abstract
The empowerment of women is an essential objective to fully engage them in economic life and achieve sustainable growth throughout the world. Providing basic facilities to women is one form of empowerment. This paper examines the extent of women’s empowerment in Punjab, Pakistan and its divisions, along with rural and urban regions. In addition, we check the effect of the gender wage differential on the current dilemma by implementing Alkire et al.’s [2013.The women’s empowerment in agriculture index (Working Paper No. 58). Oxford, UK: Oxford Poverty and Human Development Initiative. Retrieved from https://www.ophi.org.uk/wp-content/uploads/ophi-wp-58.pdf.] indexing on HIES 2013–14 datasets. Our results show that 34.91% of women are empowered in Punjab overall, with independence being the highest dimensional contributor, and ownership of assets being the least. Women are 31.43% more empowered in urban regions. The results indicate that Islamabad has significantly more women’s empowerment, while Dera Ghazi Khan has the lowest percentage of empowered women. To assess particular impacts of different socio-economic and demographic variables on women’s empowerment, logistic regression model is applied, revealing that most socio-economic and demographic variables have significant impacts on the current scenario, and variation in any variable causes significant variations in the status of women’s empowerment, with increased wage differential in particular, decreasing the probability of women being empowered.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".