The Determinants of Gender Wage Discrimination in Pakistan: Econometric Evidence from Punjab Province
Bibliographic record
Abstract
The development of labor sector has always been on the priority agenda of every country in the world. Since gender discrimination can be categorized in various forms across countries, but this paper concentrates on the gender employment positions and wage differentials in Pakistan. The major objective of this paper is to analyze the determinants of gender wage discrimination in Pakistan using descriptive and regression analysis based on the cross-sectional data of Pakistan labor force survey. It is concluded that illiteracy, poor and low levels of education as well as low vocational, technical, and professional competence are currently important facets of the labor market participants in Pakistan. The results of empirical analysis show that dissimilarity in attainment of jobs is a remarkable phenomenon between males and females. It is also proved that some socio-economic and cultural constraints also hinder the participation of females. Finally the results show that women are not different in their productivity from men and if discrimination does not occur, women can earn more as compared to men in some cases. The governments should take some concrete steps for equitable employment opportunities, improving institutions and infrastructure, provision of quality education and proper training, gender participation in decision-making and knowledge-based economy
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".