How Does Human Capital Formation Affect Labour Force Participation in Pakistan? A Primary Data Analysis
Bibliographic record
Abstract
Human Capital Formation is considered as main source of labor force participation and employment and raises economic growth and development. The major purpose of the present study is to trace out the human capital related factors which determine employment in Pakistan. The study is based on purely primary source of data, which is collected by the authors by employing multistage cluster sampling techniques. Binomial Logit regression technique is used to estimate the parameters of labor force participation model. The study concludes that the completed years of education, experience, various level of education, health status of workers significantly influenced the labor force participation and employment. In addition, it is observed that some socio economic factors like house holds’ assets, spouse participation in economic activities and number of dependents also significantly affect the employment. Therefore, it is suggested that the government should provide education and health facilities without any discrimination for all. Key words: Labor force; Human Capital; Health status; Workers’ level of education; Logistic regression; Pakistan
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".