Human Capital and Economic Growth in Pakistan: A Cointegration and Causality Analysis
Bibliographic record
Abstract
This study is an attempt to investigate the role of human capital in terms of education and health on economic growth of Pakistan during 1974-2009. Using annual data, ADF, PP and Ng-Perron tests are utilized to check the stochastic properties of the variables. Long-run relationship among variables is confirmed through Johansen and Juselius cointegration test whereas the long-run and short-run dynamics are observed by VECM specification. For causality purpose both VECM based causality and Toda-Yamamoto causality tests are employed. Stability of the model is confirmed through CUSUM and CUSUMSQ. The results indicate strong positive impact of human capital on economic growth despite the fact that Pakistan has been spending less percentage of GDP on education and health facilities to create human capital. The study concludes that in order to reap maximum benefits from human capital there is a need to formulate and implement effective economic policies related to the provision of education and health facilities to the people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".