Economic Growth and Higher Education in South Asian Countries: Evidence from Econometrics
Bibliographic record
Abstract
South Asian economies has witnessed very slow growth over the years and the gap has widened manifold between other nations of Asia particularly East Asian nations and South Asian nations. This paper examines co-integration between the economic growth and reach of higher education in South Asian nations explaining this disparity. The research employed an econometric panel co-integration investigation to analyse the long run relationship of higher education and economic growth among these nations. The research confirmed positive long run causality between the economic growth of the South Asian nations and gross enrolment ratio of higher education. So, if the South Asian nations continue with their existing pattern of paying less attention to higher education by allocating low share of investment on it, poor human capital formation would result in growing further economic disparity between developed and South Asian nations where rich nations would remain richer and poor nations would remain poor with the gap remaining unabridged. This research will serve as an aid to policy makers, educators and financers of South Asian nations to bridge the gap between high- and low-income nations. The focus on the quantum of spending on higher education by the government will help improve the reach of tertiary education and build economic prosperity in these nations.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".