Youth Unemployment and Economic Growth in South Asia: Policy Implications for Stability and Sustainable Development
Bibliographic record
Abstract
Youth unemployment poses significant challenges to economic growth and social stability in South Asia, particularly in Pakistan, India, and Bangladesh.This study uses System Generalized Method of Moments and Dynamic Panel Threshold Models to examine how youth unemployment impacts GDP growth and reveals key threshold effects.Results show that a 1% rise in YU reduces GDP per capita growth by 0.20% on average across the region, with declines reaching 0.30% when unemployment exceeds a 17% threshold.Education are positively correlated with GDP growth 0.16%, while GD in employment contribute to a 0.13% GDP decline for each 1% increase in disparity.Inflation and FDI further influence growth, with inflation reducing GDP by 0.09% and FDI increasing it by 0.11%.Comparative analysis shows that despite India's relatively higher educational attainment, labor market mismatches persist, leading to high rates of educated youth unemployment.In Bangladesh, remittances offer economic stability amid limited domestic job opportunities Unlike previous study that finds teenage unemployment distinctly, our investigation shows a precarious joblessness threshold of 17%, at which economic instability accelerates.Using Threshold Models System and GMM, we build a non-linear association among YU and GDP growth, providing empirical evidence in favour of specific policy regulations.Findings highlight the importance of targeted labor market reforms, educational alignment with job market demands, and policies to reduce gender disparity in employment to maximize the region's youth potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".