The Nexus between Employment and Economic Growth: A Cross-Country Analysis
Bibliographic record
Abstract
The main objective of this paper is to examine the relationship between employment and economic growth in developed and developing countries over the period of 1970–2019. As documented in the literature in the past, economic growth in most developing countries has been less job-generating than in developed countries, even though high economic growth is observed in most of the developing world, indicating jobless growth. Based on the Cobb–Douglas production function, we developed an employment demand model to find the employment elasticity with respect to economic growth using working hours and population as explanatory variables. The main findings of the present study reveal that the employment elasticities with respect to GDP are positive and significant in developing and developed countries. But in the developing countries, the employment elasticity is relatively very low (0.11 to 0.15) compared to the developed countries (0.43 to 0.48), which led to the conclusion that a possibility of jobless growth exists in these countries. The findings of the study imply that policymakers should focus more on employment-led growth policies instead of growth-led employment policies, especially in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".