Drivers of Labor Force Participation and Economic Growth in Gulf Cooperation Countries Region: A Dynamic Panel Analysis
Bibliographic record
Abstract
This study investigates the drivers of labor force participation and GDP growth in the Gulf Cooperation Council (GCC) region from 1990 to 2023, emphasizing the roles of youth employment, urbanization, export performance, and foreign direct investment (FDI).Using the Arellano-Bond dynamic panel data estimation method, the study models interdependent relationships between labor force participation, GDP growth, and key macroeconomic indicators while addressing endogeneity and dynamic feedback effects.The analysis reveals that lagged labor force participation has a strong positive and highly significant effect (coefficient = 0.821, p < 0.001), indicating persistence in workforce engagement.Urbanization exerts a significant positive influence (coefficient = 0.271, p = 0.007), while GDP growth shows a positive but marginally significant effect (coefficient = 0.219, p = 0.076).Exports of goods and services have a negative and significant impact on labor force participation (coefficient = -0.076,p = 0.024), suggesting structural mismatches between export industries and labor market needs.ICT imports and FDI have statistically insignificant effects.Regarding GDP growth, past growth trends (p < 0.01), labor force participation, urbanization, and general exports significantly enhance economic performance.In contrast, ICT goods exports negatively correlate with GDP growth, and FDI contributes modestly but significantly.This study provides novel empirical evidence on the differential impact of macroeconomic drivers on labor force participation and growth in GCC economies, particularly highlighting the persistent influence of urbanization, the negative association of export sectors with labor absorption, and the limited role of ICT and FDI.It contributes to the literature by dissecting the sectoral misalignments in labor demand and proposing policy directions to promote inclusive growth, especially through youth integration, urban labor planning, and strategic trade and investment alignment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".