Drivers of Labor Force Participation and Economic Growth in Gulf Cooperation Countries Region: A Dynamic Panel Analysis
Ihsen Abid · 2025 · International Journal of Sustainable Development and Planning
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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 signi
Abstract by Ihsen Abid, International Journal of Sustainable Development and Planning (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Investigating Variation in Replicability
Negative / Null Result ReportMany Labs 2: Investigating Variation in Replicability Across Samples and Settings
Failed Experiment ReportGetting Ahead in the Communist Party: Explaining the Advancement of Central Committee Members in China
Negative / Null Result ReportReducing implicit racial preferences: II. Intervention effectiveness across time.
Negative / Null Result ReportTHE IMPACT OF IMMIGRATION ON THE STRUCTURE OF WAGES: THEORY AND EVIDENCE FROM BRITAIN
Negative / Null Result ReportRevisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.18280/ijsdp.200523
