Financial Openness, Trade Openness, and Economic Growth Nexus: A Dynamic Panel Analysis for Emerging and Developing Economies
Bibliographic record
Abstract
International market openness has long been regarded as critical for economic development, and recent evidence highlights the distinct roles of financial and trade openness, particularly in emerging and developing economies. This study examines the impact of financial and trade openness on economic growth in ten emerging and developing countries from 1970 to 2023. It employs a dynamic panel generalized method of moments (GMM) model, which is selected for its ability to address potential endogeneity and dynamic relationships within panel data. The analysis finds that both financial and trade openness positively influence economic growth and that stable macroeconomic conditions and political stability enhance these growth-promoting effects. In the context of growing geo-economic tensions, trade fairness, and national security concerns, the study underscores the need for policies that balance global integration with national interests. These findings suggest the importance of designing policies that promote greater integration into global financial and trading systems while ensuring sound macroeconomic fundamentals and supportive institutions. The study recommends that policymakers pursue strategic liberalization and strengthen governance structures to achieve sustained and inclusive growth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".