Financial Development, Trade Openness and Economic Growth: A Trilateral Analysis of Bahrain
Bibliographic record
Abstract
This study investigates the relationship between trade openness, financial development and economic growth for the Kingdom of Bahrain. Time series data are utilized form 1980 till 2012. The vector error correction model (VECM) in combination with innovation of accounting (variance decomposition and impulse response function) analysis are employed to explore the causal relationship between the variables. The stationarity properties of the data and the order of integration are tested using both the Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test. All variables are found to be cointegrated indicating the existence of long-run relationship. The empirical findings show that trade openness and financial development have causal impact on economic growth. Conversely, growth is found to have no causal impact on trade and financial development, implying support for “trade-led growth” and “finance-led growth” hypotheses. Furthermore, the results show a short-run causality from financial development to trade openness. The findings suggest that trade openness and financial development are important elements in determining economic growth in Bahrain. Therefore, Bahrain should continue to patronize the development of its financial sector and to allow more trade openness in order to achieve a high and sustainable economic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".