Financial Development, Savings and Economic Growth: Evidence from Bahrain Using VAR
Bibliographic record
Abstract
This research paper investigates the linkage between the financial development and the economic growth in Bahrain during the period 1981 to 2013. The motivation for kicking off on the Bahrain economy is attributed on account of paradigm shift manifested in moving from hydrocarbons purveyor to being in the financial services and industrial hub. Given the limiting factor embedded with the bivariate causality structure, the paper encompasses savings as an intermittent variable. The paper makes an earnest investigation to gauge the long run and short run relationship among financial development, savings and the economic growth. Time series data are taken for a time span of 33 years (1981- 2013). Data are culled from the World Bank Database. Financial Development measured by M2(broad money)/ GDP is represented by F, Economic growth measured by GDP per capita is represented by Y and Savings measured by Domestic Savings/GDP is represented by S. No long term co-integration is found among the variables under consideration as represented by Johansen test. Through the employment of multiple econometrics tools under Vector Auto Regression (VAR) framework, it is unearthed that the empirical evidence supports neither the supply –leading hypothesis nor the demand –following hypothesis for the Bahrain. While savings and economic growth have bi-directional causality at 10% level of significance. In responding to inexplicit results between the purported variables, the current study recommends that more wide ranges of reforms in the financial services are entailed, so as to escalate further the economic growth in the Bahrain economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".