Financial Deepening and Economic Development in MENA Countries: Empirical Evidence from the Advanced Panel Method
Bibliographic record
Abstract
This study analyses the effect of financial deepening on economic development in 12 MENA countries for the period between 2000 and 2014. Using three financial deepening indicators which are widely used in the literature, an econometric analysis was conducted through co-integration and estimation methods which take cross-sectional dependence into account. A long-term relationship between variables was revealed with Westerlund (2008) Durbin-Hausman panel co-integration test, and then, long-term coefficients were obtained using Pesaran (2006) CCE (Common Correlated Errors) estimator. Empirical findings point to a positive relationship between financial deepening indicators - domestic credit to private sector, domestic credit provided by private sector, and liquid liabilities of the financial system ratio – and economic development. With this study, it was shown that the domestic credit to private sector causes economic growth for five countries, domestic credit provided by financial sector causes economic growth for one country, and liquid liabilities of the financial system causes economic growth for four countries.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".