Financial Deepening and Economic Development in MENA Countries: Empirical Evidence from the Advanced Panel Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it