Public-Private Investment and Macroeconomic Determinants: Evidence from MENA Countries
Bibliographic record
Abstract
This paper attempts to demonstrate the relationship between macroeconomic factors and each of Private Investment in Energy (PIE) and Private Investment in Telecoms (PIT) from 1990 to 2016 in 21 MENA countries (Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, Palestine and Yemen). Results reveal that both PIE and PIT are Granger caused by GDP, Real Interest Rate, Gross fixed capital formation, private sector, stocks traded are Granger causing PIE. Also, Inflation, Exports of goods and services and Commercial bank branches are Granger causing PIT. All of the ten macroeconomic variables taken up in study are cointegrated with Investment in energy and telecoms with private participation in the long run. Besides, shocks to all of GDP, gross fixed capital formation, private sector to GDP, general government final consumption expenditure, stocks traded and commercial bank branches (as a proxy of financial inclusion) have a positive and statistically significant effect on the private investment in energy and telecoms.
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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 | 0.001 | 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) | 0.002 | 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".