The Social Sustainability of Public Debt in the Framework of Middle East and North African Countries: Egypt, Lebanon, Morocco, Tunisia, and Turkey
Bibliographic record
Abstract
The paper’s main objective is to analyze the social sustainability of the external public debt of some MENA countries, namely, Egypt, Lebanon, Morocco, Tunisia, and Turkey between 1990 and 2018. The study carries out a dual statistical and econometric analysis to determine the impact of external public debt on the population welfare. The first analysis aims to examine the evolution of the debt social sustainability indicators and the second uses the Auto Regressive Distributed Lag (ARDL) panel data estimation technique. Statistical analysis reveals that the external public debt service weighs heavily on public spending in health, education, and public investment. While the econometric study establishes that the ratio of external public debt as a percentage of Gross Domestic Product (GDP) has a negative effect on the population’s standards of living. The study concludes that external public debt in MENA countries has been used to finance non-productive expenditures, which have no effect on the population’s living conditions. It highlights the need to consider the views of both debtors and creditors to achieve a comprehensive and sustainable approach to public debt. The latter should integrate the social and environmental consequences of debt on the well-being and living conditions of the population.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".