Evidence-based exploration of macroeconomic dynamics in ensuring the sustainability of external debt: A case study of Djibouti
Bibliographic record
Abstract
The current research examines the impact of different macroeconomic indicators on Djibouti's external debt from 1990 to 2022 using the Autoregressive Distributed Lag (ARDL) model. The empirical model provides evidence that economic growth, trade, and government expenditure have a positive and significant impact on external debt. Moreover, the findings uncovered a positive relationship between Djibouti's national savings and external debt. Interestingly, FDI inflows and population growth showcased insignificant impacts on external debt during the long run. This can be explained by the lack of a business operating environment that attracts FDI and a limited number of consuming customers due to the modest percentage of the Djiboutian population. Consequently, these findings highlight the importance of sustained economic growth, trade efficiency, and fiscal prudence in managing and potentially reducing external debt. Additionally, the insight into the relationship between national savings and external debt provides a foundation for further exploration into Djibouti's borrowing and saving behaviors .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".