A two-edged sword: the impact of public debt on economic growth—the case of Ethiopia
Bibliographic record
Abstract
This study investigates the dynamic effects of public debt on economic growth in Ethiopia using annual data from 1980 to 2021. The results from the Autoregressive Distributed Lag (ARDL) modeling approach reveal that while public debt boosts investment and enhances growth in the short term, it hinders long-term growth. Additionally, debt servicing negatively impacts growth in both the short and long term by diverting vital resources from investment. Thus, public debt acts as a two-edged sword for Ethiopia’s economic growth. On one side, it finances infrastructure and other growth-stimulating projects; on the other, high debt levels can impede growth. To mitigate the adverse impacts of public debt, Ethiopia should implement prudent fiscal discipline, mobilize domestic revenue, manage debt efficiently, address its structural trade deficit, and prioritize needs to prevent misuse and corruption. This approach should also prioritize social spending and public investment while strategically transitioning from debt dependence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".