The Asymmetric Effects of Government Debt on GDP Growth: Evidence from Somalia
Bibliographic record
Abstract
With the recent worldwide financial crisis, government debt has become a focal point of economic attention.This fundamental economic concept reflects the amount a nation owes in international loans.Rapid loan accumulation has brought Somalia's economy to the brink of a debt crisis, threatening the long-term economic stability of the region.This study examines the asymmetric relationship between government debt and GDP growth in Somalia from 1980 to 2020.It employs non-linear autoregressive distributed lag (NARDL) methods to investigate the asymmetric effects of government debt on GDP growth.The findings indicate a negative relationship between government debt and GDP growth; an increase in government debt significantly impairs GDP growth.The estimated long-run parameters for negative shocks to government debt are -0.711 and -2.88, respectively, suggesting that a decrease in government debt will lead to an increase in GDP growth.These results argue that for Somalia to stimulate GDP growth, it must maintain its obligations at a rational level and strive for fiscal sustainability.Policy implications include fiscal debt management, prioritizing public investments, and increasing revenue through tax reforms, anti-corruption measures, and promoting business initiatives to finance public investments and reduce debt.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".