The Relationship Between Foreign Aid and Economic Growth: Empirical Evidence from Somalia
Bibliographic record
Abstract
The purpose of this research investigation is to determine the effect of Sweden aid, UK aid, and US aid with control variables including the capital, and labor on economic growth utilizing Somalia data for the years 1989 to 2017. the study checked unit root problem by using both Phillips Perron (PP) and Augmented Dickey Fuller (ADF) bounds testing approach were employed to model the long-run and short-run cointegrations of the scrutinized variables and also the study uses the J & J cointegrating to regress for the long-run estimation.The study's empirical findings revealed that the variables had a long cointegration.It was discovered that while UK aid has no noticeable long-term relationship with economic growth in Somalia, whereas Swedish aid and US aid contribute the economic growth of Somalia.There are indications that Swedish assistance will boost long-term economic growth.Similarly, US aid is indicated to contribute to long-term gross domestic product (GDP), but UK aid has an insignificant impact on economic growth because UK aid relates mostly to military aid compared to those two other countries.Capital is also seen to contribute to long-term gross domestic product (GDP) which is suggesting that capital growth is more responsive to economic growth than other variables.While labor is also seen to contribute to long-term gross domestic product.Therefore, policymakers should establish a strategy to growth the economy by promoting the economy's most important drivers, such as exchange rates, capital, and inflation, and address drivers that impede the country's economic growth, such as the labor force.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".