Exploring the Determinants Influencing Somalia's Trade Balance: An ARDL Modelling Approach
Bibliographic record
Abstract
Over the past two decades, the Somali Shilling exchange rate against the US dollar and Euro currency has become unstable.The country's reliance on imports of food, fuel, building materials, and manufactured products has resulted in a chronic trade imbalance.Livestock, bananas, skins, fish, charcoal, and scrap metals are the main exports.This study offers comprehensive insights into the determinants of Somalia's trade balance, encompassing both long-term and short-term dynamics.By employing the Autoregressive Distributed Lag (ARDL) model, this research has econometrically examined the relationship between various latent variables.The findings demonstrate that exchange rates and inflation exert long-term positive and significant influences on Somalia's trade balance, yet they have short-term negative effects.Conversely, Foreign Direct Investment exhibits long-term negative effects on the trade balance but manifests short-term positive impacts.Furthermore, government expenditure displays both short-term and long-term positive and significant effects on the trade balance in Somalia.Based on the findings of the study, it is recommended that governments and policymakers implement a proactive exchange rate policy, emphasizing strategic government expenditure allocation to boost domestic production and balance trade.It urges prioritization of export-oriented industries and import-substitution sectors in Somalia.Policymakers must cautiously manage foreign direct investment to foster sustainable economic growth and trade equilibrium.This study does not control for all potential factors that could influence the relationship between the variables under investigation.To address this limitation, future research should aim to conduct a more comprehensive analysis by controlling for additional factors that may influence the relationship between the variables.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".