Relationship Between Governance and Trade: Evidence From Sub-Saharan African Countries
Bibliographic record
Abstract
This paper investigates the effect of governance in Sub-Saharan African towards trade. This study utilized panel data from 1996-2017. This employed Pooled Mean Group approach by categorizing the Sub-Sharan African (SSA) countries into Low Governance Index (LGI) and Very Low Governance Index (VLGI) countries, considering its abundant resources. The results of the findings indicate that corruption does not affect trade in LGI countries but increases that of VLGI countries, signifying that corruption “greases the wheels” of trade in countries with a high rate of corruption. However, political instability reduces trade for LGI countries, whereas, in VLGI countries, it does not affect trade, indicating that political instability only impacts in countries with relatively better governance. Government expenditure, income, and population growth increase trade in LGI countries but does not show any evidence of impacting trade in the VLGI countries. The study concludes that governance (corruption and political instability) is a significant determinant of trade in the SSA; hence, the importance of dealing with corruption and ensuring a stable political environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".