Trade liberalization, economic growth and welfare in Guinea-Bissau: a CGE modeling
Bibliographic record
Abstract
This paper examines the macroeconomic, sectoral and welfare impacts of trade liberalization policies based on import tariffs (scenario 1), partial export tax (scenario 2) and complete export tax (scenario 3) reductions in Guinea-Bissau using a dynamic computable general equilibrium model from 2022 to 2036. GDP grows at approximately 1.6%, 0.03% and 0.28% in scenarios 1–3, respectively. Scenario 1 provokes a reduction in the foreign input prices in the domestic market, boosting investment demand. In scenarios 2 and 3, the export improvement allows for the accumulation of trade gains, which are reinvested particularly in non-traditional sectors. The demand for labor increases by about 5.4% to 8.2%, 0.61% to 6.8% and 5.4% to 8.3%, respectively, as sectoral production expands. At the household level, the impact of the results varies across different settings and quantile levels. In both scenarios, urban households benefit more than rural counterparts with the same initial income level. However, reducing import tariffs has a more pronounced effect on the income and consumption of poor individuals, suggesting the potential of trade liberalization to enhance long-term welfare in a developing country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".