South Africa trade liberalization and poverty in a dynamic microsimulation CGE model
Bibliographic record
Abstract
South Africa has undergone significant trade liberalization since the end of apartheid. Average protection has fallen while openness has increased. However, economic growth has been insufficient to make inroads into the high unemployment levels. Poverty levels have also risen. The country’s experience presents an interesting challenge for many economists that argue that trade liberalization is pro-poor and pro-growth. This study investigates the short and long term effects of trade liberalization using a dynamic microsimulation computable general equilibrium approach. Trade liberalization has been simulated by a complete removal of all tariffs on imported goods and services, and by a combination of tariff removal and an increase of total factor productivity. The main findings are that a complete tariff removal on imports has negative welfare and poverty reduction impacts in the short run which turns positive in the long term due to the accumulation effects. When the tariff removal simulation is combined with an increase of total factor productivity, the short and long run effects are both positive in terms of welfare and poverty reduction. The mining sector (highest export orientation) is the biggest winner from the reforms while the textiles sector (highest initial tariff rate) is the biggest loser. African and Colored households gain the most in terms of welfare and numbers being pulled out of absolute poverty by trade liberalization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".