South Africa trade liberalization and poverty in a dynamic microsimulation CGE model
Why this work is in the frame
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Bibliographic record
Abstract
South Africa has undergone significant trade liberalization since the end of apartheid. \nAverage protection has fallen while openness has increased. However, economic \ngrowth has been insufficient to make inroads into the high unemployment levels. \nPoverty levels have also risen. The country’s experience presents an interesting \nchallenge for many economists that argue that trade liberalization is pro-poor and \npro-growth. This study investigates the short and long term effects of trade \nliberalization using a dynamic microsimulation computable general equilibrium \napproach. Trade liberalization has been simulated by a complete removal of all tariffs \non imported goods and services, and by a combination of tariff removal and an \nincrease of total factor productivity. The main findings are that a complete tariff \nremoval on imports has negative welfare and poverty reduction impacts in the short \nrun which turns positive in the long term due to the accumulation effects. When the \ntariff removal simulation is combined with an increase of total factor productivity, the \nshort and long run effects are both positive in terms of welfare and poverty reduction. \nThe mining sector (highest export orientation) is the biggest winner from the reforms \nwhile the textiles sector (highest initial tariff rate) is the biggest loser. African and \nColored households gain the most in terms of welfare and numbers being pulled out \nof absolute poverty by trade liberalization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it