Analyzing Pro-Poor Growth in Southern Africa: Lessons from Mauritius and South Africa*
Why this work is in the frame
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Bibliographic record
Abstract
Abstract: Based on the methodology of Ravallion and Chen (2003), Kakwani and Pernia (2000) and Kakwani et al. (2003) and using household survey data, we analyze poverty, inequality and pro-poor changes in South Africa over the period 1995–2005 and in Mauritius over the period 2001–2006. Conditions are very different in these two countries. South Africa is one of the least equal countries in the developing world while inequality in Mauritius is relatively low in comparison to other African countries. Similarly, using a reference threshold of US$3 a day, we find that poverty headcount was initially around 42 percent in South Africa and 6 percent in Mauritius. Moreover, in addition to these initial differences, the two countries have experienced very different pro-poor growth paths. Temporal differences reveal that inequalities have increased significantly in South Africa over the period and that the poverty headcount in 2005 would have been around 10 percentage points lower without this strong adverse redistribution effect. South African growth has been anti-poor relatively speaking. Conversely, growth was absolutely pro-poor in Mauritius over the period 2001–2006. Deeper analysis is conducted across areas of residence (urban and rural) and according to educational achievements (some schooling versus no schooling) and gender. A comparison between Mauritius and South Africa allows for a better understanding of both growth and redistribution effects on poverty and for drawing some policy recommendations towards reducing poverty in these countries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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