Aid and Poverty in Africa: Do Well‐being Measures Understate the Progress?
Bibliographic record
Abstract
Abstract: In the last 15 years international aid donors to Africa have shifted their focus dramatically toward health and education; the share of social sector support in total aid rose from 33 per cent to 60 per cent from 1990–94 to 2000–2004 alone. If this aid has been effective, it is unlikely to be captured in GDP or income poverty figures. This paper uses the Demographic and Health Survey at multiple points in time to explore changes in well‐being in ten sub‐Saharan African countries. It compares the evolution of both assets and health which are considered as the two main dimensions of well‐being. These dimensions are simultaneously estimated using the structural equation models with latent variables that have been developed in the psychometric literature. The comparisons of well‐being across time in each country are based on the stochastic dominance analysis. The main results suggest that assets and health have improved during the last two decades in most of these countries. A decline in assets is observed for three countries while health deteriorates in two countries. The reduced poverty appears to be explained less by the aid than other factors in most cases.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".