Foreign aid and economic growth in South Africa: An empirical analysis using bounds testing
Bibliographic record
Abstract
South Africa is classified as one of the wealthiest countries in Africa, yet half of its population lives below the poverty line and over a quarter of its labour force is unemployed. Foreign aid was one of the major sources of capital for the country. It poured in from many developed countries and it was very successful in promoting a stable society, especially during the first few years after apartheid ended in 1994. Thus, South Africa is a good case study for determining the relationship between and the effect of foreign aid on growth. The data on aid flow as a percentage of gross domestic product (GDP) in South Africa was only available from 1980, thus limiting the data from 1980 to 2009. Given the limitations in the data, a co-integration analysis of the autoregressive distributed lag (ARDL) was adopted, using the method of the conditional unrestricted error correction model (UECM), which accommodates small samples. The result shows that the relationship between aid and growth is negative both in the short and the long run.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".