Bibliographic record
Abstract
• South Africa may be afflicted by Dutch Disease; we present quantitative evidence that supports this hypothesis, at least in part. • If this diagnosis is correct, then it follows that certain key policy recommendations be followed, such as urgent economic diversification, political reform and financial transparency improvements. • While South Africa is not, according to our quantitative examination, afflicted by other dimensions of the resource curse per se, institutional quality appears to have been impaired by mineral rents, which will ultimately reverse the country’s human development gains if not immediately reversed. • This is the first quantitative attempt to specifically assess the role of South Africa’s mineral rents in its manufacturing and development outcomes against a set of comparable countries from 1996 to 2019. The results are, therefore, novel, and should be closely scrutinised by policymakers and academics alike. This paper addresses the question of how best to explain South Africa's prolonged economic stagnation, manifest especially in manufacturing decline, both in total employment share and value addition to the economy. Despite its wealth of natural resources, South Africa's economic performance – especially in the manufacturing sector – has been weak, especially since 2008. The extent to which the country's resource abundance determines manufacturing performance has largely been overlooked in the literature. Utilising analytic narrative, we examine the plausibility of competing hypotheses that may account for manufacturing decline in South Africa. Our primary hypothesis is that South Africa is afflicted by a particular manifestation of the resource curse known as “Dutch Disease”. After examining several explanatory hypotheses, we conclude that the decline of South Africa's manufacturing industry is strongly linked to its reliance on mineral rents, but through multiple channels. The decline is exacerbated by poor institutional quality, itself driven by "state capture," hindering the country's ability to combat corruption and inefficiencies in government effectiveness. To recover from these dynamics, we suggest that South Africa should focus on strengthening institutions, improving political governance, and enhancing financial transparency. Addressing these challenges is crucial to manufacturing recovery, diversifying the economy and fostering broad-based economic development in South Africa.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".