Higher Education and Economic Growth in Africa
Bibliographic record
Abstract
Enrollment rates for higher education in Sub-Saharan Africa are by far the lowest in the world at 6%. Yet because of conventional beliefs that tertiary education is less important for poverty reduction, the international development community has encouraged African governments’ relative neglect of higher education. This article challenges beliefs that tertiary education has little role in promoting economic growth and alleviating poverty. First, we review recent evidence that higher education can produce significant public and private benefits. Next, we analyze the relationship between tertiary education and economic growth. We find evidence that tertiary education improves technological catch-up and, in doing so, may help to maximize Africa’s potential to achieve more rapid economic growth given current constraints. Investing in tertiary education in Africa may accelerate technological diffusion, which would in turn decrease knowledge gaps and help reduce poverty in the region. We also review new developments and trends in the higher education scene in Africa. Le taux d’inscription dans l’enseignement supérieur en Afrique sub-saharienne est de loin le plus faible du monde, atteignant seulement 6%. Pourtant, parce que l’enseignement supérieur est perçu comme moins important que les enseignements primaire et secondaire pour lutter contre la pauvreté, la communauté internationale a encouragé les gouvernements africains à moins y prêter attention. Cet article conteste l’idée que l’enseignement supérieur joue un rôle peu important dans le développement économique et la lutte contre la pauvreté. Tout d’abord, nous nous intéressons à de récents résultats qui montrent que l’enseignement supérieur crée des bénéfices publics et privés. Ensuite, nous analysons la relation entre l’enseignement supérieur et la croissance économique. Nous montrons que l’enseignement supérieur permet de rattraper le retard technologique et, ce faisant, pourrait aider l’Afrique à maximiser sa capacité à accélérer sa croissance économique dans les conditions actuelles. Investir dans l’enseignement supérieur en Afrique pourrait permettre une diffusion plus rapide des avancées technologiques, qui pourrait à son tour réduire la disparité de savoir et participer à la réduction de la pauvreté dans la région. Nous passons aussi en revue les nouveautés et tendances dans l’enseignement supérieur africain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".