Health and economic growth across Sub Saharan Africa: the unobserved role of demography
Bibliographic record
Abstract
This thesis revisits the debate on the impact health has on economic growth. The majority of previous work on the subject have focused on how health affects growth in developed countries and developing countries outside Africa. However, Sub Saharan Africa (SSA) is where insights into this relationship are most of value as the continent of Africa bears one quarter of the overall global disease burden, with 69% of deaths in SSA resulting from infectious diseases like HIV/AIDS and malaria. Of the 37.4 million people living with HIV globally, 25 million live in Africa (World Economic Forum, 2019). Waage et al. (2015) suggests that achieving health and wellbeing for all can only be accomplished if poverty is reduced. These two objectives poverty reduction and good health and wellbeing for all are in line with Sustainable Development Goals (SDGs) 1 and 3. The critical nature of health and its resulting effect on growth makes it essential to find ways of improving the state of health in developing countries. \n \nThis thesis provides insights into the direct and indirect role played by health in conjunction with other factors, through the process of economic development in Sub Saharan Africa. This is vital because the realisation of strategies that are intended to improve health depends not only on understanding the economic context but also on the understanding of the epidemiological and the sociological environment. Chapters in this thesis therefore considers some of the factors that have been highlighted as important influences on the health-growth relationship, but not fully explored in the Sub Saharan African context. These include diseases load, the demographic transition, geographical variation, and causal channels through which population health affects economic development. Policy makers as well as researchers undertaking further empirical investigation into health and growth in developing countries will benefit from the contribution of knowledge this thesis provides.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".