Interaction effects of mental health disorders and labour productivity on economic growth in Africa
Bibliographic record
Abstract
Mental health disorders are major public health problems confronting millions of people globally as well as in Africa. While these disorders can negatively affect the economic productivity of affected persons which can reduce economic growth, to the best of our knowledge, empirical evidence in this regard is sparse, with none emanating from the African continent. This study therefore examines the individual and combined (interaction) effects of mental health disorders and labour productivity on economic growth in Africa. The study uses data comprising 45 African countries over the period, 2002–2019. Prevalence of schizophrenia, depression, dysthymia, bipolar and anxiety are the mental health disorders used while the log difference between the current year's real Gross Domestic Product (GDP) and the past year's real GDP is used to measure economic growth. Labour productivity is measured by the rate of growth in output (GDP) per worker. The system Generalised Method of Moments (GMM) regression is used as the estimation technique. The study finds that, in both the short-and long-run periods, while all the mental health disorders have negative significant effects on economic growth, the effect of labour productivity on economic growth is positive and significant. However, the interactions of each of the mental health disorders with labour productivity are found to have negative significant effects on economic growth in both the short-and long-run periods. There is therefore the need to enhance awareness about mental health disorders as well as access to effective and quality mental healthcare to reduce the associated enormous economic losses. • Mental health disorders are major public health problems affecting millions of people globally as well as in Africa. • This study examines the individual and combined (interaction) effects of mental health disorders and labour productivity on economic growth in 45 African countries. • We find that, the interactions of mental health disorders with labour productivity have negative significant effects on economic growth. • There is the need to enhance awareness about mental health disorders as well as access to effective and quality mental healthcare.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".