Road injuries, labor productivity, and economic growth in Africa: A panel study
Bibliographic record
Abstract
Abstract Background and Aims Globally, millions of people suffer from road injuries, with Africa having the highest burden of road injury deaths. This public health problem has the potential to reduce labor productivity and hence hamper economic growth, especially on the African continent. This study, to the best of the authors' knowledge, therefore seeks to provide the first empirical evidence of the interaction or combined effect of road injuries and labor productivity on economic growth in African countries. Methods The study uses annual data on 45 African countries over the period, 2002 to 2019. The dynamic panel system generalized method of moments regression is used as the estimation technique. Results The findings show that the interaction of road injuries with labor productivity has a negative significant effect on economic growth in both the short‐run (coefficient: −1.96, p < 0.01) and long‐run (coefficient: −1.93, p < 0.01) periods. Conclusion There is a need to increase investment in road safety to reduce the prevalence of road injuries on the African continent.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".