Internet Use and Life Expectancy in Sub-Saharan Africa
Bibliographic record
Abstract
The study investigates whether internet use is associated with rising life expectancy at birth in 45 Sub-Saharan African countries from 2000 to 2019. Following the Grossman Health Capital Model, the paper conceptualizes internet access as a productive factor in the health production function. The main estimations employ panel-corrected standard errors (PCSE), and feasible generalized least squares (FGLS) and two-stage least squares (2SLS) methods verify the results. The empirical evidence supports a positive and nonlinear effect of internet use on life expectancy. The role of other determinants of health is assessed, in particular the effect of public and private health expenditure as well as other fundamentals of public health: food production per capita, immunization coverage, and urbanization. Consistent with the health production model, public and private health spending, immunization, and food per capita are positively and significantly related to longevity. By contrast, rapid urbanization has a statistically significant adverse effect. The empirical findings of the paper have an important policy message for both closing the digital divide and investing in public health infrastructure, which are two important and complementary conditions for longer and healthier lives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".