The effects of local economic development on female obesity (overweight) in sub‐Saharan Africa
Bibliographic record
Abstract
Abstract Obesity (overweight) is a widespread concern not only in high‐income nations but also in low‐income countries across sub‐Saharan Africa (SSA). Although many studies attribute this trend to economic development triggering a shift in nutrition patterns within SSA, they tend overlook a critical factor: the level at which these determinants are measured. Assessing them nationally while drawing comparisons with individual‐level obesity data introduces a statistical challenge known as the ecological fallacy. To address this, we utilize local‐level night light data as a proxy for local economic development. Analyzing demographic and health surveys from 44 SSA countries spanning the period 1992–2019, we find that local development is associated with a 0.002% increase in the body mass index of women. In addition, we find that night light intensity is associated with 0.2%–0.3% increases in probabilities of a woman being overweight and obese. Our results remain robust when we employ an instrumental variable approach by using a control function based on peer effect. In terms of policy implication, our research highlights that local development may entail potential health costs, emphasizing the need for African governments to invest in healthcare and also build physical infrastructure that can promote active lifestyles.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".