Global country‐level estimates of associations between adult height and the distribution of income
Bibliographic record
Abstract
OBJECTIVES: This article presents the first estimates of global associations between adult height and the distribution of income, and considers the roles of regional heterogeneity, heterogeneity across low- and high-income countries, and of infant mortality as a potential mediator. METHODS: Linear parametric and semiparametric regressions predicting mean height and sexual dimorphism in height are estimated using data on one cohort born in 1996 with height measured in 2016. Measurement error in income inequality is addressed using an instrumental variables method. RESULTS: Across countries higher income per capita is strongly associated with higher mean height, and higher income inequality is associated with lower mean height after holding mean income constant. These relationships vary with mean income: at low incomes, higher mean income strongly predicts greater height but income inequality has no statistically significant effect, whereas for high-income countries, only higher income inequality predicts lower height, and only in Europe. Sexual dimorphism in height is positively associated with mean income at low incomes, but it is not related to income inequality. CONCLUSIONS: Controlling for income inequality has modest effects on a positive height-income gradient. Greater inequality predicts lower height after holding income per capita constant, suggesting that mean height should be used with caution as a proxy for standard of living in some contexts. The extent to which these associations reflect causality running from economic conditions to height cannot be determined from these results.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".