Health and wealth: Short panel Granger causality tests for developing countries
Bibliographic record
Abstract
The world has experienced impressive improvements in wealth and health, with, for instance, the world's real GDP per capita having increased by 180% from 1970 to 2007 accompanied by a 50% decline in infant mortality rate. Healthier and wealthier. Pl Are health gains arising from wealth growth? Or, has a healthier population enabled substantial growth in wealth? We contribute to understanding the dynamic links between wealth and health by examining for causal, rather than associative, links between health (as measured by infant mortality rate) and wealth (as measured by GDP per capita) for a panel of 58 developing countries using quinquennial data covering the period 1960–2005. Estimating as a panel allows us to account for unobserved heterogeneity, as well as permitting heterogeneous causal effects. We test for panel and country-specific noncausality, and we explore robustness of outcomes to level of economic development (as measured by national income), whether we account for bias in least squares estimators, and to our heterogeneity assumption on the causal coefficients. Overall, our panel tests detect bidirectional links between wealth and health, compatible with other research. However, our country-specific work suggests that the panel results arise from the dominance of a few countries, as there is evidence of noncausality between health and wealth for a majority of countries. These findings contrast with earlier research, and likely arise from different metrics being used to measure the health of a nation. Our work highlights the usefulness of panel causality tests accompanied by unit specific analysis and the importance of examining different metrics for health.
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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 | 0.028 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".