Bibliographic record
Abstract
This study evaluates the direct causal effects of household wealth on health. We discuss several specific mechanisms that that could relate poverty with worse health and hypothesize that poverty will undermine population health. This hypothesis was tested based on data drawn from a recent cross-country survey in 12 post-Soviet countries and Mongolia using classic regression (OLS) and instrumental variable 2SLS regressions. The results indicate that poverty does indeed lead to worsening health. This negative effect of poverty on health remains unchanged after controlling for a wide range of individual characteristics, healthcare performance indicators, trust in individuals, government, parliament, and political parties, as well as country-level unobserved characteristics. Using an instrumental variable increases our confidence in being able to isolate the effects of poverty on health status and confirms that our results are not due to endogeneity. In addition, the strong negative effect of poverty on health remains robust to the use of a set of country-level aggregated indicators (e.g. GDP and Gini) instead of country dummies, the employment of a subjective self-assessment indicator of poverty instead of an objective one, and an alternative conceptualization of health status as a binomial variable (for bad and very bad health) instead of a continuous one.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".