Can market structure explain cross-country differences in health?
Bibliographic record
Abstract
There is a well documented health disparity between several European countries and the United States. This health gap remains even after controlling for socioeconomic status and risk factors. At the same time, we note that the U.S. market structure is characterized by significantly more large corporations and "super-sized" retail outlets than Europe. Because big business is hierarchical in nature and has been reported to engender urban sprawl, inferior work environments, and loss of social capital, all identified as correlates of poor health, we suggest that differences in market structure may help account for some of the unexplained differences in health across Europe and North America. Using national level data, this study explores the relationship between market structure and health. We investigate whether individuals who live in countries with proportionately more small business are healthier than those who do not. We use two measures of national health: life expectancy at birth, and age-standardized estimates of diabetes rates. Results from ordinary least squares regressions suggest that, there is a large and statistically significant association between market structure (the ratio of small to total businesses) and health, even after controlling income, public percent of health expenditure, and obesity rates. This association is robust to additional controls such as insufficient physical activity, smoking, alcohol disease, and air pollution.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".