Public support for social security in 66 countries: Prosperity, inequality, and household income as interactive causes
Bibliographic record
Abstract
It is widely accepted that support for government intervention is highest among people in lower socioeconomic positions, during economic recessions and in less prosperous countries. However, the relationship between income inequality and attitudes toward government intervention is less clear. We contribute new insights to both questions by exploring how subjective household income, economic prosperity, and income inequality interact to influence attitudes. Using mixed-effects and country fixed-effects models fitted to data from 66 countries, we demonstrate that income inequality has a strong positive impact on attitudes toward government intervention in rich countries but no discernable effect in poor countries. Concomitantly, the impact of economic prosperity differs by level of inequality. It has little effect when income inequality is relatively low, a weakening effect as inequality rises, and no apparent effect when inequality is high. Consistent with these findings, the effect of subjective household income on attitudes toward government intervention is strongest in countries that are simultaneously very prosperous and highly unequal. Taken together, these findings suggest that if inequality continues to rise, especially in rich countries, public demand for social spending will eventually increase as well.
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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 | 0.006 |
| 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 | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".