The Impact of Increasing Income Inequality on Public Support for Redistribution
Bibliographic record
Abstract
While political economists provide clear theoretical foundations that connect public support for redistribution to differences in income inequality, existing empirical evidence provides only modest support for established theory. This paper incorporates a broad range of factors that have been theoretically and empirically linked to public attitudes towards redistribution in order to determine the extent to which shifts in inequality have affected citizens’ redistributive public policy preferences. Using cross-national data from both post-industrial democratic and post-communist successor states and cross-provincial data from the Canadian case, a model is presented that simultaneously tests the effects of both cross-sectional and longitudinal variations in income inequality. The findings indicate that the cross-sectional relationship between inequality and support for redistribution is negative, but the longitudinal relationship between inequality and support for redistribution is positive. These results provide support for a model that unifies both institutionalist and rational choice theoretical perspectives. Countries and provinces with lower levels of income inequality are more likely to be populated by citizens who prefer higher levels of redistribution, whereas increases in income inequality are likely to lead to increases in support for redistribution. Within the context of growing income inequality over the past three decades, these findings help to explain apparent increases in public support for redistribution. The evidence does not, however, indicate that these changes in popular sentiment have been represented in the policies that public officials have implemented for their respective citizenries.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".