Wealth and preferences for redistribution: The effects of financial assets and home equity in 31 countries
Bibliographic record
Abstract
How does wealth affect preferences for redistribution? In general, social scientists have largely neglected to study the social effects of wealth. This neglect was partially due to a dearth of data on household wealth and social outcomes, and also to greater scholarly interest in how wealth has been accumulated rather than the social effects of wealth. While we would expect household wealth to be an important component of attitudes toward inequality and social welfare policies, research in this area is scarce. In this study, the relationship between wealth and preferences for redistribution is examined in cross-national global and comparative perspective using data on 31 countries from the 2009 wave of the International Social Survey Programme (ISSP), the first wave of that study to include measures of wealth. The findings presented compare the effects of two types of wealth—financial assets and home equity—and demonstrate that there are differences in effects by asset type and by redistributive policy in question. Financial wealth is more closely associated with attitudes about income equality, while home equity is more closely associated with attitudes about unemployment benefits. Moreover, while the upper categories of financial wealth have the largest negative effects on support for income equality, it is the middle categories of home equity that are most strongly associated with opposition to unemployment benefits. Effects also differ by country, but not in patterns that theories of comparative welfare states nor political economy would adequately explain.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".