Increasingly polarized? Inequality, prosperity, and perceived socioeconomic conflict in advanced economies (1987–2019)
Bibliographic record
Abstract
Abstract Previous studies suggest that in more unequal societies, people perceive stronger antagonistic relations between opposing socioeconomic groups. Given that income inequality and social polarization have both been on the rise in most Western democracies, we expand on this body of work by investigating whether changes in macroeconomic fundamentals have triggered changes in perceived socioeconomic conflict. To assess this proposition, we fit hybrid multilevel models using time-series cross-sectional data from 26 countries spanning over three decades (1987–2019). Our evidence shows that rising economic prosperity does not reduce the level of perceived conflict once income inequality is accounted for. In contrast, growing inequality is robustly associated with increased salience of perceived socioeconomic conflict. Findings indicate a sociotropic within effect of income inequality, net of changes in economic prosperity and accounting for contextual confounders and individual-level compositional effects. Our results further suggest that income inequality exacerbates class-based polarization in conflict perceptions: it increases perceived conflict across all groups—except the upper-middle class. Alternative model specifications and extensive robustness checks lend additional support to our argument that the distribution of economic resources has a direct impact on the salience of socioeconomic conflict perceptions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".