Are the Rich Better Off than They Were Four Years Ago? Class-Biased Economic Voting in Comparative Perspective
Bibliographic record
Abstract
A growing literature has inquired into the political consequences of rising income inequality in the United States. Scholars have identified a number of mechanisms through which American democracy has become more responsive to the interests of the very rich than to the those of lower- and middle-class citizens. Among the patterns of unequal influence that analysts have observed is a strong bias in economic voting'' identified by Bartels (2008). Specifically, Bartels finds that lower- and middle-class voters are far more responsive to election-year income growth among the richest Americans than they are to overall economic growth or to growth within their own income brackets. In this paper, we examine this troubling feature of U.S. electoral politics in comparative perspective, asking (i.) how widespread class biases in economic voting are in advanced democracies and (ii.) what generates them. Analyzing electoral behavior in three OECD countries (Canada, Sweden, and the United Kingdom), we find clear evidence of class-biased economic voting with substantively important electoral consequences outside the United States. Most surprisingly, we find that the class bias is not limited to national contexts characterized by market-liberal norms and institutions. We then propose two possible mechanisms that might contribute to the class bias --- an informational mechanism and an ideological mechanism --- and test for their operation in the United States and Sweden. The results are highly consistent with the operation of both mechanisms in the United States and weakly suggestive of an informational effect in Sweden.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".