Impact of income position information on perceived tax burden and preference for redistribution: An online survey
Bibliographic record
Abstract
Abstract Many experimental studies have assessed the relationship between the provision of information on relative income and redistribution preferences. However, the influence of information on perceived tax burden, which is considered a subjective cost of redistribution, has not been examined. This study investigates how individuals' relative income positions influence their income redistribution preferences and individual perceptions of the income tax burden. This study was conducted using a customized online survey. First, I asked respondents about their perceived income position in their country, redistribution preference and perceived tax burden. In the follow‐up survey, I provided the treatment group with information on their true income positions based on the same questions as in the first survey. However, for the control group, I did not disclose their true income positions but asked them the same questions. The key findings suggest that, after learning their real income positions: (i) individuals who overestimated their income positions perceived their tax burden as higher, (ii) individuals' redistribution preferences had hardly changed and (iii) reciprocal individuals (who accounted for the largest proportion) perceived their tax burden as lower and were less likely to prefer redistribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".