Economic Inequality and Willingness to Pay for Collective Goods: Theory and Experimental Evidence
Bibliographic record
Abstract
Building on findings in diverse literatures in political science, economics and psychology,we surmise that an important effect of the steep rise in inequality in the UnitedStates may be to undermine popular support for public investment in costly collectivegoods. We argue that economic inequality may influence perceptions of the: fairness ofthe political economy system in the broad sense; fairness of the distributive features of apolicy on either the tax or spend side; and likelihood of successful delivery of promisedpolicy goals. Via any of these mechanisms, citizens who learn or attend to the fact thatthey are on the losing side of rising inequality might be expected to become less willingto pay material costs to pay for collective goods. We evaluate the impact of inequalityon collective goods support, finding support for our theory in a series of experimentalstudies
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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 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".