Buy-in for Buyouts: Attitudes Toward Compensation for Reform
Bibliographic record
Abstract
Political reforms are often impeded by concentrated interest groups who lobby to block change that would benefit the majority. One under-examined policy response is to compensate the recalcitrant group in exchange for agreeing to the reform. We refer to such mass compensation schemes as public buyouts. After laying out the theoretical case for and against buyouts, we design a series of survey experiments to gauge the determinants of public support for buyouts linked to three reforms---phasing out coal energy, simplifying tax filing, and amnesty for dictators. Partisanship appears systematically related to attitudes towards buyouts, as does program design: buyouts find significantly more favour when they target individual workers, rather than companies. Yet the chief objection to buyouts is normative: individuals' "moral aversion" to compensating actors who hold up beneficial reforms dominates other salient concerns, like moral hazard. Our results also highlight a vexing credibility problem: those who support reform also support reneging on the compensation once the reform is passed. Recipients may thus be right to fear policy reversals. Buyouts appear democratically viable as a means of passing beneficial reforms that have been blocked for decades---yet their design proves decisive.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".