A Behavioral Economic Study of Tax Rate Selection by the Median Voter: Can the Tax Rate Be Influenced by the Name of the Publicly Provided Private Good?
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Bibliographic record
Abstract
This paper presents the results of a behavioral economics study to test if the tax rates submitted to finance the public provision of a private good are influenced by changing the name of the private good. A revealed-preference laboratory decision-making experiment is used to test if participants choose significantly different tax rates to support provision of a private good named as a health care investment compared to an identical good named as a neutral monetary investment. Although some previous studies focusing on both framing and context effects find differences associated with health versus non-health environments, these studies have not involved voting over public provision of a private good. In our experimental environment, participants with different income endowments provide their preferred proportional tax rates for financing public provision of a private good in either a neutral or a health context. The implemented tax rate is the median preferred tax rate, and once the budget is determined, each participant receives the same quantity of the publicly provided private good. In each context, the payoff functions are the same. The only difference between the contexts is the name attached to the publicly provided private good, regardless of the name attached to the publicly provided private good, consuming it imposes no externalities. This controls for the positive externality characteristics of many health care goods, but not for preferences evoked by the merit good character of health care which factor into decisions about the public provision of health care. We find that the theoretical predictions of the median voter model are generally supported by the data. However, the conjecture that the implemented tax rate would be affected by context is not supported by the results.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it