Prosocial perceptions of taxation predict support for taxes
Bibliographic record
Abstract
Many people report disliking taxes despite the fact that tax funds are used to provide essential services for the taxpayer and fellow citizens. In light of past research demonstrating that people are more likely to engage in prosocial action when they recognize how their assistance positively impacts the recipient, we examine whether recognition of how one's tax contributions help other citizens-perceived prosocial taxation-predicts more supportive views of taxation and greater engagement. We conducted three correlational studies using North American samples (N = 902, including a nationally representative sample of over 500 US residents) in which we find that perceived prosocial taxation is associated with greater enjoyment paying taxes, willingness to continue paying taxes, and larger financial contributions in a tax-like payment. Findings hold when controlling for several demographic variables, participants' general prosocial orientation, and the perception that tax dollars are being put to good use. In addition, we examined data from six waves of the World Values Survey (N > 474,000 across 107 countries). We find that people expressing trust in their government and civil service-thereby indicating some confidence that their taxes will be used in prosocial ways-are significantly more likely to state that it is never justifiable to cheat on taxes. Together, these studies offer a new and optimistic perspective on taxation; people may hold more positive views and be more willing to contribute if they believe their contribution benefits others.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".