Citizens' Willingness to Support New Taxes for COVID‐19 Measures and the Role of Trust
Bibliographic record
Abstract
The COVID‐19 public health pandemic has seen governments spend trillions of dollars to limit the spread of the COVID‐19 virus as well as to soften the economic blow from the shutting down of national economies. Subsequent budget shortfalls raise the question of how governments will pay for the direct and indirect costs associated with the COVID‐19 pandemic. In this article, we study the public's willingness to contribute through paying a new tax, with a focus on Canada. We find that both generalized social and political trust are associated with a greater willingness to support a COVID‐related tax and that generalized social trust, in particular, attenuates the negative effect of an experimentally manipulated, specified level of tax burden on policy support. These findings entail important implications for the public opinion and tax policies literature, as well as for policy makers. Related Articles Gainous, Jason, Stephen C. Craig, and Michael D. Martinez. 2008. “Social Welfare Attitudes and Ambivalence about the Role of Government.” Politics & Policy 36 (6): 972‐1004. https://doi.org/10.1111/j.1747‐1346.2008.00147 Shock, David R. 2013. “The Significance of Opposition Entrepreneurs on Local Sales Tax Referendum Outcomes.” Politics & Policy 41 (4): 588‐614. https://doi.org/10.1111/polp.12028 Wagle, Udaya R. 2013. “The Heterogeneity Politics of the Welfare State: Changing Population Heterogeneity and Welfare State Policies in High‐Income OECD Countries, 1980‐2005.” Politics & Policy 41 (6): 947‐984. https://doi.org/10.1111/polp.12053
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".