Are Tax Cuts Supporters Self-Interested and/or Partisan? The Case of the Tax Cuts and Jobs Act
Bibliographic record
Abstract
In late 2017, the first unified Republican government in 15 years enacted the Tax Cuts and Jobs Act, which cut taxes for corporations and the wealthy. Why did so many citizens support a policy that primarily benefited people richer than them? The self-interest hypothesis holds that individuals act upon the position they occupy in the income distribution: richer (poorer) taxpayers should favor (oppose) regressive policy. Associations between income and policy preferences are often inconsistent, however, suggesting that many citizens fail to connect their self-interest to taxation. Indeed, political psychologists have shown compellingly that citizens can be guided by partisan considerations not necessarily aligned with their own interests. This article assesses public support for the Tax Cuts and Jobs Act of 2017. Using data from the 2018 Cooperative Congressional Election Study as well as contemporaneous ANES and VOTER surveys to replicate our analyses, we show that self-interest and partisanship both come into play, but that partisanship matters more. Personal financial considerations, while less influential than party identification, are relevant for two groups of individuals: Republicans and the politically unsophisticated.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".