Do Citizens Want Something for Nothing? Mass Attitudes and the Federal Budget
Bibliographic record
Abstract
Prior research has shown that citizens demand more public spending for government services but are unwilling to support tax increases to cover the increased costs. Yet the basis for this observed inconsistency in fiscal preferences remains unclear. Some scholars propose that the desire “something for nothing” could be the result of citizens lacking knowledge about taxation, public spending, and the inherent trade‐offs between the two sides of the ledger, while others suggest that citizens sincerely want the benefits without paying the costs. This article contributes to the literature by studying citizens' fiscal priorities under the condition of near‐complete information about the U.S. federal budget. We used an online budget simulation—based on the actual U.S. federal budget—to provide respondents with information about the structure of the federal budget and gather data on their preferred changes in the budget. Further, we conducted a population‐based survey and found that respondents express preferences that are more nuanced—and more coherent—than previously suggested. Related Articles Ariely, Gal. 2011. “Why People (Dis)Like the Public Service: Citizen Perception of the Public Service and the NPM Doctrine.” Politics & Policy 39 (6): 997‐1019. https://doi.org/10.1111/j.1747‐1346.2011.00329.x Fisher, Patrick. 2008. “The Partisan Foundations of Balanced Budget Politics.” Politics & Policy 33 (4): 617‐641. https://doi.org/10.1111/j.1747‐1346.2005.tb00216.x Smith, Robert W. 2008. “The Courage to Tax: Rational Choice versus State Budgetary Politics in the South.” Politics & Policy 32 (4): 636‐659. https://doi.org/10.1111/j.1747‐1346.2004.tb00199.x
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| 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".