Rich States, Poor States: Assessing the Design and Effect of a U.S. Fiscal Equalization Regime
Bibliographic record
Abstract
Unlike most of the world’s federations – including Australia, Canada, Germany, India, South Africa and numerous others – the United States has no system of federal equalization grants in place to reduce fiscal disparities among its subnational governments. Only at the state level, through policies designed to mitigate property tax disparities among school districts, has equalization been tried in the United States. The federal government has never adopted, nor has it ever seriously considered, an equalization policy for the states. This article represents the first comprehensive scholarly treatment of a possible U.S. fiscal equalization regime. It reviews the most recent data relating to fiscal disparities among the U.S. states and reports the results of simulations showing the overall cost and distributive effects of adopting a Canadian-style equalization regime in the United States. Two alternative policies are examined, one based on the “representative tax system” methodology employed in Canada and a second, known as the “representative revenue system,” that employs a slightly broader measure of state fiscal capacity. Depending on the methodology employed, the cost of a U.S. equalization policy (based on 2005 data) would be in the range of $70-$110 billion per year, or roughly 1 to 1.5 times the annual cost of the current income tax deduction for state and local taxes. Under both methodologies, as well as alternative formulas adjusting for regional cost-of-living differences, the principal beneficiaries would be the so-called “red states” of the South. On a per capita basis, the main winners of a U.S. equalization policy would be Mississippi, Arkansas, and West Virginia. In terms of absolute payments, the largest beneficiary is by far Texas, accounting for approximately 15 percent of total equalization payments. The article considers arguments for and against adoption of an equalization policy and offers some preliminary comments on the politics of fiscal equalization in the U.S. context.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".