Local Taxes and Local Expenditures: Strengthening the Wicksellian Connection
Bibliographic record
Abstract
One way to design a local tax system is to determine the desired size and nature of local expenditures and then put in place a tax (and transfer) system providing incentives that lead local decision-makers to choose to finance that expenditure package. In practice, however, there are seldom clear links between local taxes and local expenditures and accountability at the local level is often both confused and confusing. This paper discusses how the Wicksellian connection between local services and revenues might be strengthened by changing the ‘package’ of local services, by altering the ‘package’ of local revenues, and by altering the way in which the two packages are tied together, although it considers in depth only the second of these points. The potential importance of this issue is illustrated by a brief review of an on-going discussion about how to finance the regional public transit system in Toronto, Canada. We conclude that, although advances in technology may make an economically more rational local finance system achievable, it is unclear that people (or politicians) are willing to face the economic realities of local finance.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".