State and Local Government Finance: Why It Matters
Bibliographic record
Abstract
Abstract This article lays out the economists' view of why state and local government matters. To establish the economic framework, the article systematically works through the seminal contributions of Paul Samuelson's theoretical arguments of the importance of a public-sector role for efficiency in resource allocation; Charles Tiebout's thinking on the difference between national and local public goods; Richard Musgrave's classification of the fiscal “branches” of a decentralized federalist system; and Wallace Oates's Decentralization Theorem. It is from this platform that the article proceeds to address three fundamental fiscal policy issues for a multigovernmental society (e.g., US fiscal federalism): the sorting out of expenditure responsibilities among different types of governments (“expenditure assignment”); the question of which type of government should use which type of revenue (“revenue assignment”), and what happens when, for many state and local governments, the costs of the allocation of expenditure responsibilities are greater than that which can be financed from their “own” state/local revenues (the role of “intergovernmental transfers”).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".