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Record W2197582997 · doi:10.1787/9789264246966-3-en

The role of intergovernmental fiscal institutions: The case of tax sharing

2015· book-chapter· en· W2197582997 on OpenAlexaboutno aff
Junghun Kim

Bibliographic record

VenueOECD fiscal federalism studies · 2015
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationDebtEconomicsPoliticsLocal governmentTax reformPublic economicsEconomic policyBusinessEconomic systemPolitical scienceMacroeconomicsMarket economyPublic administration

Abstract

fetched live from OpenAlex

In the literature on fiscal decentralisation – both the first generation and second generation theories – a critical assumption made is that local governments have taxing power over local taxes. Another assumption is that a clear distinction exists between local taxes and intergovernmental grants. Tax sharing, which is widely adopted in the world except in a few countries such as the United States and Canada, makes such assumptions invalid. Tax sharing links the budgets of the central and local governments in a complicated way and creates conflicts of interest among different levels of government. It also may lead to an inefficient tax mix and higher national debt. Compared with the cases where the assignment of tax and expenditure responsibilities are clear cut, understanding the interrelationship between tax sharing, fiscal institutions, and the political system is critical for the success of fiscal decentralisation.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.016
Scholarly communication0.0080.010
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.071
GPT teacher head0.281
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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