MétaCan
Menu
Back to cohort
Record W1586783657

A Review of the Empirical Evidence on the Effects of Fiscal Decentralization on Economic Efficiency: With Comments on Tax Devolution to Scotland

2008· review· en· W1586783657 on OpenAlexaboutno aff
Paul Hallwood, Ronald MacDonald

Bibliographic record

VenueOpenCommons - UConn (University of Connecticut) · 2008
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationDevolution (biology)EconomicsGovernment (linguistics)Central governmentEconomic policyTax reformEmpirical evidencePublic economicsLocal governmentPolitical scienceMarket economyPublic administrationGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews the existing empirical evidence on tax decentralization ("tax .devolution") from central government to sub-central government. Sub-central government is taken to be levels above the local level: such as within the UK at the level of Scottish government/executive in Edinburgh, and at the provincial government level in Canada or Spain. Our interpretation of the literature is that there is increasing empirical support for the proposition that tax decentralization helps in promoting economic efficiency and economic growth. It is noted that a distinction must be drawn between tax decentralization and spending decentralization. Where tax decentralization follows spending decentralization - as would be the Scottish case, any adverse economic effects emanating from spending decentralization cannot be blamed on tax decentralization. Indeed, as we argue elsewhere, tax decentralization has the potential of correcting any negative economic effects caused by spending decentralization.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.134
GPT teacher head0.282
Teacher spread0.148 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueOpenCommons - UConn (University of Connecticut)Same topicFiscal Policy and Economic GrowthFrench-language works237,207