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Record W2427454334

Economic aspects of decentralized government: structure, functions

2016· article· en· W2427454334 on OpenAlexaboutno aff
Gordon Hughes, Stephen Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationUnitary stateFiscal federalismFederalismEconomicsGovernment (linguistics)Property taxInternational economicsEconomic systemPublic economicsEconomic policyPolitical scienceTax reformMarket economy
DOInot available

Abstract

fetched live from OpenAlex

Tax revolts have erupted against property taxation in the US and the UK. Dissatisfaction has also grown with income taxation in parts of Scandinavia and with various aspects of decentralized fiscal arrangements in Belgium, France, Spain and other European countries. Yet discussions of local tax reform often focus exclusively on the merits and demerits of a single tax instrument. Little attention is paid to the links between the structure and functions of local government. The narrowness of the debate has been further reinforced by a tendency to focus on an artificial contrast between federal and unitary states in analysing the working of decentralized fiscal mechanisms. Among the most heavily studied English-speaking countries, the contrast between federal countries such as the US, Canada and Australia and unitary UK are polar cases which may tell us little about alternative fiscal arrangements. This paper adopts a comparative approach to the analysis of fiscal decentralization.' In Section 2 we examine a wide range of industrial countries and find that there is no dominant model. Rather the diversity of existing arrangements in different countries is striking. This leads us to consider in Sections 3 and 4 how far the theory of fiscal federalism can be applied to European countries. The standard analysis emphasizes

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.009
GPT teacher head0.248
Teacher spread0.238 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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