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Record W3008779873 · doi:10.35774/jee2019.04.454

THE EVALUATION OF INDICES OF PUBLIC FINANCE DISTRIBUTION ON CENTRAL AND LOCAL LEVELS FOR EU COUNTRIES

2019· article· en· W3008779873 on OpenAlexaboutno aff
Nadiya Dubrovina, Erika Neubauerová, Michal Fabuš, Оксана Тулай

Bibliographic record

VenueJOURNAL OF EUROPEAN ECONOMY · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationFiscal federalismPublic financeDistribution (mathematics)Local governmentFederalismEconomicsFinanceState (computer science)Public economicsCentral governmentEconomic systemBusinessMacroeconomicsPublic administrationPolitical sciencePoliticsMarket economyComputer science

Abstract

fetched live from OpenAlex

Nowadays the problems of optimal taxation and tax distribution are closely connected with growth of decentralization and democracy in the world, especially in EU and other countries, such as USA, Canada, etc. Many economists and analysts studied the problems of co-operation between central and local administration in the realization of the state programmes and efficiency of public services on different levels (central, regional or local). Due to the theoretical and empirical evidence it was clear that some functions of public administration on the central level are not carried out efficiently and some competences of public administration can be transferred to the local levels. Thus, the problems of competences and public finance distribution between central level (state) and other levels (regional or local) are the main aspects to discuss in the theories of fiscal federalism and fiscal decentralization. In the theory of fiscal federalism the problem of taxes allocation between different levels of government is considered to be one of important tools for realization of stabilization and allocation functions of public finance. It should be noted that one of the theoretical and research problems is how to evaluate the measure for financial decentralization. There are different approaches to this problem in modern research such as qualitative (for example, grouping countries based on some qualitative criteria or experts’ evaluation of reforms focuse on financial decentralization) or quantitative ( a set of different ratios, geometric mean of different indicators, aggregated index, etc.). The purpose of the research is to obtain the aggregated indicators for evaluation of public finance distribution on central and local levels and to analyze the balance between these indicators for EU countries. For our research we used the idea of aggregated indicator to evaluate the measure of dependence upon central government expenditure and measure of local autonomy development. Due to the methodology for calculation of aggregated index proposed by Helwig the value of the aggregated index is varied from 0 to 1, or from minimal possible level to maximum possible level of the generalized characteristics described by the original set of indicators. Thus, if measure of public finance dependence upon central government Int_C is more closed to 1, then central government plays a greater role in expenditure for public finance. If measure of local autonomy Int_L is more closed to 1, then local government has more opportunities in their revenue and expenditure. It should be noted that for the balanced position of the country on the plot the values of the Int_C and Int_L should be equal to or lie on 45 degree line. If the bundles lie upper 45 degree line it means that level of local autonomy is more expressed, and vice versa, if the bundles lie lower 45 degree line it means that level of local autonomy is less expressed. The aggregated indices Int_C and Int_L were calculated for EU countries during the period of 2002–2017 and it makes possible to evaluate the features of national fiscal policy in balance between distribution of funds for central and local levels.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.071
GPT teacher head0.250
Teacher spread0.179 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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