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Record W2905643766 · doi:10.21272/mmi.2018.4-33

Optimization of the financial decentralization level as an instrument for the country’s innovative economic development regulation

2018· article· en· W2905643766 on OpenAlexaboutno aff
Tetiana Vasylieva, Yuriy Harust, Nataliya Vinnichenko, Alina Vysochyna

Bibliographic record

VenueMarketing and Management of Innovations · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationEconomic systemEconomicsPublic economicsProcess (computing)Panel dataBusinessEconomic policyMarket economyEconometrics

Abstract

fetched live from OpenAlex

This article generalizes arguments and counter-arguments within the scientific discussion regarding the determination of the optimal decentralization level, which will provide the country’s innovative development, since the key task of decentralization has to be not only to expand the income and expenditure powers of the subnational formations but also to understand the final goal of this process – qualitative transformation of the country’s economic system towards improving its innovativeness and competitiveness. Thus, the decentralization reform has to be the driver of the innovative economic development, which is the expected result of the managerial decision-making freedom increase at the local level, the subnational formations’ financial self-sufficiency increase and more effective spending policy (expansion of the innovative projects financing amounts that will promote the sustainable economic growth). Systematization of the scientific works on the above problems proves that there is no one idea regarding the decentralization impact on the country’s economic and innovative development among scientists. That is why it is urgent to continue the empirical searching in this area, that will enable to take into account the dual nature of consequences regarding the activation of the decentralization processes. The empirical study is carried out through using of the non-linear analysis form of dependence (GLM regression, which enables to identify the linear and nonlinear character of the relationship between variables) based on the panel data, formed for set of 23 states-OECD members (Austria, Belgium, Canada, the Czech Republic, Denmark, Estonia, Finland, France Germany, Greece, Hungary, Italy, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, Great Britain and the USA) during 2002-2015. The expenditure decentralization index, calculated as the ratio between the consolidated expenses amount at the subnational level and state consolidated expenses, expressed in parts of the whole, is chosen as the factorial variable model. The final variable (traditional for the economic growth models) is GDP per capita (dollars the USA). Besides, the set of control variables is added to this regression model (which explain the regularities of the resultative feature change and have a strong relationship with it). The control variables are selected on the basis of correlation analysis. The practical implementation of all stages in this research is performed using the software product Stata 12/SE. The results of the study confirm the non-linear character of dependence (the inverse U-shape) regarding the GDP change per capita on the expenditure decentralization level change, and also the maximum extremum of the function in the point with expenditure decentralization level 1.35. It means that excessive expenses load (above the specified norm) on the local budgets will be accompanied by inhibition of the innovative and economic dynamics, that should be taken into account by the relevant authorized executive bodies in investigation of the concrete measures regarding intergovernmental relationships reforming in direction of their decentralization, and in the formation of the well-balanced economic and innovative policies.

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)0.001
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 score0.944
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.042
GPT teacher head0.240
Teacher spread0.198 · 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".

Quick stats

Citations37
Published2018
Admission routes1
Has abstractyes

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