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Record W3111242112 · doi:10.5539/ijef.v13n1p33

Does the Quality of Fiscal Institutions Matter for Fiscal Performance? A Panel Data Analysis of European Countries

2020· article· en· W3111242112 on OpenAlexvenueno aff
Dimitra Mitsi

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsFiscal unionFiscal policyEconomicsPanel dataFiscal imbalanceEstimationFiscal federalismOrder (exchange)Fiscal sustainabilityDebtCorporate governanceStability and Growth PactMacroeconomicsGovernment (linguistics)Balance (ability)European unionEconomic policyFinanceMember statesEconometrics

Abstract

fetched live from OpenAlex

The last decade, the number of fiscal frameworks such as national fiscal rules and independent fiscal councils have increased, significantly as a consequence of fiscal indiscipline in many European Countries. In the wake of economic crisis in 2007, fiscal laxity and unsustainable public finances made the European Union to strengthen its fiscal policy in many ways in order to create an economic environment of macroeconomic stability and sustainable growth. This paper investigates the role of fiscal frameworks (fiscal rules and fiscal councils) on fiscal performance as well as the impact of other types of institutions, namely Worldwide Governance Indicators on primary balance. The empirical analysis builds on a reaction function proposed by Bohn (1998) while the estimation method builds on a fixed effect panel data estimation and a dynamic panel data estimation of Arellano-Bover and Blundell-Bond. Our main results provide that political stability, government effectiveness, fiscal rules and fiscal councils play an important role for improving fiscal performance. However, the effect of fiscal institutions on primary balance changes among different types of fiscal rules (debt rules, expenditure rules and budget balanced rules) and independent fiscal councils or fiscal councils that have access to information, respectively.

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 score0.809
Threshold uncertainty score0.267

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 science0.0010.000
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 head0.110
GPT teacher head0.295
Teacher spread0.185 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

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