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Record W2791490751 · doi:10.22158/jepf.v4n2p143

Do Financial Arrangement of the International Monetary Fund Has Impact on the Reduction of Government Spending, Evidence through Political Fiscal Cycles, the Case of Croatia

2018· article· en· W2791490751 on OpenAlexaboutno aff
Ivana Rukavina

Bibliographic record

VenueJournal of Economics and Public Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment spendingEconomicsObligationGovernment (linguistics)PoliticsQuarter (Canadian coin)LimitingMonetary economicsFiscal policyEconomic policyPolitical scienceMarket economyWelfareLaw

Abstract

fetched live from OpenAlex

This paper examines the regulatory role of the IMF on government spending through political fiscal cycles. According to theoretical views, the fiscal policy in the pre-election period reflects an increase in government spending or budget expenditures; in postelection period, it takes a restrictive course by reducing spending. In the presence of a contractual agreement with the IMF, the theory points to limiting and reducing the magnitude of government spending in the pre-election period. According to the research results in Croatia, there is an increase in government spending in the election quarter, and its decrease in the quarter after the election. On the other hand, the contractual arrangements with the IMF show significant reductions in government spending. When a country is under a contractual obligation with the IMF, it reduced the government spending in the pre-election period in relation to the period when it is not under a contractual obligation.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.091
GPT teacher head0.276
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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