Fiscal Policy in a Decentralized Space of the Financial System of Ukraine
Bibliographic record
Abstract
The article deals with fiscal policy in the decentralized space of the financial system of Ukraine. The methodology of complex, systematic assessment of fiscal policy in the decentralized space of the financial system of the state is grounded. It is proved that effective methodological approach to assessing fiscal policy in the decentralized space of the financial system of the state is a vector auto regression (VAR), which provides dynamic correlation of time series with simultaneous determination of each exogenous and endogenous variable in the system, in case of fiscal impulses (shocks) in economy. The production-institutional function is used which, when adapting to the relationship between GDP and tax burden with specific statistics, changes the type of trend of tax revenue. A method for evaluating the effectiveness of financing targeted programs for decentralized territory has been developed. The dynamics of direct and indirect taxes to the state and local budgets are analyzed and the fiscal significance of VAT in GDP, the state budget and tax revenues of Ukraine is determined. The amount of tax debt and the state budget deficit has been estimated and the structure of tax benefits in terms of taxes and fees in Ukraine is presented. The projected values of real tax revenues per capita are substantiated and the forecast parameters of the level of subsidization of local budgets of decentralized territories are given.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".