Fiscal Policy in a Decentralized Space of the Financial System of Ukraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it