Impact of Municipal Financial Resilience on Sustainable Economic Development: Case of Ukraine
Bibliographic record
Abstract
Reform of fiscal decentralization is one of the most effective modern tools to improve the efficiency of public and local finances, as it involves the transfer of some powers from central to sub-central governments according to the principle of subsidiarity. Therefore, one of the top priorities in public finance development nowadays is to ensure municipal financial resilience (financial capacity). In the paper it is proposed to measure municipal financial resilience (financial capacity) as an integral indicator of fifth parameters (ratio of revenue / cost / tax revenue of local budgets (excluding transfers) to revenue / cost / tax revenue of the consolidated budget, "1" reduced by the ratio of net intergovernmental transfers to own revenue of local budgets; ratio of own revenue of local budgets to their own expenditure) aggregated based on Fishburn formula. It is proposed to chose as a proxies of municipal sustainable economic development such indicators as consumer price index, current account balance, volume of credits to the private sector, net foreign direct investment, GDP growth, GDP per capita, gross capital formation, business density, employment ratio, R&D expenditures, trade turnover. Testing the hypothesis on relationship between municipal financial resilience (financial capacity) and its economic development is realized on data for Ukraine for the period 2008-2021. Method of modellingregression analysis in Stata software. Based on the empirical research results it might be concluded that there are national peculiarities of relationship between municipal financial resilience (financial capacity) and its sustainable economic development in Ukraine, which might be considered in terms of fiscal decentralization reform implementation, counteraction to negative consequence of coronavirus disease pandemic and municipality post-pandemic recovery strategy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".