Theory and practice of the use of fiscal sustainability indicators
Bibliographic record
Abstract
The author notes that, in their studies on fiscal policy, Ukrainian scientists are now beginning to use fiscal sustainability indicators. However, some researchers allow themselves to freely treat the formulas of these indicators (by discarding individual components or introducing new variables), which calls into question the results of calculations and conclusions drawn from them. The article reveals the origin of the formulas for fiscal sustainability indicators. The author presents the derivation of a one-period budget constraint in relative variables, a budget constraint for a finite period of time and a budget constraint for an infinite period of time (intertemporal budget constraint). The content of the no-Ponzi game condition is revealed. The author shows how the formula for calculating the indicator of medium-term fiscal sustainability S1 is derived from a budget constraint for a finite period of time, and the formula for calculating the indicator of long-term fiscal sustainability S2 – from an intertemporal budget constraint. The meaning of these indicators and their components is explained in accordance with the interpretations of the Europe-an Commission. Identified parameters whose values affect the values of fiscal sustainability indicators. It is shown which target values of public debt are selected by the countries that compile reports on fiscal sustainability. The author provides the items of revenues and expenses, whose impact on fiscal sustainability is investigated in different countries and gives a concept of basic and alternative calculation scenarios. The example of Canada and the European Union shows the values of the parameters of the medium-term fiscal sustainability indicator S1 in the baseline scenario and what values these parameters may have in the alternative scenarios. It is noted that, with the help of fiscal sustainability indicators, it is possible to assess the sustainability of both the general government budget and budgets of lower levels, as well as budgets of state social insurance funds. Presented the classification of the risks to fiscal sustainability in the medium and long-term periods, which is used by the European Commission. On the example of Ukraine, the author shows the sensitivity of medium-term fiscal sustainability indicator to changes in the values of its parameters. The process of decision-making on fiscal policy with the help of fiscal sustainability indicators S1 and S2 is illustrated. Highlighted the points that need to be considered when calculating the indicators' values.
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 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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".