Fiscal and Tax Policy Response to New Challenges and Budget Performance in the First Quarter of 2023
Bibliographic record
Abstract
The beginning of 2023 allows us to draw the first results of the Russian economy response to the impact of external and internal shocks in 2022. The structure of the Russian economy remains relatively stable, many catastrophic forecasts have not materialized. However, the sanctions that have come into force and the rapid measures of the anti-crisis policy pose a threat to fiscal stability. The main burden falls on the federal budget, but in the medium term, the depletion of federal reserves will also affect the state of the regional budgets. The paper analyzes the dynamics of indicators of the consolidated budget of the Russian Federation and the budgets of state off-budget funds, as well as separately – the federal budget. In conditions of limited access to data on budget execution, some conclusions are hypotheses and assumptions. In addition, the statistics were distorted by the introduction of the unified tax payment mechanism. The paper distinguishes between the mechanism of the impact of temporary and permanent exogenous shocks. The response of fiscal policy is considered in the context of the division of measures into temporary and structural changes, and appropriate recommendations 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.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".