The Quality of Tax Administration, Macroeconomic Stability and Economic Growth: Assessment and Interaction
Bibliographic record
Abstract
The article deals with investigating the link between the quality of tax administration, macroeconomic stability and economic growth.The paper identifies the benefits and risks of the shadow operations for macroeconomic stability of the country.Based on the analysis of indicators of the effectiveness of tax policy implementation, an approach to assessing the quality of tax administration of the country was proposed.Based on empirical calculations, a conclusion about the low quality of tax policy in the country was made.The study of the relationship between the quality of tax administration and macroeconomic stability is based on the modified least squares method.The EU countries and Ukraine are identified as the statistical base of the study and the assessment period is 2005-2019.The results of modelling on the example of Ukraine and EU countries proved the relationship between the quality of tax administration of the country and level of its macroeconomic stability and shadow economy.All indices are statistically significant at the level of 1% and 5% and 10% respectively.This research let the authors conclude that it is necessary to take into account the quality of tax administration in forecasting the level of shadow economy and economic growth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".