THE IMPACT OF THE TAX SYSTEM STRUCTURE ON THE NARROWING OF INCOME DISPARITIES IN OECD COUNTRIES
Bibliographic record
Abstract
Social disparities have a common and consistent character in the vast majority of contemporary countries. The level of income inequality in OECD countries has grown in the past 30 years and is still rising. Taxes and tax systems, aside from social transfers, are fiscal instruments widely used in compensation policy. The aim of the article is to define the optimal structure of tax systems (i.e. the share of different tax categories in tax revenues) in terms of narrowing income disparities. To achieve this aim, scatter diagrams have been used. For the purpose of the article a tentative hypothesis has been formulated that the optimal tax system in terms of narrowing income disparities is characterised by a relatively large share of Personal Income Tax and at the same time a relatively low share of consumption taxes in tax revenues. The detailed analysis is focused on the countries for which the full data is available. The group of countries covers some “old” member states of the European Union (Austria, Belgium, Finland, Greece, Ireland, Italy, Luxembourg and the United Kingdom), the South- -East European countries (the Czech Republic, Estonia, Poland, the Slovak Republic and Slovenia) as well as non-EU countries (Canada and Iceland). These countries represent different levels of socio-economic development and, as a result, the variety of situations concerning the distribution of income.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".