Rethinking Tax Systems: How Heterogeneous Tax Mix Shapes Income Inequality in European OECD Economies
Bibliographic record
Abstract
Divergences in tax policies are evident among European OECD economies, due to varying priorities of efficiency vs. equity, influenced by the forms of direct vs. indirect taxation. The special interest of this paper is to identify how different tax forms (direct—corporate and personal income taxes (CIT, PIT); and indirect—value added tax (VAT)) affect inequality in European OECD economies in the period 2003–2020. Using heterogeneous non-stationary panel models and the (Pooled) Mean Group (PMG/MG) methods of estimation, a long-run negative relationship between direct tax forms (CIT, PIT) and the Gini coefficient was discovered, meaning that utilizing progressive direct tax forms resulted in more equity. The error-correction terms are heterogeneous, showing that developed economies decrease income inequality by using direct taxes more efficiently than emerging European OECD economies. The short-run statistically significant relationships between VAT and the Gini coefficient are discovered, meaning that certain European OECD economies effectively use VAT revenue to achieve greater equity in society. This study demonstrates that the use of indirect tax forms may be beneficial in terms of collecting more tax revenues, and that using them for redistributive programs can reduce inequality while maintaining economic efficiency.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".