Tax Rates and Tax Revenues in the Context of Tax Competitiveness
Bibliographic record
Abstract
The diverse tax burdens and economic situations of EU member states are causing investors to relocate their investments to countries that offer better tax conditions and a better economic environment. The total amount of corporate tax revenue is therefore influenced by tax, macroeconomic and other indicators. This paper assesses the importance of tax revenues and tax rates in the context of tax competitiveness in EU states. The aim of the paper is to determine the impact of selected indicators on corporate tax revenues in EU states for the period 2004 to 2019. The source data were drawn from the databases of the European Commission (2021) and The World Bank (2021). The set goal was complemented by an analysis of tax rates and subsequent comparison with corporate tax revenues. Multiple regression analysis was performed to achieve the goal. Two econometric models were compiled that followed the same variables, with the EU13 model dealing with the new member states and the EU15 model dealing with the old EU member states. The results showed that the variables statutory and average effective tax rate do not have a decisive influence on corporate tax revenues in either model. In the new states, the unemployment rate has the most statistically significant effect, while in the old countries GDP has the biggest effect. The result of this work is that there are differences between the new and old member states at different levels, which was ultimately reflected in the different impact of tax and macroeconomic indicators on corporate tax revenues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".