Correlations of Taxation and Macroeconomic Indicators in the OECD Member Countries from 2014 to the First Year of the Crisis Caused by COVID-19
Bibliographic record
Abstract
This paper explores the characteristics and inter-relationships of tax systems in the OECD countries over the period 2014–2020, i.e., from a relatively consolidated economic period until the end of the first year of the COVID-19 pandemic. A predictable tax system is essential for the proper functioning of the economy. One of our two main research objectives was to develop a composite indicator for taxation, consisting of tax rates and tax administration time. This composite indicator was then tested using multivariate statistical methods. Our second research objective was to explore the correlation between tax rates, tax burden indicators and macroeconomic indicators over the period 2014–2020, focusing on three years, 2014, 2019 and 2020. An important criterion for the choice of the study years was that 2014 was considered the first overall year of recovery from the crisis, 2019 the last year before the COVID-19 pandemic, and 2020 the first year affected by the pandemic. We investigated the significant differences between the composite indicator categories and the tax burden macroeconomic indicators, and examined and tested correlations between the variables under study (tax rates, tax burden and macroeconomic variables). We found that the amount of working time spent on tax administration is decreasing, presumably due to the increasingly digitalised environment, but this trend has been slightly interrupted by the pandemic. Furthermore, we found that countries with more complex tax systems with a high tax burden perform worse on certain macroeconomic indicators, mainly in southern Europe from a geographical perspective; however, these potentially more burdensome, higher-rate tax systems of more developed countries do not put these countries at a competitive disadvantage. This reflects on the fact that these countries rely on the monetarist school rather than the Keynesian school, a fact which was also compared and considered in our paper.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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".