Effective Tax Levels using the Devereux/Griffith Methodology : Project for the EU Commission TAXUD/2008/CC/099, Final Report 2012
Bibliographic record
Abstract
[Introduction] This 2016 report of the project TAXUD/2013/CC/120 presents estimates of the effective tax rates on investment in the EU member states, FYROM and Turkey as well as Norway, Switzerland, Canada, Japan and the United States. The work presented in this report updates for the year 2016 the analyses of the previous projects within the former Framework Contract TAXUD/2008/CC/099 and the current Framework Contract TAXUD/2013/CC/120. Following the methodology used in previous work, we apply the Devereux and Griffith framework to compute effective tax levels. The report considers primarily taxes on corporations in each country, but also includes analyses of personal taxes on investment and saving. It also considers both cross-border investment and investment by small and medium sized enterprises (SME). Background information to the applied model can be found in Devereux and Griffith (1999, 2003), Schreiber et al. (2002) and European Commission (2008, p. 3-54). In addition, the European Commission has recently published studies on the specific impact of interest and inflation rates, tax planning and the debt/equity bias on forward-looking effective tax rates. This report is organized as follows. Section A introduces the tax parameters for the period 1998 - 2016 covered by this report. These tax parameters form the basis of the computations of effective tax rates. Section B provides worked examples for several countries for a better understanding of the model. Section C then provides detailed results for domestic investment in all countries covered in this report. In addition to results focusing on the corporate level, this report comprehensively includes the analysis of personal taxes on investment and saving at the shareholder level for three different types of shareholders when calculating effective tax rates on domestic investment. Section D presents estimates for effective tax burdens of cross-border investment if all countries were either locations of investment or locations of the investor. Finally, Section E provides effective tax burdens of SMEs in selected countries. Please note that all results presented in this report refer to the legal situation as of 1 July 2016.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".