Effective tax levels using the Devereux Griffith methodology: Project for the EU Commission TAXUD/2008/CC/099. Report 2009
Bibliographic record
Abstract
This report on behalf of the EU Commission presents estimates of the effective tax rates on investment in the EU member states over the period 1998 to 2009. Furthermore, the EU candidate countries Croatia, FYROM, Turkey as well as Norway, Switzerland, Canada, Japan and the United States are covered over the period 2005 to 2009. The report extents the work completed in project TAXUD/2005/DE/310. The former report covered the period 1998 to 2007. In addition to the update of previous results, report comprehensively includes the analysis of personal taxes on investment and saving at the shareholder level when calculating effective tax rates on domestic investment. The report considers primarily taxes on corporations in each country, but also includes analysis of personal taxes on investment and saving. It also considers both cross-border investment and investment by small and medium sized enterprises (SME).
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".