Trends in Top Incomes and their Taxation in OECD Countries
Bibliographic record
Abstract
The shares of top income recipients in total pre-tax income have increased in OECD countries in the past three decades, particularly in most of the English-speaking countries but also in some Nordic (from low levels) and Southern European countries. Today, the richest one percent receives between 7% of all pre-tax income in Denmark and the Netherlands up to almost 20% in the United States. This increase is the result of the top 1% capturing a disproportionate share of overall income growth over the past thirty years: around 20 – 25% in Australia and the United Kingdom, up to 37% in Canada and even 47% in the United States. At the same time, tax reforms in almost all OECD countries reduced top personal income tax rates as well as rates of other taxes affecting the highest income earners. Indeed, while top tax rates were equal to or above 70% in half of the countries in the mid-1970s, this rate has been halved in many countries by 2013.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".