Inequality in the EU in the First Quarter of the 21st Century: Unusual Trends
Bibliographic record
Abstract
The article shows the unevenness of economic growth in the European Union in 2000–2023, broken down into three country groups: North, South, and East. The post-socialist East has significantly closed the gap in terms of GDP per capita at PPP (in constant 2021 prices) with the South group, creating an effect of overall convergence in per capita GDP levels in the EU. At the same time, the noticeable growth in the East group countries was accompanied by a decline in population. The gap between the North group and the rest remained, as did significant variation in country levels, although it decreased compared to the time of the EU’s large-scale expansion. At the same time, all groups lagged behind the US during the period under review. The article also examines changes in the income levels of social groups within countries. It reveals a strengthening of the position of the wealthy 10th decile of the population. At the same time, differences between country groups in the scale of tax redistribution of income have a significant impact on it: it is greatest in the developed North, less in the South, and even less in the East. The specifics of redistribution, along with differences in income inequality before taxation, have led to a noticeable convergence of the levels of the wealthy 10th decile of Eastern countries with those of the same group of Southern countries. The parameters of convergence among EU countries seem surprising at first glance: the wealthy strata are converging to a greater extent than the rest of the population or the countries as a whole.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".