Inequality rose from above, so it shall fall again: Income skewness trends in 16 OECD countries as evidence for a second Kuznets cycle
Bibliographic record
Abstract
Advanced industrial democracies experience increasing inequalities or at least a new trade-off between equality and growth: liberal welfare states opted for growth and accepted rising inequality, while conservative welfare states tried to hold back inequality, thereby accepting lower growth. The rise in inequality is widely interpreted with regard to globalization and technological change. This article contrasts this interpretation with an alternative based on the argumentation of Kuznets’s inverted U-turn which is individually reformulated as some diffusion process of some qualification. While threats such as globalization can be reformulated as a ‘negative diffusion process’, a positive diffusion process is also possible. The two alternative mechanisms are identical regarding inequality measures as the Gini coefficient, but they are differentiated in their trend expectations with regard to income distributions’ skewness. In the globalization model, increasing inequality is accompanied first by a fall and later by a rise in skewness, while the qualification diffusion model shows the opposite sequence: rising to a maximum and falling back later on. Due to their different position in the inequality—growth trade-off, liberal and social democratic welfare states are assumed to be ahead in this evolution, while conservative welfare states lag behind. Based on the Luxembourg Income Study, skewness estimations of logged monetary income distributions form an unbalanced panel with 69 observations from 16 OECD countries. A fixed effects regression for the skewness time trend in conservative welfare states and the trend difference for the two other welfare state groups shows strong support for the positive diffusion model, giving rise to the expectation that inequality can and will decrease again.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".