Why does the amount of income redistribution differ between the United States and Europe?: The Janus face of Switzerland
Bibliographic record
Abstract
In this paper, the amount of income redistribution in the United States, the European Union, and Switzerland is compared and empirically related to economic, political, and behavioral determinants elaborated in the literature. Lying in between the two poles, Switzerland provides unique evidence about the relative merits of competing hypotheses. It tips the balance against the economic explanation, which predicts more rather than less income redistribution in the United States compared to the EU. It only weakly supports the political model linking proportional representation and multiparty structure (which also characterize Switzerland) to redistribution; yet the Swiss share of transfers in the GDP is low. Behavioral explanations receive a good deal of support from the case of Switzerland, a country that shares with the United States the belief that hard work rather than luck, birth, connections, and corruption determine wealth. In this way, the Janus face of Switzerland may help to explain the difference in the amount of U.S. and EU income redistribution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".