Embedded liberalism, economic nationalism, or Welfare Chauvinism? Experimental evidence on policy preferences in tough times
Bibliographic record
Abstract
Three policy paradigms have emerged to address globalisation-induced economic vulnerabilities: (1) Embedded Liberalism (EL), (2) Economic Nationalism (EN), and (3) Welfare Chauvinism (WC). We investigate which of these policy paradigms is better equipped to address citizens' concerns in times of economic crises, by assessing which policies citizens prefer in response to negative economic shocks: (1) social expenditure and redistribution via taxation, (2) closing domestic markets to foreign products and people, or (3) social expenditure and redistribution via taxation and strict migration policies. Our key tests involves vignette experiments in the three largest EU economies: France, Germany, and Italy (N = 11, 000). We find that voters are more likely to support politicians who increase welfare spending. Follow-up conjoint experiments, which investigate specific attributes of social expenditure and redistribution, indicate strong support for social investment, progressive taxation, and extending social expenditure to both natives and foreigners. However, we show that right-wing respondents are significantly less likely to favour social expenditure for foreigners compared to centrist and left-wing ones. Our micro-foundational evidence suggests that, while politicians who advocate redistribution in tough times will enjoy a significant political advantage, citizens are ideologically divided as to whether welfare spending should come with an exclusionary component or not.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".