Immigration and the welfare state: A cross-regional analysis of European welfare attitudes
Bibliographic record
Abstract
A growing body of research connects diversity to anti-welfare attitudes and lower levels of social welfare expenditure, yet most evidence comes from analyses of US states or comparisons of the United States to Europe. Comparative analyses of European nation-states, however, yield little evidence that immigration – measured at the country-level – reduces support for national welfare state programs. This is not surprising, given that research suggests that the impact of diversity occurs at smaller, sub-national geographic units. Therefore, in this article, we test the hypothesis that immigration undermines welfare attitudes by assessing the impact of immigration measured at the regional-level on individual-level support for redistribution, a comprehensive welfare state, and immigrants’ social rights. To do this, we combine data from the European Social Survey with a unique regional dataset compiled from national censuses, Eurostat, and the European Election Database (13 countries, 114 regions, and 23,213 individuals). Utilizing multilevel modeling, we find a negative relationship between regional percent foreign-born and support for redistribution as well as between regional percent foreign-born and support for a comprehensive welfare state. Objective immigration, however, does not increase opposition to immigrants’ social rights (i.e. welfare chauvinism). We discuss the implications of these results and conclude that traditional welfare state attitudes and welfare chauvinism are distinct phenomena that should not be conflated in future research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".