Does Immigration Reduce the Support for Welfare Spending? A Cautionary Tale on Spatial Panel Data Analysis
Bibliographic record
Abstract
There has been a long-lasting debate over whether increasing ethnic diversity undermines support for social welfare, and whether this conflict thesis applies not only to the United States, but also to European welfare states. In their 2016 ASR article, Schmidt-Catran and Spies analyzed a panel (1994 to 2010) of regional units in Germany and concluded that this thesis also holds for Germany. We argue that their analysis suffers from misspecification: their model specification assumes parallel time trends in welfare support in all German regions. However, time trends strongly differed between Western and Eastern Germany after reunification. In the 1990s, Eastern Germans’ attitudes adapted to a less interventionist Western welfare system (“Goodbye Lenin effect”). When allowing for heterogeneous time trends, we find no evidence that increasing proportions of foreigners undermine welfare support, or that this association is moderated by economic hardship (high unemployment rates). We conclude with some general suggestions regarding the conceptualization of context effects in spatial analyses.
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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 | 0.060 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".