Immigration and voting for the far right
Bibliographic record
Abstract
Extreme-right-wing (ERW) parties are on the rise in many countries. Moreover, there is an alarmingly high cross-country correlation between the election success of ERW parties and immigration. Motivated by this evidence, we explore one potentially important channel through which immigration may drive support for ERW parties: the presence of immigrants in the voters' neighborhoods. We study the case of the Freedom Party of Austria (FPÖ). Under the leadership of Jörg Haider, this party increased its share of votes from less than 5 percent in the early 1980s to 27 percent by the year 1999. We exploit specific features of the history of immigration into Austria to identify a causal effect of immigration on ERW voting results. We argue that the sudden, large inflow of immigrant workers in the 1960s generated immigrant settlement patterns that provide a plausible source of exogenous variation in the more recent spatial distribution of immigrants. We find that the percentage immigrants in a community is an important causal factor behind support for the extreme right, explaining roughly a quarter of the cross-community variance in votes for the FPÖ. The effect varies across immigrants (e.g., based on their skill levels) as well as across communities (e.g., based on the degree of skill overlap between immigrants and natives), supporting the idea that voters worry about labor market competition. We find more limited indications that compositional amenities play a role for ERW votes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".