The Political Impact of Immigration: Evidence from the United States
Bibliographic record
Abstract
In this paper we study the impact of immigration to the United States on the vote for the Republican Party by analyzing county-level data on election outcomes between 1990 and 2010. Our main contribution is to separate the effect of high-skilled and low-skilled immigrants, by exploiting the different geography and timing of the inflows of these two groups of immigrants. We find that an increase in the first type of immigrants decreases the share of the Republican vote, while an inflow of the second type increases it. These effects are mainly due to the local impact of immigrants on votes of U.S. citizens and they seem independent of the country of origin of immigrants. We also find that the pro-Republican impact of low-skilled immigrants is stronger in low-skilled and non-urban counties. This is consistent with citizens' political preferences shifting towards the Republican Party in places where low-skilled immigrants are more likely to be perceived as competition in the labor market and for public resources.
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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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".