Does anti-immigration policy lead to protectionism? Evidence from the Smoot–Hawley Tariff Act
Bibliographic record
Abstract
I study whether the imposition of anti-immigration policy affects the subsequent passage of anti-trade legislation, using the protectionist Smoot–Hawley Tariff Act of 1930 as a setting. I show that the immigration quotas resulted in a higher likelihood of voting in favour of Smoot–Hawley and raising tariffs; this effect is driven primarily by increased Republican representation. Districts who voted in an anti-immigration manner in 1924 were subsequently more likely to vote in support of Smoot–Hawley in 1930 if affected by the immigration quotas. In contrast, districts who voted against immigration restrictions were then more likely to oppose Smoot–Hawley if they became heavily affected by immigration quotas. My results therefore suggest that the immigration quotas from the 1921 and 1924 anti-immigration legislation may have increased polarization in congressional voting within the House of Representatives.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".