Why Americans Voted for Trade Protectionism Again: A Review of Political-Economic Models
Bibliographic record
Abstract
Since 2018, the USA has unilaterally imposed tariffs on imports of goods from abroad, especially from China. President Trump enacted since the inauguration for his second term further increases in tariffs, for imports from Mexico, Canada, China, und announced recently tariffs for imports from Europe. Why did a politician who explicitly advocated foreign trade protectionism become again president of the world’s most economically developed country? Why do US voters value the economic freedom of being able to buy cheaper foreign goods instead of more expensive domestic goods less than the rather dubious promise of making domestic industry great again? The paper answers these questions based on a review of political-economic models published since Trump’s first term as follows: Foreign trade protectionism has once again become politically powerful in the USA because, within the framework of international supply policy, US globalization losers could not be compensated, white lower middle-class men in the old industrial areas of the USA were tormented by status anxiety towards people of different colors and foreigners, about half of the US electorate no longer identified with the nation as a whole but only with their own social class, and after China joined the WTO, voters and party representatives who were in favor of free trade became nationalists and protectionist social conservatives. According to an economically liberal interpretation of natural law, however, free international trade does not contradict social conservatism.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".