The Effect of U.S. Import Tariff Reductions on Expanded Wage Inequality
Bibliographic record
Abstract
There is still considerable disagreement among researchers whether trade liberalization can explain the rising wage inequality. The wage inequality between skilled workers and unskilled workers expanded in the U.S. manufacturing industries during 1980 through 2000. Meanwhile, NAFTA (North American Free Trade Agreement) has provided us with the opportunity to observe the effect of significant tariff reduction during the same period. The purpose of this paper is to examine the contribution of the reductions of U.S. import tariffs from NAFTA countries Canada and Mexico to that expanding wage inequality during 1980 through 2000. Based on the essential idea of Stolper and Samuelson (1941) and following the method of Haskel and Slaughter (2003), the relationship between product prices and U.S. tariff rates is estimated first and the effect of tariff-induced product prices on wage changes is then estimated. Based on a newly developed industrial classification code, this paper finds significant evidence that U.S. tariff reductions on both Canadian imports and Mexican imports expanded wage inequality between skilled workers and unskilled workers in U.S. manufacturing industries during the period considered. That is, a 1 percent reduction of U.S. tariffs on imports from Canada resulted in a mandated rise in the wage gap by 0.69 percent. A similar result was obtained for Mexican imports, in which a 1 percent reduction of U.S. tariffs on imports from Mexico resulted in a mandated rise in the wage gap by 0.57 percent. These results indicate that U.S. tariff reduction hurts unskilled workers in manufacturing industries, which does not match the result from Haskel and Slaughter (2003), who found no significant evidence that tariff reductions widened wage inequality in the United States.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".