Trade Liberalization and Income Inequality Among Automotive Manufacturing Workers in the Automotive Industry
Bibliographic record
Abstract
Little is known about the impact trade openness has on income inequality in developed nations. Researchers have demonstrated that income inequality and globalization largely benefit underdeveloped nations. However, there is little research that exists on developed countries such as the United States. The purpose of this quantitative study was to compare similarities between trade liberalization and income inequality among manufacturing workers in the automotive industry, specifically in the United States. This study was framed using the Heckscher–Ohlin theory. The study attempted to determine how trade liberalization has affected income inequality among the automotive industry in places such as Detroit, specifically as it has impacted automotive manufacturing workers in the last two decades. Additionally, trade agreements were explored, such as the North American Free Trade Agreement/United States-Mexico-Canada Agreement, which led to increased income inequality among automotive manufacturing workers in the automotive industry. The study used secondary data publicly available and considered national- and state-level census data over a period of time to compose the sample. Correlational methods were used to measure more than two variables to determine if there was any relationship. Results demonstrated that both total import consumption values and NAFTA import consumption values increased over the sample period of 21 years, there was a negative correlation between mean weekly wages and imports for consumption, and increasing NAFTA imports did have a negative impact on the wages of automotive manufacturing workers. These results may effect positive social change and offer solutions that minimize income disparities across a formerly bustling Rust Belt.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".