A Reappraisal of Trade Deficit and Income Inequality in the United States: 1985-2007
Bibliographic record
Abstract
The recent resurgence of income inequality in the United States has spawned a wide-ranging discussion to its causes, which has often focused on America's historically high trade deficit in the past two decades. Our paper revisits this issue by investigating the latest trends in the U.S. income disparity from 1985 to 2007, and systematically examining the factors that might have influenced the income inequality. To better understand income disparity, three different measures are employed: Gini, Theil and Atkinson indices. Results show that, only in the cases of Gini and Atkinson, international trade explains a part of income inequality, but it surely cannot be the whole story. Other factors, such as the net migration rate, the changing role of women, and the sectoral distribution of employment also play important roles in accounting for America's income inequality.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".