Trade Competition and Worker Compensation: Why Do Some Receive More than Others?
Bibliographic record
Abstract
Abstract Dealing with the distributional consequences of trade liberalization has become one of the key challenges facing developed democracies. Governments have created compensation programs to ease labor market adjustment, but these resources tend to be distributed highly unevenly. What accounts for the variation? Looking at the largest trade adjustment program in existence, the US’ Trade Adjustment Assistance (TAA), we argue that petitions for compensation are largely driven by legislative attitudes. When legislators express negative views of TAA, individuals in their districts become less likely to petition for, and receive, compensation. This effect is especially pronounced in Republican districts. An underprovision of TAA, in turn, renders individuals more likely to demand other forms of government support, like in-kind medical benefits. We use roll-call votes, bill sponsorships, and floor speeches to measure elite attitudes, and we proxy for the demand for trade adjustment using economic shocks from Chinese import competition. In sum, we show how the individual beliefs of political elites can be self-fulfilling.
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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 | 0.000 | 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".