How responsive is Trade Adjustment Assistance?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract How responsive is the US’ Trade Adjustment Assistance (TAA) to the labor dislocation that results from trade integration? Recent findings suggest that the world's most ambitious trade adjustment program barely responds to import shocks, and that the shortfall is made up by disability insurance and early retirement. This holds considerable implications: TAA offers a lens onto the central question of whether developed democracies can effectively redistribute the gains from international economic integration. We take a closer look at these results. Using petition-level data over a 20-year period, we find that TAA is between 1.7 and 3.3 times more responsive than current estimates suggest. Yet the news is not all good. As we show, the responsiveness of TAA has decreased considerably since the 1990s, just as developed democracies started facing increasing pushback against liberalization. This shortfall, in turn, has political consequences: areas where TAA has been least responsive were also more likely to shift toward voting for Trump in the 2016 Presidential election. Our findings speak to the considerable challenge governments face in aiding workers “left behind” by liberalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| 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 it