Structural Transformation under Trade Imbalances: The Case of the Postwar U.S.
Bibliographic record
Abstract
A striking feature of the structural change literature is that, even though the U.S. economy is often used as a benchmark for calibration, the traditional models cannot account for the steep decline in manufacturing and rise in services in the U.S. since the late 1970s (Buera and Kaboski, 2009). In order to solve this puzzle, this paper develops a three-sector model to evaluate various factors that could have contributed to the structural transformation process from 1950 to 2005. The results show that, in addition to traditional explanations, such as non-homothetic preference and sector-biased productivity progress, international trade is another major source of structural change and is able to explain about 35.5% of the overall employment share decrease in American manufacturing. The quantitative calibration estimates that the inter-sector trade makes a moderate contribution, while trade imbalances dominate the recent contraction of manufacturing employment share. Our results suggest that calibrated models based on U.S. data have to be adjusted by trade factors.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".