Endowments, Skill-Biased Technology, and Factor Prices: A Unified Approach to Trade
Why this work is in the frame
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Bibliographic record
Abstract
We develop a multi-factor, multi-sector Eaton-Kortum model in order to examine the impact of trade costs, factor endowments, and technology (both Ricardian and factor-augmenting) on factor prices, trade in goods, and trade in the services of primary factors (value-added trade).This framework nests the Heckscher-Ohlin-Vanek (HOV) model and the Vanek factor content of trade prediction.We take the model to the data using skilled and unskilled data for 38 countries.We have two findings.First, the key determinants of international variation in the factor content of trade are endowments and international variation in factor inputs used per dollar of output.Inputusage variation in turn is driven by (1) factor-augmenting international technology differences and (2) international factor price differences.Second, our estimates of factor-augmenting international technology differences -which imply cross-country variation in skill-biased technologies -are empirically similar to those used to rationalize cross-country evidence on income differences and directed technical change.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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