Endowments, Skill-Biased Technology, and Factor Prices: A Unified Approach to Trade
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".