Factor Adjustment and Intra-industry Trade: An Application of the Adjustment Costs Model
Bibliographic record
Abstract
The hypothesis of the adjustment costs associated with factor adjustments under intraindustry trade are lower than those under inter-industry trade is initially supported by a specific-factor model analysis theoretically. Yet there is no comprehensive empirical evidence to support that. This paper therefore, tested the hypothesis that factor adjustment under intra-industry trade specialisation predominantly occurs within industries rather than between industries using a dynamic factor demand system. The test was carried out in three steps. First, the optimal solutions of factor demand and output supply were derived for the dynamic adjustment costs model when the function of production is assigned in a quadratic form. Secondly, using data obtained from the OECD’s international sectoral database in the quasi-fixed input demand equations, adjustment coefficients were estimated at the subdivision level of ISIC manufacturing industries. This derivation was employed to Canada, Germany and the United States due to the availability of data. Thirdly, specifying two effects—a trade specialisation effect and a structural change effect—in the empirical model to explain the determinants of labour and capital adjustment coefficients. The results reveal strongly that if an industry has a high degree of intra-industry
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".