GTAP-MVH, A Model for Analysing the Worldwide Effects of Trade Policies in the Motor Vehicle Sector: Theory and Data
Bibliographic record
Abstract
The Office of the Chief Economist in Global Affairs Canada (hereafter, the Office) is seeking to add to its tools for looking at the effects on Canada and other countries of higher U.S. protection. The Office is particularly interested in the motor vehicle sector. To meet the Office's requirements, we created a version of the GTAP model in which the motor vehicle sector is disaggregated. We call this version GTAP-MVH. This paper describes the process and data inputs though which we constructed a disaggregated motor vehicle sector for GTAP-MVH. The theory in standard GTAP assumes that capital is completely mobile between industries and that labor markets are characterized by either fixed real wages or completely flexible real wages that adjust to eliminate effects on aggregate employment from policy changes. These capital and labor assumptions limit the usefulness of standard GTAP as a tool for analyzing the short-run impacts of policy changes. We describe theoretical innovations to standard GTAP to enhance its depiction of both capital and labor markets. We also describe innovations in other areas, particularly in the treatments of: the accumulation by each region of foreign assets and liabilities; and the determination of savings, investment and rates of return.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".