Decomposing Movements in U.S. Non-Energy Import Market Shares
Bibliographic record
Abstract
Country market shares of U.S. non-energy imports have changed considerably since 2002, with varying volatility across three subperiods: pre-crisis (2002–07), crisis (2007–09) and post-crisis (2009–14). In this paper, we analyze market shares for four main trading partners of the United States (Canada, Mexico, China and Japan). We use shift-share analysis to decompose movements in the aggregate market shares into those related to actual shifts in product-specific market shares, versus shifts in the composition of U.S. import demand and the interaction between these two effects. Our analysis shows that separating these effects is important, since shifts in product-specific market shares explain varying amounts of movements in the overall market shares across countries and between time periods. Specifically, we find that two-thirds of Canada’s decline in U.S. market share is due to shifts in product-specific market shares and that these losses were relatively stable across subperiods. In contrast, losses associated with a shift in the composition of U.S. import demand were most important during the crisis and have in fact supported Canada’s market share since 2009. We also find that almost three-quarters of Canada’s total loss in market share was concentrated in two sectors: (i) motor vehicles and parts, and (ii) forestry products and building and packaging materials. Japan’s loss in U.S. market share was very similar to Canada’s over this period. In contrast, China and Mexico both gained market share between 2002 and 2014. China gained mostly in product-specific market share, while Mexico benefited from favourable shifts in U.S. import demand.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".