Measuring Canada's export performance in the United States using an unbiased shift‐share
Bibliographic record
Abstract
Abstract Shift‐share is a popular technique used by policymakers and researchers alike to decompose the change in a variable into within and between effects, intensive and extensive margins, or other comparable effects. One reason for the popularity of shift‐share is its relative simplicity compared to econometric techniques. However, often overlooked is that a shift‐share is actually an index number problem that uses differences instead of ratios. Techniques developed in index number theory accentuate the fact that the traditional shift‐share is biased. This paper proposes using the Bennet index to achieve unbiased measurements in shift‐share decompositions. In addition to solving the bias problem, the Bennet index removes the need for a residual and may be even simpler to calculate. While this paper is primarily theoretical, it also explores the differences between the traditional shift‐share and the Bennet index—both chained and fixed base—to measure the competitive and composition effects of Canada's export performance in the United States since 1990.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".