Does Import Competition Reduce Domestic Innovation? Evidence from the 'China Stock' and Firm-Level Data on Canadian Manufacturing
Bibliographic record
Abstract
A key economic issue in Canada is the declining Business Enterprise Research and Development in manufacturing since the early 2000s. Accompanying this, the total factor productivity (TFP) growth in manufacturing slowed after 2000. However, there has not been a definitive explanation for these trends. To deepen our understanding of this phenomenon, we focus on the increasing Chinese import share in the total domestic absorption in Canadian manufacturing since the early 2000s, which appears to be driven by positive supply shocks within Chinese manufacturing. Based on a firm-level database covering all incorporated firms in Canadian manufacturing, we find that rising Chinese import competition led to declines in R&D expenditure and TFP growth within firms but reallocated employment towards more productive firms and induced less productive firms to exit. The negative within-effects were pronounced for firms that were initially smaller, less profitable, and less productive. These firms also experienced declines in their profit margins due to rising Chinese import competition while larger and better-performing firms did not. Our estimates imply that rising Chinese import competition can explain about 7 per cent of the total decline of $1.36 billion (2007 CAD) in R&D expenditure in Canadian manufacturing between 2005 and 2010. Although it led to declines in TFP within firms, the positive reallocation effects more than offset the negative within-effect. Had there been no increase in Chinese import competition between 2005 and 2010, TFP in Canadian manufacturing would have declined by 1.26 per cent per year instead of the actual 1.09 per cent per year over this period.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".