Strength in Numbers? The Weak Effect of Manufacturing Clusters on Canadian Productivity
Bibliographic record
Abstract
In the wake of the recent financial crisis, clusters – the spatial concentration of interrelated industries, specialized services, and customers – have again captured the attention of economists, policymakers, and consultants. Clusters are viewed by many as vital to the national economy, and a possible fix to stagnating productivity and incomes. Economic research has shown, however, that most clusters do not live up to these expectations. There is little solid evidence that clusters make regions – let alone nations – prosperous. Does this also apply to Canada? Using detailed business location data, this Commentary measures the degree of clustering in Canadian manufacturing industries. It then documents changes in the spatial concentration of those industries between 2001 and 2009, and investigates whether those changes are positively associated with aggregate industry performance as measured by value added per employee or wages. Four key results stand out. First, Canada’s manufacturing industries are less strongly clustered than those of other developed countries. High-tech sectors are not, in general, more strongly localized than other sectors. Second, between 2001 and 2009, the spatial concentration of industries became slightly weaker, despite the stability of the overall spatial distribution of manufacturing. Third, there is little evidence that more clustering had significant effects on average productivity or wages in manufacturing industries. The changes in clustering that would be needed to significantly boost productivity and wages nationwide are large and arguably beyond the reach of regional or even national policy. Last, international trade has a much stronger impact on productivity than small changes in the spatial distribution of Canada’s manufacturing industries. The policy message is therefore that looking at trade – and at tax policy – might provide better and cheaper solutions to improving productivity than focusing on clusters, however tempting the latter might be.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".