Bibliographic record
Abstract
Productivity levels and productivity growth rates vary significantly over space. These differences are perhaps most pronounced between countries, but they remain acutely evident within national spaces as economic growth favors some cities and regions and not others. In this paper, we map the spatial variation in productivity levels across Canadian cities and we model the underlying determinants of that variation. We have two main goals. First, to confirm the existence, the nature and the size of agglomeration economies, that is, the gains in efficiency related to the spatial clustering of economic activity. We focus attention on the impacts of buyer-supplier networks, labour market pooling and knowledge spillovers. Second, we identify the geographical extent of knowledge spillovers using information on the location of individual manufacturing plants. Plant-level data developed by the Micro-economic Analysis Division of Statistics Canada underpin the analysis. After controlling for a series of plant and firm characteristics, analysis reveals that the productivity performance of plants is positively influenced by all three of Marshall's mechanisms of agglomeration (Marshall, 1920). The analysis also shows that the effect of knowledge spillovers on productivity is spatially circumscribed, extending, at most, only 10 km beyond individual plants. The reliance of individual businesses on place-based economies varies across the sectors to which the businesses are aggregated. These sectors are defined by the factors that influence the process of competition'access to natural resources, labour costs, scale economies, product differentiation, and the application of scientific knowledge. Neither labour market pooling, buyer-supplier networks nor knowledge spillovers are universally important across all sectors. This paper provides confirmation of the importance of agglomeration, while also providing evidence that external economies are spatially bounded and not universally im
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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 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".