Resource Curse and Regional Development: Does <scp>D</scp>utch <scp>D</scp>isease Apply to Local Economies? Evidence from <scp>C</scp>anada
Bibliographic record
Abstract
Abstract Looking at 135 Canadian urban areas over a 35‐year period (1971–2006), the paper examines the relationship between initial specialisation (using employment) in resource industries and various growth indicators via a mix of descriptive statistics and econometric modelling. The paper differentiates between two resources sectors: resource extraction (mining, logging, etc.); primary resource transformation (paper mills, foundries, smelters, etc.). The evidence for a “resource curse” is mixed. Resource transformation industries are found to be associated with slower population growth, also depressing growth in college‐educated cohorts. However, no such relationship is found for resource extraction. We find no evidence for a durable Dutch Disease wage effect. Wages fluctuate in response to resource demand as do working‐age populations. Many relationships hold only for the short run. In the end, we argue, the impact of resource specialisation depends on the particular resource and type of industry it spawns, as well as location. There is no generalisable resource curse, valid for all resources and all places.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".