Resource Curse and Regional Development: Does <scp>D</scp>utch <scp>D</scp>isease Apply to Local Economies? Evidence from <scp>C</scp>anada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 D utch D isease 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it