Dutch Disease, Factor Mobility Costs, and the ‘Alberta Effect’ – The Case of Federations
Bibliographic record
Abstract
Do reduced costs of factor mobility mitigate ‘Dutch Disease’ symptoms? The case of federations provides an indication for this. By investigating ‘Resource Curse’ effects in all federations for which complete data is available at the regional level it is observed that within federations resource abundance is more of a blessing than a curse (while between them the curse remains). In addition, it is also shown that federations with relatively worse institutional quality experience amplified reversed ‘Resource Curse’ effects within them, so that results are not driven by good institutions. A theory is then presented in an attempt to explain the difference between the cross-federal (and previous cross-country) results of the ‘Resource Curse’, and the intra-federal ones presented initially. It is argued that the reduced factor mobility costs within federations (compared to the costs of cross-country mobility) trigger an ‘Alberta Effect’ which mitigates ‘Dutch Disease’ symptoms, so that ‘Resource Curse’ effects do not apply within federations, and are even reversed. Thus, this paper demonstrates and emphasizes the significance of the mitigating role of factor mobility; also, it highlights the relative importance of ‘Dutch Disease’ theory (compared to the ‘institutions’ perspective) in explaining the ‘Resource Curse’ phenomenon. The paper concludes with empirical evidence for the main implications of the model, taking the United States and Canada as case studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".