Convergence of Income Among Provinces in Canada - An Application of GMM Estimation
Bibliographic record
Abstract
This paper tests for unconditional and conditional income convergence among provinces in Canada during the period 1981-2001. We apply the first-differenced GMM estimation technique to the dynamic Solow growth model and compare the results with the other panel data approaches such as fixed and random effects. The method used in this paper accounts for not only province-specific initial technology levels but also for the heterogeneity of the technological progress rate between the ‘richer ’ and ‘not so richer ’ provinces of Canada. One of the findings of the paper is that the Canadian provinces do not share a common technology progress rate and a homogeneous production function. The findings of the study suggest a convergence rate of around 6 % to 6.5 % p.a. whereas the previous studies using OLS and other techniques reported a convergence rate of around 1.05 % for per capita GDP and 2.89 % p.a. for personal disposable income among Canadian provinces. Keywords:
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".