The impact of trade liberalization and the fiscal equalization transfer policy on provincial income disparities in Canada: an application of GMM estimation
Bibliographic record
Abstract
This article uses the Solow growth model and the panel data method to examine the effect of trade liberalization and the federal equalization transfers on income convergence among Canadian provinces between 1981 and 2006. Estimation problems of weak instruments and endogenous regressors are addressed by the use of a system Generalized Method of Moment (GMM) estimator. The results from the empirical analysis indicate that the current rate of convergence of Personal Income (PI) in Canada is 4.41% per year. This rate is considerably higher than the range of 1.80 and 2.41% per year that previous studies using least-square estimators have reported. The findings from the policy analysis show that the launching and expansion of the North America regional integration have de-accelerated the convergence speed for Canadian provinces by 3.99 and 3.15% per year, respectively. However, consistent with the results from previous studies, the fiscal transfers, which are part of the federal equalization programme, have accelerated the convergence speed for Canadian provinces.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".