Structural breaks and labor market disparities in the Canadian provinces
Bibliographic record
Abstract
Purpose – The purpose of this paper is to use quarterly time series data from Canada and the Canadian provinces to determine if the unemployment rates in the Canadian provinces are converging to the national rate of unemployment. Design/methodology/approach – First, the authors check for existence of stochastic convergence using recent unit root statistics, see Perron and Rodríguez (2003) and Rodríguez (2007). Second, the authors verify existence of convergence using methods proposed by Volgelsang (1998) and Bai and Perron (1998, 2003). All these methods allows for structural break(s) in the data. Findings – Results from different unit root tests, without and with structural breaks, confirm that stochastic convergence exists in all provinces. The other results show strong evidence that deterministic convergence exists and the unemployment rates of the Canadian provinces are converging to the unemployment rate of Canada. This conclusion is stronger when multiple breaks are allowed in the trend function using the approach of Bai and Perron (1998, 2003). Practical implications – Since the authors have verified the existence of stochastic convergence, any intervention in the labor markets of the Canadian provinces to control the provincial unemployment rate would have a temporary effect and these policies will not have a permanent influence on the unemployment rates. However, existence of β -convergence in the Canadian provinces shows that general policies toward lowering the national unemployment rate would decrease the provincial unemployment rates as well. Originality/value – To the best of the knowledge, the paper attempts to study the unemployment rate convergence in the Canadian provinces using the above-mentioned approaches. These approaches allow the authors to take into consideration the possibility of structural breaks in order to get results that are more accurate.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".