Economic Interdependencies in the Great Lakes–St. Lawrence Region: A Dynamic Analysis of Manufacturing Connectedness
Bibliographic record
Abstract
This study investigates the evolving dynamics of economic connectedness within the Great Lakes–St. Lawrence (GLSL) region, focusing on the manufacturing sector across eight U.S. states and two Canadian provinces. Leveraging monthly manufacturing employment growth rates from January 1990 to December 2024, the analysis employs a Vector Autoregressive (VAR) model combined with Elastic Net regularization to capture the interdependencies and directional spillovers among these highly integrated regional economies. Through forecast error variance decomposition, the approach identifies the contributions of shocks originating in any given state or province to fluctuations in the others, thereby quantifying both the magnitude of influence (“Connectedness To”) and the degree of exposure (“Connectedness From”). The results reveal a complex yet discernible network of industrial linkages, with states such as Ohio and Indiana emerging as consistent net transmitters of shocks and provinces like Quebec displaying relatively lower susceptibility to external disturbances. A rolling window estimation confirms that these patterns vary over time, frequently intensifying during episodes of macroeconomic stress, such as the 2008–2009 financial crisis and the onset of the COVID-19 pandemic. The findings highlight the significance of coordinated policy interventions aimed at stabilizing key nodes in the network and underscore the importance of diversification and risk management strategies for entities that exhibit heightened exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".