Political Partisanship and Economic Outcomes: Canada, 1870–2020
Bibliographic record
Abstract
Abstract This paper examines the role of partisanship at the provincial and federal levels in relation to the functioning of the Canadian economy. At the provincial level (1976–2019), we find no evidence of a traditional partisan effect but do find evidence weakly consistent with a rational partisan cycle a′ la Alesina. At the federal level (1870–2020), we also find no evidence consistent with a distinctive expansion in output arising when the government is controlled by the left-leaning (Liberal) political party although we again find evidence of a weak rational partisan effect. The former result is reinforced by finding the absence of evidence of partisan changes in federal spending and/or taxation. But while the data do not support a theory of left-right partisan policy over the entire post-Confederation (1867) period of Canada’s history, the data do support distinctive periods of partisan influence on aggregate output. The first is consistent with Sir John A. MacDonald’s post-Confederation conservative government’s adoption of a policy of nation-building based on the railway, immigration, and tariffs. The second is the period between 1885 and 1933 where traditional left-right partisanship is evident and the third is the period following the Great Depression where a distinction between the outcomes arising under left- versus right-leaning parties is no longer apparent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".