Political Competitiveness and Fiscal Structure: A Time Series Analysis. Canada, 1870 - 2015
Bibliographic record
Abstract
We investigate the extent to which the intensity of political competition moderates the governance issues that arise in relation to Canada’s fiscal structure. By fiscal structure we mean three distinct but interrelated fiscal dimensions of the state: financial stability, long run size and short run interventions into the private economy with respect to the business cycle. The paper is distinctive in focusing on four measures of political competitiveness that reflect the degree of competition in and between national parliamentary elections: the size of the majority of the governing party in the House; the distribution of the volatility adjusted winning margins of the governing party; the proportion of electorally marginal constituencies adjusted for asymmetry between parties; and a multiparty measure of the competitiveness of elections at the constituency level. The analysis accounts for the differing time series properties of the political and economic variables and the comingling of long and short term fiscal policies in the time series data. Estimation using a sequence of ARDL models indicates that greater political competition enhances fiscal stability, speeds up convergence of government size from above on fundamentals, and helps to align fiscal deficits better with the business cycle. The potential quantitative impact of more intense electoral competition is analyzed by applying the deficit model to the period of fiscal instability that arose in the 1980’s and early 1990’s.
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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 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.004 | 0.001 |
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".