Politics versus Economics in the Explanation of Government – revised version: Studying the Role of Political Competition in the Evolution of Government Size over Long Horizons
Bibliographic record
Abstract
This paper uses annual data from 1870 and 2000 in Canada to test whether overtly political variables interact with macroeconomic variables through government size. We begin by asking whether Canada’s macro data is consistent with political cycles, i.e., the hypothesis that macro cycles have been caused by overtly political influences such as the timing of elections, the political ideology of the governing party, the size of the winning majority, and/or whether there was a minority government. After finding some evidence of a correlation between political variables and output growth (but not inflation), the paper explores whether the transmission mechanism for these cycles could be through government size. To test for this relationship, the analysis uses an error correction model constructed under the base case assumption that political variables have no separate influence on government size. Competition among political parties is assumed to lead government size to converge on an equilibrium that depends only on the underlying tastes and technology of the community. The addition of political variables to this structure then allows us to assess whether explicit political considerations can still explain sympathetic variations in real government size once a complete long and short run model of the economic factors at play has been fully specified.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".