Political Competition and Convergence to Fundamentals: With Application to the Politcal Business Cycle and the Size of the Public Sector
Bibliographic record
Abstract
We address the problem of how to investigate whether economics, or politics, or both, matter in the explanation of public policy. We first pose the problem in a particular context by uncovering a political business cycle (using Canadian data for 130 years) and by taking up the challenge to make this fact meaningful by finding a transmission mechanism through actual public choices. Since the cycle is in real growth and it is reasonable to suppose that public expenditure would be involved, we then focus on investigation of the role of (partisan and opportunistic) political factors, as opposed to economics, in the evolution of government size. We ask whether the data allow us to distinguish between the convergence and the nonconvergence hypotheses. Convergence means that political competition forces public spending to converge in the long run to a level dictated by endowments, tastes and technology. Nonconvergence is taken to mean that political factors other than the degree of political competition prevent convergence to that long run. The general idea here is that a political factor can clearly be said to play a role in the evolution of public choices if it can be shown to lead to departures from a dynamic path defined by economic fundamentals in a competitive political system. The results of applying cointegration and error correction modeling to implement this idea indicate that public expenditure cannot serve as the required transmission mechanism. Of the political factors considered, only variation in the degree of political competition leads to substantial departures of public expenditure from its long run path defined by economic fundamentals. We conclude with some general implications of the analysis for future research.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".