Political Competition and Convergence to Fundamentals: With Application to the Political Business Cycle and the Size of Government
Bibliographic record
Abstract
We address the problem of how to investigate whether economics, or politics, or both, matter \nin the explanation of public policy. The problem is first posed in a particular context by \nuncovering a political business cycle (using Canadian data for 130 years) and by taking up the \nchallenge to make this fact meaningful by finding a transmission mechanism through actual \npublic choices. Since the cycle is in real growth, and it is reasonable to suppose that public \nexpenditure would be involved, the central task then is to investigate the role of (partisan and \nopportunistic) political factors, as opposed to economic fundamentals, in the evolution of \ngovernment size. \nWe proceed by asking whether the data allow us to distinguish between the convergence and \nthe nonconvergence hypotheses. Convergence means that political competition forces public \nspending to converge in the long run to a level dictated by endowments, tastes and \ntechnology. Nonconvergence is taken to mean that political factors other than the degree of \npolitical competition prevent convergence to that long run. The general idea here, one that \nmay be applied in any situation where the key issue is the role of economics versus politics \nover time, is that an overtly political factor can be said to play a distinct role in the evolution \nof public choices if it can be shown to lead to departures from a dynamic path defined by the \nevolution of economic fundamentals in a competitive political system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".