A Contribution to the Political Economy of Government Size: 'Demand', 'Supply' and 'Political Influence'
Bibliographic record
Abstract
This paper contributes to the understanding of empirically-oriented work on the size of \ngovernment by integrating the analysis of three basic elements: (i) the 'demand' for government stemming in part from attempts to coercively redistribute, often analyzed in a median voter framework; (ii) the 'supply' of taxable activities emphasized in Leviathan and other models of taxation; and (iii) the distribution of 'political influence' when influence and \neconomic interests are distinct. The role of the first two factors have been considered in recent empirical studies of government growth by Ferris and West (1996) and Kau and Rubin (2002). Estimates of the effect of unequal political influence on the size of government have been provided by Mueller and Stratmann (2003). We combine all three elements in a spatial voting framework of a sort that has not been well explored, and use the comparative static properties of the integrative model to shed light on the analytical and empirical literatures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".