The Effects of Power-Sharing on the Size of the Public Sector: An Empirical Study
Bibliographic record
Abstract
This project seeks to comprehensively understand the relationship between power-sharing/ veto institutions and the size of a government's public sector. Specifically, I examine the horizontal and vertical power-sharing institutions of political parties and federalism respectively. Building on a comprehensive literature of the relationship between political institutions and public spending, I argue that governments that do not share power either vertically or horizontally are expected to be associated with larger public sectors. Conversely, power-sharing at both levels is expected to best constrain central government spending. I use comprehensive political and economic data for over 100 states from 1975-2000 and find that some of my argument is supported by the data, even when controlling for partisanship, regime type, economic growth and economic openness. I then look at each power-sharing institution's effect on spending more closely by employing a detailed case study on sub-national governments. It examines power-sharing in three federal states, the United States, Canada and Germany in order to look at how divided and coalition government at the sub-national level affects public spending relative to unified or single party governments. I find mixed support for my theory, though it is stronger in presidential than in parliamentary systems.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".