Public Deficits and Surpluses in Federated States: A Review of the Public Choice Empirical Literature
Bibliographic record
Abstract
The purpose of this paper is to review the empirical public choice literature explaining deficits levels in federated states. First, I describe theoretical constructs, showing how new theories have developed by releasing one of the basic Ricardo-Barro assumptions. Empirical results bearing on the federated states of Australia, Canada, Germany, Switzerland, and the United States are then reviewed to assess which hypothesis, in which setting, is confirmed by systematic observation. On the whole, this literature shows that economic cycles have an impact on budget balances. It also shows that deficits are higher in election years in German Lander, Canadian provinces, and American states, but not in Australian states nor in Swiss cantons. In addition, the literature tends to support the hypothesis that the stringency of budgetary rules is related to higher budget balances in Canada, Switzerland, and in the United States. Finally, government fragmentation has no impact on the budget balances of federated states and parties of the left do not have higher deficits than parties of the right, except in Switzerland where empirical evidence is mixed. Rather, parties of the center or of the right do have higher deficits in German Lander and in Canadian provinces. In the concluding section, I discuss two issues: the impact of rules, and the partisan cycle hypothesis.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".