MétaCan
Menu
Back to cohort

Public Deficits and Surpluses in Federated States: A Review of the Public Choice Empirical Literature

2004· review· en· W1539875341 on OpenAlexaffabout
Louis Imbeau

Bibliographic record

VenueJournal of Public Finance and Public Choice · 2004
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGermanGovernment (linguistics)Empirical researchPolitical scienceEconomicsPublic economicsEmpirical evidenceGeography

Abstract

fetched live from OpenAlex

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 die 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 Länder, 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 Länder and in Canadian provinces. In the concluding section, I discuss two issues: the impact of rules, and the partisan cycle hypothesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.317
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Public Finance and Public ChoiceSame topicFiscal Policies and Political EconomyFrench-language works237,207