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Record W4294770377 · doi:10.1093/jeea/jvac050

Bureaucrats and Policies in Equilibrium Administrations

2022· article· en· W4294770377 on OpenAlexaff
Jean Guillaume Forand, Gergely Ujhelyi, Michael M. Ting

Bibliographic record

VenueJournal of the European Economic Association · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBureaucracyEconomicsNeutralityPoliticsGovernment (linguistics)IdeologyCivil serviceSeniorityPublic sectorPublic administrationPublic economicsPublic servicePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract We develop a model of policy making with an endogenous bureaucracy. Parties choose platforms and ideologically differentiated citizens decide whether to enter the public sector, anticipating the platforms that they may be asked to implement. Bureaucrats prefer to work on policies closer to their ideal, and voters judge the performance of an administration taking both politicians’ and bureaucrats’ actions into account. The model provides an equilibrium framework to study the emergence of partisan or neutral bureaucracies and their consequences for government performance. It shows how bureaucratic partisanship can develop in modern civil service systems; why political polarization and bureaucratic partisanship reinforce each other; why bureaucratic neutrality is associated with competitive elections; and why partisanship lowers government efficiency and increases output fluctuations. Our results yield a number of policy implications regarding political appointments, public sector wages, seniority benefits, and recruiting measures that raise the intrinsic motivation of bureaucrats.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.219
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2022
Admission routes1
Has abstractyes

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