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A MODEL OF BUREAUCRACY AND CORRUPTION*

2004· article· en· W2064821313 on OpenAlex
Shouyong Shi, Ted Temzelides

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Economic Review · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBureaucracyLanguage changeIncentiveConsumption (sociology)EconomicsMicroeconomicsProduction (economics)WelfareTransaction costPrivate information retrievalPrivate goodPublic economicsPublic goodPrivate consumptionQuality (philosophy)Private sectorMarket economyMonetary economicsComputer securityComputer scienceEconomic growthPolitical science

Abstract

fetched live from OpenAlex

We analyze bureaucracy and corruption in a market with decentralized exchange and “lemons.” Exchange is modeled as a sequence of bilateral, random matches. Agents have private information about the quality of goods they produce and can supplement trade with socially inefficient bribes. Bureaucracy is modeled as a group of agents who enjoy centralized production and consumption. Transaction patterns between the bureaucracy and the private sector are fully endogenous. Centralized production and consumption in the bureaucracy give rise to low power incentives for the individual bureaucrats. As a result, private agents might bribe bureaucrats, whereas they do not bribe each other. An equilibrium with corruption and an equilibrium without corruption can coexist. We discuss some welfare implications of the model.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.060
GPT teacher head0.277
Teacher spread0.217 · 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