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Record W2035495151 · doi:10.1177/0192512111414447

Fiscal federalism and soft budget constraints: The case of China

2011· article· en· W2035495151 on OpenAlex

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 Political Science Review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFiscal federalismRevenueLocal governmentIncentiveEconomicsFederalismChinaAccountabilityPoliticsDebtCentral governmentEconomic policyCommunismBusinessFinanceMarket economyPublic administrationDecentralizationPolitical science

Abstract

fetched live from OpenAlex

China has been held up as a modern-day exemplar of ‘market-preserving federalism.’ This article challenges this popular belief by showing that its local governments face soft budget constraints. Fiscal indiscipline among subnational governments, which risks national indebtedness and macroeconomic instability, can pose serious dangers to federations. A large body of literature which proposes solutions to fiscal indiscipline through electoral incentives and political party structure cannot be applied to China. The Chinese Communist Party’s cadre-evaluation and dual accountability systems make it an imperative for local officials to augment fiscal revenue and allow them to tap resources at local credit institutions. This has resulted in mounting local government debt, the lion’s share of which is unrepaid loans owed to local credit institutions. To harden budget constraints, political institutions need to be reconfigured to allow the central government more effectively to hold local authorities accountable for resources deployed in achieving their job-performance targets.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.035
GPT teacher head0.347
Teacher spread0.313 · 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