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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 OpenAlexaff
Lynette H. Ong

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations68
Published2011
Admission routes1
Has abstractyes

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