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Record W4229447659 · doi:10.1017/s0305741022000352

Firms as Revenue Safety Nets: Political Connections and Returns to the Chinese State

2022· article· en· W4229447659 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

VenueThe China Quarterly · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsUniversity of British Columbia
FundersUniversity of OxfordUniversity of Texas at Austin
KeywordsRestructuringPoliticsRevenueIncentiveChinaBusinessContext (archaeology)State (computer science)Market economyTax revenueClientelismEconomicsFinancePublic economicsPolitical scienceDemocracy

Abstract

fetched live from OpenAlex

Abstract The political connection between the state and firms in the context of China's corporate restructuring has been little explored. Using the clientelist framework and unpacking the incentives of both firms and the state, we analyse political connections as repeated patron–client exchanges where the politically connected firms can help the state fulfil its revenue imperative, serving as a failsafe for local authorities to ensure that upper-level tax quotas are met. Leveraging original surveys of the same Chinese firms over an 11-year period and the variations in their post-restructuring board composition, we find that restructured state-owned enterprises (SOEs) with political connections pay more tax than their assessed amount, independent of profits, in exchange for more preferential access to key inputs and policy opportunities controlled by the state. Examining taxes rather than profits also offers a new interpretation for why China continues to favour its remaining SOEs even when they are less profitable.

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 score0.209
Threshold uncertainty score0.847

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 studies0.0010.000
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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