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Record W3196614893 · doi:10.1111/jbfa.12563

Government transparency and firm‐level operational efficiency

2021· article· en· W3196614893 on OpenAlex
Ole‐Kristian Hope, Shushu Jiang, Dushyantkumar Vyas

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

VenueJournal of Business Finance &amp Accounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransparency (behavior)DisseminationBusinessGovernment (linguistics)Shock (circulatory)Capital (architecture)Private sectorSample (material)Public economicsEconomicsIndustrial organizationFinanceEconomic growth

Abstract

fetched live from OpenAlex

Abstract We examine the informational role of governments in the private sector in emerging economies. Using a large sample of private firms, we show that governments’ ability and willingness to collect and disseminate economic information (government transparency) is positively associated with firm‐level operational efficiency and access to external financing. Several cross‐sectional analyses corroborate our main findings. We find that the effect of government transparency is stronger for firms operating in weaker alternative information environments. We also find a reduced effect of government transparency in countries with better‐developed capital markets that facilitate capital allocation and production efficiency. Additional analyses using the World Bank‐supported Open Government Data initiative as a staggered shock to government transparency provide further support to our primary results. Overall, our paper sheds light on the important role played by governments in emerging markets in aggregating and disseminating economic information.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
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 head0.018
GPT teacher head0.214
Teacher spread0.197 · 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