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Record W3125226672

Is Mandated Independence Necessary for Audit Quality

2011· article· en· W3125226672 on OpenAlexaff
Karim Jamal, Shyam Sunder

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCertificationBusinessAuditQuality auditIndependence (probability theory)AccountingAuditor independenceQuality (philosophy)MandateCompetence (human resources)MarketingJoint auditEconomicsInternal auditPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Independence (in fact as well as in appearance) is widely thought to be necessary for the quality of audits, and audit quality is often equated with independence. Private incentives to demand (and supply) independent certification of financial statements are thought to be insufficient, thus the need to mandate independence through regulation. This study presents data from a field experiment on the unregulated market for certification of baseball cards to assess the role of independence vis-à-vis other auditor attributes such as competence, price, and service on audit quality. In our field experiment, we examine prices of baseball cards sold on eBay with or without third party certification. In addition, the certifier was either independent or deeply immersed in providing other services to market participants. We find that market participants pay a significant premium for certified cards. Certifiers who are deeply immersed (and therefore apparently less independent) also provide higher quality service in the form of being stricter graders, command larger price premiums, and dominate in market share. Implications for independence and audit quality are discussed.

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)0.014
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.023
GPT teacher head0.253
Teacher spread0.231 · 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 designObservational
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

Citations6
Published2011
Admission routes1
Has abstractyes

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