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Record W2996771616 · doi:10.1287/mnsc.2018.3254

The Effect of Auditing on Promoting Exports: Evidence from Private Firms in Emerging Markets

2020· article· en· W2996771616 on OpenAlexaff
Agnes Cheng, Weihang Sun, Kangtao Ye, Ning Zhang

Bibliographic record

VenueManagement Science · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsQueen's University
Fundersnot available
KeywordsAuditEmerging marketsBusinessExploitAccountingRegression discontinuity designMonetary economicsCausality (physics)Sample (material)EconomicsFinance

Abstract

fetched live from OpenAlex

We investigate the effect of auditing on promoting exports for private firms in emerging markets. Using a sample of private firms from 125 countries between 2006 and 2015, we show that firms that have their financial statements audited have more exports than firms that do not have their financial statements audited. To infer causality, we employ a regression discontinuity design (RDD). Using the discontinuity around the mandatory financial audit threshold, we find that firms slightly above the threshold have more exports than do firms that are slightly below the threshold. We also exploit the countries with exogenous regulation shocks to the mandatory audits. Using the difference-in-differences (DiD) design, we find that firms that are exempted from mandatory audits have less exports subsequent to the regulation change. Further analyses reveal that the effect of auditing is more pronounced in countries with higher audit quality and for firms with limited alternative information. Our findings suggest that the auditing function promotes exports—an important economic consequence for the global economic development. This paper was accepted by Shivaram Rajgopal, accounting.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.111
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 teacher head, 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

Citations42
Published2020
Admission routes1
Has abstractyes

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