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Record W3123826009 · doi:10.2308/accr-50865

Do Accounting and Audit Quality Affect World Bank Lending?

2014· article· en· W3123826009 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 Accounting Review · 2014
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAccountingAuditBusinessLanguage changeCorporate governanceQuality auditQuality (philosophy)External auditorAccounting information systemPoliticsFinanceInternal auditPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT We investigate the role of accounting and audit quality in the allocation of international development aid loans provided by the World Bank. This aid is crucial to improve governance functions, infrastructure, and capital markets, and the accounting and audit environments in a country can provide the World Bank with confidence that aid is being used as intended rather than being diverted for personal or political gain. We find that development aid loans are higher for countries with stronger accounting quality, where IFRS use is mandated, and where the audit environment is stronger. However, we also find that United States geo-political interests influence these results. Specifically, the World Bank appears to “overlook” accounting and audit quality in countries where geo-political interests are relatively aligned with those of the U.S. Finally, we find that accounting and auditing matter only in countries with relatively high corruption levels, indicating that the World Bank has greater trust that accounting and auditing are of relatively high quality in low-corruption countries. Data Availability: All data are publicly available.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.287
Teacher spread0.259 · 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