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Record W4226372249 · doi:10.1111/1475-679x.12417

The Economic Consequences of Financial Audit Regulation in the Charitable Sector

2021· article· en· W4226372249 on OpenAlexafffund
Raphael Duguay

Bibliographic record

VenueJournal of Accounting Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
FundersSocial Sciences and Humanities Research Council of CanadaBooth School of Business, University of ChicagoYale School of ManagementUniversity of ChicagoYale University
KeywordsAuditReputationBusinessAccountingPublic economicsEmpirical evidenceInformation asymmetryFinanceEconomicsLaw

Abstract

fetched live from OpenAlex

ABSTRACT I provide evidence on the effects of financial audit mandates in the charitable sector, in particular their influence on donor behavior. My empirical strategy relies on variation in size‐based exemption thresholds across states and differences in size driven by the nature of charities’ activities. Consistent with audit mandates reducing donors’ reliance on charity reputation, I find audit mandates are associated with a lower concentration of donations on the largest, most well‐known charities. I show this reallocation of resources allows the charitable sector to serve more diverse geographic areas and social needs. In terms of the effect on willingness to give, I document that audit mandates are associated with a higher proportion of taxpayers who donate. However, I only observe a sizable impact on total contributions in dollars for charities with high inherent information asymmetry. Collectively, these results suggest financial audit regulation reduces information frictions and thereby affects resource allocation in the market for charitable giving.

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)0.013
metaresearch head score (Gemma)0.003
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 score0.104
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.003
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 head0.079
GPT teacher head0.392
Teacher spread0.313 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

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