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Record W4403360092 · doi:10.1002/bse.4002

Common ownership and investor‐focused disclosure: Evidence from ESG financial materiality

2024· article· en· W4403360092 on OpenAlexaff
Eduardo Schiehll, Sam Kolahgar

Bibliographic record

VenueBusiness Strategy and the Environment · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Prince Edward IslandHEC Montréal
Fundersnot available
KeywordsMateriality (auditing)BusinessAccountingFinancial system

Abstract

fetched live from OpenAlex

Abstract In this study, we investigate whether information demands made by common agents, specifically common institutional owners, drive firms to adopt a reporting framework that enhances the comparability and financial materiality of environmental, social, and governance information. Using a sample of 3659 unique US firms from 2015 to 2021, we collected data on the adoption of the Sustainability Accounting Standards Board's reporting framework—identifying first movers, followers, and non‐adopting firms—and their levels of common institutional ownership. Our results are robust across changes in the levels of common institutional ownership, various combinations of fixed effects, and the application of an instrumental variable approach. Our findings support the idea that investors' demand drives comparability and financial materiality in sustainability reporting and that common ownership enhances such disclosure by alleviating the concern over the proprietary costs of revealing sensitive information. Our study offers new insights into patterns of intra‐industry disclosure behavior and a better understanding of how a group of increasingly significant market participants (i.e., common institutional owners) influences firms' commitment to investor‐focused disclosure.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.031
GPT teacher head0.202
Teacher spread0.172 · 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

Citations25
Published2024
Admission routes1
Has abstractyes

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