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Record W4367722657 · doi:10.22495/jgrv12i2art10

Environmental, social, and governance disclosure impact on cash holdings in OECD countries

2023· article· en· W4367722657 on OpenAlexaboutno aff
Aws AlHares, Noora AlEmadi, Tarek Abu-Asi, Ruba Al Abed

Bibliographic record

VenueJournal of Governance and Regulation · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceBusinessAccountingCashInstrumental variableControl variableMonetary economicsEconomicsFinanceEconometrics

Abstract

fetched live from OpenAlex

In this research, we investigate how cash holdings are affected by the environmental, social, and governance (ESG) disclosure practices of corporations. This research provides valuable insights into the ongoing discussion all across the world on ESG disclosure, and mainly 5 countries from the Organisation for Economic Co-operation and Development (OECD), which are the United States of America, Canada, the United Kingdom, Japan, and Australia, over the period 2012–2021. We used Refinitiv Eikon database to measure the variables. The results show there is a significantly negative relation between ESG disclosure and cash holdings in the introduction, growth, and shake-out/decline stages. Lower cash holdings are associated with higher firm performance and a positive value of cash. In spite of using different econometric parameters, other measurements, extra control variables, propensity score matching, and an instrumental variable approach, our results remained unchanged (Arayssi et al., 2020). This paper has recommendations for policymakers, investors, and business organizations. Importantly, our study reveals how higher levels of ESG disclosure lead to better cash-holding practices (Buallay, 2022).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.532

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 communication0.0000.002
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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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