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Record W3162803685 · doi:10.5267/j.msl.2021.4.007

Does corporate social responsibility reduce financial distress? Evidence from emerging economy

2021· article· en· W3162803685 on OpenAlexvenueno aff
Naeem Khan, Qaisar Ali Malik, Ahsen Saghir, Muhammad Haroon Rasheed, Muhammad Husnain

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsEndogeneityCorporate social responsibilityPanel dataEquity (law)BusinessFinancial distressPopulationGuard (computer science)Financial marketEconomicsFinanceFinancial systemEconometrics

Abstract

fetched live from OpenAlex

This work investigates the relational behavior of corporate social responsibility (CSR) and its effect on firms' financial distress (FD). The population of the study consists of all the non-financial firms presently listed in the equity market of Pakistan. The yearly data set of 213 non-financial companies is selected from 2005 to 2017 with total observations of 2769. The analysis of the study based on OLS regression, fixed effect, and random effect models. The study also uses the GMM technique to guard against potential problems of endogeneity and heteroskedasticity that arise from the use of panel data. Results indicate that higher investment in CSR leads to reduced/lower financial distress. It suggests that investment in CSR raises the reputation and creditworthiness of firms. Key findings are robust as confirmed by alternative proxies of financial distress. Overall findings advocate that CSR helps in reducing default risk or financial distress and creates a better corporate environment that ultimately improves organizations' economic outlook.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0020.004
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 head0.039
GPT teacher head0.273
Teacher spread0.234 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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