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Record W2947268313 · doi:10.1080/14766086.2019.1618605

Does religiosity affect the dividend payouts of Italian-listed companies?

2019· article· en· W2947268313 on OpenAlexaff
Fabrizio Rossi, James R. Barth, Yanfei Sun

Bibliographic record

VenueJournal of Management Spirituality & Religion · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReligiosityDividendDividend policyAgency (philosophy)Affect (linguistics)Principal–agent problemAgency costStock (firearms)Demographic economicsFinancial economicsBusinessEconomicsMonetary economicsEconometricsPsychologyAccountingSocial psychologySociologyCorporate governanceShareholderFinanceGeography

Abstract

fetched live from OpenAlex

This study investigates the relationship between local culture, as measured by religiosity, and dividend payouts based on a sample of 155 Italian firms over the period 2000–2016, yielding 2,382 firm-year observations. We find a significantly positive relationship between religiosity and dividend payouts, with firms headquartered in higher religiosity areas paying more dividends than firms located in lower religiosity areas. We also find no relationship between religiosity and stock returns. An additional test also suggests that religiosity of Italian firms could mitigate agency costs. Our results are consistent with the “bird-in-the-hand theory” and generally consistent with the empirical literature on the relationship between social values (local culture) and corporate decisions. Since religiosity is a determinant of dividend payouts, it also is an appropriate explanatory variable when examining the behavior of a firm’s dividend policies.

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)0.002
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 score0.441
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.240
Teacher spread0.228 · 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
Published2019
Admission routes1
Has abstractyes

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