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Record W7117304008 · doi:10.1016/j.ememar.2025.101434

Do state religions affect entrepreneurial financing? A cross-country analysis

2025· article· en· W7117304008 on OpenAlexaff
Min Maung

Bibliographic record

VenueEmerging Markets Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsState (computer science)Affect (linguistics)Reduction (mathematics)Developing countryPoverty reductionReligious belief

Abstract

fetched live from OpenAlex

We find robust evidence that the presence of state religions reduces entrepreneurial financing in a large cross section of countries. The effect of state religions is mediated by increased aversion to risk, stricter business restrictions, lower financial development, and reduced participation in religious memberships. Religious participation increases entrepreneurial financing in countries without state religions. However, in countries with state religions, the effect of religious participation is either negative or insignificant. The reduction in financing in countries with state-sponsored religions likely comes from the institutional and cultural environments associated with the presence of state religions. • The presence of state religions reduces entrepreneurial financing. • The reduction in financing is mediated by higher risk aversion. • The reduction in financing is mediated by lower financial development and less economic freedom. • The reduction in financing is mediated by lower active participation in religious organization. • The reduction in financing could be related to institutional and cultural environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.353
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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