MétaCan
Menu
Back to cohort
Record W4409393597 · doi:10.1142/s1084946725500037

THE IMPACT OF MACRO FACTORS ON ENTREPRENEURSHIP AT THE PROVINCIAL LEVEL IN A DEVELOPING COUNTRY

2025· article· en· W4409393597 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Developmental Entrepreneurship · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsEntrepreneurshipMacroMacro levelBusinessEconomic geographyDeveloping countryEconomic growthEconomic systemEconomicsComputer science

Abstract

fetched live from OpenAlex

This study analyzes the effect of macroeconomic, social and institutional factors on provincial entrepreneurship, defined as the aggregate number of newly created enterprises per year in a province within a country. Using a robust fixed-effects regression model with data from 63 provinces in Vietnam between 2014 and 2021, the findings reveal that the GDP growth, trained labor force, reduced time costs for regulatory compliance, lower informal charges and enhanced business support services positively influence provincial entrepreneurship. Conversely, economic openness and poverty rates negatively affect entrepreneurship in the provinces. Therefore, we can confirm the effect of some key macro factors on provincial entrepreneurship, implying that the aggregate entrepreneurship rate varies among provinces in a developing country because of differences in their macro factors. Our study contributes to the literature on provincial entrepreneurship and its macro determinants, providing practical implications for policymakers aiming to foster entrepreneurial activities across their provinces in contexts like Vietnam.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.273
Teacher spread0.242 · 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