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Record W2144512694 · doi:10.5539/ijef.v5n3p62

The Determinants of Informal Capital in the Financing of Small Firms at Start-Up: An Ethnic Comparison of Small Firms in Sweden

2013· article· en· W2144512694 on OpenAlexvenueno aff
Darush Yazdanfar, Saeid Abbasian

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupCapital (architecture)ImmigrationUnivariateDemographic economicsSample (material)BusinessVariablesAffect (linguistics)Multivariate analysisInstrumental variableFinanceMultivariate statisticsEconomicsPolitical scienceSociologyEconometrics

Abstract

fetched live from OpenAlex

This study explains empirically the differences in the use of informal financing between native- and immigrant-owned small businesses in terms of ethnicity and other relevant variables. A sample of 2814 native- and immigrant-owned small businesses, consisting of a unique database gathered, was analysed and several univariate and multivariate methods employed. The results suggest that ethnicity is a significant explanatory variable and an important factor in informal capital access in the start-up stage in terms of loans from family members and friends. Moreover, the other independent variables, namely gender, age, experience of starting businesses, the amount of start capital, and firm size, affect loans from family members, whereas loans from friends are influenced by age, size, and industry affiliation. Since knowledge about informal capital determinants is limited, the results of this study add to our understanding of the variables that explain the financing behaviour of small businesses at start-up.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.266
Teacher spread0.224 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Economics and FinanceSame topicFamily Business Performance and SuccessionFrench-language works237,207